Scaling Theory #9 – Arvind Narayanan: Myths and Policies in Scaling AI
My guest is Arvind Narayanan, a Professor of Computer Science at Princeton University, and the director of the Center for Information Technology Policy, also at Princeton. Arvind is renowned for his work on the societal impacts of digital technologies, including his textbook on fairness and machine learning, his online course on cryptocurrencies, his research on data de-anonymization, dark patterns, and more. He has already amassed over 30,000 citations on Google Scholar.
In just a few days, in late September 2024, Arvind will release a book co-authored with Sayash Kapoor titled “AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference.” Having had the privilege of reading an early version, our conversation delves into some of the book’s key arguments. We also explore what Arvind calls AI scaling myths, the reality of artificial general intelligence, how governments can scale effective AI policies, the importance of transparency, the role that antitrust can, and cannot play, the societal impacts of scaling automation, and more. I hope you enjoy our conversation.
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References:
➝ AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference (2024)
➝ AI scaling myths (2024)
➝ AI existential risk probabilities are too unreliable to inform policy (2024)