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Session

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:

  • Datasets are evolving assets, requiring continuous refinement
  • Smaller, high-quality data can outperform large, unfocused datasets
  • Strategies for sourcing relevant data are in an uncertain landscape
Presentation

Speaker

Daniel Langkilde

CEO & Co-founder, Kognic

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.⁠

Company

Kognic

Kognic provides the only software available in the market tailored to measure and improve perception performance for autonomous mobility. Our Perception Analytics empowers engineers and product teams to argue safety in an objective and data-driven way while helping balancing needs across departments.

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