Computers Still Have a Long Way to Go on Visual Reasoning According to Larry Zitnick of Facebook

Larry Zitnick, AI Research, Research Lead, Facebook

Larry Zitnick, AI Research, Research Lead, Facebook

Join us at the next annual LDV Vision Summit.  This is transcript of the keynote by Larry Zitnick, AI Research Lead at Facebook, “A Visual Stepping Stone to Artificial Intelligence” from our 2016 LDV Vision Summit.

Larry Zitnick got his PhD at CMU and after that he went to Microsoft Research where he established an excellent track record in object recognition and other parts of computer vision. Now he's at Facebook and, again, he's doing world class research. He's the leader of a very influential project called COCO which is Common Objects in Context and he also works at the intersection of images and language - which is an exciting area involving things like visual question answering.

At Facebook AI Research, what we try to do is advance state of the art AI in collaboration with the rest of community. Given that my background is in computer vision, I find myself thinking a lot, what is the role of computer vision in AI? That's what I want to talk about today.

Imagine that you could go back to 1984 and you could find yourself a graduate student and you said “here, read this paper. It's got a really cool title, it's called Neocognitron and if you want to solve recognition, all you need to do is three things. You need to figure out how to learn weights, you need to collect a huge amount of data, and you need to find a really fast computer and you would solve recognition.”

Now graduate students being graduate students, they would go in and they'd look at, they'd look at the weights part and they'd look at algorithms and they'd say “I would want to solve the algorithm.” That's exactly what they did.

They went and solved the algorithm. They developed Backprop. Now, graduate students being graduate students, say, “now all we have to do is collect more data.”

That took a lot longer unfortunately. That took maybe another 30 years to finally collect enough data to then to do the learning.

Now we're in 2016 and we find ourselves asking the question, how are we going to solve AI? Which direction do we need to go in to solve AI? Well, the answer is obvious. All we need is more data, more compute and apply it to Backprop. This is exactly what we've done the last few years. We took a problem which is seemingly AI complete, image captioning, and we basically took tons of data, tons of compute and we ran it on these images and we got some really amazing results.

A man riding a wave on a surfboard in the water. Great image, great caption. A giraffe standing in the grass next to a tree. Again, fantastic caption for this image and I think a lot of people were really excited about this. Then after a little bit of introspection, we began to realize, this doesn't work if you don't have similar images in the dataset. If the images are too unique, suddenly the algorithm starts falling apart.

How many people have read the paper, Building Machines that Learn and Think Like People?It is a paper from NYU, MIT, and Harvard. It's great read. If you haven't read it yet, please read it. They took this state of the art image caption generator and they ran these images through it and they got a man riding a motorcycle on the beach. Yeah, kind of correct, but kind of missed the point all together. You see this over and over again. If the test image is from the head, you nail it. If it's from the tail, it's a little bit unique and it completely falls apart. More data is not the solution.

Then as computer vision people you might think to yourself, “we want to solve AI which direction should we push in?” Let's just make our recognition problems harder. What's something really hard that we can try to recognize? Mirrors. You have a mirror like in this image here, we nail it, we can do a really good job. Right? What about this image? Can you detect the mirror in this image? In order to do this, you have to have a much more deep understanding about the world. You need to understand how selfies are actually taken. Some of the older people here might not get it.
 

Unfortunately finding really difficult images like this is really hard. It's already hard enough to create datasets, so I don't think this is the right direction either. If we want to solve AI, which direction should we go in? That's what I want to talk about. There's two things we need to do.

The first thing is learning. There's many different types of learning. There's the very friendly nice type of learning, which is supervised. Where you get data, it's complete. It doesn't have any noise in it, it's fantastic. It's our favorite friend.

You have semi-supervised learning which is a little lazy, let's say, where you don't always get the data that you want. You have reinforcement learning which is always trying to give you money or give you rewards for doing the right thing. Then you have unsupervised learning which is really, really annoying. There is a huge amount of unsupervised learning, we have a huge amount of data but we don't have any labels for it.


Supervised learning. This is our bread and butter. This is why we've had the advances we've had so far because of huge supervised datasets like Imagenet and more recently COCO.

-Larry Zitnick


Supervised learning. This is our bread and butter. This is why we've had the advances we've had so far because of huge supervised datasets like Imagenet and more recently COCO. But, creating these datasets is incredibly difficult and frustrating. Just ask anybody here who's tried to do this. Ask the graduate students. It's really hard to get graduate students to work on problems like this.

Semi-supervised learning. Let me give you an example of semi-supervised learning and why this is tough. If you want to learn a concept such as yellow and you have a bunch of image captions, you can identify images which have yellow in them and the caption actually mentions yellow. But there are other times where almost the entire image is yellow yet the caption doesn't actually mention the fact that there is any yellow in the image.

Now you can learn really cool things using data like this. You can learn whether people actually say a certain concept is present in an image or not. We can learn a classifier which says ‘hey, when there's a fence in the background of a soccer game, nobody ever mentions a fence.’ Where as if there's a fence that is blocking a bear from eating you, somebody is going to be mentioning that fence.

Reinforcement learning. Now reinforcement learning in computer vision is kind of a weird mismatch right now because in reinforcement learning, generally, you have some sort of interactive environment and it's hard to do that with the static datasets that we're used too. What you find is a lot of reinforcement learning is being done with gaming type interfaces or with interactive interfaces. I think it's still a really exciting area to be looking into.

Then you have unsupervised learning. This is kind of the elephant in the room because there's a huge amount of data. If we can figure out a way to learn features using this unsupervised data, we could do amazing things. People have been trying to propose all sorts of different tasks they can do. And they see what type of visual features they can learn from doing these sort of tasks. It has worked kind of okay but still not as good as supervision.


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The next thing I want to talk about is reasoning, and specifically visual reasoning but not about the task of reasoning itself. What I want to give you is a sense for how difficult this problem is, and where we are as a community in solving reasoning.

Very recently, there's a paper that proposed the following tasks. You're given three statements. Mary went into the hallway. John moved to the bathroom. Mary traveled to the kitchen. You have to answer a very simple question, where is Mary? Computers have a really hard time of answering that question. Let's let that sink in.

This is trivial. Really trivial yet computers can not do it because they can't understand what these statements are actually saying - and people are worried about AI taking over the world.

If you're going to do reasoning, you need to be able to predict the world. You need to be able to see how things are going to be able to progress into the future. Where are we right now? Right now, when we think about prediction, we're dealing with these sort of baby tasks where we have, for instance, we have a bunch of blocks that are stacked up on top of each other. All you have to do is predict, are those blocks going to fall over or not? It's incredibly simple. If they do fall over, which way are they going to fall? This is something that can be done by a baby. Yet this is a state of the art in the research right now. You think about more complex prediction tasks where we model human behavior, where we have to simulate driving down roads and that sort of thing. We still have a long way to go.

Data. This is something that's interesting because when you think about these AI tasks that we're looking at, a lot of them are dealing at a higher level of reasoning. They're not looking at pixel level things. It doesn't matter if you start with real data or you start with more abstract data. There's been a lot more work in looking at abstract scenes. Cartoon scenes. Looking at Atari games. Looking at Minecraft. These other areas where we can isolate the reasoning problem that we want to explore without having to worry about this messiness, that is the recognition problem in the real world.

Finally, even if we've solved reasoning, how would we know that we solved it? We all know the problems with the Turing Test and how incredibly frustrating it is to measure intelligence based upon the Turing Test because there are all sorts of different ways of gaming it. One of the more recent things that we've proposed is to use visual question answering as a sort of Turing Test for reasoning and vision. What you do is you have an image, you have a question, and it combines the visual recognition - that is now beginning to work better. If you can do both of them well then you can do good on the VQA task. So far, what we've seen, is progress in this task hasn't been moving that quickly and I think a lot of it is due to the fact that reasoning is not progressing that quickly unlike recognition.

Looking forward. Up until 2016 we've made incredible strides in recognition. I said before that recognition is solved, but recognition is not solved, there is still a lot more work to be done. Only compared to 1984, is it essentially solved. Now, if we actually want to solve AI, we need to turn. We can't just keep pushing on recognition. We can't keep thinking that AI is recognition. We need to start thinking of AI as AI and start solving these problems that have been ignored over the last thirty years.

If you look at reasoning in particular, we're just at the beginning stages of this and for me this is what's so exciting. There's still so much work to be done. High level, what's interesting is there's not a clear road map. We don't know how reasoning is going to be solved. We don't know how learning is going to be solved. We don't know how we're going to crack the unsupervised learning problem and because of this it's hard to give a time frame.

One thing I can guarantee is, as we explore AI, computer vision is going to be our playground for research in this area. Thank you.

The annual LDV Vision Summit will be occurring on May 24-25, 2017 at the SVA Theatre in New York, NY.

Are OTT and Live the Hottest Features of Video Streaming?

Patricia Hadden, SVP of NBCUniversal © Robert Wright/LDV Vision Summit

Patricia Hadden, SVP of NBCUniversal © Robert Wright/LDV Vision Summit

Join us at the next annual LDV Vision Summit.  This transcript of the panel, “Is OTT the New Black” is from our 2016 LDV Vision Summit. Moderator: Rebecca Paoletti, CEO of Cakeworks featuring: Ed Laczynski, CEO of Zype; Patricia Hadden, SVP NBCUniversal; Steve Davis, VP & GM East of Ooyala.

Rebecca: Just to start off, if you guys could each give one sentence about what your company does and one sentence about what you do at your company. You can start, Ed.

Ed: Sure. Hi, I'm Ed Laczynski. I am CEO and founder of Zype. We are a direct to consumer video business platform, and we make it easy for content owners and brands to build and grow OTT streaming businesses.

Patricia: Hi, I'm Patricia. I work for NBC Universal. We create content. Within in NBCU, I work specifically for the digital enterprise group, and so we're responsible for creating direct to consumer SVOD services that complement the NBCU portfolio.

Steve: I'm Steve Davis. I work for Ooyala. Ooyala is a digital video platform technology company based in Silicon Valley and New York and all over the world, and power some of the largest broadcasters and operators, their entire OTT platform, which we'll talk about, I'm sure, today.

Rebecca: Yes, we will. Okay, so we've had some nice chats about “paradigm shifts.” There are lot of these shifts happening in digital, digital media, and particularly in video, especially in the last, I would say, nine to 12 months. Obviously the proliferation of technology and new devices has really changed the way consumer are dealing with video and they way advertiser and publishers are also dealing with video and the responsibilities there. Digital video companies and digital media businesses have been looking at OTT and have been asking a lot of questions about OTT.

I, personally, at my company, CakeWorks, which is a boutique digital video agency, and we sort of problem solve across the spectrum of digital video. There was this moment last July when I turned to my co-founder and I was like, "Seriously, OTT is the new black." It started because there was a week, you think it's the summer, July, people are supposed to be on vacation, we were getting overwhelming inbound questions, what is OTT? I need OTT, I'm in Hollywood, I'm building an OTT app, I have to have OTT. I'd look at my staff, and we would say, "Do they even know what OTT is? Do they know what they're asking for and what they're asking to be built?"

Before we get into OTT, this is a very acronym heavy business, digital video, we have SVOD, AVOD, OVP, CPMs, CPVs, and I'm pretty sure we're all OC about OTT, but so far as that is, let's define it, because I'm not sure that everybody in the room is particularly familiar with it, and certainly the industry that we work in actually doesn't understand quite all the way what OTT is. OTT stands for Over The Top, but would each of you like to take a one sentence on what is OTT actually?

Ed: Sure, OTT is distribution of content without a middleman.

Patricia: Yup. It is, at least in the broadcasting world, it is a being able to deliver video or content or entertainment directly to a consumer without a MSO. I'm going to throw in acronym to describe a MSO.

Rebecca: MSO, MVPD, these are the big cable operators, so the Comcast, the Time Warner Cables, the Telstras of the world.

Patricia: That's right.

Steve: Yeah, it's a disruptive shift on how to reach consumers, so just piling on what these two just said. It's another way for a consumer to get content without having to be beholden to the large cable companies.

Rebecca: This is important, because there's a whole world of people that think any app is OTT, so just because you have an app on your iPhone, that is not an OTT experience and it doesn't come with any of the issues that we are working with in the OTT space. Okay, so for you all out there, how many of you do not have cable TV at home?

Ed: I do not.

Rebecca: Well, there you go, Patricia, there's your answer. How many of you pay for Hulu? Not steal your roommates, your neighbors, pay actually for Hulu? How many of you watch Netflix more than once a month? More than once a week? How many of you have a Xbox? Roku? Apple TV? How many of you have all three?

Ed: Sorry, I'm kind of in the business.

Patricia: I was like, "That's odd."

Rebecca: Ooyala also. With these devices, do you watch TV or video on those devices at home more than once a month? Streaming video on devices more than once a week? Every day? Awesome, we have a very OTT savvy room, which is excellent. We, I think, are at the proverbial inflection point on this whole distribution of video content or do you think we're well beyond it when it comes to OTT? Steve, this is like you've been living the dream, the technology front of this.


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Steve: Yeah, this is the last 18 months of life. I would start with we've come a long way in 18 months. Most of the opportunities that our company has talked to 18 months ago folks were asking us to define it for them. We need to OTT, but we're not quite sure what the hell it is, 18 months later now it's much more of I don't want to be live at apps, but can you help us describe the best approach to get an OTT offering and should we do it? Where we are now is much different than we were 18 months ago? We're at not quite the tipping point, now we're at the it's much more being adopted on a larger scale, where every single company at least is having a strategy, whether it's good or bad, is having a strategy about OTT to go to market.

Rebecca: Let's define that. Every company, meaning every media creator, publisher in possession of premium video content?

Steve: Well said. I mean broadcasters, operators, digital publishers, everyone realizes that a direct to consumer or a new way to reach that consumer is a must, they just might know the best approach to do it yet; where 18 months ago it was much more questioning, “Steve, can you guys come in and help explain what OTT is and what we should be doing.” It's gone, in 18 months. It is the fastest adoption technology I've seen, probably, in my career.

Rebecca: Patricia, from the content perspective when do you feel like you guys made this decision? Like we want to go in this direction, we kind of understand the ad model, we understand the subscription model, how did you set out to do Seeso? Actually, why don't you tell everybody what Seeso is?

Patricia: What Seeso is? Let me explain kind of the genesis of how we thought about it first. We wanted to solve a consumer problem, not a business problem, so what we were seeing is these big OTT players were really great services if you knew exactly what you wanted to watch or if you were in the middle of a binge. If you just want to watch something good, you kind of sit there and surf for 20 minutes and then you get frustrated and then you leave. The reason is because they've become this kind of supermarket of content, they have kids and horror and everything in between, and it's kind of the Costco effect. You guys have ever gone to Costco, I have three kids at home, so I go all the time, it's overwhelming. What we wanted to do is to essentially be the anti-Costco. We wanted to be the neighborhood restaurant, the neighborhood café.

Patricia: What Seeso is, it's an SVOD service, which is a subscription video on demand. It's 3.99 a month, it's ad free, and we wanted to be very, very specific and go with one niche genre, and that is comedy. We did a ton of research and we did a lot of ethnographies and realized that comedy, to Trina's earlier point, is this kind of universal language, but there isn't one place where you can find all the comedy you want. I think we have pretty good internet service here, if you guys have not subscribed yet, just feel free to go on your app and download Seeso, it's S-E-E-S-O. It has, really, there's so many kind of nuances and faucets within comedy, so we have standup and sketch and animation and scripted and by the end of the year we'll have 20 original series.

Rebecca: Thank you. Okay, so enters Ed, who has a new startup that is, I lovingly call, OTT in a box. Where did you guys start from and what problem did you think you were solving when you set out to do this?

Ed Laczynski, CEO of Zype © Robert Wright/LDV Vision Summit

Ed Laczynski, CEO of Zype © Robert Wright/LDV Vision Summit

Ed: One thing that resonated with me from Trina's presentation was the idea of this identity crisis in video and I kind of think that-

Rebecca: We actually saw that slide, right?

Ed: Yeah, before the click fast forward, and I thought about that in relationship to what the industry is going through now and it's kind a way to answer your question. I think that there is sort of an identity crisis as content owners are trying to figure out are they direct to consumer businesses, are they simply content owners that are part of an existing supply chain and continue to do business that way. In those traditional ways, and for us, at Zype, we saw a confluence of market changes. There was the availability of bandwidth, availability of devices, the consumers had all these devices in their hands and in their homes, and the advent of cloud computing - that's a technology for developers like us. That we can scale up a business that traditionally would require a tremendous amount of capital to deliver a service to a customer became available to us in the last five years.

Rebecca: It seems like forever.

Ed: Yeah.

Rebecca: I mean just to put it in context, because we've been looking at so many amazing technological advances in the past day here, and this morning especially, I think just the reality for us living in old, traditional media land is that these things have happened really slowly. I mean Evan can tell you that I've been saying this is the year of digital video since about '97, so this is the year it's really happening. I think with these devices, you really can do almost anything you want to from your living room remote or from your phone, it's changed the game for everybody.

Ed: Yeah, exactly. For us, we wanted to build a service that was really easy for business people to use. Content owners that are business people or technologists that need to meet performance goals, we did not want them to have to worry about the assembly as much as the outcomes. We focused our product that way and saw that demand in OTT, particularly over the last 18 months it's been the number one inbound driver for us. We do a lot of online marketing, a lot of content marketing, and we have people coming into Zype.com every day who own content and want to sign up for our service. 90% of them are looking for OTT solutions one way or another, whether it's Apple TV or Roku, whether it's a native mobile experience, but delivered with subscription or transactional video, it's what's driving our business.

Rebecca: Which is great.

Ed: Which is great. We love it.

Rebecca: It's growing fast, which we love. In this world of so much content out there and obviously everybody here who's accessing all these apps constantly in their living room. There was just a piece in Vulture called "The Business of Too Much TV," it was like a couple of weeks ago it came out, and the data point they had in there was between 2009 and 2015 the number of scripted shows (TV-like content) has nearly doubled from just over 200 to 409 last year.

Netflix alone says it will produce 600 hours of original television, which they're probably going to go over, and spend five billion on programming, including acquisition. There's so much content being created, let alone all the YouTube influencers and rising Facebook Live stars, which we're going to be talking about after today. How much TV is enough? When do you feel like your OTT apps are what you guys are empowering and Patricia's creating, when does it become too much? When does the consumer decide they just can't watch it all?

Ed: We know with cord cutting trends there's $1,500 per year of consumer wallet up for grabs for services and you're going to spend half of that for your internet access. So you figure about $750 worth of content, that's what's available.

There is sort of a wallet share, and I think we should look at that for a sense of how much can a consumer spend or the supply chain through advertising afford to create. Apparently it hasn't been enough yet.


I don't think it's necessarily too much TV, I think there's just an endless appetite for premium content...There's no excuse to watch bad TV.

-Patricia Hadden, SVP of NBCUniversal


Rebecca: Not enough?

Patricia: Yeah, I don't think it's necessarily too much TV, I think there's just an endless appetite for premium content. I think it's more about how do we package and deliver it in a way that's convenient for the user. There's no excuse to watch bad TV these days, right? You can watch only what you want to watch and so TV's just part of our social fabric and I think if you go onto any social platform you'll see the conversations are typically around TV - except for now, they're political, because that's entertaining. Again, I think it goes back to who are the platform that are able to package and deliver them in a way that's convenient and that provides a little bit more choice for the user. I think Amazon's doing this incredibly well, because not only can you stream through Amazon, but they have these kind of add on subscriptions or niche genre services.

Now you can stream your videos, but then they have these add-on subscriptions, which are very specific to genres or to your individual tastes. While the audience is way more fragmented, they're also incredibly specific in what they want to watch and so you can see these kind of small, niche services or add-on subscriptions start to emerge that really speak to your individual tastes.

Steve: Yeah, I would just add - what we're seeing in the market is there is a proliferation of tons of content. I always get the question from the wife, “why do we have 863 channels when you watch eight?” She's right, but a lot of guys who want to watch sports don't want to give up ESPN and that's the big gorilla. That's our customer, ESPN thinks “we make a ton of money from the cable companies” and when they do the math, it just doesn't add up.

One thing that we try to help our customers with is using analytics to cut through what your consumers actually want to watch. When I get my subscription to the comedy channel, I can quickly use analytics to get me content I want to see. I don't want to shift through reams of libraries of content. Our customer wants to learn about that consumer and what they like and use algorithms in real time to figure out what Ed likes and what I like. Content is king and with that big of a library in the world of premium content, you need to be very smart about how you stick above and keep that consumer. For us, analytics are a big play in that.

Rebecca: Yeah, getting the eyeballs for the content is the key and for Patricia, you're flanked by awesome technology up here, when you set out on the Seeso mission - was a bigger challenge the content piece or the technology piece or the revenue profitability piece? When you guys looked out and like there's this huge opportunity, but what's the biggest challenge? What did you feel like it was?

Patricia: That's a great question. We're a content creator, so that comes very naturally to us. I think the way that Seeso approaches the program is very, very different from NBC proper. We invest in a comedian or a piece of talent and often times we'll go straight to series, as opposed to the kind of typical linear model, which is a pilot and then green lighting and that whole thing. We have much more flexibility in terms of our programming, but I think technology is always the backbone of the product, especially when you're doing kind of an OTT or SVOD service. We are using a great company called thePlatform, that Comcast partially owns.

Rebecca: Breaking the hearts of the guys on either side of you, by the way.

Patricia: I know, we've already talked about it, but there's other ways that we're going to work together.

The benefit of a product like Seeso, or any SVOD product, is exactly what Steve was saying, which is the real time analytics - the amount of data that we have and the ability to target based on your viewing behavior and based on your sessions. It just gets so granular and that is the key on how we look at what programming is working, what is the programming that is driving the most acquisition versus retention and we can really kind of slice it that way. It's incredibly important.

Rebecca: Steve, in your world of Ooyala, in that universe of hundreds of publishers are you feeling like technology hurdles are becoming easier for them to handle? We have all the content, we can do technology or are you still trying to win them over? I mean you're always trying to win them over, but from an understanding perspective.

Steve Davis, VP & GM East of Ooyala © Robert Wright, LDV Vision Summit

Steve Davis, VP & GM East of Ooyala © Robert Wright, LDV Vision Summit

Steve: Yeah, you're always trying to separate out, right? The technology hurdle, again, in the last 18 months has changed dramatically. The bigger hurdle is getting 10 people in a room to agree on what the OTT strategy means and is and we always try to help say the business case needs to be there for that technology. We can do SVOD, TVOD, AVOD, as you said, and these are all the things that get thrown out, but if you don't have a subscriber database already and you're already a freemium content provider, switching it completely to SVOD might not make sense right away.

Rebecca: Well, I think that, like we remind our clients all the time, if you're going to make an app, whether it's an app for Apple or an app for Roku or whatever, there are screens and screens and screens of apps. It's not just that you've chosen your target demographic of female Millennials living in coastal cities, per se, and you think that you're just going to find them if you create an awesome app and you leverage this technology or some others, because there are so many out there. What are you doing to actually market to them and how are you getting them, whether it's social or other tech? I mean Zype, I think, answers some of these questions, right?

Ed: Yeah, and we try to educate our customers who have only worked within a traditional distribution supply chain have relied on others to market. They have relied on others for discovery and promotion and part of it is this ecosystem is teaching these content owners how to market, how to promote, how to discover, how to do CRM. The fact that they can know who their viewers are is a big deal.

It sounds simple, but it's a really big deal for them. We have tools that when someone cancels or we think they're going to cancel, because they haven't logged in a while, it gives them some alerts and then they can plug into MailChimp or some other email service and do something about it; they can use the ecosystem of software that's available out there.

Rebecca: Actionable data.

Ed: Actionable data.

Rebecca Paoletti, CEO of Cakeworks, Ed Laczynski, CEO of Zype,  Patricia Hadden, SVP NBCUniversal, Steve Davis, VP & GM East of Ooyala (L to R) © Robert Wright/LDV Vision Summit

Rebecca Paoletti, CEO of Cakeworks, Ed Laczynski, CEO of Zype,  Patricia Hadden, SVP NBCUniversal, Steve Davis, VP & GM East of Ooyala (L to R) © Robert Wright/LDV Vision Summit

Rebecca: Love actionable data.  

Ed: And do something with it and so the engagement data is really important, but also the subscription metrics and all that stuff is what they really care about, that's revenue. We often talk to our customers about having that business strategy up front, have it decided. If you've never done this before there's budgets you need to put together. While we, I think, are offering a service at a very disruptive price point, there's still going to be costs and marketing and promotion, discovery. This isn't just hit a button and all of a sudden you'll have a million subscribers tomorrow. It's really hard to build subscription businesses of any sort.

Rebecca: Okay. Wait, we're all building within organizations that have to stay profitable, so nobody has the opportunity to just invest in OTT, like it has to make money out of the gate. My last question is, are we living in app world? Thank you, Steve Jobs, for putting us there in the first place.

Ed: I think we are. I think that not only us of a certain age demographic on this panel, but as we're minting more consumers every day, they live in that world. As human beings in the modern era we're all trained to use apps, purchase through apps, subscribe to things, buy things, and I think the genie's out of the bottle on that, so absolutely.

Rebecca: Yup. A universal agreement. Okay, so audience, are there any questions for the panel? I have dozens more.

Audience Question 1: Good morning and thank you all. Interesting panel. Adaora Udojo. Rothenberg Ventures. Question to each of you, thoughts on what incumbents or legacy media companies are doing well with their digital strategy and figuring out how to integrate not only the technology, but also the strategy, as you mentioned, Steve?

Patricia: I've been incredibly impressed. I've only been at NBC for a year, and the fact that NBC and Comcast are investing in companies like Vox and BuzzFeed really speaks to being very open to being part of the digital conversation, which typically you don't see networks participating.

Steve: Probably not a popular thing to say, I don't think any of them are doing it wonderfully. I mean the closest that came out, but also has had tons of issues, HBO and HBO Go and HBO Now. They're on the right track, but even as a consumer, I mean I moved to HBO for one reason and it was Game of Thrones, right? You want your fix, you want GoT, even if it has had glitches, hiccups, all that stuff. I would say they're a leader. Leslie Moonves of CBS, he's out there every day. As a leader position, I would say CBS and every statement Les makes is about it...he gets it. He's going direct to consumer and he doesn't care about what the cable companies are going to make him or make him not do.

Again, in the pragmatic approach, you're looking at subscribers of their Showtime channel and everything else. If you look at the revenues and you look at how they're really doing, there isn't a single, large, top 10 media company, who's absolutely killing it. The guys who are killing it are the digital companies, who may not have had the old way of thinking and there's a number of companies you can talk about, like Toca Boca - if anyone has kids, it’s kids apps. There isn't a kid who 6-12 years old who hasn't downloaded a Toca Boca app, right? Those are the guys who are killing it, because they're not beholden to old approaches and old way of thinking. The sky's the limit still for all of the old time kind of media companies as they are.

Ed: I'd agree. I think that to give some faint praise for a huge media company, who's done something with a popular thing that fans really care about, is the fact that you could buy Star Wars Episode VII through EST on an Amazon Fire device. To me, it was like, okay, that door's opening there, where they let that happen. Disney was a company that sold limited VHS tapes and you can only get that tape for six months back in the day. I think that it will change, but I would agree that none of them are killing it, except for Patricia.

Rebecca: Oh yeah, next question.

Audience Question 2: I'm Michael Cohen, Facebook. Looking out into the future, I'm wondering what you think the role of Live will be in the entertainment market?

© Robert Wright/LDV Vision Summit

© Robert Wright/LDV Vision Summit

Steve: Ooyala was one of the four companies named at the F8 Facebook keynote speaker event, so we were first to market with our live platform being fed directly to Facebook Live. With that said, social is ruling ... Again, 18 months ago people were asking “what are we going to do, what is OTT, we don't even know?” Now it's not good enough just to have a live stream and an app, it's “how do I get it to Facebook and actually capture my audience in Live?” Look at the messaging and change - that's what happened in 18 months! For me the number one, two and three things that come up are apps, which apps, and how to work with different social channels.

Rebecca: The only thing I would add there is live hasn't been able to be monetized until really recently. This is a thing, like we were streaming live, we had the capabilities to stream live, but you couldn't put an ad, you could barely run an ad on the same page as a live stream. There was no way to make money, so we're all bearing these huge bandwidth and hosting costs for live streaming, but you couldn't actually do anything with it. Now, that's totally changing. You can clip out. You can insert brands. You can do products. All of these things.

© Dean Meyers, VizWorld/LDV Vision Summit

© Dean Meyers, VizWorld/LDV Vision Summit

Ed: We believe live is huge for us. This year we're seeing almost every new deal we do has some sort of live component. Our most popular subscription business customers - these are either broadcast natives, a talent that came from like a Sirius Radio or a morning TV - are now doing their own daily show or politics or news, it's all around live. They're all monetizing it with subscriptions and pass plans, like buy five days of content. We're also seeing sports as being a big driver for live.

Much like ESPN started with niche sports, like darts and bowling and stuff like that. We just live streamed the U.S. men's polo championship and they had 12 drones in the air and feeding it through a video switcher on a truck onsite through our platform and then out. They did really well with it. Live is going to be really big and I think the social platforms are the discovery platforms for live. That's where you're going to engage that audience and then the trick is how do you get them to monetize outside that social platform in a meaningful way?

Rebecca: Or pay for it, right? Consumers will pay for a lot, definitely will pay for a lot of access to live, especially when it comes to sports.

Patricia: We actually have once a month we do a live streaming comedy show from the Barrel House and we just send out a quick email before, and we do see an uptick in engagement and an uptick in subscriptions right around that show. I agree with everyone, live is not going away. I think there is still that draw and so we're going to continue to do that.

Audience Question 3: Hi guys. My name is Brian Storm, I run a small media company called MediaStorm. I'm curious what you guys think about really independent, small niche companies. Can we play in this space? I mean I've got a five, six person company, we do 30 films a year, we have a 150 countries hit our site, but we're not on any of these devices yet, because it's just so hard. Are you guys building a solution for niche, independent media players? I used to work at NBC and things were easier, but now I don't and it's harder. Can you meet our needs?

Ed: We do. Our platform could work for you, so we should talk after the meeting.

Steve: I was just going to answer, for us it's not what we would build. Our platform is for customer of ours like an ESPN or Vice and that kind of thing.

© Robert Wright/LDV Vision Summit

© Robert Wright/LDV Vision Summit

Audience Question 3: The big boys who have big money. We're a small, little pain in the ass.

Steve: There's no question, that is our market, but what I was going to say is, there's so many levels of solutions, you have to find the one and if Ed's company is the one that fits, then that's perfect. You don't want to waste your own time talking to companies like ours, because we will lose money working with company like yours. There are tons of companies on the smaller end that would be a great fit for you, so you that's why you want to kind of get through that.

Rebecca: The great thing about now is everybody can be a creator and everybody can have an app on any device, it's just the reality. Even the smaller influencers, I only have a million subscribers and not a 100 million, there's really room to play there. You're being modest, your content is awesome.

Audience Question 4: Hi, I'm Jacob Loewenstein, I run MIT's VR and AR community, formerly of BuzzFeed. As a "Millennial," I'm a little confused about the use case for live. I get watching live sports and wanting to be a part of that experience you can only consume while it's happening, I get wanting to interact with an influencer, because this is someone I think is cool and I want to talk to that person. But beyond that, the use case for live doesn't really fit, at least for me personally, with how I consume content, especially if you're talking about OTT and Netflix and the idea of like curation. I guess I'm just confused about if I'm scrolling through my social stream am I really going to stop to watch something live outside of those two use cases?

Rebecca: Is that because it feels inconvenient to you?

Audience Question 4: Inconvenient, not curated necessarily in the moment to what I actually want, I can't develop my own sort of lineup of things to watch in a sequence. It doesn't fit some of the other trends, at least I feel like, I'm seeing and how content fits for people like me.

Rebecca: Patricia, first, then I know Ooyala and Steve you have a lot of data around this, and, Ed, you're starting to, but Patricia, when you guys set out to do live, you definitely see engagement.

Patricia: Well the reason we set out to do live is because the service that we created is specific for a comedy nerd, right? While we're very lucky, people who live on the coasts, we have access to comedy clubs, there's this whole kind of middle part of the country that may not necessarily have access that we have, and so what we're seeing is people want to be part of this comedy club. They actually want to be part of the audience, whether it's virtual or actually sitting in the stands watching, or in the audience watching, a comedian perform.

There is an energy exchange that is going on, so that is what the intent behind what we're trying to do with our live comedy stand-up and it seems to be working. It's really interesting to see, again, back to data, but it's really interesting to see the engagement across the country, and exactly to your point, we see on the coast people watching it on demand. They're going to watch it when they're going to watch it, and we see kind of everyone else, they're going to watch it live.

Ed: Imagine if you're a gamer and you use Twitch, you're watching live gaming, taking that same concept, we have customers that are doing live viewing parties, so everyone's kind of chatting and talking about what they're watching and because it's meaningful for them. It's a way to break through the mold, where live used to be only way you could consume a content. In traditional broadcasting it was all live. Now it's sort of an exceptional way to maybe drive some additional marketing or draw or interest from activated consumers that really care about that content.

Rebecca: That's a great question. Are there any other questions?

Audience Question 5: Hi, my name is Christina, I'm the CEO and founder of Seena Books and we are into the consumer behavior measurement. Steve, you were talking about analytics and the importance of this, how are you measuring engagement and what are the current trends that you see on this? For example, some companies are already integrating facial recognition of emotions, for example, to integrate at another level of engagement, and what are you doing in terms of that?


All the analytics we do is within video and we can pull in data from other places, but it's always around video.

-Steve Davis, VP & GM East of Ooyala


Steve: That's a great question. For Ooyala, specifically, it's all within video. All the analytics we do is within video and we can pull in data from other places, but it's always around video. Then within video it's broken out into a million different segments. Is it a live stream? Is it VOD? Is it by app or device? Is it by geography? Then when you get into engagement that opens up an entire other universe of metrics. Engagement to our publishers, though, typically leads back to revenue, it's all about driving the business. The analytics we offer, facial recognition could be the coolest thing in the world and emotional and if it doesn't drive revenue, typically, our publishers don't care. I'm not saying it's not valid for someone else.

For us, they want to tie back in this video did the consumer drop off after we added a mid-roll, and if so, we wanted to keep that consumer all the way to our post-roll. Did we put too many adds in? Should we have no adds in? That's what they're trying to figure out, so when we talk analytics and video, it's in real time, it's algorithms, and it's getting discovery and content recommendation, so in an ad-based world you get that consumer to click one more video and those are all the pennies dimes that add up for our consumers. That's the analytics we're looking at. In a SVOD world you're trying to reduce churn; how do you keep those subscribers on your system? All right, so that's what we mean by analytics. The word analytics is funny, but within video it can take off. We have customers who are click to buy, they're retailers, so the analytics that are important them.

North Face has a video of a guy climbing up and you put your cursor over the backpack, they want to get that backpack to the shopping cart. It's not ad-based, but it's tied to revenue at the end of the day. A lot of it leads back to how do you help your business with video, and it's funny, the live question over here from the gentleman from MIT, our data follows exactly what he just said about, which is Millennials will sign up for VOD, unless it's a BlizzCon event, then they've got to watch that live.

Patricia: Well, I was going to say, I think it's a great question, and it also really speaks to there's no standardization of digital analytics, which is what we're all kind of just circling around. We do the same exact thing. We have our typical KPIs are around engagement and retention and churn and how to mitigate that. But what we're also thinking about, and at Seeso we talk about a lot, is how do you quantify customer delight and how do you get to a better laughter score? We've kind of created this algorithm internally, where we take these four pillars, curation and time to choice and brand equity and shareability, and measure ourselves against a competitive set to see if we can actually get you to laugh more, to laugh better. Again, all this to say that there's this space available where it's not a Nielsen rating, where we're still trying to figure out what is that digital standard.

Rebecca: Thank you. Thank you. All right, we're done. Now, lunch?

The annual LDV Vision Summit will be occurring on May 24-25, 2017 at the SVA Theatre in New York, NY.

Computer Vision Will Disrupt Many Businesses Especially Manufacturing and Consumer Shopping

Howard Morgan, Founding Partner at First Round Capital & Director at Idealab

Howard Morgan, Founding Partner at First Round Capital & Director at Idealab

[Reposted with updates for our next annual LDV Vision Summit on May 24-35, 2017 in NYC.]

Howard Morgan is a founding partner at First Round Capital and director at Idealab. He began his career as a professor of decision sciences at the Wharton School of the University of Pennsylvania and professor of computer science at the Moore School at the University of Pennsylvania from 1972 through 1985.

We are honored that he was one of ~80 expert speakers at our 2016 Annual LDV Vision Summit. This post was the virtual start of our fireside chat with Howard virtually and you can watch the extended live version here

Evan: First Round Capital “FRC” has invested in many visual technology businesses that leverage computer vision. In the next 10 years - which business sector will be the most disrupted by computer vision and why?

Howard: Both the manufacturing inspection area, which has had various types of visual tech over the years, and the consumer sectors, particularly shopping, will be disrupted. You will be able to shop either by asking Alexa for something, or showing it to her - or her equivalents with a camera.

Evan: There are many AdTech companies in FRC's portfolio. Several sessions at our LDV Vision Summit will cover how computer vision is empowering the advertising industry with user generated content, to tracking audience TV attention and increasing ROI for marketing campaigns. What intrigues you about how computer vision can impact this sector?

©Peter G.Weiner Photography

©Peter G.Weiner Photography

Howard: Our GumGum investment, along with our investment in Curalate, both make heavy of use of computer vision to determine and target users in the visual sectors.  People will be shown contextually relevant ads or additional information based on the pictures they’re looking at, or creating on the various social media platforms.  This will get much more specific and lead to directly shopping from images - something that’s been tried but has been hard to do well with mediocre image recognition.

Evan: FRC has a very unique approach to celebrating the New Year with your great annual holiday video. It seems like a successful video marketing initiative. How did that idea originate and what was the goal? After years of doing this holiday video - what is your advice for your companies that wish to create an annual video marketing initiative.

First Round Annual Holiday Video 2013 "What Does the Unicorn Say?"

First Round Annual Holiday Video 2013 "What Does the Unicorn Say?"

First Round Annual Holiday Video 2015 "This Time It's Different"

First Round Annual Holiday Video 2015 "This Time It's Different"

Howard: Josh and the team decided we wanted to have fun with our companies, and show the power of the First Round Network as one which not only has high performing companies, but also great fun. And it was our way to feature the companies, and not just our partners in our holiday greetings to the world.  Our advice to those who want to do an annual video marketing piece is to be creative - we have chosen the parody, but there are lots of other ways to be creative.

Evan: You frequently experience startup pitches. What is your one sentence advice to help entrepreneurs improve their odds for success.

Howard: Get really good at crisply telling your story - why the product is needed, and how you’re going to make money with it.

Evan: What are you most looking forward to at our LDV Vision Summit?

©Heather Sullivan

©Heather Sullivan

Howard: LDV Vision Summit has a great view of the future of vision and related technologies. I always want to be learning about the way early technologies that will impact us over the next decade, and LDV is where I hope to find some of that information.

Howard Morgan spoke in a fireside chat with Evan Nisselson at the 2016 Annual Vision Summit. Our next LDV Vision Summit will take place on May 24 & 25 (Early bird tickets at 80% discount available until March 31).

Other expert speakers at our 2017 Summit are from Union Square Ventures, Facebook, Google, IBM Watson, Twitter, Pinterest, Lyft, Twilio, Glasswing Ventures, Cornell Tech, and many more...

Entrupy Secured over $8M in Inventory for Louis Vuitton, Gucci and Others after Being a Finalist at the 2015 LDV Startup Competition

Vidyuth Srinivasan, Co-founder and CEO of Entrupy, Inc ©Robert Wright/LDV Vision Summit

Vidyuth Srinivasan, Co-founder and CEO of Entrupy, Inc ©Robert Wright/LDV Vision Summit

The LDV Vision Summit is coming up on May 24-25, 2017 in New York. Through March 31 we are collecting applications to the Entrepreneurial Computer Vision Challenge and the Startup Competition.

Vidyuth Srinivasan, Co-founder and CEO of Entrupy, Inc was a finalist in the LDV Vision Summit 2015 Startup Competition. Entrupy’s goal is to enable trust in high-value goods transactions by providing on-demand authentication solutions to every business. They now support luxury brands such as Louis Vuitton, Chanel, Gucci, and many more. Vidyuth gave us an update on Entrupy since their stellar performance at the Startup Competition:

How have you and Entrupy advanced since the LDV Vision Summit?
We have since launched a product, acquired over 100 paid business customers, expanded our team to 10 (+4) and signed partnerships with large marketplaces.

What are the 2-3 key steps you have taken to achieve that advancement?
Focus on product, demonstrating value of the product to our customers, and building strategic partnerships to demonstrate value at scale.

What is your proudest accomplishment over the last year?
We helped secure over US$8M worth of inventory for our customers in 2016 by authenticating and assisting in the sale of high-end handbags with our on-demand device. In the process, we helped remove over US$1M worth of fake goods from the market. We also rapidly grew our customer base to over 100 paying business customers.

Vidyuth Srinivasan ©Robert Wright/LDV Vision Summit

Vidyuth Srinivasan ©Robert Wright/LDV Vision Summit

What was a key challenge you had to overcome to accomplish that? How did you overcome it?
Scaling up our data collection and launching more product to customers was a challenge. We overcame it through focusing on the right data partners and being patient about our learning before launching more product support. It helped us avoid a ton of pitfalls in launching too early and helped us gauge product experience even when we deliberately added lag into the user experience.

What are you looking to achieve in 2017?
Scale our customer base, make the product experience so compelling that they think it's magic!

Did our LDV Vision Summit help you and Entrupy? If yes, how?
The Vision Summit helped us meet some good folks in the Vision field and also put in perspective the different ways to solve vision problems.

What was the most valuable aspect of competing in the Startup Competition?
We received very specific feedback on which parts of the business to focus on and which secondary details to leave out. This led to a lot of refinement of our ‘story’. Also, we were asked fairly specific questions which helped us realize where we needed to have stronger answers (which meant doing more homework).

We ended up having some abstract conversations on the technology and application with technical experts, which helped reinforce the importance of what we’re doing and where this could go.

What recommendation(s) would you make to teams submitting their companies to the LDV Vision Summit Startup Competition?
Be clear, concise and lead with the best thing about your startup. Your pitch isn't your entire company, it's your screenplay for a great movie. Leave things that are 2nd order details and be choosy about what adds clarity to help people understand/appreciate your company more.

What is your favorite Computer Vision blog/website to stay up-to-date on developments in the sector?
http://news.mit.edu/topic/computer-vision

Applications to the 2017 ECVC and the Startup Competition at the LDV Vision Summit are due by March 31, apply now.

The Conscious Home is Achievable in the Next 15 Years: Your home will leverage visual sensors to be smart enough to understand what you want or need

In no order: Jan Kautz, Director of Visual Computing Research at NVIDIA; Gaile Gordon, Senior Director Technology at Enlighted; Chris Rill, CTO & Co-Founder of Canary at the 2016 LDV Vision Summit ©Robert Wright/LDV Vision Summit

In no order: Jan Kautz, Director of Visual Computing Research at NVIDIA; Gaile Gordon, Senior Director Technology at Enlighted; Chris Rill, CTO & Co-Founder of Canary at the 2016 LDV Vision Summit ©Robert Wright/LDV Vision Summit

Join us at the next LDV Vision Summit. Visual Sensor Networks Will Empower Business and Humanity panel discussion is from our 2016 LDV Vision Summit.

Moderator: Evan Nisselson, LDV Capital with Panelists: Jan Kautz, Director of Visual Computing Research at NVIDIA; Gaile Gordon, Senior Director Technology at Enlighted; Chris Rill, CTO & Co-Founder of Canary

Evan Nisselson: Thank you very much for joining us. You heard and saw my recent article about Internet of Eyes being larger than IoT market opportunity. Before we jump into that, I'd love to hear two sentences from each of you. What do you do at your company and what does the company do? Gaile, why don't you take it away.

Gaile Gordon: Okay. So, I'm at Enlighted. Enlighted introduces dense sensor networks into commercial buildings through lighting. We introduce dense sensor networks into commercial buildings through lighting. We use the sensor networks to control how much energy is used by the lighting, which is what pays for that venture, but it also produces interesting data sources which are used for HVC control and space planning. I've been there since January and I work with the CEO on the next generation of sensors and applications that run on that network.

Jan Kautz: I lead the visual computing research group at NVIDIA. That is to say I do the research which my team on computer vision and machine learning. NVIDIA probably doesn't need a lot of introduction. You all probably know graphic cards are what we're known for. Recently, we use them more and more. Generally, we sell them to all types of markets, including self-driving cars to cloud and so on.

Chris Rill: I'm one of the founders and CTO at a company called Canary here in New York City. We build devices that protect your homes and other environments. We've packed an entire security system; an HD camera, microphone, and light safety sensors into a device the size of this bottle of water. We connected ones to AWS and we send you alerts when we detect anomalies in your environment. You can control this system all from your smartphone.

Evan Nisselson: So, one of the things that I love is a very diverse panel from three totally different perspectives and that can be challenging, but there's also going to be a lot of synergies. Gaile, why don't you kick it off and tell us a little bit about what's a smart commercial building?

Gaile Gordon: The primary thing that makes a building smart is that is has sensors. It has a way of reacting to what's going on in the environments. Using, for instance, where the people are in the building to control at a very fine degree how much energy is used by the lighting and also how much light is coming in from the windows to change the dimming levels, etc. But more than that, the source of data that you use to study what the behavior patterns in the building are, which is really interesting thing for other applications.

Evan Nisselson: For what?

Gaile Gordon: For space planning, for instance. So, to make sure that you're using the building to its best efficiency. That your conference rooms are being used, that they are sized appropriately. Then, I think going forward, there's going to be a lot of new interesting things that we can do with the network that's already there. Pay for it through the energy savings. To do things like, active tracking, indoor location, lots of really, really, interesting things that I think people will find valuable to their daily lives.

Evan Nisselson: What's the main ROI for a building to start using these sensors?

Gaile Gordon: The energy savings that is introduced is about 75%.

Evan Nisselson: Okay.

Gaile Gordon: So, it's a no-brainer.

Evan Nisselson: Right.

Gaile Gordon: As opposed to previous applications which studied where people were in the building, which had to first pay for putting that network there and sometimes that was obvious in terms of its value and sometimes it was not. But in this case, it's completely obvious. The money is immediately covered through the energy savings. Which gives really interesting business model to figure out what else to do with the platform that's there.

Evan Nisselson: Right. Chris, take it away from there. Evolution of the sensors in commercial buildings, you're obviously more focused on the home. Tell us a little bit about some of the sensors, in addition to the camera, that are on the Canary devices.

Chris Rill: Sure. When we started designing Canary about four years ago, we knew that video and audio is so important because of our smartphones and the fact that we have been looking at these really crisp images. We wanted to give a better picture of what was going on within the home. That's really the things you can't see, especially when you're not in that environment. So, the temperature, humidity and air quality were really important for us to really understand the context of that environment. For situations where you're monitoring, say an elderly parent or a child, understanding their comfort and understanding the well-being of that environment goes far beyond video and audio. That's one of the reasons why we included those additional sensors. So, that you were almost telepresence in those environments that you were monitoring.

Evan Nisselson: So as a co-founder, why did you start this business? I'm sure there's been some surprises in the technology challenges. Why'd you start it, in a short sentence or two. What's the biggest surprise once you started connecting all this data, because it relates to the signals that are either useful or not.

Chris Rill: For me, it comes from personal experience. About six years ago, my apartment was broken into while I was living abroad. I sought a system to monitor my apartment. Like any engineer, I went to the store to look for something that I could put in my home and it was a local hardware store. There was nothing that you could simply buy and place in the corner of your home. So, I bought these sensors you stick on the windows and doors and I hacked together a camera and a server in the cloud. That was my security system.

Today, companies like Canary are enabling everyone to do that. Not everyone's an engineer. Not everyone has the resources to go and put that system together. That's one of the reasons why I got connected with my co-founders, Adam and John, and why I'm so passionate about using technology to understand what's going on in environments, because I forever have that baggage because of the trauma in my life.

On the question about surprising; I would say it's been so surprising to see how hard it is to build a consumer product company. Not just a device, but to have it be available and working at the quality it needs to work all day, every day. I think that's something, especially, something I'm passionate about, which is security. Which is securing these products at scale, because, I'm actually terrified of the internet of eyes. Because I know it's not just about the algorithms, but it's also about the security of the information that these algorithms are analyzing.

Evan Nisselson: Jan, you're kind of perfectly sitting on the panel in-between both of these opportunities. I liked how you orchestrated that. Tell us some use cases that you're working on today that will relate to both of these. And maybe one that you're most excited about in working on with your team.

Jan Kautz, Director of Visual Computing Research at NVIDIA ©Robert Wright/LDV Vision Summit

Jan Kautz, Director of Visual Computing Research at NVIDIA ©Robert Wright/LDV Vision Summit

Jan Kautz: We recently started working on, which really relates to both of these cases. Using visual sensors to do activity and action recognition in videos. One of the newest cases at home, might be, you have lots of cameras in your home and although you might be able to monitor what's going on remotely, for instance you have an elderly parent that you care for and you're not at home. Your parent falls, you wouldn't know, but your sensors are able to recognize that your parent fell. Call the ambulance directly. Those are use cases that wouldn't be possible otherwise. Those are the things that I think will make a big difference in people's lives in the future.

Evan Nisselson: Is that something that's possible right now? Or are we talking five years out or eight years out? Without giving away the secrets. I mean, how soon is this? Because Chris is smiling. He says, "I want to use that."

Chris Rill: When can I have it?

Evan Nisselson: Exactly.

Jan Kautz: It's a question robustest, right.

Evan Nisselson: How do you define robustest?

Jan Kautz: How many false alerts are you willing to deal with?

Evan Nisselson: So, Chris, how many false alerts are you willing?

Chris Rill: Oh, man. Well, as an industry, security is 99% wrong. So, there's a very low bar that you need to meet-

Evan Nisselson: So, you want it today? That's what you're saying?

Chris Rill, CTO & Co-Founder of Canary ©Robert Wright/LDV Vision Summit

Chris Rill, CTO & Co-Founder of Canary ©Robert Wright/LDV Vision Summit

Chris Rill: Yeah, today. All joking aside, we've seen with the application of computer vision, that we've been able to reduce false alarms by up to 80% and it continually gets better as we get better labels and better data to train our models. So, I 100% agree that false alarms, with that, the first time you freak out because your mother, you think your mother or father falls, you're going to cry wolf and eventually going to shut the system off. That's what traditional security has kind of been having to deal with for years.

Jan Kautz: There's still some way to go.

Evan Nisselson: Okay. Give us another use case while you're on the hot seat. Give me another use case that you're really excited about.

Jan Kautz: I think the other one is self-driving cars. I think that's going to be a big use case for sensors. Sorry, it's not just visual sensors, wherein your case, there will be additional sensors on cars. But it will be a big change to the way we live as well. In 10 years time, everybody will have-

Evan Nisselson: Okay, so today most of the cars that are on the road, how many sensors are in the car?

Jan Kautz: There's still a lot of sensors.

Evan Nisselson: Roughly, what do you think?

Jan Kautz: There's radars, ultra-

Evan Nisselson: Are there 10, 20?

Jan Kautz: 10.

Evan Nisselson: Okay. In 10 years or 15 years, how many will be in the car? Or just one controlling more?

Jan Kautz: No. There will be more sensors. There is disagreement which ones and how many. There will be more, but which ones and exactly is unclear. I'm betting on cameras and radar. Not LiDAR, because I think they make your car look ugly because they're big and bulky.

Evan Nisselson: Just because of a look factor?

Jan Kautz: No one wants to put a big spinning LiDAR on top of a car.

Evan Nisselson: Right, right. Okay.

Gaile Gordon: It doesn't need to spin.

Gaile Gordon, Senior Director Technology at Enlighted ©Robert Wright/LDV Vision Summit

Gaile Gordon, Senior Director Technology at Enlighted ©Robert Wright/LDV Vision Summit

Jan Kautz: It doesn't need to spin. Right!

Chris Rill: There was a Kickstarter campaign that did a small LiDAR unit about $200. I'm not sure how it did. Anyone back that-

Evan Nisselson: There's a bunch of them working on it.

Gaile Gordon: I think it's safe to say there will be 3D sensors on the car.

Jan Kautz: Yes. Which form they take is the question.

Evan Nisselson: What are the options? Cause a technical audience here, so. A technical first day.

Jan Kautz: It could be stereo cameras, that's one way. LiDAR is the other way. We might just need more of them if we don't have a spinning LiDAR, but you could do that.

Evan Nisselson: So, Gaile talk to us a little more about the technical side for the very technical folks in the audience. Because it is our first technical day. What is the capacity of the sensors you have now? Talk to some of the challenges that you're seeing today and in the near future.

Gaile Gordon: So for Enlighted, the sensors that we have now are relatively simple sensors. They're based on thermal data, ambient light, temperature. Things like that. They're already quite powerful. But the challenge's that we see, lighting has to be extremely interactive and all of the competition that you're going to be doing is local. So the big challenges are doing the processing locally, so that when you walk into a room, it reacts quickly. When your network is down, if you happen to have a network interruption, your lights are still working. So, that's processing on the edge, is probably the biggest issue and security, I think is the next one. You don't normally think of hacking lights, but that can have very broad impact. You don't want your smart building system to be hacked, but you also want people to be able to have personal control over their environment, and so the push-pull there is another huge issue that we have. I think getting more advanced sensors into our networks at a cheap enough and pervasive enough level is the next challenge.

Evan Nisselson: You're sitting very close to someone that might be able to influence that. What would you ask for?

Gaile Gordon: Well, it has to be cheap.

Chris Rill: A discount.

Gaile Gordon: I think that's the primary issue, right? The price point that we're currently at, you'd have to be able to form at least the tasks that you can do today, better.

Evan Nisselson: So, just for perspective, how many sensors are on the Canary? How many visual and how many total?

Chris Rill: So we have about, well it depends on what you consider visual.

Evan Nisselson: Your definition.

Chris Rill: Camera, so we've got one.

Evan Nisselson: Okay, so anything that can see thermal, see anything that is not actually a camera as we know it.

Chris Rill: Yeah, correct. We have one camera and then first thing, we've got many different sensors. We have about half dozen of sensors that we use. Some of them we use for user value. Like our temperature and humidity. But also, ambient light sensing so we can turn on night vision and other such features. But the main one's that the user can see are CNR, APAR, the camera, temperature, humidity and air quality.

Evan Nisselson: Talk to us a little bit about how do they talk to each other? Are they triggering actions locally on the device? Are they communicating back with the cloud? How is the smartness connected?

Chris Rill: Sure. Today we leverage the sensors on device to change the internal state of the Canary device itself. We have algorithms that interpret the visual data to try to understand at a very low compute, because the Canary device only has about, since we're talking tech today, only has about 400 megahertz of computational power. So you know, you can't really do a lot there.

So, we try to understand what's going on in the Canary device visually and then we upload that to the cloud for further contextual analysis to try to understand whether we have people, pets or just background noise like a shadow or a light change. Then, from there, if we do detect that there's something of interest for you, we will send you a notification and let you know what's going on.

Some of the other sensors that we use that are really about understanding the environment are temperature and humidity. If the temperature dips or the temperature spikes or the humidity changes more than between 30 to 50%, which is really the comfort zone for the home, we'll send you a notification to say, "Hey, we see that your humidity is low." From an air quality standpoint, that's another whole bag of worms. What is air quality? Is it a pollutant? Is it carbon dioxide? I'm sure people in offices really want to understand what air quality is. So, that's really a qualitative sensor for us to really understand. Is the air changing? So, things like cigarettes and cooking actually influence that. But today, it's still really-

Evan Nisselson: Cooking in the home, you mean?

Chris Rill: Oh yeah.

Evan Nisselson: When a steak smells really good it effects Canary?

Chris Rill: Yeah, I'll get an alert sometimes that says your air quality is very bad. Which, for me, is actually a good thing.

Evan Nisselson: So that brings up the perfect segway of will it tell you when that steak is ready? Medium-rare.

Chris Rill: We're working on it! R&D. One day! There are connected frying pans, though.

Evan Nisselson: So, that's kind of where, you know, that's the interesting things that I think the connection of different signals, not only from Canary, but from the phone and other things that maybe interact with Enlighted or other companies. Where the APIs that we are used to online are then going to become more of the APIs of internet things and internet of eyes. Give an example of what would excite you.


That's the opportunity, really, is the synergy for playing Bingo today. The synergy between all of these different signals. Today I would say we don't do a really good job at integrating all of the different signals in your home and all of the different signals that are publicly available to add the right context.    

-Chris Rill


Chris Rill: That's the opportunity, really, is the synergy for playing Bingo today. The synergy between all of these different signals. Today I would say we don't do a really good job at integrating all of the different signals in your home and all of the different signals that are publicly available to add the right context.

One thing that really excites me that, I don't know if I've talked about this publicly, I have talked to some of you about this, but it's using the excel orometer on Canary. I believe that we will have the largest seismic activity detection system in the world because of the number of units we have deployed, but we haven't yet started looking at that excel orometer data and correlating that with the seismic readings that we get from the different government agencies around the world.

I would say that from an interactive perspective with all of these other companies, we have an integration with Wink. They're an OS for the home that you can control your lights and your thermostat and add a few different sensors. We have an integration with them and surprisingly there are a lot of people who have started to integrate with Canary and with Wink. That's something that we're not super focused on because we feel like the opportunity for us is to empower people with data. Meaningful insights from the environment around them. The control of things is really just a small tangential niche that we're eventually going to get to, but there are companies like Apple and Google and Samsung. They are all fighting over your light switches and your refrigerators and all those other things in your home that you may or may not have. The information in your home, everyone has that and everyone should have access to that so that they can make decisions about the changes that go on.

Evan Nisselson: Gaile, similar to Chris, give us a perspective in the photograph that was in my presentation that came from Enlighted, which is the heat map of the office. A perspective of how many sensors are there that you guys are part of the network and is that going to exponentially increase or are sensors, the next question shortly you'll hear, is it going to decrease but have more power? So you don't need to have zillions. So, give us a perspective.

Gaile Gordon: The Enlighted network is basically spaced every 10 feet. So, there's a sensor package every 10 feet in a grid around the facility. Because every fixture essentially has a sensor package in it, so it's very dense. However, there are other things in the office as well. An RF is one of the big things that is also intriguing. We're here talking about visual technology, but we also have to understand that the sensor fusion solution will be very interesting. You can do a lot of things that traditionally the cavision community had tried to do only with vision sensors, but now that everyone is carrying around a very powerful computing device with radios on it, a lot of things like indoor location can also be either done entirely or augmented with RF as well.

Evan Nisselson: Just as an example for those that are not technical in the audience, when you say sensor packages, what does it looks like? Is it a little box? Can we see it? Is it very small, how-

Gaile Gordon: Yeah. The Enlighted fixtures you can barely see it. It's probably the size of a dime, is what you see. It's not very noticeable.

Evan Nisselson: I assume they're going to get smaller. Which brings me to Jan with the question of: When will these sensors be painted on the wall? So you actually can't see them, so that I'm sure, Gaile's company would love to network the paint-, wall with thousands of sensors inside of them and they can actually talk to Canary.

Jan Kautz: Well, as you mentioned, Apple patented the 2 millimeter camera, so you can place lots of cameras if they're the size of 2 millimeters. You can easily make cameras the size of a dime and place them all over your house. It will be possible. There are questions about security that becomes a real issue at that point. Do you want to stream all this data to the cloud? I would be very hesitant to stream from my hundreds of cameras that are in my house to the cloud.

Evan Nisselson: Okay, let's talk about it for a second. It's the big issue. What's the difference between streaming two cameras or a thousand cameras? Aside from the, on a security level? It's either secure or it's not.

Jan Kautz: The more cameras you have the more they see, right. If you place it just in your living room-

Evan Nisselson: It's less about the vantage point of how many you have. So, if you know where your sensors are or cameras are in the house you can do the things you shouldn't do in a different room. Your issue is, if they're everywhere-

Jan Kautz: If they're everywhere, if I want a smart house, right. If I want to see everywhere in my house so I won't have any blind spots. I don't want my elderly parent to fall in the one blind spot in my house.

Evan Nisselson: Right. So you can't have both. You have to have them everywhere-

Jan Kautz: You process it at home.

Evan Nisselson: Is that going to happen?

Jan Kautz: I think so.

Evan Nisselson: You think so, Gaile?

Gaile Gordon: Yeah, I think it always-

Evan Nisselson: So, what if you processed at home and kept at home, in theory, is safer, which it really isn't if it's connected to the internet, versus on the cloud which is the same thing?

Gaile Gordon: Well, I think one of the things we've gotta think about is the data ownership. Who owns the data and I think in 10 years you might see the camera as not being in the environment but on you. Because when you own it, when you control the cameras you own the data and then the question is a little bit easier to ask.

Evan Nisselson: That's right. Chris.

Chris Rill: I was going to add that the companies that are building these products should build in privacy and control, so that if you don't want cameras on you should be able to turn them off and trust that they're off. But there are other sensors that you can put around your home that still respect your privacy. Trying to understand if someone falls, well there are other types of cameras that you may not really care, necessarily, about the information and if it's going to the cloud. But I do agree that when we look at edge computing, that as semiconductors become less expensive and consumer product companies can put more compute at the edge, that allows us to do some very interesting things both respecting privacy and providing the value and context you need from these signals.


Tickets for our annual 2017 LDV Vision Summit are on sale
May 24-25 in NYC


Evan Nisselson: We've got about eight minutes left, so, we're gonna keep on talking here. Who has questions in the audience? Raise your hand and we've got mics that are going to be passed back there, but, there you go. We're ready already. Let's dig in, go.

Audience Question 1: Hey. My name's David Rose from Ditto Labs in Boston. I have a quick question about Canary. Once you have all these perches in people's homes, do you send video to any other cloud-based APIs for analysis? Or do any of your competitors?

Chris Rill: We do not. There are many competitors now. We've been around for four years, so people have kind of gotten word of us on Indiegogo a couple years back, so I don't know about any other companies that are sending their data to other third party APIs. I do know that there are, kind of, even in this room, other companies that do analysis to try to add context to video, which we do in-house.

Audience Question 1: Gotcha. Is that because of latency concerns? Or why wouldn't you send data to other cloud-based APIs for other services?

Chris Rill: Because, as a company, we do everything in-house or we try to do everything in-house. We have a computer vision team and we want to make sure that that's a defensible intellectual property that we have, so that if you're using a third party, you're gonna have to pay for that.

David Rose: A secondary question is, since you do have cameras in the homes, do you see that there's an opportunity to doing other services, like interior design consulting or bring my cleaner around or give me diet coaching. Based on the things you might know about behavior in the home.

Chris Rill: Not in the near term. We're really focused on the security value proposition. What's going on in my home? Are my kids okay? Is my pet okay? Aging and place type monitoring. Make sure my parents are okay. In the future, maybe. But really, there's so much to do in the security field that I don't see us going into that in the near term.

Evan Nisselson: Who else? Okay, okay. You'll have more chances later. Does anybody have questions? One of the things they say at this point is that one of the biggest challenges of any conference is everybody wants to meet other people. I always feel that when nobody asks questions it's impossible to know who you are. But those that ask the smartest, most interesting questions will inspire those other people to go find you. Look at that! It always works, it's amazing! It's just a very simple statement that just connects.

Patrick Gill: Thank you very much. I'm Patrick Gill from Rambus. I'm a computational, optical research scientist. We're developing, sorry a little bit of shameless self-promotion. We're developing a new kind of sensor that will not produce photographic quality of monitoring wide-angle, sort of in visual space, but may be able to address some of the privacy questions you might have.

Evan Nisselson: So, what's the question?

Patrick Gill: The question is, would it be worthwhile for companies like you to have some kind of sensor that's of intermediate resolution? Much more resolution than a PIR sensor for instance and yet would not be able to read the text if documents you're working on or be able to identify people by face. Is that a pressing concern that you folks are seeing from the industry of providing privacy against facial recognition data and snooping in case your devices are hacked?

Evan Nisselson: Gaile, Chris?

Chris Rill: Yes, we should talk. You know, there are places that cameras just aren't meant to go right now. Like bathrooms and other places where there's an expectation of privacy. Different types of sensors perhaps that fits in the obligation you have to create private environments in your home, for instance. That's something that I'm really excited about, because there are places our current Canary cannot go. We want to make sure that we're able to monitor all environments, but do so responsibly.

Gaile Gordon: I would agree and I think that beyond, and I don't know about your product, but thermal data, 3D data, there's a lot of types of data that can be used to understand occupancy and what's going on in a room that are not really invasive of your privacy.

Evan Nisselson: So, Jan, tell us, you mentioned in emails with me one of the aspects that you guys are working on is gesture and that's a big project.

Jan Kautz: Right.

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

Evan Nisselson: Give us some use cases of that. What you're working on that you can share and how does it relate to this topic?

Jan Kautz: Right. Gesture recognition is something we've been working on for awhile now. The first use case was in car user interfaces. If you buy a BMW 7 series it already has a gesture recognition interface.

Evan Nisselson: What does it do, what does it-

Jan Kautz: It's fairly limited. You can take a phone call. I think stop playing music. Things like that. There's only five or six different-

Evan Nisselson: So what do you do to take the phone call? You point to it?

Jan Kautz: I think you make a specific gesture. I forget what it was.

Evan Nisselson: Okay.

Jan Kautz: You point to it. There's sort of a cube in space where you gesture and it recognizes whatever gesture you did, but there's only five or six different-

Evan Nisselson: So those people I see doing that are not crazy? They're talking to their car?

Jan Kautz: They are gesturing to their car, yeah.

Evan Nisselson: Okay.

Jan Kautz: We have a system that allows more gestures and you can add new gestures on the fly and it's more reliable than the BMW one. The challenge here is that for user interfaces you really don't want latency. When you press a button you want immediate feedback, you don't want to wait half a second. The same for gestures. If you make a gesture and it takes half a second for anything to happen, you never know. Did it work? Did it not work? So you want immediate feedback. That's actually quite tricky for this type of interface. Because you don't know if the gesture's ending, has it ended, is it still ongoing? So you need smart algorithms to be able to very quickly, rule out any latency. That's something we have. It's actually related to action and activity recognition videos, the similar problems the gesture is slightly easier because it's more specific. So you know, yeah. There's only 20 gestures so it's easier to know what they will be.

Evan Nisselson: Got it. Any more questions from the audience and also questions you guys have for each other? You have three different opinions here that you can ask tough questions and we'll see if we get answers. Go.

Audience Question 2: Hi. You guys have all touched on the security industry in many ways. Do you see any trends that are going to get the security industry to use extra sensory data? Stereo cameras, range finders. Flir, I guess, is doing some infrared stuff, but is there any hope on the horizon for the very traditional security camera, mono camera?

Gaile Gordon: Maybe to just jump in with a quick ... There's security and then there's a lot of other applications which are very related. Retail, for instance. The retail environment has always been very interested in slightly different data compared to security. For instance, they want to know whether there's adults or children in the space. Which is a classic use of 3D sensors. So it's something required since you happened to mention 3D.

Audience Question 3: I've been working on a project called the Human Face of Big Data for the past couple years and one of the things we've found in the course of doing this project was that General Electric and GE were working on a series of products aimed at aging at home. I'm curious if the Canary ... One of them is called the Magic Carpet. You install a carpet in the home of your loved one and it actually builds, sort of a model, of what's normal for your own parent and then predicts that your parent may fall just from muscle weakness or a change in their base behavior. These devices that you always see on TV, you know, I've fallen and I can't get up. They have this sort of shame factor that nobody wants to wear them, but now there's this gamification of health. Are you integrating with any of the Jawbone or Apple watches or any of these other devices that are not your own stand alone device?

Chris Rill: We're not, but I do think that's an opportunity to help those that are aging in place. Twenty percent of our, sorry, different stat, about a third of our customers are actually 50 and over. They will start to age in place as they get older and it's an opportunity for Canary and other companies like Canary to provide services and technology to kind of monitor those folks that are getting older and may ultimately start living alone and need the assistance of people or technology to be more independent.

Evan Nisselson: Okay, great. We're just about out of time, but two quick questions that if you guys can answer in one sentence answers. What are you most excited about in this internet of eyes and/or visual sensor sector that's going to happen in 15 years or 10 years? Or sometime in the future that says, "Wow, I can't wait for-."

Gaile Gordon: My favorite would be augmented memory.

Evan Nisselson: Which is in one sentence?

Gaile Gordon: Which is, where did I meet this person last?

Evan Nisselson: Okay, perfect. Jan.

Jan Kautz: For me it's a confluent of three areas, which is computer vision, machine learning and robotics. In 10 years or in 15 years we'll see a lot of new, very interesting robots that have capabilities that we've never dreamt of.

Evan Nisselson: Like what? In one sentence.

Jan Kautz: People helping in your home. Like a butler.

Evan Nisselson: A butler. It's going to happen. Okay.


The conscious home is definitely achievable in the next 15 years...your home will be smart enough to really understand what you want or need

-Chris Rill


Chris Rill: I think the conscious home is definitely achievable in the next 15 years and I think cameras will help allow the computers to get the context that they need of what's going on. But it will be the combination of all these different signals that are coming in that will provide all the context. Hopefully your home will be smart enough to really understand what you want or need. Because today that's just not the case.

Evan Nisselson: Okay. So that last question before we go to network and there's a lot of topics I'm sure people will hunt you down and talk in more detail. Each of you, there's a lot of smart people in the audience that want to start businesses. What would you suggest they focus on that would be great for the industry? Separate from what you guys are focusing on. What's another thing that leveraging visual sensors that you can't wait for someone to start working on? Go, Chris.

Chris Rill: Well, I would say the advice to anyone looking to go into entrepreneurship is be very self aware of what you're not good at. You're not going to be able to do it alone. You're going to have to find partners that are exceptional at the things that you're not good at.

Evan Nisselson: Great. Jan.

Jan Kautz: I've thought about this for awhile. I couldn't come up with a good answer. I think I'm a researcher at heart, so for me, it's hardest to tell people what businesses they should start.

Evan Nisselson: Or even from your space. What should someone work on as research?

Jan Kautz: I think pick hard and interesting problems.

Evan Nisselson: What's the difference from hard?

Jan Kautz: Something people haven't solved for a long time. Computer vision was one of those problems, which now finally it's starting to work because of machine learning. I think pick hard and difficult problems.

Evan Nisselson: Gaile.

Gaile Gordon: I think taking the systems, full systems approach is the answer to success. You need to have something that works top to bottom and was made to work together.

Evan Nisselson: Fantastic. Round of applause for this panel. Thank you very much.

The annual LDV Vision Summit will be occurring on May 24-25, 2017 at the SVA Theatre in New York, NY.

The Smalls raised capital and have grown 300% year over year since competing at the LDV Vision Summit

Kate Tancred, CEO & Co-Founder of The Smalls at the 2015 LDV Vision Summit ©Robert Wright/LDV Vision Summit

Kate Tancred, CEO & Co-Founder of The Smalls at the 2015 LDV Vision Summit ©Robert Wright/LDV Vision Summit

The LDV Vision Summit is coming up on May 24-25, 2017 in New York. Through March 31 we are collecting applications to the Entrepreneurial Computer Vision Challenge and the Startup Competition.

Kate Tancred, CEO & Co-Founder of The Smalls, finalized in the 2015 Startup Competition at the LDV Vision Summit. The Smalls is a content marketplace that connects the world’s filmmaking talent with the world’s brands and agencies. Since the Vision Summit, Kate has been building The Smalls global operations. We caught up with Kate in February to find out more:

How have you and The Smalls advanced since the LDV Vision Summit?
Following the Vision Summit The Smalls raised investment with Russell Glenister an angel investor who was in the audience. The funding was used to make key hires and improve technology. Following this The Smalls has continued to grow at 300% per year and now has offices in both London and Singapore.

What are the 2-3 key steps you have taken to achieve that advancement?

  1. Surrounded myself with smart people both on my board and in my own time who have helped in all areas of the business.
  2. Focused on technology.

  3. Looked to international markets that are growing quickly in our space.
Kate Tancred at 2015 LDV Vision Summit ©Robert Wright/LDV Vision Summit

Kate Tancred at 2015 LDV Vision Summit ©Robert Wright/LDV Vision Summit

What project(s)/work is your focus right now?
I am focusing on adding key personnel to the business to further cement our position in the UK and assist in the growth in APAC. We are also focusing on new technologies for the business.

What is your proudest accomplishment over the last year?
Opening up office in APAC and our 2016 results.

What was a key challenge you had to overcome to accomplish that? How did you overcome it?One challenge we encountered recently was adapting the makeup of the team to suit our growing roster of clients and their needs. We decided to restructure and bring new skills into the business which really helped to ensure we were providing the best service possible. We have also found communication has become a new challenge as we start to operate in other markets. Gone are the days of us all sitting around a desk together discussing our thoughts and plans.

What are you looking to achieve in 2017?
Continued growth and expansion of our team and community.

Did our LDV Vision Summit help you and The Smalls? If yes, how?
Yes, it introduced us to our now director and angel investor Russell. It was also a great networking opportunity for the business and myself.

What was the most valuable aspect of competing in the Startup Competition?
The exposure and the feedback received from the amazing judging panel.

2016 Judges of the Startup Competition (in no particular order): Josh Elman - Greylock, Brian Cohen - Chairman of NY Angels, Jessi Hempel - Senior Writer at Wired, David Galvin - IBM Ventures Watson Ecosystem, Christina Bechhold - Investor at Samsung, Evan Nisselson - Partner at LDV Capital, Jason Rosenthal - CEO of Lytro, Barin Nahvi Rovzar - Exec. Director of R&D & Strategy at Hearst, Steve Schlafman - Principal at RRE Ventures, Alex Iskold - Managing Director of Techstars, Taylor Davidson - Unstructured Ventures, Justin Mitchell - Founding Partner of A# Capital, Richard Tapalaga - Investment Manager at Qualcomm Ventures ©Robert Wright/LDV Vision Summit

2016 Judges of the Startup Competition (in no particular order): Josh Elman - Greylock, Brian Cohen - Chairman of NY Angels, Jessi Hempel - Senior Writer at Wired, David Galvin - IBM Ventures Watson Ecosystem, Christina Bechhold - Investor at Samsung, Evan Nisselson - Partner at LDV Capital, Jason Rosenthal - CEO of Lytro, Barin Nahvi Rovzar - Exec. Director of R&D & Strategy at Hearst, Steve Schlafman - Principal at RRE Ventures, Alex Iskold - Managing Director of Techstars, Taylor Davidson - Unstructured Ventures, Justin Mitchell - Founding Partner of A# Capital, Richard Tapalaga - Investment Manager at Qualcomm Ventures ©Robert Wright/LDV Vision Summit

What recommendation(s) would you make to teams submitting their projects to the LDV Vision Summit competitions?
Hone your pitch, make it visual and entertaining.

Applications to the 2017 ECVC and the Startup Competition at the LDV Vision Summit are due by March 31, apply now.

Bijan Sabet Invests in Founders Building Inspiring Products That He Would Want to Work For

Bijan Sabet, General Partner & Co-Founder, Spark Capital with Evan Nisselson, General Partner, LDV Capital ©Robert Wright/LDV Vision Summit

Bijan Sabet, General Partner & Co-Founder, Spark Capital with Evan Nisselson, General Partner, LDV Capital ©Robert Wright/LDV Vision Summit

Join us at the next annual LDV Vision Summit.

This Fireside Chat, "Future investment trends and early stage opportunities in businesses leveraging visual technologies" is from our 2016 LDV Vision Summit. Featuring Bijan Sabet, General Partner & Co-Founder, Spark Capital and Evan Nisselson, General Partner, LDV Capital.

Evan: Next up is our next Fireside Investor Chat. I'm honored to bring up Bijan from Spark Capital. We're honored to have Bijan here for multiple reasons. Serial entrepreneur, successful investor, and passionate photographer. We started a pre-interview session a little bit ago, and tell us, the audience, which is a mixture of entrepreneurs, and researchers in computer vision, technology execs, why do you shoot with Hasselblad?

Bijan: It's a funny contrast given what I do for a living.

Evan: Exactly.

@Bijan

@Bijan

Bijan: Hasselblad, they make still cameras, and digital cameras, and medium format cameras, but I shoot a 20-year-old Hasselblad that shoots film, it shoots 120 millimeter film. For me, I discovered film, or rediscovered film, I guess, about three years ago.

Evan: What was that moment where you're like I'm going the other creative direction?

Bijan: I just started reading books about some of the masters, and was amazed at what I saw, and I just wanted to frankly emulate it. Not that I'm coming anywhere close to it, but I just found it really inspiring, and decided to explore film again.

Evan: Are you processing in the darkroom as well, and doing your prints?

Bijan: I don't do that, no. I did that in undergrad.

Evan: That's what I miss. You did do it in undergrad?

Bijan: Yeah, I did do it in undergrad.

Evan: The brown fingers from the fixer, and smelling afterwards.

Bijan: All the chemicals, yeah. No, I found a great lab in Southern California, and I send everything to them. It feels like the right compliment to spending time with digital products all day to have kind of an analog experience. Everything is slow, my Hasselblad takes 12 photos at a time.

Evan: Exactly. I used to shoot with a Rolleiflex, so very similar. Started with a Nikon F, Nikon FM, Rolleiflex, and unlike you I've gone the other direction rather than backwards. In 2003, I got rid of all of them and started with my camera phone, which you'll see in a photo if you're here tomorrow, the Sony P800, in 2003, and I've never gone back. Those twelve pictures, it's interesting, you mentioned you do one or two investments a year...

Skateboard Park, Venice, California. Leica M3, Kodak Portra 400 © Bijan

Skateboard Park, Venice, California. Leica M3, Kodak Portra 400 © Bijan

Bijan: Yeah, so I make more photos than new investments, I guess.

Evan: Actually, probably per fund how many investments would you make?

Bijan: We make about 30 investments per fund.

Evan: No, but you personally?

Bijan: Oh, personally about five or six.

Evan: So there's almost a connection between the number of images in a roll, and...

Bijan: Yeah, I hadn't thought about that.

Evan: That's my role as a moderator.

Bijan: Thank you.

Evan: Do you say take pictures or make pictures?

Bijan: I say make pictures.

Evan: Yeah. Well done.

Bijan: For me, it feels quite good. I don't make fire with two rocks and all that. I still am really excited about what's happening with digital, and connected products, and computer vision, and all sorts of social experiences, but I think if you haven't picked up a film camera in awhile you should do it.

Evan: I agree. We both were operators and went to the investing side. You've been investing a lot longer at a big fund with some great successes. What was the hardest part of that transition? How did you get past any difficulty that might have existed?

Bijan: The hard part on a personal level was I didn't know what I was getting into. Literally 11 years ago I was not an investor, so I didn't know - would I be any good at it? would I like it? What would it be like actually doing this every single day? There were a lot of questions, it was mostly the unknown, and I would say I'm still figuring it out. What the next ten years will look like, compared to the last ten years, it will probably look completely different. I think that's the fun part, but it's been humbling for sure.


Tickets for our annual 2017 LDV Vision Summit are on sale now
May 24-25 in NYC


Evan: One of my challenges was that as an entrepreneur I always felt that, every single day I could tell whether I was moving the ball forward or backwards. As an investor, it's more of a coach rather than doing, and getting too into the weeds is the wrong thing to do. That for me after 18 years as an entrepreneur was hard. Have you ever experienced that kind of thing?

Bijan: Yeah, for sure. Especially this pace I'm on one or two new investments a year, I'm meeting a lot of companies but only getting involved in a select few. When I first started it was kind of like am I being productive, and I had some mentors that really helped me think about it differently. I'm learning everyday, I'm helping or at least trying to help people, and I guess I am moving the ball forward that way. But in the beginning I felt like, was this really what I was supposed to be doing?

Evan: Sometimes every week I felt like oh, no, don't do that, back up again, no, no, and kept on going back. From the entrepreneur world we have success and failures, and also on the investors' side. What's the biggest mistake you made as an entrepreneur that you learned the most from?

Bijan: I think the late '90s were quite instructive. I was at a company called WebTV in the mid-'90s and that was a great success on many levels: the people, product, outcome was fantastic. The next company was a company called Moxy Digital, that didn't work. We started the company in 1999, we raised $60 million in our series A at 200 pre, with no product, and we spent it, and learned a lot of lessons.

Evan: What was the one big learning lesson? Is there one that actually helps you now on the other side of the table?

Bijan: For sure. I think some of this stuff is fairly well discussed today around MVP and being capital efficient. In those days - it feels like 100 years ago but it's still very painful to some extent - really thinking about being much more mindful and focused and capital efficient are things that I'll never forget from that experience. We were thinking about things completely differently in terms of trying to get big fast, we thought we knew it all and we didn't.

Evan: A couple months ago you wrote a blog post which I loved, Less Things, Better, and it's about setting priorities and focus in business and personal life. It's a challenge I try to figure out all the time. You wrote a list, and then towards the end I think I recall you said you reviewed the list and you said “I think maybe I've got too big of a list, but it’s pretty core stuff.”

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

Bijan: I think that is the challenge in startups or in personal life - you sit in a board meeting, and you see there all these things to do this year. It's like do we really have to do all these things? Oftentimes, I'm finding as an investor, I'm in a board meeting thinking this stuff is so obvious, and then when I think about our own firm or my own life you realize that this stuff is really hard. This list I made for myself, you're referring to this blog post I wrote, looking back now the list is ridiculous, there's way too many things.

Evan: I read it the first time, I said oh, that makes sense. Then I got to your comment ‘maybe it's a little long,’ I reread it, and it's like yeah, well, it's probably impossible to do all that, but it's the right goals for a year. What were a couple of things, do you remember a couple of those things really quickly just so the audience knows?

Bijan: Yeah, I'm trying to be a better father, better husband, better partner. I think I'm going to lose a couple hundred pounds. I was going to run around the world. It's almost in every dimension I was trying to improve. I think this work life balance we're all trying to juggle, this is still a work in progress with me, but I think it's a bit of a myth. I think you have to kind of pick which ones you really want to excel at.

Evan: You have to sacrifice some of the others.

Bijan: Yeah, something's going to give.

Evan: Right. Looking at this list and the priority question about entrepreneurs, and the ones that succeed are probably focusing on different priorities. When you see a company in your portfolio, how do you address that if they're doing too many things, or how do you coach them in a way so they might prioritize different things?

©Dean Meyers/Vizworld

©Dean Meyers/Vizworld

Bijan: I think the most compelling time is when the company is struggling, you really have to pick and choose. We see some founders do this almost instinctively, and others it's more difficult. It's like if we don't do this, then this is going to be a failure, or why should we do it. One example I went through recently, I've been on the board of a company called Run Keeper for about four or five years. The company was just sold recently, and it was a great outcome for the founders and team and all that, but that company was in a crisis period. It had maybe six months of cash, and had a bunch of products in the market, and was going through a tough time. Nobody wanted to invest in the company, no new investor.

The CEO on his own, he didn't do it in the middle of the night, he came to the board and said this is what I want to do. He cut the burn by a third, he dropped one of the products, just shut it down completely, focused on the core business, had a real chance of getting to profitability in a short period of time, and then he had this great outcome. He sold the company for just under $100 million, he owned the biggest stake in the company, the employees did great, and it wouldn't have happened if he'd kept going down this path of we got to have all these initiatives.

Evan: I think that's great, the outcome is great. Could you filter down that transitional period? In order to help the audience, the entrepreneurs how do you figure out “what should I try to do,” “how should I try to filter” or analyze or rate the things? Do you have any processes that you guys go through with some of your companies or entrepreneurs personally, one on one, like how do we figure this out together?

Bijan: I don't have a secret answer here, but I do think it's trying to be as focused as you can on thinking what's the most important thing we have to get done. Oftentimes we get involved in the company, it's in your earlier stages of the business, and almost by definition that means the capital we're investing is going to be insufficient for the next phase of the company, or for the company to reach its fullest potential. I think at that moment since we're all mindful that we're basically in deficit financing mode, we have to think about it like what is the most important thing we have to accomplish for the company to get to the next stage of the company's mission. I think distilling it down to that, trying to make it a more simpler task is key.


If you could stay capital efficient, and you could find great partners and investors, then you can be singularly focused on what's the most important thing at the time. 

-Bijan Sabet


Evan: Relating around the mission a very direct statement of which everything else that doesn't directly tie to that is not dealt with.

Bijan: Yeah. Oftentimes you hear about companies like “hey, there's no business model” initially, and I feel that can be confusing as well because if you start firing up the business model too early it's just another thing you got to worry about, and then you're diluting efforts and everything else. If you could stay capital efficient, and you could find great partners and investors, then you can be singularly focused on what's the most important thing at the time.

Evan: At that point you're talking about capital efficiency. Many in the audience know you and Spark, but for those that don't, could you give a couple sentences about fund size, ideal profile investment, and how early.

Bijan: There's no too early for us. We have offices now in Boston, New York, and San Francisco, and we get involved either at the seed stage or the Series A stage, but we also I would say 20% of what we do now are later stage investing.

Evan: The Seed Stage is roughly how much?

Bijan: Seed Stage is half a million dollars and up.

Evan: And the A?

Bijan: It varies, but it's maybe $4 to $6 million, something like that.

Evan: There's a lot of researchers also in the audience that this is black magic, they have no idea. We're balancing a little bit of the “those that know and those that don't.” Also you've done several deals that relate to visual content, leverage it, it's a byproduct, or it's core to the business, Twitter, OMG Pop, and another one, Lilly Robotics. Tell us what got you excited about that, and what were the kind of behind the scenes questions, was it going to work, is it not going to work.

Bijan: With Lilly, in particular?

Evan: Yeah.

Bijan: With Lilly Robotics, if people don't know, it's a flying camera essentially. Some people look at it as a drone, or something like that. For me, the real excitement was it's a flying camera.

Evan: It's like the polar opposite to the Hasselblad.

Bijan: I guess you're right.

Evan: I love both. I'm just trying to understand.

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

Bijan: The connective tissue here is that you're making art, or you're making creative media, and our frustration with the other products in the market, it was really tailored towards people that were interested in flying and piloting. If you look at products like DGI, or products like DGI, it's this big honking controller, and there's a serious learning curve because it's a piloting system. We really felt like there's the untapped potential here is what happens when you take a camera and you make it a flying camera, and you're not piloting, you're just creating work. I think that's the opportunity, and that's why I got excited about it.

Evan: The double edged sword there is for the success in the business you cannot wait for all of us to have our hovering cameras waiting for us outside like a limo.  The negative obviously is that there's 500 cameras outside waiting and floating in the air for us.

Bijan: Right. I think it's like anything. You go to a concert, and you'll see fans just have their camera phones out, and you can ask “are they enjoying the music or are they just too busy Snapchat’ing the content?” I think it's both, and I think this stuff will find its happy equilibrium.

Evan: What's the one activity that you cannot wait to do, that you cannot wait to be photographed by your Lilly Camera?

Bijan: For me personally it's going to be hiking, but I think we're hearing all sorts of different use cases from people, soccer, swimming, windsurfing, family birthday parties. Somebody recently talked to us about a wedding. I think the use cases are pretty diverse, but it's been very interesting. There's this one recent moment we went to a park in San Francisco after a board meeting, and it's right near a school playground, so there's a fence between the school playground and the public park, and we were out there flying it and all of the school kids see the Lilly guys testing it all the time, so they climbed on the fence and they were like it's the Lilly guys. It was really exciting.

Evan: That's cool. That's great. That kind of ties in across the whole spectrum of this summit. You guys invested in Cruise, and had a great exit with that, Oculus, and also content sites, Tumblr and Twitter, so both spectrums which relate to many of our sessions here. One became the title of our pre-interview, which I loved your statement, was the photographs can tell a powerful story that is unique to any other creative format. One of the things we're going to talk about is 2D, 3D, 360. Why to you is a photograph better than a gif, or better than a video? What are your thoughts there to come up with that conclusion?

Bijan: I just think if you look at history or contemporary times it can tell the story in the most compelling way. In some ways, whether it's that US airplane that landed in the Hudson River, with that Tweet, that photo that went around the world...

Evan: When they were standing on the wings, right, all the people?

Bijan: Right. That person took that photo on a camera phone that by today's standards is fairly low res, but that one picture told that story better than any other news report or anything. You see it today with the Syrian refugee crisis, with that child. These photographs are iconic, and I think we've seen over and over again that this is probably the most powerful format ever, and I think it's going to continue to be that way.


Speakers for the annual 2017 LDV Vision Summit include Albert Wenger of Union Square Ventures, Clement Farabet of Twitter, and many more. Check out the evolving list here.


Evan: In relation to that, the great segue is I've still got questions, I've got some evolved ideas the more I've seen recently, 3D content.

Bijan: VR or 3D?

Evan: 3D. Forget about VR. I mean 360 and 3D, without looking at VR for now, do people you think, will people eventually want to see more 3D than 2D?

Bijan: Yeah.

Evan: Let's put 3D roughly like 360, this interactive still, which is not a video, or it's not a gif, but it's this interactive, will five ten years that be the norm and 2D that be like black and white film?

Bijan: There's nothing wrong with black and white film.

Evan: It's a great art, which is all I'm saying. I love black and white, and I still turn some of my colors black and white. It's not a negative thing, but I'm trying to figure out is Instagram, will it be fully immersive pictures, or some other site that's fully immersive, that will be more engaging?

Bijan: It's hard to predict what Facebook's going to do with Instagram. They own Oculus, they own Instagram, so where that's all headed ... I think each of these are going to be its own experience. I think 3D and 360 is a Band Aid to get to VR.

Evan: Why's that?

Bijan: In my view, it's neither fish nor fowl. It's a 'tweener. What we have today with 2D photographs, printed or on screen, that's a durable format for the ages. Gifs are great, I don't think they're going anywhere, but I think 360 is a bit of a hack until VR is fully mainstream, and I think it's going to be mainstream.

Evan: How long do you think it's going to take?

Bijan: It's going to take awhile.

Evan: I know that's the hard question, but I have to ask it or I wouldn't be doing my job. For everybody in the audience, is it five years, is it ten years, is it twenty years?

Bijan: When we invested in Oculus, I think our view was that it was five years out.

Evan: To the masses?

Bijan: Mass market, yeah, for gaming. That was our projection, for gaming. Post acquisition, obviously we have nothing to do with the business anymore, but I think in some ways it's five years out again. You know, this was five years ago, but the significant difference is that Facebook's ambition and audacity for Oculus is much bigger than our ambition, and I was going to say audacity, but maybe naïveté. They're not just in it for gaming, they're in it for everything. I think that's why another five years isn't kicking the can, it's more the aperture got bigger, not to have too many double entendres.

Evan: I like that. That was good.

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

Bijan: I think that's the reason why it's just probably going to take longer, but I think that the experience is so compelling that I really believe that it's going to happen. It's just too compelling. Versus 3D movies, it's like I don't feel like that's compelling. It's cute.

Evan: I guess the question there is that experiencing that content whether or not it's gaming or other activities, wearing the gear or holding up some cardboard version or evolution, there's going to be a different activity, and it might be 24/7 one day.

Bijan: I hope not. Yeah.

Evan: It might not be, but up until then I wonder whether or not that 3D and 360 images will be everywhere, and become more normal until headsets are prevalent.

Bijan: It might be, but I don't think you're going to live inside the Oculus 24/7, or HDC or whatever competing thing. I think it's going to be when you want that experience you're going to have that experience, and then when you don't want that experience you're going to live your life, and that's okay. I don't think this is an “or”, it's an “and.” I think that's where it's exciting. I mean there's no reason why, in the future, I couldn't do this with you, but I could be in Boston, and it will be as lifelike and realistic as me being here.

Evan: I cannot wait. It'll be fun.

Bijan: Maybe I'm not here right now.

Evan: Nobody will be here right now, we'll be in many different countries.

Bijan: It's possible, yeah. We'll have people from all over the world participating in a way that they cannot do that today, and I think that's exciting.

Evan: I think it is if it becomes immersive enough where we feel like we're there. We purposely do not do live streaming of this event because either you're here or you're not, at least for now.

Another thing I read I'd like to try to understand a little more is how you personally look for companies, and the types of companies you look for. At least on your profile page it says you like to look for companies with new approaches to building communities through the sharing of ideas and interests. Obviously Twitter and Tumblr relates to that, but what do you see going forward with this world of computer vision and others? Are there intrigues that you might have as are you waiting for that to happen, or is it more serendipity when they come through along with the trends?

Bijan: I think what you just described and what I've been excited about is more around shared experiences whether it's in these creative tools like Tumblr, or even in workplaces and teams like what you see with Slack and Trello.

Evan: Trello is one of your deals as well, right?

Bijan: Yeah, we're investors in both. I think that that to me is exciting. There's a lot of energy these days around bots, and things like Alexa and this Google Home thing, and I think they're amazing products from a computer science point of view, or AI/ML point of view, but they don't really do anything for me. I really feel like they're missing the people side of these things. The reason why when I walk into a venue and I see all the FourSquare tips from other people, I find it much more exciting than Alexa telling me to go grab a cappuccino down the street. Her the movie wasn't as exciting as a FourSquare tip. I'd rather have people power the internet versus some machine in Mountain View.

Evan: If AI is working along the learning from the masses, it's basically a collective group of people delivering Her, and so it could be the next generation FourSquare when we come back a couple years, and you say actually I did invest in the next one, which is delivering.

Bijan: The synthesis of the planet.

Evan: Exactly.

Bijan: Remember in Her she was dating a thousand people at the end of that.

Evan: Exactly. That's great. It's insane. We've got plenty more time, and I want to offer up to the audience any questions, and we're going to interact, and so raise your hands if you've got questions. I will keep on firing away. Anybody have one right now? Go ahead.

Audience Question 1: What are your tips for entrepreneurs who are looking to raise capital, especially in the current funding situation?

Bijan: My own take on the current funding environment is at the early stages what you read in the headlines, just ignore it, literally ignore it. I think it's mostly irrelevant. I think the headlines about this volatility, and it's tougher times ahead, it's really for companies with massive valuations and big burn rates. If you're starting a company today, you don't have to worry about either.


I don't think anything's happening in the public markets or macro volatility should discourage anybody from building the next great company.

-Bijan Sabet


Bijan: My own take on the current funding environment is at the early stages what you read in the headlines, just ignore it, literally ignore it. I think it's mostly irrelevant. I think the headlines about this volatility, and it's tougher times ahead, it's really for companies with massive valuations and big burn rates. If you're starting a company today, you don't have to worry about either.

The issue really is, we've got this funk where you have tremendous amount of pressure - public markets are putting pressure on tech companies, and as a result it just kind of goes downwards. At the same time, you have entrepreneurs building extraordinary companies based on amazing ideas, and you have venture capitalists that have never had bigger funds ever, ever, ever. There's no shortage of great ideas getting funded. It's not the case where entrepreneurs are out of ideas, and VCs don't have any money. We're exactly the opposite. We have both of those pieces at work here. I don't think anything's happening in the public markets or macro volatility should discourage anybody from building the next great company.

Audience Question 2: We talked a lot about VR. What's your view on AR? How far do you see that away from being a mass market product?

Bijan: I want to like AR. I really want to like AR. I haven't gotten there yet. I like the concept a lot, but I haven't seen any AR demos, and I've seen a number of them recently in the past, and I don't feel like we're there. I guess the most interesting ones, although it's not exciting, has been like automotive heads-up display systems. I think those are valuable, but by and large I thought even if Google Glass is a real thing, and it was displaying content on the real world, for me that's not, I just haven't found those product demos super compelling. I'm much more in the VR camp than the AR camp. I think the VR one is more intentional, and the experiences are much more interesting.

I want to like AR. I just haven't gotten there yet.

Audience Question 2: Do you think the situation is different for enterprise applications?

Bijan: There might be vertical applications I'm just not thinking about where AR can play a big role, and the experience maybe is more of a functionality than otherwise, but I just haven't seen it. Like for example I've seen some around architecture and things like that, but even that I'd rather have videos or VR. I'm open to other things. Like I said, I want to see it, I just haven't yet.

Evan: The example I'll give you is we invested in a company called Apx Labs[now called UpSkill], which is AR for B2B enterprise, so manufacturing, healthcare, logistics, and it's amazing what they're doing in those spaces on an ROI basis. I think before we get to VR it's actually going to be more AR opportunities, even though VR might be a holy grail until the next holy grail happens of dating thousands of people simultaneously.

Back to this kind of attributes of visual content combined with people sharing, I love this kind of seems like a core passion of yours and belief in value creation. When companies come to you, let's not talk about the ones that you already know, the more challenging for the audience are the entrepreneurs saying wow, “Bijan's smart, I want to meet him at the right time for our business,” which leads to the question, when is that? The second is when is there enough validation in a new relationship of a company that will inspire you?


The product vision or product itself must be inspiring, that's the paramount thing. The other part is, can I imagine if I wasn't doing what I was doing for a living, would I imagine that I would legitimately want to work for these founders. If those two things don't compile, then I tend not to want to invest. 

-Bijan Sabet


Bijan: We get involved pre-product as well as post-product, so it's not a particular stage. I really look for personally a few things that I care about, and then the stage is almost less specific.

Evan: What are those?

Bijan: I feel like it is the product - product vision or product itself must be inspiring, that's the paramount thing. The other part is, can I imagine if I wasn't doing what I was doing for a living, would I imagine that I would legitimately want to work for these founders. If those two things don't compile, then I tend not to want to invest.

Evan: That second one, I like the way you said that because you actually would be working for them and vice versa, as a coach as we talked about earlier.

Bijan: Yeah.

Evan: It's basically investing in somebody that you would want to collaborate with, and your example is join them on a team, but you're in effect joining them as an investor.

Bijan: Yeah, and I actually mean, literally joining as an employee, if I wasn't doing what I'm doing. If I was unable to say yes to that question then how am I going to convince their future VP of Marketing that this is where that person should work, and so on and so forth. At the times where I've kind of strayed from that in the last 11 years is the times where I'm like, ah, I wish I had kept that personal criteria going, and the times it's worked out is where that criteria I kind of stayed true to it. It doesn't work for every investor has their own point of view of what makes them excited, but on a personal level that's what I consider.

Evan: Have you noticed traits of those people that work better?

Bijan: Yeah. There's big character differences or people differences between like a David Karp and a Biz Stone, they're very different people, or Palmer Lucky or Michael Pryor of Trello. They're quite different, but they all have a very mission driven sense of why they're doing what they're doing, and I feel like it resonates for me.

David Karp: Leica MP, Leica 50mm Summilux, Kodak Tri-X 400 ©Bijan

David Karp: Leica MP, Leica 50mm Summilux, Kodak Tri-X 400 ©Bijan

Audience Question 3: As a fellow photography enthusiast, more like a personal question to you - connecting startup to photography and such, do you have at a personal level things you believe are missing in the current photography, from technical as in camera point of view to post processing like out of focus issues or low light issues, or how to make a better picture composition? There are a bunch of things which I feel are open there, it's not solved yet. Do you think there's something out there which could be a company out there on any of these, and which one would you bet on?

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

Bijan: On the device hardware side I feel we have all the capability we need now from an iPhone to a Hasselblad. If you're not making great photos, it's not the camera's fault anymore. It may not even be true 50 years ago, but it's certainly not true today, I think that there's still a lot of headache with sharing, collaborating, storing, backing up. That's still a mess. Whether it's a venture startup opportunity or not, it remains to be seen I guess. In families I find, it's not even my own family, although it's certainly true with our case, sharing, and kind of like oh, you took those pictures on family vacation, I did too, kind of that whole thing seems to be kind of crazy now. We're all uploading to places like Facebook, but they compress the hell out of the photos, they look like shit after a while. I just think that cannot be the answer long term.

There's still I feel like a missing piece on, loosely speaking, I'll call it workflow, backups, sync sharing amongst people that you care about. I don't know where that leads us.

Evan: When you make a picture in any format, how do you choose where to share it?

Bijan: Tumblr's my go-to place for things that I want to publicly share. If not, I'm still old-school. I have a private Flickr account just for our family, my own family, my brother's family, my mom and dad, because my parents aren't on Facebook.

Evan: They're on Twitter ... I mean they're on Flickr.

Old Car, Mission Street, SF: Hasselblad 503cw, Kodak Portra 400 ©Bijan

Old Car, Mission Street, SF: Hasselblad 503cw, Kodak Portra 400 ©Bijan

Bijan: They are on Twitter, yeah, but they're part of the private Flickr group. It seems a little ridiculous that I'm still using Flickr, but that's the one use case.

Evan: It isn't really coming from the spectrum of our conversation, it seems like it makes sense.

Bijan: It does, but given what's happening in the market it feels like I better find a new answer.

Evan: I feel you should stay there.

Bijan: I adore Tumblr. I think it's still my favorite way to share my photos.

Evan: Do you ever put any photos on Twitter? I think I've seen some.

Bijan: I do. I use Twitter for different things, but yeah, I definitely do.

Evan: Is it different type of photographs, or different kind of public events, versus your own photography that you do with your Hasselblad and it's more going to make pictures?

Bijan: I definitely share photos from my Hasselblad on Twitter, but it's something about Hasselblad is this six-by-six, it's this big photo, and then sharing it on a mobile phone, I'd like to think people are seeing it on a bigger screen, but that may just be naive thinking on my part. Twitter for me is just being part of the public conversation. I love Twitter, but it's less about photo sharing than everything else.

Evan: One of the questions that I frequently ask everybody, and I like the composite of all the answers, is very simply, and I asked Howard Morgan earlier, in one word answers your favorite personality trait of an entrepreneur, and your most disliked personality trait, in one word answers.

Bijan: Creative, on the plus. On the negative, for the ones that I have a harder time, indecisive. Indecision with the founders, if I were to pick on one thing. It's hard to beat. We're all human, we're trying to figure this stuff out.  

Evan: Mine is passion and selfishness. Obviously some people would say selfishness is a good thing or a bad thing, but I look at it as a horrible thing because it's not team player, it's not building the company, it's all about the individual, which sometimes works sometimes doesn't work for that. The reason I like the one word answers even though they're hard is because they're very easily actionable to people when they hear it. On that note, round of applause for Bijan. Thank you very much.

Bijan: Thank you very much for having me.

The annual LDV Vision Summit will be occurring on May 24-25, 2017 at the SVA Theatre in New York, NY.

Hired at Facebook After Showcasing Research in Visual Technology at the LDV Vision Summit: An interview with Divyaa Ravichandran

Divyaa Ravichandran, from CMU and she showcased her project “Love & Vision” ©Robert Wright/LDV Vision Summit  

Divyaa Ravichandran, from CMU and she showcased her project “Love & Vision” ©Robert Wright/LDV Vision Summit
 

The LDV Vision Summit is coming up on May 24-25, 2017 in New York. Through March 31 we are collecting applications to the Entrepreneurial Computer Vision Challenge and the Startup Competition.

Divyaa Ravichandran was a finalist in the 2016 Entrepreneurial Computer Vision Challenge (ECVC) at the LDV Vision Summit. Her project, “Love & Vision” used siamese neural networks to predict kinship between pairs of facial images. It was a major success with the judges and the audience. We asked Divyaa some questions on what she has been up to over the past year since her phenomenal performance: 

How have you advanced since the last LDV Vision Summit?
After the Vision Summit I began working as an intern at a startup in the Bay Area, PerceptiMed, where I worked on computer vision methods to identify pills. I specifically worked with implementing feature descriptors and testing their robustness in detection tasks. Since October 2016, I’ve been working at Facebook as a software engineer. 

What are the 2-3 key steps you have taken to achieve that advancement?
a. Stay on the lookout for interesting opportunities, like the LDV Vision Summit
b. ALWAYS stay up-to-date in the tech industry so you know what counts and who's who

What project(s)/work is your focus right now at or outside of Facebook?
Without any specifics, I'm working with neural networks surrounded by some of the brightest minds I have come across as yet, and along with the use of Facebook's resources, the opportunities to improve are boundless.
 

Divyaa Ravichandran  ©Robert Wright/LDV Vision Summit

Divyaa Ravichandran  ©Robert Wright/LDV Vision Summit

What is your proudest accomplishment over the last year?
Snagging this gig with Facebook was kind of the highlight of my year; working on projects that have the potential to impact and improve so many lives has me pretty psyched!

What was a key challenge you had to overcome to accomplish that? How did you overcome it?
I think visibility was one big point: I wasn't highly visible as a candidate for the Facebook team since I had only just graduated from school and didn't have any compelling publications or such to my name. Fortunately, my attendance at the LDV Vision Summit last year gave me that visibility, and the Facebook team got in touch with me because of that.

Did our LDV Vision Summit help you? If yes, how?
Yeah, it was through LDV that I  came in contact with my current employer at Facebook! I also met some really interesting people from some far-off places, like Norway, for instance. It put into perspective how the field is growing the world-over.
 

Divyaa Ravichandran  ©Robert Wright/LDV Vision Summit

Divyaa Ravichandran  ©Robert Wright/LDV Vision Summit

What was the most valuable aspect of competing in the ECVC for you?
The fact that the summit puts the guys with the money (the VCs) in touch with the guys with the tech (all the people making Computer Vision pitches) really bridges the gap between two shores that I think would do very well juxtaposed with each other. Personally, it opened my eyes to new ideas that people in the field were looking at and what problems they were trying to tackle, something that I wouldn't have been able to think up myself.

What recommendation(s) would you make to teams submitting their projects to the ECVC?
Stay current, but if you're bringing something entirely new to the table, that would be best! Everybody at ECVC is looking to be blown away (I think) so throwing something totally new and unexpected their way is the best way to get their attention.

What is your favorite Computer Vision blog/website to stay up-to-date on developments in the sector?
I generally read Tombone's CV blog, by Tomasz Malisiewicz*, and follow CV conferences like ECCV, ICML, CVPR to look up the bleeding edge in the industry and this usually gives a fair idea of the biggest problems people are looking to tackle in the current age.

*Editor’s Note: Tomasz Malisiewicz was a speaker at the 2016 Vision Summit

Applications to the 2017 ECVC and the Startup Competition at the LDV Vision Summit are due by March 31, apply now.

You Are Brilliant and You Want More Exposure

Our LDV Vision Summit is coming up on May 24-25, 2017 in New York. We bring together top technologists, researchers, startups, media/brand executives, creators and investors with the purpose of exploring how visual technologies leveraging computer vision, machine learning and artificial intelligence are revolutionizing how humans communicate and do business.   

Through March 31 we are collecting applications to the Entrepreneurial Computer Vision Challenge and the Startup Competition.

Every second of every day, people around the world are publishing research papers and launching new startups that leverage computer vision, machine learning and artificial intelligence.

Researchers and professors want their work to be noticed in the midst of a flood of new work.

Entrepreneurs want to build valuable businesses, get covered in Techcrunch, Wired, Wall Street Journal, want to raise financing and want happy customers.

We want to help you!

We have been organizing the premier annual visual technology summit since 2014 called the LDV Vision Summit with the main focus of showcasing brilliant people like YOU!

Entering competitions increases your odds of being recruited, raising capital, or selling for over $100M because your work becomes visible to an audience of actors working to advance your field.  The key is to focus on attending and competing where it is most contextually relevant for you to further your goals. If you’re in visual tech, that means the LDV Vision Summit.

We bring together top technologists, researchers, startups, media/brand executives, creators and investors with the purpose of exploring how visual technologies leveraging computer vision, machine learning and artificial intelligence are revolutionizing how humans communicate and do business. 

Speakers and judges come from Apple, Cornell Tech, Qualcomm, NBCUniversal, Stanford, Facebook, MIT, Greylock Partners, CMU, Wired, Spark Capital, Nvidia, First Round Capital, Flickr, Refinery29, Lytro, Timer Warner, Samsung, Magic Leap, Ooyala, Hearst, Google and many more.

Enter and present your brilliance at our 2017 LDV Vision Summit Startup Competition or the Entrepreneurial Computer Vision Challenge (ECVC). Application deadline is March 31, 2017.   

Sean Bell, CEO & Co-Founder, GrokStyle from Cornell Tech. ©Robert Wright/LDV Vision Summit

Sean Bell, CEO & Co-Founder, GrokStyle from Cornell Tech. ©Robert Wright/LDV Vision Summit

Past competitors in the ECVC, like 2016 winner, GrokStyle, have reaped the rewards of competing. “The most valuable part of the Vision Summit was connecting with three different companies potentially interested in building on our technology, and with four different potential investors/advisors,” said CEO & Co-founder Sean Bell after the Vision Summit.

Divyaa Ravichandran, from CMU and she showcased her project “Love & Vision” ©Robert Wright/LDV Vision Summit

Divyaa Ravichandran, from CMU and she showcased her project “Love & Vision” ©Robert Wright/LDV Vision Summit

For other 2016 ECVC competitors like Divyaa Ravichandran, who was a recent graduate of Carnegie Mellon University at the time, “attendance at LDV Vision Summit last year gave me visibility and I came in contact with my current employer at Facebook!”

Rosanna Myers CEO & Co-Founder, Carbon Robotics Startup Competition Winner ©Robert Wright/LDV Vision Summit

Rosanna Myers CEO & Co-Founder, Carbon Robotics Startup Competition Winner ©Robert Wright/LDV Vision Summit

2016 Startup Competition winner, Carbon Robotics, was looking to “connect with recruits and investors in NYC. The experience was great for that. Getting to pitch on the main stage was amazing, because it made it easy for people to learn about what we’re working on. After the pitch, we were approached by tons of high-quality engineers and potential partners, so it was a great success,” said Rosanna Myers CEO & Co-founder.

“Following the summit, [London based startup] The Smalls raised investment with an angel investor who was in the audience. The funding was used to make key hires and improve technology. The Smalls has continued to grow at 300% per year and now has offices in both London and Singapore,” reports CEO & Founder of The Smalls, Kate Tancred after finalizing in the 2015 Startup Competition.

The 2017 LDV Vision Summit on May 24 & 25 in NYC will include over 80 international speakers with the purpose of exploring, understanding, and shaping the future of imaging and video in human communication. The best startups and computer vision experts who compete in the Startup Competition and the ECVC will be showcased alongside these industry leaders.

The Startup Competition is for promising visual technology companies with less than $1.5M in funding.

The ECVC is for any Computer Vision, Machine Learning and/or Artificial Intelligence students, professors, experts or enthusiasts working on a unique solution that leverages visual data to empower businesses and humanity. It provides contestants the opportunity to showcase the technology piece of a potential startup company without requiring a full business plan. It is a unique opportunity for students, engineers, researchers, professors and/or hackers to test the waters of entrepreneurism.

Competitions are open to anyone working in our visual technology sector such as: photography, videography, medical imaging, analytics, robotics, biometrics, LIDAR, radar, satellite imaging, computer vision, machine learning, artificial intelligence, augmented reality, virtual reality, autonomous vehicles, media and entertainment, gesture recognition, search, advertising, cameras, e-commerce, visual sensors, sentiment analysis and much more.

Judges for the competitions include top industry venture capitalists, entrepreneurs, journalists, media executives and companies that are recruiting. Past judges included Josh Elman of Greylock, Tamara Berg of U. North Carolina, Chapel Hill, Larry Zitnick of Facebook, Andy Weisman of Union Square Ventures, Ramesh Raskar, of MIT Media Lab, Alex Iskold of Techstars, Gaile Gordon from Enlighted, Jessi Hempel of Wired and many more. The list of phenomenal 2017 judges continues to evolve on the 2017 Competition’s website.

All competition sub-finalists will receive remote and in-person coaching by Evan Nisselson and in person mentoring during the sub-finalist judging session by Jan Erik Solem, Rebecca Paoletti, Andy Parsons, Evan Nisselson, Serge Belongie and other experts.

It would be a horrible feeling to be sitting behind your computer or in the audience when someone else presents an idea that you had years ago. Take a risk, prove yourself, compete. 

We are waiting to to see YOUR brilliance!

Enter and present your brilliance at our 2017 LDV Vision Summit Startup Competition or the Entrepreneurial Computer Vision Challenge (ECVC). Application deadline is March 31, 2017.   

Meet LDV Capital’s Expert In Residence, Computer Vision Leader Serge Belongie

LDV Vision Summit 2016. We are a family affair! Serge and August Belongie thanking the audience for a fantastic, inspirational gathering. ©Robert Wright

LDV Vision Summit 2016. We are a family affair! Serge and August Belongie thanking the audience for a fantastic, inspirational gathering. ©Robert Wright

We are honored to announce that computer vision and machine learning expert Serge Belongie has joined LDV Capital’s team as our first Expert in Residence [ER]. Serge is a professor of Computer Science at Cornell University and Cornell Tech and a technical leader in our field of visual technologies. He has co-founded several computer vision startups that have been acquired, and he thrives on empowering others to succeed.  

“I am excited to deepen my collaboration with Evan and LDV Capital in our mutual pursuit of empowering students, PhDs, and our ecosystem to leverage deep technical skills to solve problems and build valuable businesses that benefit society,” says Serge.

I met Serge in March 2013, and I have been inspired by his insights, expertise, passion, and modest genius since the day we met. We share a passion and curiosity for how visual technologies will empower businesses and humanity. His unique combination of industry expertise, professorial guidance, enthusiasm to empower students to build businesses, and technical advisory guidance is an extremely unique combination. We have been working closely together on our LDV Vision Summit since 2014, and we are honored to collaborate more deeply going forward.

We joined forces with others to create the successful annual LDV Vision Summit, which started as a one-day event in June 2014. It has since evolved into a two-day event with over 500 attendees, 80 speakers, 40 sessions, and 2 competitions [Startup Competition & Computer Vision Challenges]. Our summit is unique in that it gathers visionaries and cutting-edge technologies across our visual technology ecosystem, including startups, investors, CV/ML/AI researchers/professors, technology/media executives, and creators, in one place to inspire, raise capital, recruit talent, and facilitate commercial deals.

“Frequently I cross paths with talented students, researchers, and PhDs who want to explore commercialization but are overwhelmed by the options. LDV Capital, Cornell Tech, and I are always looking for ways to further empower technological advancements and our ecosystem,” says Serge.

Serge is also a co-founder of several companies, including Digital Persona (which merged with CrossMatch 2014), CarCode (which was acquired by Transport Data Systems), Anchovi Labs (which was acquired by Dropbox in 2012), and Orpix. He also serves as technical advisor to Osmo and other companies.

LDV Community Dinner, October 2014. Serge introducing himself and, as always, keeping it lively. ©Robert Wrigh

LDV Community Dinner, October 2014. Serge introducing himself and, as always, keeping it lively. ©Robert Wrigh

LDV Vision Summit 2016, Entrepreneurial Computer Vision Challenge Winner: Grokstyle, co-founded by Cornell researchers Sean Bell and Kavita Bala. Serge congratulating CEO Sean Bell. ©Robert Wright 

LDV Vision Summit 2016, Entrepreneurial Computer Vision Challenge Winner: Grokstyle, co-founded by Cornell researchers Sean Bell and Kavita Bala. Serge congratulating CEO Sean Bell. ©Robert Wright 

“As Computer Vision vision finds more success in practical domains, my research interests have shifted from the fundamentals of object recognition to the challenges of human-in-the-loop computing. I find this area fascinating since it involves humans and machines working together to solve problems that neither can solve in isolation.”

Serge is also pushing the envelope with regard to computer vision and machine learning research across multiple projects, such as:

Residual Networks Behave Like Ensembles of Relatively Shallow Networks with Andreas Veit and Michael Wilber.

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks with Subarna Tripathi, Zachary Lipton, and Truong Nguyen.

Visipedia: Fine Grained Visual Categorization with Humans in the Loop, with Pietro Perona

Learning to Match Aerial Images with Deep Attentive Architectures with James Hays and Tsung-Yi Lin

Boosted Convolutional Neural Networks with Mohammad Moghimi, Mohammad Saberian, Jian Yang, Li-Jia Li, and Nuno Vasconcelos

View more research from Serge.

LDV Capital’s Experts in Residence work part time on helping us track interesting companies, collaborating on due diligence, providing valuable advice to portfolio companies, analyzing trends with our team, and continuing to collaborate closely on our annual LDV Vision Summit. We look forward to adding more experts to our LDV Capital team to continue empowering people, universities, and our ecosystem to support technical people in their pursuit to commercialize deep technical research. Many of LDV Capital’s portfolio companies leverage deep technical research, and they are always recruiting smart people.

LDV Vision Summit 2014.  How will image recognition disrupt businesses and empower humanity? How can we inspire more researchers to bring their visions to market? Moderator: Evan Nisselson Panelists: Serge Belongie, Professor, Cornell NYC Tech, Computer Vision Expert; Gary Bradski, Magic Leap, VP Computer Vision & Machine Learning; and Moshe Bercovich, Shutterfly, GM Israel. ©Dan Taylor

LDV Vision Summit 2014.  How will image recognition disrupt businesses and empower humanity? How can we inspire more researchers to bring their visions to market? Moderator: Evan Nisselson Panelists: Serge Belongie, Professor, Cornell NYC Tech, Computer Vision Expert; Gary Bradski, Magic Leap, VP Computer Vision & Machine Learning; and Moshe Bercovich, Shutterfly, GM Israel. ©Dan Taylor

Serge Belongie received a B.S. (with honors) in electrical engineering  from Caltech in 1995 and a PhD in Electrical Engineering and Computer Sciences from Berkeley in 2000. While at Berkeley, his research was supported by an NSF Graduate Research Fellowship. From 2001 to 2013, he was a professor in the Department of Computer Science and Engineering at University of California, San Diego. He is currently a professor at Cornell Tech and in the Department of Computer Science at Cornell University. His research interests include computer vision, machine learning, crowdsourcing and human-in-the-loop computing. He is also a co-founder of several companies, including Digital Persona, Anchovi Labs, and Orpix. He is a recipient of the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review “Innovators Under 35” Award, and the Helmholtz Prize for fundamental contributions in computer vision.

 

 

How do you know if your audience is laughing, talking or sleeping?

Individual real-time audience analysis leverages infrared and optical cameras to detect whether viewers are watching television while lying under a blanket, lying down on the couch, or laughing.

Individual real-time audience analysis leverages infrared and optical cameras to detect whether viewers are watching television while lying under a blanket, lying down on the couch, or laughing.

Historically, television audience viewing analyses have been based on households rather than individuals and the currency of TV advertising has been based on 30-year-old technology. Audience insights from computer vision analyses will deliver more valuable individual attention-based viewing engagement statistics.

The mission of TVision Insights is to fix the broken parts of television advertising. LDV Capital is excited to invest in the TVision team alongside Accomplice, Jump Capital, ITOCHU Technology Ventures, and other investors.

TVision is an audience measurement company pioneering the way in which brand advertisers, TV networks, and over-the-top content [OTT] platforms measure attention. Using TVision’s data showing how many seconds viewers actually pay attention to what they are watching, media teams can optimize their allocations and networks can improve their programming. Advertisers can see higher returns on ad spending, and networks can make better, more engaging programming.

TVision relies on cameras and computer vision to deliver real-time individual viewer analyses for advertisers and content programmers. They leverage proprietary facial recognition technology that allows users to understand engagement and sentiment in viewers’ natural watching environments.

Nielsen has been around for years, and to date they track household viewing using 30-year-old technology.  

Yan Liu, co-founder and CEO, said, “When I was running my own digital ad agency, I was able to leverage lots of data and tools for digital media. Then I started to work with other agencies that handled TV ads. I was surprised that there is very little data available for optimization. By providing better data to the TV world, we can make it much more efficient."

TVision’s per-person ratings panel size has surpassed all its competitors to become the largest in Boston, which is the eighth largest designated market area (DMA) in the country, comprising 600 households and more than 2,000 people. TVision currently captures and reports on viewer attention across 285 channels and 99% of the content on Netflix, Amazon, and Hulu.

The living room is the new research lab for advertisers and media programming companies, who can rely on this data instead of using expensive, non-real-time focus groups. Advertisement and content measurement can be tested in real time and on an individual basis.

At what point in a show do people leave the room, and when are they distracted by other people in the room? To help answer these questions and more, TVision also captures real-time data on actual in-room engagement based on demographic, time, and program, such as during the first 2016 presidential debate.

TVision reported that viewers paid the most attention during the debate when Hillary Clinton responded to Donald Trump, “You live in your own reality.” TVision captured viewers’ attention based on key demographics, finding that Hillary Clinton scored +26% with Hispanics, Donald Trump received +8% more attention from male viewers, Hillary led +13% with African Americans and Hillary got +3% more attention from female viewers.

At the 2016 Oscars, TVision reported that viewers paid the most attention when Lady GaGa received a standing ovation on stage and at home as she addressed campus assault. The top "smiled moment" was when Leo won the award for Best Actor in a Leading Role, garnering a Smile Index rating of  2.75.

Check out TVision’s other exciting audience attention deep dives for Super Bowl 50, the Patriots vs. Broncos AFC championship game, the 2016 American Music Awards , and the 2015 Emmys.

TVision never stores images or videos, so viewers do not need to  be concerned that cameras are watching them while they watch television.   

Dan Schiffman, co-founder and CRO, said, “We take privacy very seriously. It is honestly our primary concern. We do not store or transmit any images or videos ever. We analyze the living room scenario in real time, store the data as 0s and 1s with no personally identifiable information, and then upload that data to our servers for analysis. We own our data, and we rely on our panel homes to provide it. We would never compromise that trust.”  

LDV Vision Summit 2016: Dan Schiffman, Co-Founder & CRO, TVision Insights

LDV Capital invests in people who are building visual technologies to solve problems and empower businesses and humanity. The TVision Insights team is another great example of domain experts leveraging visual technology to solve problems and build a valuable business. We are honored to collaborate with them.

Programmers and advertisers can finally understand in real time when their audiences are laughing, crying, sleeping, or mad. We hope content production, advertising, and entertainment around the world will become more contextually relevant and inspiring for all viewers.

TVision Insights is hiring.

Clarifai raises $30M Series B - Delivering The Power Of Artificial Intelligence Into Everyone’s Hands

We are proud to share the news that our portfolio company Clarifai has raised a $30M Series B financing led by Matt Murphy at Menlo Partners.

“One of the biggest reasons for our fundraise is to “continue enabling anyone in the world to train and use AIsays Matthew Zeiler, CEO & Founder, Clarifai.

We first invested in Matthew, their team and the Clarifai vision in June 2014 when “artificial intelligence” was still a matter of science fiction. We invested again in April 2015 in their Series A led by Albert at Union Square Ventures and again in their Series B alongside Menlo Partners, Union Square Ventures, Lux Capital, Qualcomm Ventures and other investors.

The Clarifai team has made solid progress adding marquee customers and expanding their developer community. Success never happens overnight and it is always critical to gather brilliant people who collaborate toward a shared vision.

Clarifai was founded in 2013 to solve real-world problems with Artificial Intelligence, starting with visual recognition.  Matthew was working on early Clarifai algorithms at NYU along with his professor Rob Fergus. Clarifai won top awards in the ImageNet Challenge in 2013 beating out teams from IBM, Adobe and other major companies (results).  In June 2014, Rob Fergus, Research Scientist at Facebook AI & NYU Professor gave a presentation highlighting the potential for Clarifai at our annual LDV Vision Summit.

May 2016: Matthew Zeiler, CEO of Clarifai gives a presentation at our LDV Vision Summit “The revenue potential is tremendous for accurately, automatically and efficiently keywording all the videos in the world.”

In a blog post announcing Clarifai’s Series B financing today, Matt outlined their Clarifai vision:

We want to teach computers how to see the world like humans. Recognizing objects is one piece of the puzzle, but by itself, it’s not very “human.” When people see the world, they don’t just see objects. They see complexity, context, and relationships that make up a greater understanding - an understanding that differs from person to person.

So, when we’re building artificial intelligence tools at Clarifai, what we’re really thinking about is human intelligence and how we can amplify it to help solve real-world problems.

We have a really exciting roadmap that continues to position us as the independent A.I. company out there, and the only bottleneck on executing is the number of people in the company. We plan to grow all functions, from research to engineering to developer evangelists to sales and marketing.”  Train your own visual recognition model and search any image with custom training and visual search.

Everyday more of our world is optimized and empowered by people building visual technology businesses. These businesses analyze visual data via computer vision, machine learning and artificial intelligence. Self-driving cars, baby monitors, shopping recommendations, news feeds, mapping, personal assistants, biometrics, gesture recognition and this will exponentially impact every business vertical and become a core part of humanity.  

Matt Zeiler at one of our monthly LDV Community Dinners. ©Ron Haviv

Matt Zeiler at one of our monthly LDV Community Dinners. ©Ron Haviv

We are thrilled to continue collaborating and investing in Matt, the Clarifai team and their vision!

 

Who Created That Image? Solving the online content identity problem leveraging the blockchain.

Love The Living of Life: Bumbershoot Festival Seattle, WA 9/4/95 ©Evan Nisselson  

Love The Living of Life: Bumbershoot Festival Seattle, WA 9/4/95 ©Evan Nisselson
 

Every day, billions of images, songs, videos, and written works are created and shared online to communicate to loved ones, to friends, and to the masses. The most interesting content is then shared exponentially across multiple social networks. Each time this content is shared, it loses important metadata, such as the attribution for who created it and important associated caption information.

I have been a photographer since I was 13 years old. I have worked as a photo agent and a photo editor, and many of my friends create original visual content.  The holy grail for all creators and content owners, including myself, is the ability to track our creativity so we can better understand how and where it is being enjoyed and create future monetization opportunities—or at least have our names associated with our content wherever it is viewed.

As the commercial web has grown over the last 20 years, many different companies and solutions have tried to deliver for digital rights management solutions, but none have succeeded yet.

Love The Living Of Life: Billiards, San Francisco, CA 4/26/96 ©Evan Nisselson

Love The Living Of Life: Billiards, San Francisco, CA 4/26/96 ©Evan Nisselson

For example, imagine that a photographer’s image is published on the National Geographic website, then shared to Facebook, then copied and posted to Reddit, then published on an individual’s blog, and then posted to Pinterest, Instagram, and back to Reddit. The creator’s attribution and valuable caption data would not continue to be associated with that image each time the photo is shared to a new website on the Internet.

Mediachain Labs is working to solve this content identity problem by building an open, universal media library leveraging the blockchain. Jesse, Denis, and their team are building Mediachain to automatically connect media to its creator and relevant metadata.

LDV Capital is excited to invest in the Mediachain Labs team and this ambitious goal along with Union Square Ventures, Andreessen Horowitz, RRE Ventures, and other investors.

The Mediachain Labs team says, “What if the information about all media ever created was completely open, and you could instantly know everything about whatever you were viewing, watching, reading, or listening to — who made it, what it was, where it originated — regardless of how you came across it?”

Mediachain connects media to information through the content itself. It combines a decentralized media library with content identification technology to enable collaborative registration, identification, and tracking of creative works online. In turn, developers can automate attribution, preserve history, provide creators and organizations with rich analytics showing how their content is being used, and even create a channel for exchanging value directly through content no matter where it is.

Rick Smolan, photojournalist and creator of the “Day in the Life” series, said, “Mediachain Labs is tackling a serious problem that’s faced creators from day one: how can I make sure I am credited for the work I create? Mediachain’s Blockchain technology tethers ownership to content, ensuring to both creators and to publishers—in any medium—that proper attribution (and hopefully financial remuneration) is perpetually connected to each work of art. This has been a long time coming, and Mediachain Labs seems to have broken the code.”  

Rick elaborates on the story behind his Muhammad Ali photo, which could provide valuable additional metadata associated with the photo when it appears online. Rick said, “I ran into Muhammad Ali when I stepped into an elevator in Tokyo’s Keio Plaza Plaza hotel in 1976 with my friend David Burnett, an incredibly talented photojournalist who had invited me to become part of his fledgling photo agency [Contact Press Images, which he co-founded with photo impresario Robert Pledge].

Muhammad Ali, 1976 ©Rick Smolan

Muhammad Ali, 1976 ©Rick Smolan

In the elevator with Ali was Howard Bingham, Ali’s personal photographer. David and Howard were old friends and in the space of that 30 second elevator ride Howard told us that in a few hours he and Ali were headed to Korea to tour US Army bases for a week.

Howard told us they two extra seats on the plane and invited us to join them on the tour (that’s how things worked back then!). So off we went on a weeklong fascinating behind the scenes tour of Korea with the champ. At every base Ali would get in the ring to spar with a few soldiers.

It was surreal and I kept thinking if we had pushed the elevator button 30 seconds later none of this would happened. I always thought about these wonderful moments of serendipity as the “fate stream”.

Mediachain’s codebase is completely open-source leveraging the blockchain. This makes it an ideal environment for collaboration and innovation between media organizations, distribution platforms, and independent creators and developers who want to retain control over their data while broadening their reach.

Millions of images and related metadata records have been contributed to Mediachain by participating organizations including The Museum of Modern Art (MoMA), Getty Images, the Digital Public Library of America (DPLA), and Europeana.

Dan Taylor, Founder and Principal Photographer at Heisenberg Media said “This sounds like a great solution to a time old problem. I can imagine this cutting hours off my standard routine of tracking and gaining proper credit for works I've produced. Probably one of the most innovative uses of the blockchain I've seen yet. Can't wait to give this a go myself!”  

Dan elaborates on how one of his photos is re-published exponentially online “I was on the job covering the Web Summit in Dublin, Ireland, and just happened to be walking through the audience when I spun around and saw this. Luckily, I had a fisheye in my pocket and quickly swapped over to capture the scale of what I was viewing.

As this image was selected by the Web Summit as one of their top marketing images (and one they’re still using in some collateral today), naturally, it became quite a popular one for bloggers, journalists, and general fans of the event. In most cases, I don’t blame the second, third, and fourth level individuals who use the image, because they have no way of tracking down who created the work.

Web Summit in Dublin. ©Dan Taylor/Heisenberg Media

Web Summit in Dublin. ©Dan Taylor/Heisenberg Media

In fact, when doing a Google Images search, there’s no reference to me until the 19th page. And you know the best place to hide a body? Page 2 of Google search. Again, I don’t blame those that use it, just wish there was a way that they could know who the original photographer was/is. If the Mediachain solution can retain and provide that information, we really are looking at the holy grail.”

Developers who are interested in the project can find out more and get involved by joining the community on GitHub or through their public Slack.  

Other People’s Weddings: Emma & Josh’s Wedding, Brooklyn, NY 1/2/99 ©Evan Nisselson

Other People’s Weddings: Emma & Josh’s Wedding, Brooklyn, NY 1/2/99 ©Evan Nisselson

Love The Living Of Life: Jim and his son Matt, Glacier, WA 9/13/95 ©Evan Nisselson

Love The Living Of Life: Jim and his son Matt, Glacier, WA 9/13/95 ©Evan Nisselson

Content creators and owners should also get involved by reaching out to the team at Mediachain Labs.

 

Grokstyle wins LDV Vision Summit 2016 Entrepreneurial Computer Vision Challenge

Entrepreneurial Computer Vision Challenge Winner: Grokstyle, Sean Bell, CEO & Co-Founder ©Robert Wright/LDV Vision Summit

Entrepreneurial Computer Vision Challenge Winner: Grokstyle, Sean Bell, CEO & Co-Founder ©Robert Wright/LDV Vision Summit

Our annual LDV Vision Summit has two competitions. Finalists receive a chance to present their wisdom in front of hundreds of top industry executives, venture capitalists, top industry executives and companies recruiting. Winning competitor also wins $5,000 Amazon AWS credits.

1. Startup competition for promising visual technology companies with less than $1.5M in funding?

2. Entrepreneurial Computer Vision Challenge (ECVC) for any Computer Vision and Machine Learning students, professors, experts or enthusiasts working on a unique solution to empower businesses and humanity.

Competitions are open to anyone working in our visual technology sector such as: empowering photography, videography, medical imaging, analytics, robotics, satellite imaging, computer vision, machine learning, artificial intelligence, augmented reality, virtual reality, autonomous cars, media and entertainment, gesture recognition, search, advertising, cameras, e-commerce, visual sensors, sentiment analysis, and much more.

The Entrepreneurial Computer Vision Challenge provides contestants the opportunity to showcase the technology piece of a potential startup company without requiring a full business plan. It provides a unique opportunity for students, engineers, researchers, professors and/or hackers to test the waters of entrepreneurism in front of a panel of judges including top industry venture capitalists, entrepreneurs, journalists, media executives and companies recruiting.

In the 2014 and 2015 Summits the ECVC was organized into predefined challenge areas (e.g., "estimate the price of a home or property," "estimate how often a photo will be re-shared") plus a "wildcard" category.

Initially we proceeded in the same way for the 2016 ECVC, but we found that the most exciting entries were overwhelmingly the wildcards, so we decided to go all-in on that category. Attendees at this year's summit bore witness to the outstanding lineup of finalists, including GrokStyle (visual understanding for interior design) from Cornell, MD.ai (intelligent radiology diagnostics) from Weill Cornell, DeepALE (semantic image segmentation) from Oxford University and Vision+Love (automated kinship prediction) from Carnegie Mellon.

Congratulations to our 2016 LDV Vision Summit Entrepreneurial Computer Vision Challenge Winner: Grokstyle, Sean Bell, CEO & Co-Founder  

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

What is GrokStyle?

GrokStyle, co-founded by Cornell researchers Sean Bell and Kavita Bala, is developing state-of-the-art visual search.  Given any photo, we want to tell you what products are in it, and where you can buy them.  We want to help customers and retailers connect with designers, by searching for how others have used and combined furniture and decor products.  The world is full of beautiful design -- we want to help you find it.

As a PhD Candidate - what were your goals for attending our LDV Vision Summit? Did you attain them?

My goals were to understand the startup space for computer vision, to connect with potential collaborators, find companies interested in building on our technology, and generally get our name out there so we can have a running start.  The event definitely far exceeded our expectations and we attained all of our goals.

Why did you apply to our LDV Vision Summit ECVC competition? Did it meet or beat your expectations and why?

Serge Belongie recommended that we apply, and saw the value that the summit would have for us.  We were excited, but certainly did not expect the amount of positive feedback, support, and connections that we made.  My pocket is overflowing with business cards, and I’m excited to continue these conversations as we turn our technology into a company.

Why should other computer vision, machine learning, and artificial intelligence researchers attend next year?

I think that all CV/ML/AI researchers should attend events like the LDV Vision Summit.  The talks here are interesting and varied, and it is inspiring to see how algorithms and computer vision research are having a real impact in the world. You don’t get that at academic conferences like CVPR.

We try to have an exciting cross section of judges from computer vision experts, entrepreneurs, investors and journalists. Asking a question is Barin Nahvi Rovzar, Hearst, Exec. Dir., R&D & Strategy. Judges included: Serge Belongie (Prof., Cornell Tech, Computer Vision), Howard Morgan (First Round, Partner & Co-Founder), Gaile Gordon (Enlighted, Sr. Director, Technology), Jan Erik Solem (Mapillary, CEO), Larry Zitnick (Facebook, AI Research, Research Lead), Ramesh Jain (U. California, Irvine, Prof., Co-Founder Krumbs), Evan Nisselson - LDV Capital, Partner),  Nikhil Rasiwasia (Principal Scientist, Snapdeal), Beth Ferreira (WME Venture Partners, Managing Partner), Stacey Svetlichnaya (Flickr, Software Engineer, Vision & Machine Learning), Adriana Kovashka (U. of Pittsburgh, Assist. Professor Dept. Computer Science) ©Robert Wright/LDV Vision Summit

We try to have an exciting cross section of judges from computer vision experts, entrepreneurs, investors and journalists. Asking a question is Barin Nahvi Rovzar, Hearst, Exec. Dir., R&D & Strategy. Judges included: Serge Belongie (Prof., Cornell Tech, Computer Vision), Howard Morgan (First Round, Partner & Co-Founder), Gaile Gordon (Enlighted, Sr. Director, Technology), Jan Erik Solem (Mapillary, CEO), Larry Zitnick (Facebook, AI Research, Research Lead), Ramesh Jain (U. California, Irvine, Prof., Co-Founder Krumbs), Evan Nisselson - LDV Capital, Partner) Nikhil Rasiwasia (Principal Scientist, Snapdeal), Beth Ferreira (WME Venture Partners, Managing Partner), Stacey Svetlichnaya (Flickr, Software Engineer, Vision & Machine Learning), Adriana Kovashka (U. of Pittsburgh, Assist. Professor Dept. Computer Science) ©Robert Wright/LDV Vision Summit

What was the most valuable part of your LDV Vision Summit experience aside from winning the competition?

The most valuable part of the summit was connecting with three different companies potentially interested in building on our technology, and with four different potential investors/advisors.  Last year, a key potential collaborator had presented at LDV Vision Summit, looking for computer vision researchers to solve challenging problems in visual search, interior design, and recognition.  This year we were able to connect and say “we solved it!”

Sean Bell, CEO & Co-Founder of Grokstyle ©Robert Wright/LDV Vision Summit

Sean Bell, CEO & Co-Founder of Grokstyle ©Robert Wright/LDV Vision Summit

Do you have any advice for other researchers & PhD candidates that are thinking about evolving their research into a startup business?

My advice would be to keep potential commercial applications in mind, early on in the project, so that what you end up with at the end is easier to take out of the lab and sell to the world.  For me, one of the most challenging aspects of research is deciding which problems are solvable and which are worth solving -- if you are interested in startups, this is even more important.  There is the extra step of understanding who cares and who wants to use it.

What was the timeline for you to take your idea for your research to evolving it into a startup plan?

We presented a research paper at SIGGRAPH 2015 about our ideas from last year.  It has taken us a year to flesh out the work, develop it from a research prototype to a product prototype.  But there is still a lot to do.  I am graduating in a few months, and Prof. Kavita Bala is joining full time on sabbatical.  We plan to hit the ground running this summer with our engineer Kathleen Tuite, and two interns we are taking on.  As technologists, we are looking to partner with business people to take the lead on evaluating which markets and customers can benefit the most from our technology.  Starting in the fall, we plan on fundraising to help scale up our technical infrastructure.

Judges for our Entrepreneurial Computer Vision Challenges ©Robert Wright/LDV Vision Summit

Judges for our Entrepreneurial Computer Vision Challenges ©Robert Wright/LDV Vision Summit


 


 

Carbon Robotics wins LDV Vision Summit 2016 Startup Competition

Rosanna Myers, Co-Founder & CEO Carbon Robotics during her 4 minute startup competition presentation.   ©Robert Wright/LDV Vision Summit

Rosanna Myers, Co-Founder & CEO Carbon Robotics during her 4 minute startup competition presentation.  
©Robert Wright/LDV Vision Summit

Our annual LDV Vision Summit has two competitions. Finalists receive a chance to present their wisdom in front of hundreds of top industry executives, venture capitalists, top industry executives and companies recruiting. Winning competitor also wins $5,000 Amazon AWS credits.

1. Startup competition for promising visual technology companies with less than $1.5M in funding?

2. Entrepreneurial Computer Vision Challenge for any Computer Vision and Machine Learning students, professors, experts or enthusiasts working on a unique solution to empower businesses and humanity.

Competitions are open to anyone working in our visual technology sector such as: empowering photography, videography, medical imaging, analytics, robotics, satellite imaging, computer vision, machine learning, artificial intelligence, augmented reality, virtual reality, autonomous cars, media and entertainment, gesture recognition, search, advertising, cameras, e-commerce, visual sensors, sentiment analysis, and much more.

Each year we review over 100 applications to select about 20 sub-finalists. Sub-finalists receive remote presentation coaching from Evan Nisselson and invitation to our final mentoring and judging by a group of experts in person before the Summit. Finalists are invited to present on stage at the LDV Vision Summit in front of the audience and judges.

Finalists included: Reconstruct, SmartPlate, GeoCV, Simile, Shelfie, Faception and Carbon Robotics.  

Congratulations to our 2016 Startup Competition Winner: Carbon Robotics, Rosanna Myers, Co-Founder & CEO, Carbon Robotics.

[L-R] Rosanna of Carbon Robotics, Rebecca of CakeWorks, Serge & August Cornell Tech, & Evan of LDV Capital ©Robert Wright/LDV Vision Summit

[L-R] Rosanna of Carbon Robotics, Rebecca of CakeWorks, Serge & August Cornell Tech, & Evan of LDV Capital
©Robert Wright/LDV Vision Summit

We asked Rosanna some questions about her goals and experience at our LDV Vision Summit.

What were your goals for attending our LDV Vision Summit? Did you attain them?

We were excited about coming to the LDV Vision Summit for a few reasons. First, we wanted to learn from people working on interesting and disparate aspects of visual technologies. It's usually the combination of disciplines and backgrounds that yields the most creative results, so we were attracted to the theme.

We also wanted to connect with potential recruits and investors in NYC since we're based in SF and so hadn't ever connected with the ecosystem. The experience was great for that. Getting to pitch on the main stage was amazing, because it made it easy for people to learn about what we're working on. After the pitch, we were approached by tons of high-quality engineers and potential partners, so it was a great success.

We try to have an exciting cross section of judges from Computer Vision Experts, Entrepreneurs, Investors to Journalists. We typically have more judges than other events because we strongly believe more data will deliver better results. We also hope to have more opportunities for our competitors to raise capital when relevant. Startup Competition judges:  Josh Elman (Greylock, Partner), Brian Cohen (NY Angels, Chairman), Jessi Hempel (Wired, Senior Writer), David Galvin (IBM Ventures, Watson Ecoystem), Christina Bechhold (Samsung, Investor), Jason Rosenthal (Lytro, CEO), Susan McPherson (McPherson Strategies, CEO), Steve Schlafman (RRE Ventures, Principal), Taylor Davidson (Unstructured Ventures), Justin Mitchell (Founding Partner, A# Capital), Adaora Udoji (Rothenberg Ventures, Chief Storyteller), Varun Jain (Qualcomm Ventures, Investment Manager), Josh Weisberg (Microsoft, Principal PM, Computational Photography), Tamara Berg  (UNC, Chapel Hill, Assist. Prof., Computer Vision). ©Robert Wright/LDV Vision Summit

We try to have an exciting cross section of judges from Computer Vision Experts, Entrepreneurs, Investors to Journalists. We typically have more judges than other events because we strongly believe more data will deliver better results. We also hope to have more opportunities for our competitors to raise capital when relevant. Startup Competition judges:  Josh Elman (Greylock, Partner), Brian Cohen (NY Angels, Chairman), Jessi Hempel (Wired, Senior Writer), David Galvin (IBM Ventures, Watson Ecoystem), Christina Bechhold (Samsung, Investor), Jason Rosenthal (Lytro, CEO), Susan McPherson (McPherson Strategies, CEO), Steve Schlafman (RRE Ventures, Principal), Taylor Davidson (Unstructured Ventures), Justin Mitchell (Founding Partner, A# Capital), Adaora Udoji (Rothenberg Ventures, Chief Storyteller), Varun Jain (Qualcomm Ventures, Investment Manager), Josh Weisberg (Microsoft, Principal PM, Computational Photography), Tamara Berg  (UNC, Chapel Hill, Assist. Prof., Computer Vision). ©Robert Wright/LDV Vision Summit

Judge: Brian Cohen, NY Venture Partners, Founding Partner. NY Angels, Chairman ©Robert Wright/LDV Vision Summit

Judge: Brian Cohen, NY Venture Partners, Founding Partner. NY Angels, Chairman ©Robert Wright/LDV Vision Summit

Why did you apply to our LDV Vision Summit competition? Did it meet or beat your expectations and why?

Same reason as above. We wanted to come to the Summit to meet with and learn from people and we wanted to pitch on the stage to amplify signal. It definitely exceeded expectations. I ran out of business cards in the first 20 minutes after pitching and had really interesting conversations.

What were the most valuable parts of your LDV Vision Summit experience aside from winning the competition?

I would say that the best part was forging new relationships. Talks are great, but they can be watched asynchronously. The magic of having events with tons of interesting and creative people is the engineered serendipity. In a few hours, we met with computer vision experts, storytellers, roboticists, writers, investors, students, designers, and even a manufacturer. You just can't do that with a LinkedIn search. Business is really about people and it's nice to develop relationships organically.

Did you benefit from the pre-summit competition mentoring? If yes, how?

Yes, the pre-summit mentoring helped us hone our messaging, which I think was instrumental in helping us tell our story effectively. We also liked getting to know the LDV Vision Summit team personally, albeit briefly, and we're sorry we had to leave the city so quickly. Hopefully we can connect again very soon once everyone recovers.

 Josh Weisberg, Microsoft, Principal PM, Computational Photography asking a question. ©Robert Wright/LDV Vision Summit

 Josh Weisberg, Microsoft, Principal PM, Computational Photography asking a question. ©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

Any advice to other entrepreneurs that might be thinking about applying to our next Startup Competition? Why should they apply?

Definitely apply, even if you are not sure how in-thesis you are. When we applied, I wasn't sure if we would be too tangential, because while we leverage a lot of computer vision to make applications easy and intuitive, we're not a computer vision company per se. We build robots and we build a platform. However, what we learned through the process is that LDV Summit is about the ecosystem and the impact, which can take many forms.

My advice if you get selected to pitch is to get to know the other founders. They are likely awesome people who are going through a lot of the same things you are and who have a high potential to become close friends and allies. It's called a competition and they do select a winner, but I think “winning” at these events is way more people than titles.  

Power To The People! You Made Our LDV Vision Summit A Success. Thank you!

Investing in visual technology businesses panel: Steve Schlafman/RRE Ventures, Josh Elman/Greylock Partners, Allison Goldberg/Time Warner Investments & moderated by Evan Nisselson/LDV Capital. ©Robert Wright/LDV Vision Summit

Investing in visual technology businesses panel:
Steve Schlafman/RRE Ventures, Josh Elman/Greylock Partners, Allison Goldberg/Time Warner Investments & moderated by Evan Nisselson/LDV Capital. ©Robert Wright/LDV Vision Summit

Wow - another fantastic Summit thanks to all of you brilliant people!

YOU are why our annual LDV Vision Summit gathering is special and a success every year. Thank You!

We are honored that you fly in from around the world each year to share insights, inspire, do deals, recruit, raise capital and help each other succeed!  

Congratulations to our competition winners:
- Startup Competition:  
Carbon Robotics, Rosanna Myers, Co-Founder & CEO, Carbon Robotics.
Entrepreneurial Computer Vision Challenge: Grokstyle, Sean Bell, CEO & Co-Founder

Startup Competition Winner:  Carbon Robotics, Rosanna Myers, Co-Founder & CEO, Carbon Robotics. Rosanna says "The magic of having events with tons of interesting and creative people is the engineered serendipity. In a few hours at the LDV Vision Summit, we met with computer vision experts, storytellers, roboticists, writers, investors, students, designers, and even a manufacturer. You just can't do that with a LinkedIn search. Business is really about people and it's nice to develop relationships organically. ©Robert Wright/LDV Vision Summit

Startup Competition Winner:  Carbon Robotics, Rosanna Myers, Co-Founder & CEO, Carbon Robotics.
Rosanna says "The magic of having events with tons of interesting and creative people is the engineered serendipity. In a few hours at the LDV Vision Summit, we met with computer vision experts, storytellers, roboticists, writers, investors, students, designers, and even a manufacturer. You just can't do that with a LinkedIn search. Business is really about people and it's nice to develop relationships organically. ©Robert Wright/LDV Vision Summit

Entrepreneurial Computer Vision Challenge Winner: Grokstyle, Sean Bell, CEO & Co-Founder We had a blast this week!  The LDV Vision Summit was the most rewarding event we have attended since starting GrokStyle, by a large margin.  The talks were interesting and varied, and it was inspiring to see the kinds of real-world applications of computer vision that you might not see at academic conferences like CVPR. We are grateful for the opportunity to present our startup, and our presentation allowed us to connect with many new potential customers, investors, business partners, and collaborators.  It’s given us a running start to take our technology out of the lab and into the world. ©Robert Wright/LDV Vision Summit

Entrepreneurial Computer Vision Challenge Winner: Grokstyle, Sean Bell, CEO & Co-Founder
We had a blast this week!  The LDV Vision Summit was the most rewarding event we have attended since starting GrokStyle, by a large margin.  The talks were interesting and varied, and it was inspiring to see the kinds of real-world applications of computer vision that you might not see at academic conferences like CVPR. We are grateful for the opportunity to present our startup, and our presentation allowed us to connect with many new potential customers, investors, business partners, and collaborators.  It’s given us a running start to take our technology out of the lab and into the world. ©Robert Wright/LDV Vision Summit

A special thank you to Rebecca Paoletti and Serge Belongie as the summit would not exist without collaborating with them!

Beyond the fascinating sessions, there is the serendipity and the inspirational networking that leaves everyone wanting more. Until next year.” Paul Melcher, Kaptur, Editor

The quotes below from our community is why we created our LDV Vision Summit. We could not have succeeded without the tremendous support from all of our partners and sponsors:

Organizers:
Presented by Evan Nisselson, LDV Capital
Video Program: Rebecca Paoletti, CakeWorks, CEO
Computer Vision Program: Serge Belongie, Cornell Tech
Computer Vision Advisor: Jan Erik Solem, Mapillary  
Universities: Cornell Tech, School of Visual Arts, International Center of Photography
Sponsors: Amazon AWS, Facebook, FLIR Systems, GumGum, IDA Ireland, Microsoft Research, Qualcomm,  Vidlet
Media Partners: Kaptur, VizWorld
Coordinators Entrepreneurial Computer Vision Challenge: Andreas Veit, Cornell University, Doctor of Philosophy, Computer Science and Oscar Beijbom, UC Berkeley, Postdoctoral Researcher  

Day 1 Panel: Visual Sensor Networks Will Empower Businesses & Humanity. More and more visual sensors are being leveraged in the commercial and home markets. From energy and real-time data management in smart commercial buildings, to home video security and monitoring our babies when they sleep. How will they empower businesses and humanity? [Front to Back] Moderator: Evan Nisselson, LDV Capital Panelists: Gaile Gordon, Enlighted, Senior Director Technology,  Jan Kautz, NVIDIA, Director of Visual Computing Research, Chris Rill, Canary, CTO & Co-Founder ©Robert Wright/LDV Vision Summit

Day 1 Panel: Visual Sensor Networks Will Empower Businesses & Humanity.
More and more visual sensors are being leveraged in the commercial and home markets. From energy and real-time data management in smart commercial buildings, to home video security and monitoring our babies when they sleep. How will they empower businesses and humanity? [Front to Back] Moderator: Evan Nisselson, LDV Capital Panelists: Gaile Gordon, Enlighted, Senior Director Technology,  Jan Kautz, NVIDIA, Director of Visual Computing Research, Chris Rill, Canary, CTO & Co-Founder ©Robert Wright/LDV Vision Summit

"LDV has been ahead of the pack in identifying and analyzing the visual tech space (far beyond just AR and VR) as one that is becoming an increasingly important and viable theme for many businesses. The convening of researchers, investors, and companies that are literally inventing our future is an immersive and instructive way to spend two days."  Barin Nahvi Rovzar, Hearst, Executive Director, R&D & Strategy

"LDV Vision Summit is one of those rare events that brings a very focused group of people together to talk about something that they all care about - computer vision & visual technologies. If you are investing or working in the space, you should definitely attend to meet other experts and startups building the next great products and companies." Josh Elman, Greylock Partners, Partner

“It was my first time at a summit as a student, and it was quite an eye-opening experience: lots of interesting people to meet with game-changing ideas in the works. I feel like it is a necessary bridge to even out the disparities between academia and industry in this rapidly growing field, and the opportunity to network with some of the greatest names in the field is something that should definitely not be overlooked!” Divyaa Ravichandran, Carnegie Mellon, Research Assistant

"LDV Vision Summit exposed me to some amazing entrepreneurs and thought leaders thinking about the world through a different perspective than any other tech conference I've been to.  Anyone that wants to get a glimpse at the future of how we process everything around us would benefit from attending." Ed Laczynski, Zype, CEO & Co-Founder

“The LDV Vision Summit is computer mad scientists meets visual storytellers with world class investors lurking in every corner. Two days that will stretch your brain and open your eyes to countless emerging possibilities in the imaging world.” Brian Storm, Founder & Executive Producer, MediaStorm

Day 2 Keynote: An Image Is Really Hundreds Of Data Points That Tell Us Who We Are. Anastasia Leng, Picasso Labs, CEO & Founder Anastasia worked many years at Google and is a serial entrepreneur leveraging technology to better understand visual content. Inside every image lies hundreds of unique data points that provide priceless information about your audience and their revealed visual preferences. Find out how technology can unearth this data to help you make smarter creative decisions and improve your visual strategy.   ©Robert Wright/LDV Vision Summit

Day 2 Keynote: An Image Is Really Hundreds Of Data Points That Tell Us Who We Are. Anastasia Leng, Picasso Labs, CEO & Founder
Anastasia worked many years at Google and is a serial entrepreneur leveraging technology to better understand visual content. Inside every image lies hundreds of unique data points that provide priceless information about your audience and their revealed visual preferences. Find out how technology can unearth this data to help you make smarter creative decisions and improve your visual strategy.   ©Robert Wright/LDV Vision Summit

“I believe all CV/ML/AI researchers should attend the LDV Vision summit and its competitions. LDV Vision Summit is a unique event. Different from traditional conferences where I mostly only meet researchers, at LDV Vision Summit, I made a lot of good contacts from the whole computer vision technology ecosystem, lots of CV researchers, investors, entrepreneurs, and VR/AR content creators. I was thrilled to participate.” Shuai Kyle Zheng, University of Oxford, Graduate Research Assistant

"The last 10 years of advances in mobile and cloud computing have been life changing. Visual computing feels like that next life changing tech movement, and the questions and ideas explored at the LDV Vision Summit will be critical to any tech player serious about being involved." Rohit Dave, Samsung, Corporate Development & Strategy
 

“My first experience at the LDV Vision Summit was a slam dunk:  true thought leadership content, a never-boring pace, and a great platform to drive awareness of our technology.  I was also very impressed by the caliber of speakers as well as the attendees.” Travis Merrill, FLIR Systems, SVP, CMO

Truly inspired by inspired by the LDV Vision Summit and Evan Nisselson’s dedication to creating an exceptional audience experience. I was fortunate to join this year as a judge reviewing the selected start-ups and felt confident that they were well-vetted ahead of time. Hats off to the entire team for bringing this event to life — a very important annual gathering for those in the VC/Tech space.” Susan McPherson, McPherson Strategies, CEO

“As a first time attendee to the LDV summit, I was mostly expecting to see numerous technical presentations by various startups from the computer vision field.  I was pleasantly surprised that LDV Summit also covered important investment related topics and trends that helped me gain greater understanding of business aspects related to Visual Tech.  I am definitely looking forward to LDV 2017!” Jack Levin, Nventify, CEO & Founder, ImageShack, CEO & Co-Founder

“I enjoyed the unique atmosphere and fresh perspectives that come from bringing together vision researchers, entrepreneurs, and investors. I can't get that within the usual academic conference. Computer vision specialists who want to make an impact should make every effort to participate in this lively summit.” Derek Hoiem, University of Illinois at Urbana-Champaign, Associate Professor, Computer Science

Day 1 Keynote:  reCAPTCHA: anti-spam, crowdsourcing, and humanity.   reCAPTCHA was created 9 years ago as an anti-spam tool which also crowdsourced books digitization. reCAPTCHA has been pushing the boundary of research on OCR that today machines can read text much better than human. With the re-imagined ""No CAPTCHA"" reCAPTCHA, it pivots into the natural image recognition space and empowers deep learning system from millions of brilliant human minds everyday. Ying Liu, Google, reCAPTCHA, Manager @Robert Wright/LDV Vision Summit

Day 1 Keynote:  reCAPTCHA: anti-spam, crowdsourcing, and humanity.  
reCAPTCHA was created 9 years ago as an anti-spam tool which also crowdsourced books digitization. reCAPTCHA has been pushing the boundary of research on OCR that today machines can read text much better than human. With the re-imagined ""No CAPTCHA"" reCAPTCHA, it pivots into the natural image recognition space and empowers deep learning system from millions of brilliant human minds everyday. Ying Liu, Google, reCAPTCHA, Manager @Robert Wright/LDV Vision Summit

"The LDV Vision Summit was the most rewarding event we have attended since starting GrokStyle, by a large margin. The talks were interesting and varied, and it was inspiring to see the kinds of real-world applications of computer vision that you might not see at academic conferences like CVPR. We are grateful for the opportunity to present our startup, and our presentation allowed us to connect with many new potential customers, investors, business partners, and collaborators.  It’s given us a running start to take our technology out of the lab and into the world." Sean Bell, GrokStyle, Co-Founder and CEO [Winner of the LDV Vision Summit Computer Vision Challenge]

“It’s amazing to see all the innovation around creativity at the Summit,” said Brian Hunt, EVP Head of Believe Studios & Development. “I think our panel spurred a valuable conversation around the importance of not losing sight of the storytelling amidst the chase to keep up with it all."

“For computer vision entrepreneurs and investors, the LDV Vision Summit is not to be missed. There simply is no other place where you can find such high caliber ideas being explored, and high impact people discussing them.” Samson Timoner, Founder/CTO Mythical Labs.

“LDV brings together thinkers from many fields, which is key for productive, cross-disciplinary discussion about the present and future of imaging. As a professor who approaches photography from a humanities perspective, I found the talks and panels, from experts in fields beyond my own, illuminating. This is an excellent space for the generation of important conversations for anyone (photographer, academic, investor, branding expert, artist, software developer…) interested in the role of the image.” Lauren Walsh, New York University, Professor & Director NYU Gallatin Summer Photojournalism Lab.

Day 2 Panel: Content Creation For Virtual Reality Is Critical For Success - How Will Pros and Consumers Create 360, 3D, & Virtual Reality Content? Moderator: Jessi Hempel, Wired, Senior Writer. Panelists:  Jason Rosenthal, Lytro, CEO, Brian Cabral, Facebook, Dir. Engineering, 360 Camera, Koji Gardiner, Jaunt VR, VP of Engineering ©Dean Meyers/Vizworld

Day 2 Panel: Content Creation For Virtual Reality Is Critical For Success - How Will Pros and Consumers Create 360, 3D, & Virtual Reality Content? Moderator: Jessi Hempel, Wired, Senior Writer. Panelists:  Jason Rosenthal, Lytro, CEO, Brian Cabral, Facebook, Dir. Engineering, 360 Camera, Koji Gardiner, Jaunt VR, VP of Engineering ©Dean Meyers/Vizworld

"Oh, I heard about your company" - presenting on stage in the startup competition gives you prior visibility among investors, whom you want to get connected to. It also brings some inbound meeting requests from Venture Capitalists." Anton Yakubenko, GeoCV, Co-Founder & CEO

"The LDV Summit is truly a unique synergy of smart business, cutting edge research, and inspiring creativity. The LDV Summit provided a platform and focused audience that can't be found anywhere else. Through the pitch competition we were instantly connected with several very interested investors. " James George, Simile, Co-Founder

“It was definitely one of the best learning and networking experience.  I love listening to all the rapidly disseminated information.  Also loved feedback from people on my thoughts.  So many people came and provided me positive feedback on Context and intent are essential for finding meaning in photos; and language divides but visual unites.” Ramesh Jain, Professor at UCI and CoFounder at Krumbs  

Future of how humans will interact with their phones in a world of 360 photos during Evan Nisselson's Day 2 keynote. ©Robert Wright/LDV Vision Summit

Future of how humans will interact with their phones in a world of 360 photos during Evan Nisselson's Day 2 keynote.
©Robert Wright/LDV Vision Summit

“The LDV Vision Summit bests itself each year, and 2016 was no exception: the panels, talks and competitions are absolutely world class. I used to say that entrepreneurial CTOs, regardless of company size or focus, *should* attend. I’m now telling them it is absolutely essential!” Andy Parsons, Kontor, CTO & Co-Founder
 

“Photographs and images enable us to imagine ourselves in another place, another time and as if we were another person. They enable us to empathize with others across time and space. What images do for people symbolically, the LDV Vision Summit did practically for its attendees who came across space and time (zones) to attend. We were brought up close to new problems, solutions, burgeoning fields and concepts. We were able to empathize with founders up close, learn about new emerging technologies and solutions to today's and tomorrow's biggest problems.” Dan Schiffman, Tvision Insights, Co-Founder & CRO

“The LDV Vision Summit is a must for anyone interested in images, video or computer vision.  This event is unique in bringing together scientists, investors, entrepreneurs and artists all willing to share knowledge and ideas in a stimulating and dynamic atmosphere. The discussions are excellent. The connections made invaluable.” Thomas Jelonek, envision.ai, Founder

“The LDV Vision Summit is a fast-paced mix of technology, business, and academic perspectives.  You can hear insights from seasoned venture capitalists, major industry players, and young entrepreneurs developing their first vision or machine learning-based startups, packed into just two days.  It's invigorating!” Dr. David S. Touretzky,  Carnegie Mellon University, Research Professor Computer Science Department & Center for the Neural Basis of Cognition

“The LDV Vision summit is an opportunity to listen, learn and share ideas that are impacting everyone connected to the world of visuals. As photographers we too often wait to see what ideas and tools are coming down the pipeline that will have impact on the way we work. I prefer learning about potential visual technologies in advance and speaking with technologists during the process of creating them to hopefully have a beneficial impact for all. I hope other photographers will join our discussion at the next Summit.” Ron Haviv, VII Photo, Photographer & Co-Founder

Day 1 Panel: Autonomous Driving Would Not Be Possible Without Leveraging Visual Technologies.  Moderator: Mike Murphy, Quartz, Reporter. Panelists: Sanjiv Nanda, Qualcomm Research, VP, Engineering and Laszlo Kishonti, AdasWorks, CEO. ©Dean Myers/Vizworld

Day 1 Panel: Autonomous Driving Would Not Be Possible Without Leveraging Visual Technologies. 
Moderator: Mike Murphy, Quartz, Reporter. Panelists: Sanjiv Nanda, Qualcomm Research, VP, Engineering and Laszlo Kishonti, AdasWorks, CEO. ©Dean Myers/Vizworld

“In Silicon Valley - Augmented & Virtual Reality & Robotics are all hottest trends. The annual LDV Vision Summit in NYC always gathers the top-tier experts, brains and the ideas which drive these trends. It showed how early we are and simultaneously that we are embarking on technology paradigm shift. Excited for Keepy to leverage these technologies to save memories on any device and to collaborate with the people I met at this unique gathering of inspiring experts. I truly enjoy this conference each year.” Offir Gutelzon, Keepy, CEO and Co-Founder

“The summit gave us insight on emerging technologies that will be valuable for our network of photographers to anticipate as tools for their work in the near and distant future!” Susan Meiselas and Emma Raynes, Magnum Foundation

“Being surrounded with computer vision experts from academia, industry and startups was a great networking and learning experience. It was a very surprising moment for me to listen and meet such a great people at one place. I have made plenty of connections and learnt different perspectives about future of Computer Vision & AI. I am sure LDV Vision Summit will attract larger crowd in future and makes huge impact to humanity by bringing 2 different worlds (Academia & Industry) on to the same platform. I will try to be part of future LDV summits and contribute to them in some way.” Harsha Vardhan, Carnegie Mellon University, Graduate Student

As an early stage investor in robotics, computer vision, autonomous mobility, and remote sensing,The LDV Summit is an invaluable resource to have in our venture community.  Reflecting on last week’s presentations, I am still impressed by the curated experience of industry leaders, innovative startups and quality connections all under one roof.” Oliver Mitchell, Mach 5 Ventures

“I wanted to let you know your conference is hands down the best conference in NYC. I was absolutely blown away by the caliber of people you have assembled at every vertical of the ecosystem.” Jonathan Ohliger, CEO at VeePiO

“Concealed behind all the incredible people you meet, everything you learn and the amazing discoveries is the formidable inspiration that empowers you for many days after the event is over. The LDV Vision Summit only gets better with each edition.” Paul Melcher, Kaptur Magazine, Founder & Editor

"Being involved in the LDV Vision summit was a pleasure and an amazing opportunity to network across all disciplines of entrepreneurial computer vision. Big congrats to this years winners and everyone else for a great meeting!" 
Oscar Beijbom, UC Berkeley, Postdoctoral Scholar

"Easily the best curated speakers for any conference I have been to in awhile and great coverage of computer vision/machine learning. The startup pitches were high level. The doodle notes on screen were unique and enjoyable to watch." Nisa Amoils, Investor and Co-Chair Frontier Tech, New York Angels

“LDV Vision Summit assembled a fantastically eclectic group of people from investors to inventors who are all passionate about the how computer vision will change the world.  I look forward to going again in 2017!” Paul Kruszewski, wrnch, CEO & Founder

"As a first-timer at the LDV Vision Summit, one never knows what to expect... will it be too academic, too commercial, to many early stage start-ups, or over-run with larger tech companies?  Instead, the LDV team has curated the perfect blend of people and topics - all tackling disparate, but related applications leveraging computer vision.  In one quick trip, I engaged with potential hires, strategic partners, future investors - and learned about myriad new applications being tackled by this robust ecosystem!  Definitely planning on coming back next year." Richard Lee, Netra, CEO & Co-Founder

Day 2 Keynote: How funny videos can help build cultural understanding. Trina DasGupta, Single Palm Tree Productions, CEO ©Robert Wright/LDV Vision Summit

Day 2 Keynote: How funny videos can help build cultural understanding. Trina DasGupta, Single Palm Tree Productions, CEO ©Robert Wright/LDV Vision Summit

“The Summit's clear technology focus sets it apart - I was truly impressed by the quality of companies using computer vision across a range of verticals, from VR to robotics, and by the caliber of conversation amongst speakers.” Christina Bechhold, Samsung Global Innovation Center, Investor. Empire Angels, Co-Founder, Managing Director.  

"LDV Vision Summit is a unique event focused on visual technologies with a rare mix of great entrepreneurs, researchers, investors and global technology companies. Definitely worth the time and the trip to NYC.” Jan Erik Solem, Mapillary, CEO & Co-Founder

“LDV brought together entrepreneurs, practitioners and investors for a multidisciplinary discussion about where the future of the visual image is heading. It was a great mix of people who are working inwhat lies ahead for visual media. It is clear we are only at the beginning of what is possible. It was exciting to be part of it.” Doreen Lorenz, Vidlet, Co-Founder

“I wanted to let you know your conference is hands down the best conference in NYC. I was absolutely blown away by the caliber of people you have assembled at every vertical of the ecosystem.” Jonathan Ohliger, VeePiO, CEO

“The LDV Summit is always inspiring – for those who are creating, those who are operating, those who are investing – and for all of us who are constantly learning. For those of us obsessed with video, the ongoing discussions about live streaming were particularly great. Thanks to all who participated and asked great questions!” Rebecca Paoletti, CakeWorks, CEO & Co-Founder

Day 1 Keynote: A Visual Stepping Stone to Artificial Intelligence. What do the recent advances in computer vision mean for AI? Computer vision and AI are intertwined, yet insights gained in one may not be applicable to the other. The future of AI research depends on identifying these differences and finding new and creative solutions. Larry Zitnick, Facebook, Artificial Intelligence Research Lead. ©Robert Wright/LDV Vision Summit

Day 1 Keynote: A Visual Stepping Stone to Artificial Intelligence. What do the recent advances in computer vision mean for AI? Computer vision and AI are intertwined, yet insights gained in one may not be applicable to the other. The future of AI research depends on identifying these differences and finding new and creative solutions. Larry Zitnick, Facebook, Artificial Intelligence Research Lead. ©Robert Wright/LDV Vision Summit

“Real nice mix of very technical solutions with real-world application. It’s nice to see the results of research applied on a real market and how those solutions have to adapt to this environment” Brunno Attore, CTO, Brohan

“The LDV Vision Summit is at the forefront of what's changing in the image space, bringing founders and CEOs of image-related companies, academic researchers, technology developers, brands and cultural creators, and even photographers and visual artists together in one space. It's a great place to learn about what's happening in visual technology and meet the people that are defining the space.” Taylor Davidson, Unstructured Ventures, Managing Director

"IDA Ireland were thrilled to be part of such a fantastic event. It was a real privilege to spend two days with some of the smartest people in Computer Vision, Artificial Intelligence, Deep Learning, and Augmented Reality. The summit provided invaluable insights into what is happening at the forefront of vision technology" Jessica Benson, IDA Ireland, @IDAIRELAND

The LDV Vision Summit program and quality of attendees never fail to exceed expectations. Evan, Rebecca and Serge do a masterful job of curating a kaleidoscope of speakers, topics and sponsors and it works. Please set the date for 2017 so I can save it! Myron Kassaraba, Managing Director, MJK Partners, LLC

“We are three CS students, just graduating from Cornell Tech, spinning out our CV-driven video ad tech company. We are actively raising seed, actively trying to build partnerships with video platforms, and actively recruiting CV talent. So, for us, LDV was a hydra-headed home run. We met dozens of potential investors (and got a sense of who else to target). We connected with a few video player platforms and already set up a meeting with one. Lastly, we left with a fistful of solid recruiting leads.Next year, I'll have major FOMO if I can’t make LDV. It felt, for those two days, that we were at the center of the vision world. Even if we're not raising money, I feel like we'll have to be here to scope out the competition and see what’s on the horizon for CV.”   Bill Marino, CEO, Brohan
 

Day 2 Panel:  Is OTT the New Black? Monetizing digital video has confounded creators and programmers, with syndication leading the list of potential levers to pull. With the promise of OTT, suddenly new revenue streams can be unlocked, new audiences tapped, and money can flow. Right? But how easy is it, after all… [L-R] Moderator: Rebecca Paoletti, CakeWorks, CEO. Panelists:Ed Laczynski, Zype, CEO, Patricia Hadden, NBCUniversal, SVP, Digital Enterprises, Steve Davis, Ooyala, VP & GM East. ©Robert Wright/LDV Vision Summit

Day 2 Panel:  Is OTT the New Black? Monetizing digital video has confounded creators and programmers, with syndication leading the list of potential levers to pull. With the promise of OTT, suddenly new revenue streams can be unlocked, new audiences tapped, and money can flow. Right? But how easy is it, after all… [L-R] Moderator: Rebecca Paoletti, CakeWorks, CEO. Panelists:Ed Laczynski, Zype, CEO, Patricia Hadden, NBCUniversal, SVP, Digital Enterprises, Steve Davis, Ooyala, VP & GM East. ©Robert Wright/LDV Vision Summit

Learn more about our partners and sponsors:

AWS Activate Amazon Web Services provides startups with low cost, easy to use infrastructure needed to scale and grow any size business. Some of the world’s hottest startups including Pinterest, Instagram, and Dropbox have leveraged the power of AWS to easily get started and quickly scale.  

CakeWorks is a boutique digital video agency that launches and accelerates high-growth media businesses. Stay in the know with our weekly video insider newsletter. #videoiscake

Cornell Tech is a revolutionary model for graduate education that fuses technology with business and creative thinking. Cornell Tech brings together like-minded faculty, business leaders, tech entrepreneurs and students in a catalytic environment to produce visionary ideas grounded in significant needs that will reinvent the way we live.

Day 1 Fireside Chat: Bijan Sabet, Spark Capital & Evan Nisselson, LDV Capital. ©Robert Wright/LDV Vision Summit

Day 1 Fireside Chat: Bijan Sabet, Spark Capital & Evan Nisselson, LDV Capital. ©Robert Wright/LDV Vision Summit

Research at Facebook Our mission is to give people the power to share and make the world more open and connected. At Facebook, research permeates everything we do. We believe the most interesting research questions are derived from real world problems. Working on cutting edge research with a practical focus, we push product boundaries every day. At the same time, we are publishing papers, giving talks, attending and hosting conferences, and collaborating with the academic community.

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GumGum is a leading computer vision company with a mission to unlock the value of every online image for marketers. Its patented image-recognition technology delivers highly visible advertising campaigns to more than 400 million users as they view pictures and content across more than 2,000 premium publishers.

The International Center of Photography is the world’s leading institution dedicated to the practice and understanding of photography and the reproduced image in all its forms. Since its founding in 1974, ICP has presented more than 700 exhibitions and offered thousands of classes, providing instruction at every level.

Day 1 Keynote: Design Patterns for Evolving Storytelling Through Virtual and Mixed Reality Technologies.  Heather Raikes has a PhD in Digital Arts and Experimental Media and is currently Creative Director at Seattle-based virtual and mixed reality development studio 8ninths. She will discuss archetypes that underscore the fundamentals of storytelling and emerging design patterns that can be applied to virtual and mixed reality technologies. Heather Raikes, 8ninths, Creative Director, Augmented | Virtual Reality ©Robert Wright/LDV Vision Summit

Day 1 Keynote: Design Patterns for Evolving Storytelling Through Virtual and Mixed Reality Technologies. 
Heather Raikes has a PhD in Digital Arts and Experimental Media and is currently Creative Director at Seattle-based virtual and mixed reality development studio 8ninths. She will discuss archetypes that underscore the fundamentals of storytelling and emerging design patterns that can be applied to virtual and mixed reality technologies. Heather Raikes, 8ninths, Creative Director, Augmented | Virtual Reality ©Robert Wright/LDV Vision Summit

IDA Ireland is Ireland's inward promotion agency, we partner with international companies, working with them every step of the way to achieve a smooth, fast and successful set-up of their operations in Ireland. Ireland is one of the best places in the world to do business for large multinationals and high-growth companies.  

Kaptur is the first magazine about the photo tech space. News, research and stats along with commentaries, industry reports and deep analysis written by industry experts.

LDV Capital Investing in people around the world who are creating visual technology businesses with deep domain expertise.

Mapillary is a community-based photomapping service that covers more than just streets, providing real-time data for cities and governments at scale. With hundreds of thousands of new photos every day, Mapillary can connect images to create an immersive ground-level view of the world for users to virtually explore and to document change over time.

Microsoft Research has contributed to nearly every product Microsoft has shipped, including Kinect for Xbox, Cortana, cool free photography apps like Hyperlapse, and other programs that help secure your data in the cloud. We have world-renowned scientists at the forefront of machine learning, computer vision, speech, and artificial intelligence. Our external collaborations include efforts to prevent disease outbreaks and solve problems facing large cities such as traffic and pollution.

The MFA Photography, Video and Related Media Department at the School of Visual Arts is the premiere program for the study of Lens and Screen Arts. This program champions multimedia integration, interdisciplinary activity, and provides ever-expanding opportunities for lens-based students.

Qualcomm Research is a world-class, global R&D organization comprised forward-thinking researchers that engage in a wide variety of exciting and technically challenging areas of research. Each focus area pushes the envelope of what is possible in mobile technology, paving the way for the devices, applications, services, and business models of tomorrow. We are leading the way in next-generation wireless technologies, including 5G. For more than 25 years, our ideas and inventions have driven the evolution of digital communications, linking people everywhere more closely to information, entertainment, and each other.   

Day 1 Panel: Where will Computer Vision Be in 5, 10 & 20 years? There is exponential growth recently with businesses leveraging computer vision. Dozens of computer vision companies have recently been acquired by Google, Microsoft, Yahoo, Apple, Facebook, Salesforce, GoPro, Twitter and other major players. How will image recognition disrupt businesses and empower humanity? How can we inspire more researchers to bring their visions to market? Could computer vision become a commodity? If yes, when? Moderator: Taylor Davidson, Unstructured Ventures, Managing Director Panelists: Ramesh Jain (UCI, Prof., Computer Vision), Serge Belongie (Cornell Tech, Prof., Computer Vision), Stacey Svetlichnaya (Flickr, Software Developer, Vision & Machine Learning), Nikhil Rasiwasia (Snapdeal, Principal Scientist)

Day 1 Panel: Where will Computer Vision Be in 5, 10 & 20 years?
There is exponential growth recently with businesses leveraging computer vision. Dozens of computer vision companies have recently been acquired by Google, Microsoft, Yahoo, Apple, Facebook, Salesforce, GoPro, Twitter and other major players. How will image recognition disrupt businesses and empower humanity? How can we inspire more researchers to bring their visions to market? Could computer vision become a commodity? If yes, when? Moderator: Taylor Davidson, Unstructured Ventures, Managing Director
Panelists: Ramesh Jain (UCI, Prof., Computer Vision), Serge Belongie (Cornell Tech, Prof., Computer Vision), Stacey Svetlichnaya (Flickr, Software Developer, Vision & Machine Learning), Nikhil Rasiwasia (Snapdeal, Principal Scientist)

Vidlet taps the power of mobile video for business. The company’s mobile-first B2B video platform makes it easy for large enterprises to use mobile video for a wide range of business communications, from conducting market research for innovative new products, to training a world-class workforce and employee engagement, to surfacing valuable insights from troves of video.

VizWorld covers news and the community about visual thinking, from innovation and design theory to applied visual thinking in technology, media and education. From the whiteboard to the latest OLED screens, graphic recording to movie making and VFX, VizWorld readers want to know how to put visual thinking to work and play. SHOW US your story!

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Robert Wright Photography Clients include Bloomberg Markets, Budget Travel, Elle, Details, Entrepreneur, ESPN The Magazine, Fast Company, Fortune, Glamour, Inc. Men's Journal, Newsweek (the old one), Outside, People, New York Magazine, New York Times, Self, Stern, T&L, Time, W, Wall Street Journal and more…

Hybrid Events Group is the only real-time solution for turning conferences and meetings into web-ready videos. Using our proprietary CaptureProTM System we digitally capture your presentation materials, combine them with HD video of your presenters and then edit everything live, right there, during your event. You’ll have your finished videos before you leave the venue.

We are a family affair! Serge and August Belongie thanking the audience for a fantastic and inspirational gathering. See you next year! #carpediem ©Robert Wright/LDV Vision Summit

We are a family affair! Serge and August Belongie thanking the audience for a fantastic and inspirational gathering. See you next year! #carpediem ©Robert Wright/LDV Vision Summit

We're Taking Billions Of Photos A Day—Let's Use Them To Improve Our World!

Join us at the next LDV Vision Summit.
This keynote is from our 2015 LDV Vision Summit from our LDV Vision Book 2015

Pete Warden, Engineer, Google

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

My day job is working as a research engineer for the Google Brain team on some of this deep learning vision stuff. But, what I'm going to talk about today is actually trying to find interesting, offbeat, weird, non-commercial applications of this vision technology, and why I think it's really important as a community that we branch out to some weird and wonderful products and nonprofit-type stuff.

Why do I think we need to do this? Computer vision has some really deep fundamental problems, I think, the way that it's set up at the moment.

The number one problem is that it doesn't actually work. I don't want to pick on Microsoft because their How-Old demo was amazing. As a researcher and as somebody who's worked in vision for years, it's amazing we can do the things we do. But if you look at the coverage from the general public, they're just confused and bewildered about the mistakes that it makes. I could have picked any recognition or any vision technology. If you look at the general public's reaction to what we're doing, they're just left scratching their heads. That just shows what a massive gap in expectations there is between what we're doing as researchers and engineers and what the general public actually expects.

What we know is that computer vision, the way we measure it, is actually starting to kind of, sort of, mostly work now -- at least for a lot of the problems that we actually care about.

This is one of my favorite examples from the last few months, where Andrej Karpathy from Stanford actually tried to do the ImageNet object recognition challenge as a human, just to see how well humans could actually do at the task that we'd set the algorithms. He actually spent weeks training for this, doing manual training by looking through and trying to learn all the categories, and spent a long time on each image. Even at the end of that, he was only able to beat the best of the 2014 algorithms by a percentage point or two. His belief was that that lead was going to vanish shortly as the trajectory of the algorithm improvements just kept increasing.

It's pretty clear that, by our own measurements, we're doing really well. But nobody's impressed that a computer can tell them that a picture of a hot dog is a picture of a hot dog. That doesn't really get people excited. We really have not only a perception problem, when we're going out and talking to partners and talking to the general public and talking to people. The applications that do work tend to be around security and government, and they aren't particularly popular either. The reason this matters is not only do we have a perception problem, but we aren't actually getting the feedback that we need to get from working with real problems when we're doing this research.

What's the solution? This is a bit asinine. Of course we want to find practical applications that help people. What I'm going to be talking about for the rest of this is just trying to go through some of my experiences, trying to do something a little bit offbeat, a little bit different, and a little bit unusual with nonprofit-type stuff -- just so we've actually got some practical, concrete, useful examples of what I'm talking about.

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

The first one I'm going to talk about is one that I did that didn't work at all. I'm going to use this as a cautionary tale of how not to approach a new problem that's trying to do something to help the world. I came into this with the idea that...I was working at my startup Jetpac. We had hundreds of millions of geotagged Instagram photos that were public that we were analyzing to build guides for hotels, restaurants, bars all over the world. We were able to do things like look at how many photos showed mustaches at a particular bar to give you an idea of how hipster that particular bar was. It actually worked quite well. It was a lot of fun, but I knew that there was really, really interesting and useful information to solve a bunch of other problems that actually mattered.

One of the things that I thought I knew was that pollution gives you really, really vivid sunsets. This was just something that I had embedded in my mind, and it seemed like it would be something that I should be able to pull out from the millions of sunset photos we had all over the world. I went through, I spent a bunch of time analyzing these, looking at public pollution data from cities all over the US, with the hope that I could actually build this sensor, just using this free, open, public data to estimate pollution and track pollution all over the world almost instantly. Unfortunately it didn't work at all. Not only didn't it work, I actually had worse sunsets when I was seeing more pollution.

image03.png

At that point, I did what I should have done at the start, and went back and actually looked at what the atmospheric scientists were saying about pollution and sunsets. It turns out it’s only at really high atmospheres, with very uniform particulate pollution -- which is what you typically get from volcanoes -- is what actually gives you vivid sunsets. Other kinds of pollution, as you might imagine if you've ever lived in LA, just gives you washed out, blurry, grungy sunsets. The lesson for me from this was I really should have been listening and driven by the people who actually understood the problem and knew the problem, rather than jumping in with my shiny understanding of technology, but not really understanding the domain at all.

image02.png

Next I want to talk about something that really did work, but I didn't do it. This is actually one of my favorite projects of the last couple of years. The team at onformative, they took a whole bunch of satellite photos and they ran face detectors across them. Hopefully you can see, there appears to be some kind of Jesus in a cornfield on the left hand side, and a very grumpy river delta on the right. I thought this was brilliant. This is really imaginative. This is really different. This is really joining together a data set with a completely different set of vision technologies and shows how far we've come with face recognition.

image05.png

But shortly after I saw this example, I actually ran across this news story about a landslide in Afghanistan that had killed over a thousand people. What was really heartbreaking about this was that the geologists looking at just the super low-res, not-very-recent satellite photos and the elevation data on Google Earth said that it was painfully, painfully clear that this landslide was actually going to be happening.

What I'm going to just finish up with here is that there's a whole bunch of other stuff that we really could be solving with this:

       - 70 other daily living activities

        - pollution

        - springtime

        - rare wildlife.

What I'm trying to do is actually just start a discussion mailing list here at Vision for Good, where we can bring together some people who are working on this vision stuff and the nonprofits who actually want to get some help. I'm really hoping you can join me there. No obligation, but I want to see what happens in this.

 

Join us at the next LDV Vision Summit.
This keynote is from our 2015 LDV Vision Summit from our LDV Vision Book 2015

How Can Data Science Evaluate Why Some Advertising Creative Content Resonates More Than Others?

Join us at the next LDV Vision Summit
This keynote is from our 2015 LDV Vision Summit from our LDV Vision Book 2015

Claudia Perlich, Chief Scientist, Dstillery

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

When Evan invited me to come and talk at a vision event, my initial response was: “I am not sure. I actually don't do vision. I typically don't even try to visualize my data.” But I thought about it and there's some very interesting development that's going on in digital advertising, in our company right now. I wanted to share the premise and maybe some of the promise of this work. It all starts with what moves us. Ultimately you would argue that the whole point of advertising is to affect people, to touch them emotionally in some way, maybe bring them closer or at least generate some interest in your product.

What I'm going to do is I'm going to show you a couple of images. And I'm asking you: What moves you?

This is a pretty well known campaign. I'm sure you have seen many of those before, maybe not all of them. I will not embarrass you and ask you to raise your hand on which of those you felt most touched or affected. But chances are that we all had very different reactions to these images. I am not going to tell you which one my favorite is,  but I wanted to take the opportunity to tell you a little bit of a story that few people know about me.

I grew up in East Germany and that in particular means -- and that's the irony of my life -- until age 15 I had never seen an ad because in East Germany there was no such thing as advertising. There was nothing really to sell anyways so why the hell would you want to advertise it? When the wall came down I became fascinated by ads. Not because of the product, but what I discovered was photography: beautiful images of things and nature. And where I discovered it was in cheap magazines that had incredibly well-printed and produced pictures (by my East German standards at least) . The dirty little secret is I spent my time as a teenager collecting those. It was truly because I was just incredibly amazed and touched by some of that photography. Not that I ever bought any of these things, but still.

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

Images have the ability to connect to us, to touch us. I want to challenge you right now: do you think you're able to express why a given image had such an impression on you? I'm sure you can tell me which one it was and I will recognize it, but do you even know why? I personally feel that we are very restricted by language when we try to explain things, when we annotate images, when we bucket them: "This is a happy cat and this is a grumpy cat." Ultimately, language is limiting in the ability to express our emotions.

Whether or not the tale is true, that northern tribes have an exceeding number of words to describe the various types of snow, just consider that the average person’s vocabulary is estimated to be only around 17K. While this may sound like a lot in a specific context, that is all there is for all the things we may ever wish to say. Anyone ever trying to describe the subtle details of an image will soon realize how limiting already our ability to characterize colors are. How many words for different colors can you come up with? I have seen a list of about 150 and they contained a lot of analogies like “forest green.” Do you want to guess how many colors your Internet browser uses? HTML allows for 16 million. That's one of the challenges that I feel we face in machine learning when we try to explain, characterize, categorize, and annotate things. We're stuck with language and in that process, a lot of the magic and subtlety gets lost. 

As I said, I work in advertising so I want to show you some of the alternatives where we're trying to avoid having to characterize what it is about an image that touches you. I don't actually have results for Equinox, so what I'm going to show you are results from a food brand. We ran an experiment where we show digital ads with a set of creatives that vary both in image and message. The interesting question is not so much “which of these variations is ‘best’” but rather to understand how different people react differently to these variations. The methodology is a combination of a random experiment alongside with machine learning. I will start initially looking at the impact of just different images.  

What is this graph here? It's showing the impact of the image as a factor for different groups of people. Unfortunately I cannot show you the exact six images, and now I have to eat my words and describe them a little but in terms of what they looked like: family oriented (probably a picture with the family), individual, lifestyle, just showing the logo, some variety of the product, or just a very close up shot of the product itself. And in order to give you a talk, I also have to describe the characteristics of different groups of people to you rather than the actual millions of details that our machine-learning approach is actually processing. Specifically, I have grouped people by where they physically go -- fast food restaurants in this first comparison and gyms in the next. (We obtain the information about device locations from mobile advertising bid requests.)

The first thing you observe is that people who go to Chick-fil-A are really, really hard to sway to buy this product. No matter what it is that you show them, they're kind of happy with where they are, thank you very much. The next observation is that the “lifestyle” image is sometimes having no effect at all whereas the product image was overall the most effective across groups.  

But you also see a lot of variance between these subgroups -- they react very, very differently to some of the differences in the images. Right now, this is just a very high-level picture characterizing people by the fast foods they like. But you see clear differentiation and you can think about how would you use that information to schedule or to choose separate images for sub-population if you wanted to actually specifically reach out to any of these groups.

image10.png

Let’s take a look at how the messages themselves fare. Some related more to the natural process of the production. Others talked more about taking a snack at a certain time of the day. Some tried to tell you that they're really healthy and good for you, talking about the benefit of this product. What you see here is now broken up by people who go to certain gyms.

In general here you see that there's overarching effect on the emphasis of benefit: it's good for me. I mean yes, people who go to the gym probably care about the benefit and this is very consistent. But what is fascinating to see are the implicit groupings of the gyms: Equinox and Crunch are similar, and so are YMCA and LA Fitness. The populations are similar to each other in how they are affected by the message of the creative. In the case of the YMCA and the LA Fitness, the descriptive message "This is a great product, it will taste perfect" is very effective. This is the sensory stimulation that is not just limited to taste but also includes texture and how it will make you feel beyond taste.

I wanted to use this analysis as an example to really challenge our industry as we move on. What's important here is, that I wasn't trying to characterize the images in any way, but just let a machine-learning algorithm estimate how different people are affected by the message and the image. If you think this forward, maybe in the future I actually don't need to ask you whether it was the guy carrying the little statue. I might be able to predict from observed data alone which of those creatives will most likely speak to you as an individual.

Join us at the next LDV Vision Summit.
This panel discussion is an excerpt from our LDV Vision Book 2015

The Power And Promise Of Emotion Aware Machines

Join us at the next LDV Vision Summit.
This keynote is an excerpt from our LDV Vision Book 2015

Marian Stewart Bartlett, Co-Founder & Lead Scientist, Emotient.
Apple acquired Emotient in January 2016 after she spoke at our 2015 LDV Vision Summit.

Technology that can measure emotion from the face will have broad impact across a range of industries. In my presentation today, I am going to provide a picture of what's possible with this technology today and also provide an indication of what's possible for the future, where the field may be going. But first I will show a brief demo of facial expression recognition.

You can see the system detecting my face and then when I smile, the face box changes blue to indicate joy. On the right, we see the outputs over time. Okay, so that's over the past 30 seconds or so. Next, I will show sadness.

image06.png

That was a pronounced expression of sadness. Here is a subtle sad. Natural facial behavior is sometimes subtle but other times, it's not necessarily subtle. Sometimes, it's fast. These are called micro expressions. These are expressions that flash on and off your face very, very quickly. Sometimes in just one frame of video.

I will show some fast expressions, some fast joy. Then, also surprise. Fast surprise. Anger. Fear. Disgust. Now, disgust is an important emotion because when somebody dislikes something, they'll often contract this muscle here, the levator muscle, without realizing they are doing it. Like this. And then there is contempt. Contempt means unimpressed.

Some things that are possible today are to do pattern recognition on the time courses of this signal. If we take a strong pattern recognition algorithm, capturing some of the temporal dynamics, we are able to detect some things sometimes better than human judges can. For example, we've demonstrated the ability to detect faked pain and distinguish it from real pain and we can do that better than human judges. Other things that we can detect are depression, student engagement, and my colleagues have also demonstrated that we're able to predict economic decisions. I will tell you a little bit more about that decision study.

Facial expressions provide a window into our decisions. The reason for that is that the emotional parts of our brain and particularly the amygdala, which is part of the limbic system, plays a huge role in decision making. It's responsible for that fast, gut response, that fast value assessment that drives a lot of the decisions that we end up making. One of the other co-founders of Emotient, Ian Fasel, collaborated with one of the leaders in neuroeconomics, Alan Sanfey, in order to ask whether they could predict decisions in economic gains from facial expression.

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

Here is the game. It's the ultimatum game. In this game, Player One is given some money and then Player One has to offer some of the money to Player Two. They can offer none, all, or anything in between. If Player Two accepts, then both get the money. But if Player Two rejects, then neither one gets any money.

The optimal solution, according to most economic theories, is to always accept because you will get more money if you always say yes. However, humans don't always behave optimally in this sense. They get mad. This guy is a jerk. I am going to punish him and reject his offer and nobody is getting any money. Rossi, Fasel, and Sanfey asked whether they could predict decisions in this game. They used our system to measure individual facial muscle movements and then they gathered dynamic features of these facial muscle movements. They passed these features to a gentle boost classifier trained to detect whether the player would accept or reject the offer.

They also compared the machine learning system to human judges looking at the same videos. What they found is that the human judges were at chance. They could not detect who was going to reject the offer. However, the machine learning system was able to do this above chance. It was 73% accurate. It was able to predict decisions in this game. They could also find out which signals were contributing to the decision, being able to detect the rejection in this offer. What they found was that facial signals of disgust were associated with bad offers, but they didn't necessarily predict rejection. What predicted rejection was facial expressions of anger. They could secondly ask which temple frequencies contain the most information for this discrimination. That is where they found that the discriminative signals were fast facial expressions. These were facial movements that were on the order of about half a second cycle. On the other hand, they found that humans were basing their decisions on much longer time scales. The way they did that, was they trained a second general boost classifier. But this time they trained it to try to predict the observer guesses. Then they went back and looked at which features were being selected. The observer guesses were being driven by facial signals on time scales that were too long.

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

There are many commercial applications of facial expression technology. Some of you may remember the Sony Smile Shutter. The Smile Shutter detects smiles in the camera image and that was based on our technology back in UCSD prior to forming the company. That was probably one of the first commercial applications of facial expression technology. What I've shown here on the screen is one of the more prominent applications at this time. This is an ad test focus group and here the system is detecting multiple faces at once and is also summarizing the results into some key performance indicators: attention, engagement, and sentiment.

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Now where this is moving in the future, is that we're moving towards facial expression in the wild. We're moving towards recognition of sentiment out in natural context where people are naturally interacting with their content. Deep learning has contributed significantly to this because it has helped provide robustness to factors such as head pose and lighting to enable us to operate in the wild. This shows some of the improvement that we got when we moved to a deep learning architecture. Blue shows our robustness to pose prior to deep learning and then green shows the boost that we got when we changed over to deep learning with an equivalent data set.

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Here is an example of media testing in a natural context. What we have is people watching the Super Bowl halftime in a bar. Watch the man in green. He shows some nice facial expressions in just a moment.

Next, we have the system aimed at a hundred people at once during a basketball game. Here we are gathering crowd analytics and getting aggregate information almost instantly and it's also anonymous because the original video can be discarded and we only need to keep the facial expression data. Here we have a crowd responding to a sponsored moment at a particular basketball game.

There are also a number of applications of this technology in medicine. The system is able to detect depression and it can be employed as a screening mechanism during tele-medicine interviews, for example. It can track your response over time, your improvement over time, and also quantify your response to treatment.

©Robert Wright/LDV Vision Summit

©Robert Wright/LDV Vision Summit

Another area where it can contribute in medicine is pain. We can measure pain from the face. It's well known that pain is under-treated in hospitals today and we have an ongoing collaboration with Rady Children's Hospital where we have demonstrated that we can measure pain in the face postoperatively right in the hospital room. Now this contributes both to patient comfort but also to costs because under-treated pain leads to longer hospital stays and greater re-admission rates.

Education is another area where this technology will have broad impact. This image shows three facial behaviors related to learning.

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The girl in the middle is distressed. The one on the left is engaged in her task and the one on the right is moving away. These are behaviors that can be detected right now with this technology. We can also take this a step further and we can make online education and adaptive tutoring systems adapt to the emotional state of the student the way good teachers do.

In summary, facial expression technology is enabling us to measure sentiment in locations and scales that were previously not possible. It has the potential to predict consumer decisions and behavior and will have broad impact across a large range of fields. I showed you some in advertising, ad copy testing, medicine, and education. It will be a game changer.

©VizWorld

©VizWorld

Join us at the next LDV Vision Summit.
This panel discussion is an excerpt from our LDV Vision Book 2015

 

Faces Are The Key To Success For Social Platforms

Josh Elman is a partner at Greylock Partners. He has extensive operational experience working at social networks & platforms such as Linkedin, Twitter & Facebook.

We are honored that he will be one of ~80 expert speakers at our next LDV Vision Summit May 24 & 25 in NYC. We are starting our fireside chat with Josh virtually and hope you join us at our summit next month to hear the extended live version.

Evan: What is your favorite camera today and why? What do you think will be your favorite camera in 20 years?

Josh: My favorite camera today is my iPhone. It’s my favorite because it’s the one that’s with me all the time. It’s in my hands a lot (too often?!) so whenever I come across something new or a moment I want to remember, I just open the camera and take it. Sometimes I share pictures on Facebook, Twitter, Snapchat, Instagram, etc but most of the time I just take them for me. My camera roll is full of random moments of my days – mostly amazing memories.

In 20 years I think my favorite camera will still be the one that’s with me all the time. I’m guessing it will be built into my glasses. Wouldn’t it be cool if all I had to do was think about saving a moment and it's automatically saved? Or have all moments be recalled with just a thought? Or will there be flying cameras around us at all times capturing ourselves in the activity instead of only from our vantage point? That would be cool too.

Evan: You had extensive work experience at social networks & platforms such as Linkedin, Twitter & Facebook. As a partner at Greylock, you have invested in networks and platforms such as Medium, Meerkat, and WhoSay. They all leverage visual content. What are the most valuable attributes of visual content which exponentially drive network effects?

Josh: What people love about social platforms is that most of the content they read, interact with, and share is personal. It’s intimate. It’s authentically written and shared by another person.

Faces are the key to social platforms – whether it’s looking at pictures that someone else took of you (very popular in early Facebook) or the now ubiquitous selfie. Profile pictures and avatars frame every single post on Facebook, Twitter and more – and in eye tests, we’ve seen that users linger first over the face/avatar before reading the content.

LinkedIn took many years to add photos to profiles – for exactly this reason – in a professional context, they were worried that faces would color how someone perceived the profile.

Evan: You wrote a great Medium article looking back at 2015 and forward to 2016. You believe that the days of Geocities and Myspace were more emotionally expressive and that we have lost some of that online. What might be some examples of more expressive activities that you would like added to your daily life today and tomorrow?

Josh: In the real world, we express ourselves all the time – by the clothes we wear, by what we carry, jewelry, shoes, and more. We decorate our personal spaces in the same way – colors of paint, style of furniture, art and posters on the wall, accessories everywhere.

In the days of Geocities and MySpace, everyone who participated in those platforms had a space online they could decorate however they wanted. People did crazy things with backgrounds, fonts, and sounds. In today’s online social systems, we can differentiate ourselves by the content we share, but it’s very constraining – every profile looks nearly the same, you can customize your profile picture and maybe a banner.

When I used to go to someone’s MySpace page, I could learn so much about them just by the look of the page – whether it had ponies or goth skulls all over it. I’d love to see that return to our social platforms so you can get to know people much more visually and expressively.

Evan: You meet a ton of new entrepreneurs everyday but you only invest in a small percentage of the people you meet. What are the most important personality traits of entrepreneurs that you prefer investing in?

Josh: I meet so many entrepreneurs every week and month, and given how passionate everyone is, I often wish I could work with them all. What I get most excited by is when someone paints a vision of how they believe the world will work in a few years, and how they are building the products and services that will enable that.

The best visions are incredibly intoxicating. But beyond just having a great vision, I look for someone who is very pragmatic, and who understands how to break this down into just the first step, then maybe the next step, and the step after that to show progress towards that goal.

We often use the term “Learner” to describe the founders we most enjoy funding and working with. They are people who treat everything they do, and everyone they meet as an incredible learning opportunity to get more information to build their dreams faster.

Evan: What are you most looking forward to at our LDV Vision Summit?  

Josh: I’m very excited to meet all of the people thinking about computer vision, machine learning, and how these great innovations can be applied to make products that change people’s lives. It’s rare to see so many great people come together around one important topic like this.

Evan: I look forward to speaking with Josh and all of you in more detail during our fireside chat at our LDV Vision Summit in NYC on May 24 & 25 [50% discount off tickets until April 30]. We try to make our sessions very interactive and look forward to your questions.

Other expert speakers at our Summit are from Google, Refinery29, Facebook, Cornell Tech, Qualcomm, First Round, Lytro, Greylock Partners, Olapic, Quartz, Mapillary, Microsoft Research, CakeWorks, NBCUniversal, RRE Ventures, Magic Leap, Mine, Samsung, Enlighted, Flickr, IBM Watson, and many more….

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