Arvind Narayanan is a leading voice disambiguating what AI does and does not do. His work, with Sayash Kapoor at AI Snake Oil, is one of the few beacons of reasons in a AI media ecosystem with quite a few bad Apples. Arvind is a professor of computer science at Princeton University and the director of the Center for Information Technology Policy. You can learn more about Arvind and his work on his website, X, or Google Scholar.

This episode is all in on figuring out what current LLMs do and don’t do. We cover AGI, agents, scaling laws, autonomous scientists, and past failings of AI (i.e. those that came before generative AI took off). We also briefly touch on how all of this informs AI policy, and what academics can do to decide on what to work on to generate better outcomes for technology.

Transcript and full show notes: https://www.interconnects.ai/p/interviewing-arvind-narayanan

Chapters

* [00:00:00] Introduction

* [00:01:54] Balancing being an AI critic while recognizing AI's potential

* [00:04:57] Challenges in AI policy discussions

* [00:08:47] Open source foundation models and their risks

* [00:15:35] Personal use cases for generative AI

* [00:22:19] CORE-Bench and evaluating AI scientists

* [00:25:35] Agents and artificial general intelligence (AGI)

* [00:33:12] Scaling laws and AI progress

* [00:37:41] Applications of AI outside of tech

* [00:39:10] Career lessons in technology and AI research

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