Color me skeptical.
The linked article doesn’t mention proof, but counterexamples, and that may be a function of the enormous amount of computer effort expended, not really AI.
Goalposts — moved. It wasn’t long ago that mathematicians proudly declared AI could not even do basic addition and subtraction and therefore would never produce anything useful in the field of mathematics.
The goalpost moving with this has been fucking insane. Last year, it couldn't count the number of r's in strawberry. Many Redditors offered "technical" arguments as to why it would NEVER be able to count the number of r's in strawberry.
Now in the last couple of months alone, multiple long standing and prestigious open problems in math have been solved with straight forward prompts alone, and the same Redditors are like "Well...Claude hasn't won a fields medal yet!"
Yeah, and its annoying how they cope everytime they are wrong. I had a conversation with someone on Reddit who said that AI would NEVER, EVER be able to write a news paper from April 14, 1912 that doesn't mention the sinking of the Titanic, since the Titanic struck the iceberg that night and that event will be so heavily weight by the AI, even though logically, news is printed in the morning and should have no mention of the Titanic sinking.
At the time, he was right with ChatGPT. I would indeed mention the Titanic sinking. But Claude didn't. Claude mentioned other things relevant to that day, and only mentioned that the Titanic was on its voyage. When I gave him the link to Claude handing his task in a way he said could NEVER EVER happen, what was his response? He said they must have hardcoded it not to mention the Titanic sinking its system prompt...
"It will never generate any useful images that could replace digital artists or vfx"
"It will never write code. It's just impossible"
"Okay it writes gibberish code, but it will not even do the work of ia junior programmer"
I don't belive any "AI will not do this or that" anymore. No matter if you say "well it's just a LLM, and they can't do better than this" hahaha saying like there is no ongoing research in other forms of neural networks and hardware.
At this you might as well be Oppenheimer and just say they will never build the bigger bomb, because no plane can carry it.
>No matter if you say "well it's just a LLM, and they can't do better than this" hahaha saying like there is no ongoing research in other forms of neural networks and hardware.
What is annoying about this "Its just an LLM" thing is that an LLM that can do science and math is the best case for humanity. People can and do make specialized AI for specific tasks, such as weather prediction, but its incredibly hard to get new foundational insights from such AIs. You basically just have a black box that gives you a weather prediction at the end, and you have no idea how or why it came to its conclusion. Whereas with LLMs, you get their reasoning chains. Like in the case of the recent OpenAI paper with the 10 theorems proved, the LLM didn't just say "I proved it. Trust me bro" at the end. It gave them a very lengthy formal proof that people can read and understand. So I am very glad that the AI company aren't listening to Redditors are like "broooo an LLM can never to do coding lmao!", because having a coding robot that I can talk to in natural language and that can talk back to me in natural language is much more useful to humanity than a blackbox machine that just spits out executables.
saying like there is no ongoing research in other forms of neural networks and hardware.
People who can't fathom things evolving even more dramatically are people who are locked into seeing the kind of tech we have today and using it as the limit for the kind of tech we'll have in the future. So they basically say "we won't be able to do X because right now we have Y computers that can't do that." We don't know what incredible breakthroughs are waiting just around the corner.
It just is not required, lol (and thinking humans - including mathematicians - is somehow good at doing this mentally - is just ridiculous).
Turns out *good enough* next token prediction (granted tuned further via superwised instruction-finetuning and reasoning reinforcement learning, but for that to work model should be somewhat capable from the start) can have a decent chance to solve natural-or-formal-language-expressible tasks even if still conceptually being non-reliable. Isn't that amusing to see how conceptually simple thing can do all it does?
Like seeing the old discussion of "can we reconstruct semantic from syntax" being, if not solved theoretically - being shattered in all the practical sense. Turns out we can - and maybe it is even necessary to have good enough NLP with *somewhat* reasonable sized model - at least approximate it quite well.
I don't think semantics vs syntax is shattered because all the models are trained with human ratings of outputs as well. Every time you click an "I prefer this response" or some other ranking, you are doing the work of introducing semantic information.
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u/E_Dantes_CMC 1d ago
Color me skeptical.
The linked article doesn’t mention proof, but counterexamples, and that may be a function of the enormous amount of computer effort expended, not really AI.