#828

Mentioned in 25 episodes

AI Snake Oil

What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference

Book • 2024

Arvind Narayanan

AI Snake Oil cuts through the confusion surrounding AI by explaining how it works, where it might be useful or harmful, and when companies are using AI hype to sell ineffective products.

The book acknowledges the potential of some AI, like ChatGPT, while uncovering rampant misleading claims and describing serious harms in areas such as education, medicine, hiring, banking, insurance, and criminal justice.

It explains the differences between types of AI, why organizations fall for AI snake oil, and warns of the dangers of unaccountable big tech companies controlling AI.

Mentioned by

Pat Pataranutaporn

Sam Charrington

Arvind Narayanan

Harry Stebbings

Megan McCarty Carino

Michael Krigsman

Mentioned in 25 episodes

Mentioned by

Pat Pataranutaporn

when discussing the need for specific terminology in the AI field.

Echo Chambers of One: Companion AI and the Future of Human Connection

Mentioned by

Cory Hixson

as the next book to be discussed on the podcast.

227: Lean Learning by Pat Flynn

Mentioned by

Sam Charrington

in the introduction, highlighting

Arvind Narayanan

's and Sayash Gopur's collaborative work on AI risks.

AI Agents: Substance or Snake Oil with Arvind Narayanan - #704

Mentioned as a listener recommendation that Cory wants to read to learn more about AI.

226: The 5 Resets by Dr. Aditi Nerurkar

Mentioned by

Patrick Chase

as a leading professor in computer science who wrote the book.

Ep 54: Princeton Researcher Arvind Narayanan on the Limitations of Agent Evals, AI’s Societal Impact & Important Lessons from History

Mentioned by

Harry Stebbings

in the introduction as a co-author.

20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

Sinan Ozdemir

is excited to read about the modern takes on snake oil in AI.

903: LLM Benchmarks Are Lying to You (And What to Do Instead), with Sinan Ozdemir

Mentioned by

Josh Kale

as an argument against the imminence of AGI's transformative power.

The Intelligence Curse: How AGI Makes Us All Obsolete | Luke Drago & Rudolf

Book for today's discussion, authored by Arvind Narayanan and Sayash Kapoor, both from Princeton.

228: AI Snake Oil by Arvid Narayanan & Sayash Kapoor

Mentioned by

David Hoffmann

when introducing the debate with the man who also wrote it.

The New OpenAI Gadget Will Change The World | AI Calls The Cops | AI Agent OnlyFans

Mentioned by

Tim Scarfe

at the end of the podcast as a book to pre-order.

Sayash Kapoor - How seriously should we take AI X-risk? (ICML 1/13)

Discussed by

Sayash Kapoor

and

John Xavier

as a critical examination of artificial intelligence, focusing on its limitations and exaggerated claims.

Ep22: Demystifying AI and separating hype from genuine progress

Mentioned by

Andy Steiger

as a book that calls out crystal ball AI and predictive AI.

Has A.I. Become an Idol?

Mentioned by

Martin Reeves

as a timely new book discussing AI capabilities and limitations.

AI Snake Oil with Sayash Kapoor

Mentioned by

Megan McCarty Carino

, referencing the book's discussion of predictive algorithms in high-stakes areas.

NYC's child welfare agency uses AI to scrutinize marginalized families, recent investigation finds

Mentioned by

Greg Epstein

in the context of AI companies pivoting from building a god to creating products.

Tech Agnostic (with Greg Epstein)

Mentioned by

Michael Krigsman

as the book written by

Arvind Narayanan

and Sayaj Kapoor on the topic of AI.

AI Snake Oil: Princeton Professor Exposes AI Truths | #867

Mentioned by

Thomas Smith

when discussing AI and the law.

Ok, but Would AI Judges Really Be Any Worse?

Mentioned by

Megan McCarty Carino

as a book co-authored by Sayash Kapoor.

Small tweaks to AI prompts can have significant impacts on output

Mentioned by

Nathan Labenz

while discussing AI capabilities and generalization.

AI Discourse Deranged: Assessing LLM Generalization Takes and Polarizing Regulatory Debate

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