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GPT-5.2

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1053 points atgctg | 1 comments | 11 Dec 25 18:04 UTC | HN request time: 0s | source
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svara ◴[12 Dec 25 08:08 UTC] No.46241936[source]▶
In my experience, the best models are already nearly as good as you can be for a large fraction of what I personally use them for, which is basically as a more efficient search engine.

The thing that would now make the biggest difference isn't "more intelligence", whatever that might mean, but better grounding.

It's still a big issue that the models will make up plausible sounding but wrong or misleading explanations for things, and verifying their claims ends up taking time. And if it's a topic you don't care about enough, you might just end up misinformed.

I think Google/Gemini realize this, since their "verify" feature is designed to address exactly this. Unfortunately it hasn't worked very well for me so far.

But to me it's very clear that the product that gets this right will be the one I use.

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phorkyas82 ◴[12 Dec 25 08:18 UTC] No.46241987[source]▶
Isn't that what no LLM can provide: being free of hallucinations?
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kyletns ◴[12 Dec 25 08:37 UTC] No.46242093[source]▶
For the record, brains are also not free of hallucinations.
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delaminator ◴[12 Dec 25 09:13 UTC] No.46242289[source]▶
That’s not a very useful observation though is it?

The purpose of mechanisation is to standardise and over the long term reduce errors to zero.

Otoh “The final truth is there is no truth”

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1. michaelscott ◴[12 Dec 25 10:58 UTC] No.46242930[source]▶
A lot of mechanisation, especially in the modern world, is not deterministic and is not always 100% right; it's a fundamental "physics at scale" issue, not something new to LLMs. I think what happened when they first appeared was that people immediately clung to a superintelligence-type AI idea of what LLMs were supposed to do, then realised that's not what they are, then kept going and swung all the way over to "these things aren't good at anything really" or "if they only fix this ONE issue I have with them, they'll actually be useful"