As a field, artificial intelligence has always been on the border of respectability, and therefore on the border of crackpottery. Many critics <Dreyfus, 1972>, <Lighthill, 1973> have urged that we are over the border. We have been very defensive toward this charge, drawing ourselves up with dignity when it is made and folding the cloak of Science about us. On the other hand, in private, we have been justifiably proud of our willingness to explore weird ideas, because pursuing them is the only way to make progress.
Metadata: Crossref. Abstracts retain their original authors’ rights.
In the conversation
Original HN comments, with labels from simple text rules. A label describes the whole comment and may miss context or irony. Read the discussion to make up your own mind. About the labels
"You know, as impressive as these systems are, they sure are bafflingly stupid. I am wondering if perhaps there is more to human intelligence than technical mastery of human language."
Drew McDermott, 1976, "Artificial Intelligence Meets Natural Stupidity": https://dl.acm.org/doi/10.1145/1045339.1045340
I wish to rail against a pervasive sloppiness in our thinking, the tendency to see in natural language a natural source of problems and solutions. Many researchers tend to talk as if an internal knowledge representation ought to be closely related to the "corresponding" English sentences; and that operations on the structure should resemble human conversation or "word…
Read the full comment
problems". Because the fault here is a disregard for logic, it will be hard for my criticism to be logical and clear.
Note that 1976 had some cool AI, including theorem-provers and story-tellers (Knuth wrote a story-telling program, I think it's an exercise in TAoCP). Yet the underlying theory of intelligence was "intelligence is logical processing of symbolic information"; this is obviously inadequate, but the ideology of analytic philosophy is a helluva drug for a scientist. Likewise with the 21st century "intelligence is about correctly predicting things based on past information,"; this is obviously inadequate, but the ideology of big data technocracy is a helluva drug for a scientist.
> it's people not wanting to lose control or relative status in the world.
It's amazing how widespread this belief is among the HN crowd, despite being a shameless ad hominem with zero evidence. I think there are a lot of us who assume the reasonable hypothesis is "LLMs are a compelling new computing paradigm, but researchers and Big Tech are overselling generative AI due to a combination of bad incentives and sincere ideological/scientific blindness. 2025 artificial neural networks are not meaningfully intelligent." There has not been sufficient evidence to overturn this hypothesis and an enormous pile of evidence supporting it.
I do not necessarily believe humans are…
Read the full comment
smarter than orcas, it is too difficult to say. But orcas are undoubtedly smarter than any AI system. There are billions of non-human "intelligent agents" on planet Earth to compare AI against, and instead we are comparing AI to humans based on trivia and trickery. This is the basic problem with AI, and it always has had this problem: https://dl.acm.org/doi/10.1145/1045339.1045340 The field has always been flagrantly unscientific, and it might get us nifty computers, but we are no closer to "intelligent" computing than we were when Drew McDermott wrote that article. E.g. MuZero has zero intelligence compared to a cockroach; instead of seriously considering this claim AI folks will just sneer "are you even dan in Go?" Spiders are not smarter than beavers even if their webs seem more careful and intricate than beavers' dams... that said it is not even clear to me that our neural networks are capable of spider intelligence! "Your system was trained on 10,000,00 outdoor spiderwebs between branches and bushes and rocks and has super-spider performance in those domains... now let's bring it into my messy attic."
The title wordplay dates back to at least Drew McDermott's 1976 essay "Artificial Intelligence Meets Natural Stupidity" [0]. The intro is phenomenal.
---
> As a field, artificial intelligence has always been on the border of respectability, and therefore on the border of crackpottery. Many critics <Dreyfus, 1972>, <Lighthill, 1973> have urged that we are over the border. We have been very defensive toward this charge, drawing ourselves up with dignity when it is made and folding the cloak of Science about us. On the other hand, in private, we have been justifiably proud of our willingness to explore weird ideas, because pursuing them is the only way to make progress.
>…
Read the full comment
Unfortunately, the necessity for speculation has combined with the culture of the hacker in computer science <Weizenbaum, 1975> to cripple our self-discipline. In a young field, ,self-discipline is not necessarily a virtue, but we are not getting any younger. In the past few years, our tolerance of sloppy thinking has led us to repeat many mistakes over and over. If we are to retain any credibility, this should stop.
> This paper is an effort to ridicule some of these mistakes.
---
[0]: Drew McDermott. 1976. Artificial intelligence meets natural stupidity. SIGART Bull., 57 (April 1976), 4–9. https://doi.org/10.1145/1045339.1045340
>> This doesn't really engage with my points.
Sorry. I thought you described a system that did what MuZero did. It still seems to me that's what you're describing.
"In-context learning" is a big potato. It's just some jargon that OpenAI came up with to play up the capabilities of GPT-3, just another example of the time-honoured tradition in AI of inventing grandiloquent terms to describe something much simpler. See Drew McDermot's "Artificial Intelligence meets Natural Stupidity", a paper from 1976, that castigates this already very common habit of AI researchers: https://dl.acm.org/doi/10.1145/1045339.1045340
To clarify, "in-context learning" is what we used to call…
Read the full comment
"sampling from a model" back in my time (in 2014, when I did my Master's and studied neural nets and language modelling, and also game-playing algos; not coincidentally, because I usually engage in conversation about things that I understand, so you won't catch me talking about quantum computers, say, or physics). Models got better and sampling from them gets better results, so OpenAI figured they could embiggen it and call it something more impressive than "sampling". But it's sampling, not learning.
>> People who argue LLMs are just another example in the history of narrow AI don't seem to have a strong response to the example of in-context learning.
I don't think it's very productive to try and fit opinions into "people who say" kinds of boxes. I don't treat my interlocutors like that. Perhaps I haven't yet heard all the arguments on any particular claim. I'll never know if I assume everyone is just repeating the same old stuff.
Anyway there's no point in arguing anything here. If you've used language models before, you know how sampling works and it's obvious it's what OpenAI call "in-context learning". They're not even pretending it's not sampling. They're just claiming it's different because they're sampling from a large Language model.
Edit:
>> Neural net based evaluations result in moves that more closely match human expert choices[1], which suggests a similarity in mechanism compared to prior evaluation mechanisms.
The paper you linked describes a system that was explicitly trained to predict human moves. That's probably why it can do better at simulating the way humans play chess than a system that wasn't trained to simulate the way humans play chess.
My eternally grumpy point is that if we are on the cusp of human-level AGI, then surely we can make frog-level and fish-level AI right now. But ANN-powered robots (or AI agents in physics-based video games) seem extremely slow and stupid compared to a frog or a fish. And instead of asking difficult scientific questions about what intelligence really means, AI researchers keep shoving human facts and heuristics in LLMs to give an illusion of human-level intelligence. This is precisely the same mistake yesteryear's AI researchers made when developing Lisp expert systems: ignore any tricky science, focus on trivia, tricks, and (most importantly) vibes.[1]
I am not even…
Read the full comment
convinced transformer ANNs are as smart as spiders, and I don't mean the cleverer jumping spiders. Here is my thought experiment: lets say you trained a transformer-powered robo-spider on one gazillion examples of spiderwebs in nature - between rocks, bushes, etc - and verified that in such natural environments you had "superspider" performance (whatever that means). Now test the robo-spider on a pantry, attic, garage, etc. Will the robo-spider be able to reliably spin a functional indoor web as well as a real spider? I doubt it.
I could be wrong of course! Maybe transformers can figure out the underlying geometric/physical principles in a reliable way. But zooming out a bit: despite the success of Be My Eyes / etc, I don't think any of us will live long enough to see an AI replacement for a seeing-eye dog. ("We are very sorry about your mother, there was an edge case where the AI didn't realize that green trucks were dangerous.")
[1] More people should seriously consider that LLMs are similar to Lisp expert systems with an easier user interface, but trading reliability for breadth and ease of development. I use Scheme all the time, clearly Lisp expert systems are useful, as are LLMs. But it is also clear that Lisp will never be a model for human/etc intelligence. See also Drew McDermott's classic paper, "Artificial Intelligence Meets Natural Stupidity" https://dl.acm.org/doi/10.1145/1045339.1045340
Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition.
The largely dominant meritocratic paradigm of highly competitive Western cultures is rooted on the belief that success is due mainly, if not exclusively, to personal qualities such as talent, intelligence, skills, efforts or risk taking.
Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai +3
Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools.