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
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
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?
Sorry, why is it misleading to recognise that all neural networks are created manually by human engineers?
Meh... I've been trying to pounce on HN posts that review books (or even mention them), as it's difficult to find titles to download. Jump into this one (it has four!) only to discover that I've got two of them already.
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[ 0.32 ms ] story [ 39.1 ms ] threadAI 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
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:
That's just blatantly untrue, and even when it was true (pre-2023[1]), it's a misleading anecdote that obscures an overwhelming trend. That&#x...Sorry, why is it misleading to recognise that all neural networks are created manually by human engineers?