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Mar 11, 2025 | 
bookoftheday.nextbigideaclub.com | Michael Kovnat |Arvind Narayanan |Sayash Kapoor
Upgrade to paid to play voiceoverA few days ago, we published a list of our favorite new books on AI, and today I’d like to call attention to one of those books, AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference. Written by two Princeton computer scientists, and , the book brings a clarifying lens to the hype currently surrounding artificial intelligence.
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Jan 13, 2025 | 
cxotalk.com | Arvind Narayanan |Michael Krigsman
Join this provocative conversation with Princeton Professor Arvind Narayanan, co-author of AI Snake Oil, as we cut through the AI hype cycle to examine what artificial intelligence can and cannot deliver for business. While generative AI represents genuine technological progress, many AI applications - particularly in predictive analytics - are fundamentally flawed.
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Dec 13, 2024 | 
technewstube.com | Arvind Narayanan |Sayash Kapoor
Tech News Tube is a real time news feed of the latest technology news headlines.Follow all of the top tech sites in one place, on the web or your mobile device.
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Dec 13, 2024 | 
wired.com | Arvind Narayanan |Sayash Kapoor
OpenAI CEO Sam Altman expects AGI, or artificial general intelligence—AI that outperforms humans at most tasks—around 2027 or 2028. Elon Musk’s prediction is either 2025 or 2026, and he has claimed that he was "losing sleep over the threat of AI danger."But predictions of imminent human-level AI have been made for over 50 years, since the earliest days of AI. Those predictions were wrong—not merely in terms of timing but in a deeper, categorical sense.
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Nov 19, 2024 | 
spectator.org | Arvind Narayanan |Sayash Kapoor |Leonora Cravotta
AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the DifferenceBy Arvind Narayanan & Sayash Kapoor (Princeton University Press, 360 pages, $21.52)Artificial intelligence has become the proverbial elephant in the room. We vacillate between the fear that the mammoth creature will trample us into intellectual oblivion and the gut feeling that a mirage of our own making has duped us.
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Oct 15, 2024 | 
ssir.org | Arvind Narayanan |Sayash Kapoor
AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the DifferenceArvind Narayanan & Sayash Kapoor360 pages, Princeton University Press, 2024Buy the book »Imagine an alternate universe in which people don’t have words for different forms of transportation, only the collective noun “vehicle.” They use that word to refer to cars, buses, bikes, spacecraft, and all other ways of getting from place A to place B. Conversations in this world are confusing.
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Sep 11, 2024 | 
afr.com | Arvind Narayanan |Sayash Kapoor
Misleading claims about the capabilities of AI are rampant, and the technology is already causing serious harm from hiring to education, medicine and hiring, write two computer scientists in this extract of their new book, AI Snake Oil.
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Jun 28, 2024 | 
slguardian.org | Arvind Narayanan |Sayash Kapoor
So far, bigger and bigger language models have proven more and more capable. But does the past predict the future? One popular view is that we should expect the trends that have held so far to continue for many more orders of magnitude, and that it will potentially get us to artificial general intelligence, or AGI. This view rests on a series of myths and misconceptions. The seeming predictability of scaling is a misunderstanding of what research has shown.
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Apr 13, 2024 | 
bookshop.org | Arvind Narayanan |Sayash Kapoor
From two of TIME's 100 Most Influential People in AI, what you need to know about AI--and how to defend yourself against bogus AI claims and products Confused about AI and worried about what it means for your future and the future of the world? You're not alone. AI is everywhere--and few things are surrounded by so much hype, misinformation, and misunderstanding.
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Mar 13, 2024 | 
slguardian.org | Arvind Narayanan |Sayash Kapoor
The assumption that AI safety is a property of AI models is pervasive in the AI community. It is seen as so obvious that it is hardly ever explicitly stated. Because of this assumption:Companies have made big investments in red teaming their models before releasing them. Researchers are frantically trying to fix the brittleness of model alignment techniques. Some AI safety advocates seek to restrict open models given concerns that they might pose unique risks.
