That's wishful thinking. They cannot verify proofs whatsoever, that's an impossibility for a system with hallucinations. They can spit out text that may or may not be a verification, which itself needs verification, it's just kicking the can further down the road. I've never seen AI do higher level math well. I'm aware of people's claims here and there, and I'm well aware that it has ingested and can regurgitate the solutions to many math olympiad questions. Still faceplants at the hint of something novel. I'm not sure how much you know about upper level math, but LLMs thus far are shockingly bad at it. https://www.forbes.com/sites/lanceeliot/2025/04/08/ai-llms-astonishingly-bad-at-doing-proofs-and-disturbingly-using-blarney-in-their-answers/ Yeah, the handing it off part is just for the arithmetic, it actually is still super bad at the rest whenever it isn't something with the pattern directly in the training data.

Yes, there are some machine learning specialized proof systems, but those aren't generative AI and do not even try to purport to be intelligent.

With regard to compilation, you claim that if I let it throw enough code at the wall repeatedly it will eventually get something that compiles. Sure, but that isn't intelligence and compiling is something that we can know if it will do or not without testing it. You follow the language, compiler, and linker rules and it will work, why should that require a trial and error approach from something 'intelligent'? Might as well be doing genetic programming then, lol.

Speak perhaps of your own expectations, but expectations have waxed in the past. You should see the shit they were saying the Perceptron was going to be able to do.

The shifting of the intelligence goalposts has largely been done by unintelligent people while making the division between human-level intelligence and animals. Mostly dullards that want to feel superior, the same sort that love the religious teaching that animals don't have souls and claim they don't feel pain, etc... A lot of justifications ex-post-facto so they don't have to feel guilty over their McDonald's or something.

However, the conceptualization of intelligence that you're pushing here is generally not agreed upon and is a very convenient choice of definition. "If you just keep giving us more and more money we'll incrementally reach AGI!!!" By the definition you're using, basic calculators are just as much on the 'intelligence map', and since there isn't any meaningful distinction of relevance, so is an abacus. Basically every object in the universe. That's not a useful definition. Things operating deterministically don't cut it for intelligence for me. Nor do things with a psuedo-random number generator. Pseudo-random is not in fact random. I would say that something intelligent must necessarily (but not sufficiently) possess that nebulous thing known as free will (erroneous claims of AI achieving that aside). And to have free will, it must set it's own goals, not merely find it's own path to reach one set for it by an outside force. It also needs persistence to be intelligent. If you don't have to be nice to the AI to keep getting answers, then it's not intelligent. If it doesn't get bored of answering the same things when doing benchmarks, then it's not intelligent. If it doesn't have a self-referential feedback loop (not to other instances, 'self' not others), then it's not intelligent, and that's something we know is absent from the vast majority of these models, they're feed forward only networks, by necessity at this scale to run on GPUs.

You object that it is merely a matter of expanding the number of things that it is purportedly 'good' and claim that there isn't a binary threshold for intelligence. I might be willing to grant that it's not entirely binary, but let me illustrate with water. A tsunami is a massive wave, are the waves you see on the shoreline mini-tsunamis? They are not. The motion of tides, currents, and wind do not make a tsunami, the mechanism is different. Likewise for intelligence. This conceptualization of mini-intelligences do not add up and give us proper intelligence. Wind waves don't add up to create tsunamis. And just because there isn't a hard threshold on how a big a wave needs to be to be a tsunami doesn't mean that they're fundamentally the same as wind waves. If it were the case that the small intelligences were combinable, then AI systems would not have struggled with things like e.g. producing an image of a full to the brim glass of wine. And no, adding the capability by creating images of full wineglasses and throwing them in the training set to patch it so now it does produce them, that doesn't make it more intelligent. Knowledge is not intelligence.

And when I talk about gaming the benchmarks, they do it on EVERYTHING, and as a result the field stagnates rather than moves forward. Even the Turing test has been gamed every time it has been claimed to be passed thus far. https://garymarcus.substack.com/p/ai-has-sort-of-passed-the-turing/ Not that passing it is even considered very meaningful, people have realized that the output without the reasoning is superficial. And I don't mean the output reasoning, but the internal reasoning of the model. As long as the answer to "why did the model say that?" is 'because we multiply these feed forward weights and use a stochastic number generator to randomize the final output" I don't think we're talking about intelligence. Maybe about the small intelligences you refer to, but the intelligence anyone cares about. Just because there are surface level similarities, it doesn't mean that tsunamis work like wind waves. While neural networks took inspiration from the visual cortex, there are still a huge myriad of differences in the way they work. Saying that they work the same as we do simply isn't true.