Rudina Seseri is the Founder & Managing Partner of Glasswing Ventures. With over 14 years of investing and transactional experience, she has led technology investments and acquisitions in startup companies in the fields of robotics, Internet of Things (IoT), SaaS marketing technologies and digital media.
Rudina will be sharing her knowledge on trends and investment opportunities in visual technologies as a panelist and startup competition judge at the 2017 Annual LDV Vision Summit. We asked her some questions this week about her experience investing in visual tech and what she is looking forward to at the Vision Summit...
You are investing in Artificial Intelligence (AI) businesses which analyze various types of visual data. In your perspective, what are the most important types of visual data for artificial intelligence to succeed and why?
Nowadays, a key constraint for AI to succeed in perception tasks are good (i.e. labeled) datasets. Deep learning has allowed us to achieve "super-human" performance in some tasks, and computer vision is a key pioneering area - from LeCun's OCR in the 90s, to the new wave of AI excitement spurred by Andrew Ng – and others – in the unsupervised tagging of YouTube videos and deep nets performance in ILSVRC competition (an annual image recognition competition which uses a massive database of labeled images).
Image recognition has now moved from single object labeling, to segment labeling and full scene transcription. Video has also seen impressive results. An important next step will be to see how we can move from perception tasks like image recognition, to autonomous decision making. The results achieved already in games, and self-driving cars are promising. One can think of applications in just about anything from autonomous vehicles, visual search, (visual) business intelligence, social media, visual diagnostics, entertainment, etc. However, I think the most important thing for success is to be able to match the type of data and algorithm to whichever problem you're trying to solve. The ability to create valuable datasets in new use cases will be essential for startups.
I believe AI and vision will have a massive impact across sectors and industries which is why we decided to launch [Glasswing Ventures].
What business sector do you believe will be most disrupted by computer vision and AI?
That’s a tough one because I believe AI and vision will have a massive impact across sectors and industries which is why we decided to launch the firm. From a vision point of view, we need to ask which are the business sectors that rely (or could rely) the most on images, and those are likely to be the ones "most disrupted" by AI. Within the enterprise, marketing and retail are likely to be one of the earliest adopters. In terms of sectors, it's easy to see the impact that AI will have on e-commerce, transportation, healthcare diagnostics, security etc.
You are speaking and judging at our LDV Vision Summit. What are you most excited about?
The LDV Vision Summit is a key event for anyone involved in computer vision. Being a speaker and a judge, sharing the stage with some of the pioneers in the domain and hearing the pitches of some of the most promising entrepreneurs in the area. Being able to spend two days with all of you and discuss trends and the future of computer vision is invaluable.
You’ve said “the skillset of data scientists will be rendered useless in 12-18 months. They will need to either evolve with new AI tools or become a new category of Machine Language Scientists.” How does this rapid evolution in AI impact your investing strategy?
Data science is indeed evolving at a very fast pace. The exponential improvement in computing power, the ability of GPUs to parallelize data processing (crucial for CNNs), and the sheer abundance of data available, has required data scientists to rethink how they can better leverage these capabilities and experiment with what was previously unthinkable. While most of the algorithms considered state-of-the-art today have been developed over decades, the way in which data scientists use them, has changed considerably - i.e. moving from feature engineering to architecture engineering.
Additionally, the community has fully embraced open-source, with most breakthroughs being published and algorithms shared. This means that savvy data scientists have to: know the advantages and limitations of each approach for their use case given the new computing/data constraints; be willing to experiment with new methods and embrace open-source while being able to build a sustainable competitive advantage; and be on top of the new developments in their area.
Finally, the emergence of data science at the center of AI development has created a new, major stakeholder in product teams (along with engineering & PM) so a good dynamic between these three teams, with constant collaboration to push the limits of technology, while always focusing on creating a product that delivers superior value vs status quo to their target customer is key.