An untidy history of AI across four books
34 comments
·September 19, 2025rishi_rt
dang
randomwalker
Thanks! HN was part of the origin story of the book in question.
In 2018 or 2019 I saw a comment here that said that most people don't appreciate the distinction between domains with low irreducible error that benefit from fancy models with complex decision boundaries (like computer vision) and domains with high irreducible error where such models don't add much value over something simple like logistic regression.
It's an obvious-in-retrospect observation, but it made me realize that this is the source of a lot of confusion and hype about AI (such as the idea that we can use it to predict crime accurately). I gave a talk elaborating on this point, which went viral, and then led to the book with my coauthor Sayash Kapoor. More surprisingly, despite being seemingly obvious it led to a productive research agenda.
While writing the book I spent a lot of time searching for that comment so that I could credit/thank the author, but never found it.
dang
> While writing the book I spent a lot of time searching for that comment so that I could credit/thank the author, but never found it.
Sounds like a job for the community! Maybe someone will track it down...
Edit: I tried something like https://hn.algolia.com/?dateEnd=1577836800&dateRange=custom&... (note the custom date range) but didn't find anything that quite matches your description.
NooneAtAll3
what does irreducible error mean?
CamperBob2
It's hard to miss the similarity between your book's title and Cliff Stoll's 1995 Silicon Snake Oil, an indictment of the general concept of the "information superhighway" that was starting to resonate with the public. Stoll is a really smart guy, but that particular book hasn't held up too well:
Few aspects of daily life require computers...They're
irrelevant to cooking, driving, visiting, negotiating,
eating, hiking, dancing, speaking, and gossiping. You
don't need a computer to...recite a poem or say a
prayer." Computers can't, Stoll claims, provide a richer
or better life.
So, was this something that you guys were conscious of when you chose your own book's title? How well have you future-proofed your central thesis?
eco
That's one of the things that drives me nuts about all the public discourse about AI and our future. The vast majority of words written/spoken on the subject are by generic "thought leaders" who really have no greater understanding of AI than anyone else who uses it regularly.
mmaia
A characteristic of the field since the beginning. Reading What Computers Can't Do in college (early 2000s) was an important contrast for me.
> A great misunderstanding accounts for public confusion about thinking machines, a misunderstanding perpetrated by the unrealistic claims researchers in AI have been making, claims that thinking machines are already here, or at any rate, just around the corner.
> Dreyfus' last paper detailed the ongoing history of the "first step fallacy", where AI researchers tend to wildly extrapolate initial success as promising, perhaps even guaranteeing, wild future successes.
https://en.wikipedia.org/wiki/Hubert_Dreyfus's_views_on_arti...
libraryofbabel
And the article agrees with you, and is pretty scathing about all the books except Narayanan’s (which is also the only book with a balanced anti-hype perspective):
> A puzzling characteristic of many AI prophets is their unfamiliarity with the technology itself
> After reading these books, I began to question whether “hype” is a sufficient term for describing an uncoordinated yet global campaign of obfuscation and manipulation advanced by many Silicon Valley leaders, researchers, and journalists
red75prime
Expert futurologist? Anyway. The article has very little substance. "See those ridiculous predictions," mostly. If there's anything about fundamental or practical limitations of the current machine learning approaches (deep learning, transformers, RL, and so on), I haven't seen it.
PeterStuer
Read "brainmakers", even though it completely ignores Europe's and the East's significant contributions to AI history https://www.newquistbooks.com/brainmakers/brainmakers.html
bmau5
Just finished this and I enjoyed it, though as you mention is very America-centric
kouru225
Ok so what is this publication? Because apparently they’ve been around since the 90s. I’ve never heard of them though. Their title and its reference suggests a very strong philosophical stance about something and I imagine that because of that they have political leanings, but I can’t tell what their leanings are
FungalRaincloud
The Hedgehog Review? Yes, they've been around since 1999, and publish a few times a year. But I'm not sure where you're leaping to a strong political leaning. They're an academic journal published by the University of Virginia. I don't religiously follow them, but I've been cursorily aware of them for a while. I don't think I've ever considered them to lean one way or another when reading their publications.
jayers
They don't have any political leanings but they do have a philosophical project. If you dig into the site a little you'll find that they're published by the Institute for Advanced Studies in Culture (housed at UVA) and IASC exists to promote research into the contradictions of modernity, by examining how culture manifests itself in metaphor, symbol, ideals, principles, institutions, and material objects [1]. I've been a reader of THR for a few years and I'd say generally they publish articles that promote moral realism and humanism. They're sort of metaphysically open-minded.
RyanShook
Just finished reading The Thinking Machine. Highly recommend it if you're interested in how Nvidia became the most valuable company on earth: https://amzn.to/42z8JPF
jstanley
The four books discussed in that passage are:
AI Snake Oil – by Arvind Narayanan and Sayash Kapoor
Nexus – by Yuval Noah Harari
Genesis – by Henry Kissinger, Craig Mundie, and Eric Schmidt
The Singularity Is Nearer – by Ray Kurzweil
DebtDeflation
In 20 years, Kurzweil will write another book entitled, "The Singularity is Almost Here".
FungalRaincloud
He's 77 years old. Let the man retire, damn.
kelseyfrog
Pills in; books out. That's the deal.
ks2048
Henry Kissinger, noted AI expert.
imperfect_light
He did so well with Theranos
Igrom
As criticized in the featured article, yes.
homarp
well craig mundie and eric schmidt not much better
KerrAvon
it's not complimentary about them, FWIW
adastra22
“Machines Who Think” is conspicuously missing from the list.
bbor
Apologies in advance for the passionate critique, but I just can't help but attack what I see as a faux-intellectual, misleading piece. It starts with a notoriously-biased pop-science book that assumes its conclusions before any investigations begin ("AI is bad" hidden behind a thin veneer of "oh but not good AI"), and just goes downhill from there. It's honestly shocking that the brief discussion of that book is intended in a laudatory manner:
A big part of the problem, the authors maintain, is confusion about the meaning of artificial intelligence itself, a confusion that sustains and originates in the present AI commercial boom.
Narayanan and Kapoor are particularly worried about the conflation of generative AI, which produces content through probabilistic response to human input, and predictive AI, which is purported to accurately forecast outcomes in the world, whether those be the success of a job candidate or the likelihood of a civil war. While products employing generative AI are “immature, unreliable, and prone to misuse,” Narayanan and Kapoor write, those using predictive AI “not only [do] not work today but will likely never work.”
2. The idea that using ML to predict outcomes "does not work" is so obviously wrong that I don't really feel the need to argue against it. Perhaps weather models, content moderation systems, NLP analyzers, spatial modelers, and the vast universe of other examples are all not really AI in the first place, in their book? In that case, what is "predictive AI"? Just a few cherry-picked examples of local governments trying to cheap out on bureaucratic processes, I guess?
After this brief intro, we arrive at the meat of the article. Picking on a Harari book seems like beating a dead horse, but y'know, sometimes that's fun! Still, the specific criticisms fall flat:
[Harari] offers the example of “present-day chess-playing AI” that are “taught nothing except the basic rules of the game.” Never mind that Stockfish, currently the world’s most successful chess engine, is programmed with several human game strategies
Harari fails to explain that while machine-learning models assemble a template of solutions to a specific problem (e.g., the best possible move in a given chess position), the framework in which those problems and solutions are defined is entirely constructed by engineers.
Harari bungles straightforward issues and ideas concerning artificial intelligence.. But Harari, attempting to argue that the alignment problem is a timeless conundrum, applies [the alignment problem] to historical events that did not materially involve artificial intelligence
Finally, there's the criticism about the COMPAS system that ProPublica uncovered (the true GOATs in any story). But what exactly is the criticism there? "He was critical, yes, but not critical in exactly the way I prefer"? That applies to pretty much every book ever in some way or another...
I'll skip going through the other two as closely--because I'm on the anti-markdown site, where walls of text are the only option--but it's all just the same tired assumptions wrapped in a condescending attitude. The writers of Genesis are far from experts in AI, but regardless, the criticisms of both them and Kurzweil come down to variations on one theme: "these people think AI is a big deal, which is obviously wrong, because it's not". I don't think you need me to tell you that this is not a solid argument.
I mean... Ugh. Criticizing the idea of a technological singularity as an "imaginary event" that "consists almost entirely in extrapolation" is again technically true, but the implied pejorative usage of these terms is completely unfounded; it is no more imaginary than climate change, nuclear war, or the simple empirical assumption that the sun will rise again tomorrow.
It's especially tiring to read this when we're literally in the middle of the singularity right now, which is quite obvious if you hear the real meaning of the term ("a point where our models must be discarded and a new reality rules"[3]), rather than the somewhat-bungled description here that relates more to Intelligence Explosions (" sufficiently advanced machine intelligence could build a smarter version of itself, which could in turn build an even smarter version, and that this process could continue to the point of vastly exceeding human intelligence"[4]).
The only people who still think the future of AI('s effect on humanity) is predictable post-2022 are the ones who are dogmatically certain that computers as we know them will always be crappy tools at best. I implore you, privileged reader: do not fall into this comforting trap. Face the future with us, despite the terror. Posterity is counting on us.
[1] https://github.com/official-stockfish/Stockfish/commit/af110...
[2] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Aravind Narayanan seems to be the only guy qualified enough to be called an expert.