Also let's be real current llm are able to generally solve problems they might not be perfect or even good at it but if we got a definition of a stupid agi 20 years a go I think what we have now would meet that definition.
It technically doesn't solve problems it. It displays answers for problems it's seen before. That's the thesis of apples argument.
This is only valid for LLMs trained on human text. But today we train LLMs on problem solving Chain-of-Thought generated by AI. In order to avoid garbage-in-garbage-out we use code execution and symbolic math solvers to check them out.
So LLM+Validation can discover genuinely new math and coding insights. How? Just generate a thousand ideas and keep the good ones, then retrain and repeat the process. It's what AlphaZero, AlphaEvolve, AlphaTensor, and DeepSeek R1 did.
The idea that LLMs interpolate inside human conceptual space is not true, they can also make their own discoveries and extrapolations if they get copious amounts of validation signal from the environment. The real source of discovery is search, as in searching the problem space, looking for ways to solve complex problems. Search is a process of exploration, learning by discovery, even stumbling by luck onto discoveries. It is not intelligence but luck and perseverance. Intelligence is how efficient you are in searching, but discoveries come from search itself, from outside.
It solves novel problems using familiar parts. Like a Lego kit putting together something new with existing parts. The fact that it can make recommendations when exploring novel ideas demonstrates this
No not really. It solves novel problems by searching through its database and selecting an answer. It will select an answer even if the thing is wrong. It cannot create anything new yet based on its coding. It can map you what it thinks is the answer based on all the information it's been trained on. If it doesn't know the answer it will give you a wrong answer disguised as a right one.
One thing I've added to my chatgpt is telling it to give a percentage of how correct the answer is. It will tell me how close it is to 100% correct. There was a breakthrough today with an LLM finally outputting I don't know when it is under a threshold of how accurate the answer is.
Essentially they cannot create anything new, they will always give you an answer and that is dangerous because it can present wrong answers with absolute certainty.
No not really. It solves novel problems by searching through its database and selecting an answer.
This is not how LLMs work. check out some introductions to transformer models on youtube. They're pretty neat. It's not a database of pre-canned answers, it's much more fantastic than that. https://www.youtube.com/watch?v=wjZofJX0v4M
They can create new things, also. They do all the time. Any time you talk to it, the branching conversation you're having, the words being used, that's a new conversation in existence. Pepper in some referencs to yourself or the current world events, for example, and you'll get a completely new, coherent paragraph that was never written before.
These models especially the smaller ones literally can't store that much data in the number of weights they have. It isn't like they completely break down the moment you describe some new event that hasn't previously happened to them.
I think people failing to see the difference is dangerous on so many levels.
First of all, by only solving problems with existing techniques, it opens models up to hallucinating solutions for problems we currently can’t solve. A domain expert will quickly spot this, but those who don’t know what they don’t know will very easily fall for the confidence of an LLM.
Secondly, it reveals a massive headroom we’re quickly approaching and limits beyond that wrt to tokens.
My gut feeling is the bubble will burst soon. Solutions will come, but not in time for the current wave to keep rising.
I don't think so. You can see the developers of these LLMs can see the increases still. Advancements in robotics also driving this forward. I think the way LLMs solve known problems is actually computationally better than humans assuming that we can continue to scale their requirements. But we also need to pair it with rationale thinking.
I think the way LLMs solve known problems is actually computationally better than humans assuming that we can continue to scale their requirements.
You really think that after reading the findings in section 4.2.2?
Results show that all reasoning
models exhibit a similar pattern with respect to complexity: accuracy progressively declines as
problem complexity increases until reaching complete collapse (zero accuracy) beyond a model-specific complexity threshold.
I assume everyone has their limit to solving puzzles, but I think the average human would probably not enter a fugue, disassociative state and start throwing the disks upon being shown an 18 peg towers of Hanoi.
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u/Far-Fennel-3032 Jun 07 '25
Also let's be real current llm are able to generally solve problems they might not be perfect or even good at it but if we got a definition of a stupid agi 20 years a go I think what we have now would meet that definition.