The hosts look at utility functions as the mathematical basis for making AI systems. They use the example of a travel agent that doesn’t get tired and can be increased indefinitely to meet increasing customer demand. They also discuss the difference between this structured, economic-based approach with the problems of using large language models for multi-step tasks.
What if we've been approaching AI agents all wrong? While the tech world obsesses over large language models (LLMs) and prompt engineering, there's a foundational approach that could revolutionize how we build trustworthy AI systems: mechanism design.
Part 2 of this series could have easily been renamed "AI for science: The expert’s guide to practical machine learning.” We continue our discussion with Christoph Molnar and Timo Freiesleben to look at how scientists can apply supervised machine learning techniques from the previous episode into their research.
What if privacy could be as dynamic and socially aware as the communities it aims to protect? Sebastian Benthall, a senior research fellow from NYU’s Information Law Institute, shows us how privacy is complex.