Recent breakthroughs in AI have unlocked new value for companies to create and capture. Venture capital firms are spraying money around, hoping to back the next AI decacorn. However, it’s far from clear which companies will succeed in both creating and capturing value — i.e. which ones ultimately prove to be financially sustainable.
This talk is about how to operationalize AI as part of a business, not how to develop it. We will explore aspects of building a successful AI business, such as "How do you establish a data moat?", "What are the trade-offs between vertical, domain-specific solutions and more general applications?", "How important is explainability, particularly in regulated industries?", and "How sensitive are users to issues with precision/recall?". The talk will not pretend like there are easy answers but rather discuss what we've learned so far as an industry.
Daniel Langkilde is an experienced machine learning developer and entrepreneur. He started his career as Team Lead for Collection & Analysis at Recorded Future. Since 2018 he is co-founder and CEO of Kognic. Kognic provides the most productive annotation platform for sensor-fusion data. Daniel earned his M.Sc. in Engineering Mathematics at Chalmers University of Technology, where he also served as President of the Student Union and a Member of the Board of Directors. He has been a Visiting Scholar at both MIT and UC Berkeley.
