Скачать или смотреть Auto.AI USA 2025 - Kognic - Machines Learn Faster with Human Feedback

Solution Study: Machines Learn Faster with Human Feedback

Speaker: Daniel Langkilde

As autonomy accelerates from pilot projects to global deployment, a new challenge has emerged: data alone is not enough. Machines need scalable, reliable human feedback to learn safely and adapt to the real world. In this session, we will share how leading autonomy programs are converging on data pipelines that combine automated systems with precisely targeted human feedback, enabling continuous learning and rapid validation of complex AI models. We will discuss the pivotal role of human judgment in curating rare edge cases, ensuring regulatory compliance, and instilling the trust that society demands from autonomous vehicles and robotics. Drawing on Kognic and customer experiences, Daniel will outline why the ability to route, validate, and scale human feedback is now the critical bottleneck and decisive market advantage in the race to build safe, interpretable, and deployable AI. Hear how annotation is evolving from manual labeling to high-leverage policy shaping, and why “machines learn faster with human feedback” is not just a slogan, but the underlying principle shaping the future of trustworthy AI.⁠

In this session, you will learn more about:

Combining automation with targeted human feedback for continuous learning
Human judgment in edge cases, compliance, and public trust
Scaling feedback pipelines as a key to AI deployment