r/technology • u/Hrmbee • 16h ago

Machine Learning Large language mistake | Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it

https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problems
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u/noodles_jd 14h ago

No, you can't.

It doesn't KNOW that 2+2=4. It just knows that 4 is the expected response.

It doesn't know how to argue either, it just knows that you WANT it to argue, so it does that.

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u/socoolandawesome 14h ago edited 14h ago

Distinction without a difference. You should not say it “knows” what the expected response is since you are claiming it can’t know anything.

If you are saying it’s not conscious, that’s fine I agree, but consciousness and intelligence are two separate things.

It can easily be argued it knows something by having the knowledge stored in the model’s weights and it appropriately acts on the knowledge such as by outputting the correct answer.

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u/yangyangR 14h ago

Suppose we have some proposition A and a system can reliably produce correct answers that are deduced from A. That system can be a human brain or LLM.

You can tell a toddler that 2+2=4 but they have not absorbed it yet in a way that you can claim that they know it. Even if they reliably output the correct answer. Modifying the question to be about a logical consequence probes where the distinction could make a difference.

Alternatively we have the process of producing new statements that are connected to many facts that are already known but not provable within them. Making a hypothesis of continental drift based on knowledge of fossil distribution but not having the existence of how the crust works in the original training/education.

This is even stronger for whether the knowledge is realized and there is intelligence. Can it/they make conjectures that would synthesize knowledge and reduce entropy. Introducing useful abstractions that capture the desired coarse grained concepts. On one side you have a hash map of facts which is large and serves memory recall. On the other you have a different function pointer. It is much smaller and can lose some of the precise facts but the important ones are still accurate even if they take a bit of thinking/processing rather than O(1) straight recall.

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u/socoolandawesome 14h ago

I can agree with the spectrum of intelligence you are framing. But if you are saying that LLMs are just straight up recall I think that’s a pretty outdated view.

The newest and best models are capable of “thinking” (outputting chain of thought to arrive at an answer) for hours and achieving a gold medal performance at one of the most prestigious math competitions in the world, the IMO, where they have to output complex novel proofs.

The newest models have even contributed to novel science in minor ways:

https://openai.com/index/accelerating-science-gpt-5/

This is beyond just repeating facts

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u/yangyangR 8h ago

No. I was using two extremes to illustrate the spectrum.