791–800 of 1001 posts
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Isn't that what no LLM can provide: being free of hallucinations?
For the record, brains are also not free of hallucinations.
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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I think it's more likely to be the old base model checkpoint further trained on additional data.
Is that technically not a new pretrained model? (Also not sure how that would work, but maybe I’ve missed a paper or two!)
But it's just semantics.
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Isn't that what no LLM can provide: being free of hallucinations?
For the record, brains are also not free of hallucinations.
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Do people other than Elon fans use grok? Honest question. I've never tried it.
I can't understand why people would trust a CEO that regularly lies about product timelines, product features, his own personal life, etc. And that's before politicizing his entire kingdom by literally becoming a part of government and one of the larger donations of the current administration.
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…
As a user I want it but as webadmin it kills dynamic pages and that's why Proof of work aka CPU time captchas like Anubis https://github.com/TecharoHQ/anubis#user-content-anubis or BotID https://vercel.com/docs/botid are now everywhere. If only these AI crawlers did some caching, but no just go and overrun the web. To the effect that they can't anymore, at the price of shutting down small sites and making life worse for everyone, just for few months of rapacious crawling. Literally Perplexity moved fast and broke things.
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When I saw that it labeled DP ports as HDMI I immediately decided that I am not going to touch this until it is at least 5x better with 95% accuracy with basic things. I don't see any advantage in using the tool.
That's a far more dangerous territory. A machine that is obviously broken will not get used. A machine that is subtly broken will propagate errors because it will have achieved a high enough trust level that it will actually get used. Think 'Therac-25', it worked in 99.5% of the time. In fact it worked so well that reports of malfunctions were routinely discarded.
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Isn't that what no LLM can provide: being free of hallucinations?
I think the better word is confabulation; fabricating plausible but false narratives based on wrong memory. Fundamentally, these models try to produce plausible text. With language models getting large, they start creating internal world models, and some research shows they actually have truth dimensions. [0] I'm not an expert on the topic, but to me it sounds plausible that a good part of the problem of confabulatio…
One demo of this that reliably works for me:
Write a draft of something and ask the LLM to find the errors.
Correct the errors, repeat.
It will never stop finding a list of errors!
The first time around and maybe the second it will be helpful, but after you've fixed the obvious things, it will start complaining about things that are perfectly fine, just to satisfy your request of finding errors.
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No-one should have the expectation LLMs are giving correct answers 100% of the time. It's inherent to the tech for them to be confidently wrong Code needs to be checked References need to be checked Any facts or claims need to be checked
According to the benchmarks here they're claiming up to 97% accuracy. That ought to be good enough to trust them right? Or maybe these benchmarks are all wrong
Makes you wonder what 97% is worth. Would we accept a different service with only 97% availability, and all downtime during lunch break?
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Google’s standard problem is that they don’t even use their own products. Their Pixel and Android team rocks iPhones on the daily, for example.
You cant buy an iPhone without a director approval. And it's like 3 gen behind as well. So no, they don't use iPhones.
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Not necessarily. GPT-4.5 was a new pretrain on top of a sizeable raw model scale bump, and only got 0.5 - because the gains from reasoning training in o-series overshadowed GPT-4.5's natural advantage over GPT-4. OpenAI might have learned not to overhype. They already shipped GPT-5 - which was only an incremental upgrade over o3, and was received poorly, with this being a part of the reason why.
I jumped straight from 4o (free user) into GPT-5 (paid user). It was a generational leap if there ever has been one. Much bigger than 3.5 to 4.
But o3 existing and being good at what it does? Took the wind out of GPT-5's sails.