Solution Study
Monday, June 30
10:00 AM - 10:30 AM
Live in Berlin
Less Details
In autonomous vehicle development, data isn’t a static asset but a constantly evolving resource. This presentation challenges the notion that more data equals better models, arguing that iteration and refinement are more critical than sheer volume. We’ll explore why datasets for machine learning must adapt over time, and how to source the right data when future needs are unpredictable.
In this session, you will discover why:
Daniel Langkilde is the co-founder and CEO of Kognic, a company specializing in high-performance data annotation for AI, particularly in the automotive and robotics industries. Before founding Kognic in 2018, Daniel was the first machine learning hire at Recorded Future, contributing to its growth before the company’s acquisition for nearly $780 million. He holds an M.Sc. in Engineering Mathematics from Chalmers University of Technology and was a visiting scholar at both MIT and UC Berkeley. Known for his deep technical roots and hands-on leadership, Daniel is a lifelong robotics enthusiast and an advocate for ethical AI development, aiming to improve the working standards in the global data annotation ecosystem. At Kognic, he drives the vision of scalable, trustworthy AI through the collaboration of human expertise and cutting-edge technology.