Comment by 343242dfsdf
2 years ago
> There is no "superintelligence" or "AGI"
There is intelligence. The LLM current state-of-the-art technology produces output analog to natural intelligences.
This things are already intelligent.
Saying that LLMs aren't producing "intelligence" is like saying planes actually don't fly because they are not flapping their wings like birds.
If you run fast enough, you'll end flying at some point.
Maybe "intelligence" is just enough statistics and pattern prediction, till the point you just say "this thing is intelligent".
> There is intelligence.
There isn't
> Maybe "intelligence" is just enough statistics and pattern prediction, till the point you just say "this thing is intelligent".
Even the most stupid people can usually ask questions and correct their answers. LLMs are incapable of that. They can regurgitate data and spew a lot of generated bullshit, some of which is correct. Doesn't make them intelligent.
Here's a prime example that appeared in my feed today: https://x.com/darthsidius1985/status/1802423010886058254 And all the things wrong with it: https://x.com/yvanspijk/status/1802468042858737972 and https://x.com/yvanspijk/status/1802468708193124571
Intelligent it is not
> Even the most stupid people can usually ask questions and correct their answers. LLMs are incapable of that. They can regurgitate data and spew a lot of generated bullshit, some of which is correct. Doesn't make them intelligent.
The way the current interface for most models works can result in this kind of output, the quality of the output - not even in the latests models - doesn't necessarily reflects the fidelity of the world model inside the LLM nor the level of insight it can have about a given topic ("what is the etymology of the word cat").
The current usual approach is "one shot", you've got one shot at the prompt, then return your output, no seconds thoughts allowed, no recursion at all. I think this could be a trade-off to get the cheapest most feasible good answer, mostly because the models get to output reasonably good answers most of the time. But then you get a percentage of hallucinations and made up stuff.
That kind of output could be - in a lab - fully absent actually. Did you you notice that the prompt interfaces never gives and empty or half-empty answer? "I don't know", "I don't know for sure", "I kinda know, but it's probably a bit shaky answer", or "I could answer this, but I'd need to google some additional data before", etc.
There's another one, almost never, you get to be asked back by the model, but the models can actually chat with you about complex topics related to your prompt. It's obvious when you're chatting with some chatbot, but not that obvious when you're asking it for a given answer for a complex topic.
In a lab, with recursion enabled, the models could get the true answers probably most of the time, including the fabulous "I don't know". And they could get the chance to ask back as an allowed answer, asking for additional human input, relaying on a live RHLF right there (it's quite technically feasible to achieve, not economically sound if you have a public prompt GUI facing the whole planet inputs).
but it wouldn't make much economic sense to make public a prompt interface like that.
I think it could also have a really heavy impact in the public opinion if they get to see a model that never makes a mistake, because it can answer "I don't know" or can ask you back to get some extra details about your prompt, so there you have another reason to do not make prompts that way.
> The current usual approach is "one shot", you've got one shot at the prompt, then return your output, no seconds thoughts allowed, no recursion at all.
We've had the models for a while and still no one has shown this mythical lab where this regurgitation machine reasons about things and makes no mistakes.
Moreover, since it already has so much knowledge stored, why does it still hallucinate even in specific cases where the answer is known, such as the case I linked?
2 replies →