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336 points mooreds | 2 comments | | HN request time: 0.001s | source
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raspasov ◴[] No.44485275[source]
Anyone who claims that a poorly definined concept, AGI, is right around the corner is most likely:

- trying to sell something

- high on their own stories

- high on exogenous compounds

- all of the above

LLMs are good at language. They are OK summarizers of text by design but not good at logic. Very poor at spatial reasoning and as a result poor at connecting concepts together.

Just ask any of the crown jewel LLM models "What's the biggest unsolved problem in the [insert any] field".

The usual result is a pop-science-level article but with ton of subtle yet critical mistakes! Even worse, the answer sounds profound on the surface. In reality, it's just crap.

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richardw ◴[] No.44485483[source]
They’re great at working with the lens on our reality that is our text output. They are not truth seekers, which is necessarily fundamental to every life form from worms to whales. If we get things wrong, we die. If they get them wrong, they earn 1000 generated tokens.
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jhanschoo ◴[] No.44486058[source]
Why do you say that LLMs are not truth seekers? If I express an informational query not very well, the LLM will infer what I mean by it and address the possible well-posed information queries that I may have intended that I did not express well.

Can that not be considered truth-seeking, with the agent-environment boundary being the prompt box?

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sleepybrett ◴[] No.44486263[source]
They keep giving me incorrect answers to verifiable questions. They clearly don't 'seek' anything.
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1. jhanschoo ◴[] No.44487161[source]
In the sense that I expressed, has it not already then sought out an accurate meaning that you have asked? And then failed to give a satisfactory answer? I would also ask: is said model an advertised "reasoning" model? Also, does it have access to external facts via a tool like web search? I would not expect great ability to "arrive at truth" under certain limitations.

Now, you can't conclude that "they clearly don't 'seek' anything" just by the fact that they got an answer wrong. To use the broad notion of "seeking" like you do, a truth seeker with limited knowledge and equipment would arrive confidently at incorrect conclusions based on accurate reasoning. For example, without modern lenses to detect stellar parallax, one would confidently conclude that the stars in the sky are a different thing than the sun (and planets), since one travels across the sky, but the stars are fixed. Plato indeed thought so, and nobody would accuse him of not being a truth-seeker.

If this is what you had in mind, I hope that I have addressed it, otherwise I hope that you can communicate what you mean with an example.

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2. sleepybrett ◴[] No.44490937[source]
I spent an hour on thrusday trying to get some code that would convert one data structure to another in terraform's HCL (which I only deal with once every few years and I find it's looping and eccentricities very annoying).

I opened my 'conversation' with a very clearly presented 'problem statement'. Given this datastructure (with code and an example with data) convert it to this datastructure (with code and the same example data transformed) in terraform.

I went through seven rounds of it presenting me either code that was not syntactically correct or produced a totally different datastructure. Every time it apologized for getting it wrong and then coming back with yet another wrong answer.

I stopped having the conversation when my junior who I also presented the problem to came back with a proper answer.

I'm not talking about it trying to prove to me that trump actually won the 2020 election or that vaccines don't cause autism or anything. Just actual 2+2=4 answers. Much like, in another reply to this post, the guy who had it try to find all the states that have w in their name.