by bbor | 2025-09-20 02:55:49+00:00
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.

This is just blatantly untrue to anyone who bothered to learn the names skipped with a brief "once apon a time, there was symbolic AI" -- from Turing to Minsky, Neumann to Pearl, Shannon to McCarthy, on and on and on. This incredible article from "Quote Investigator" lays out the situation well going all the way back to 1971: https://quoteinvestigator.com/2024/06/20/not-ai/ Personally, my favorite phrasing of this sentiment is the one preferred by Hofstadter: "AI is whatever hasn’t been done yet."

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.”

1. That distinction is vacuous at best. Even if we exclude all symbolic AI (pure and hybridized) from the term "AI", literally all machine learning models produce probabilistic responses to inputs -- that's why it's called the "inference" step! This kind of false dichotomy is employed regularly by passionate amateurs on bsky and Reddit to allow them to hate bad AI while leaving a vague carveout for things they can't argue against like cancer detection systems, but without any real basis it's more obfuscation than distinction. God forbid any of these people convince the EU parliament to pass laws based on this idea...

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

That's just blatantly untrue, and even when it was true (pre-2023[1]), it's a misleading anecdote that obscures an overwhelming trend.

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.

That's an absurd way to describe modern deep learning, where the Bitter Lesson[2] is cited as gospel. Yes, technically all neural network topologies are laid out by humans at some level, but just saying that is another misleading snippet of the truth at best; even the author later acknowledges "the opacity of machine-learning tools is a genuine technical problem". How can both things be the case?

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

Yes, he's applying the concept in a broader way than usual. That doesn't make it invalid, and I'm 100% sure that even someone like Harari is well aware of what he's doing there. Describing this as "bungling straightforward ideas" rather than "saying something I disagree with" is, well... bungled!

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

[3] https://edoras.sdsu.edu/~vinge/misc/singularity.html

[4] https://intelligence.org/files/IEM.pdf