241–250 of 406 posts
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There is no reason to think that teapots were sent into orbit or spontaneously formed there. Likewise, there is no reason to think the brain employs super-Turing or quantum computations that cannot be approximated by LLMs.
The point is that you're asking someone to prove a negative.
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I have been given a list by security. "We had an automated tool scan that machine. It reported these. Fix anything medium severity and above. Never mind that some of them involved vulnerabilities in some part of the bluetooth stack (servers in our datacenter don't even have bluetooth). But they just didn't care
So run apt full-upgrade and get the new bluetooth driver. Why bother with a fight over something that isn't even used? Just do the quickest thing to get it off your plate.
We can chalk this up as another example of over-exhuberance by what folks believe LLMs can accomplish vs. what they actually are. LLM-based “AI” is able to use its vast corpus of inputs and calculate the most statistically likely output in a given situation. It is probabilistic, and when you are dealing with probabilities in a situation where certainties, not probabilities, matter, you’re going to get dinged on credi…
I've thought about this for quite some time now.
No. A human doesn't need to verify everything. And the argument is really simple: stochastic.
Think of self-driving cars: We can show today - based on evidence and real data - that self-driving cars are safer than human drivers. That's a fact and the consequences are clear, more self-driving cars, less human-driven cars, less accidents, less hurt people, less dead people.
Are the cars 100% safe and NEVER make a mistake? No. But they don't need to. Nothing is ever 100% (in the real world).
Now back to AI for software creation. "Review is the bottleneck because EVERYTHING must be judged by a human." No. It doesn't. We just need to build AI review systems, that will do reviews better than (or at least as good as) humans. The human review quality bar is far below 100%. Far far far. If we can show (likely in the next 12-24 months I think) that AI review quality is consistently above the human review quality - again, based on evidence, based on real data - then that's it, then there's no good reason to have humans review the code.
Yes, there will be another layer in the system, another level of abstraction that will/must end at the human boundary.
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> but being able to generate probabilistic outputs based on inputs is not what makes us intelligent. ??? Of course it is. The brain is mechanically not capable of doing anything other than that. Do you believe the brain is something other than a bundle of probabilistic physical interactions? Or are brains not the source of what we call intelligence?
Yours is a controversial view. It is lazy and selfish to try to get other people to explain their case that it is not exclusively that, when saying that it is exclusively that is the weaker case, and you back it up with nothing but a snarky proclamation. Are newly born babies reacting due to statistical probabilities that they have derived, or are they using something other than their brains?
The answer is obviously yes lol.
The creature is an assemblage of electrical, chemical, and kinetic relationships.
Watching a baby develop is exactly what you'd expect from a system that's predominantly electrical noise triggering behaviors and then gradually refining denoising the relationship between inputs and outputs, with the goal function of achieving more desirable inputs.
Surely you can at least gesture toward one thing in the brain that appears not to be a probabilistic relationship between input and output?
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Are they useful or not? Will they continue changing the world or not? People who choose one way or the other for describing them typically fall on one side or the other in these questions imo. What do you think? Will these next token predictors change the world or not?
They are useful. They will continue to change the world. They are still next token predictors with all the problems that comes with that. For them to change the world you have to work with them as next token predictors. Ensure that the next token predictor has enough prediction paths to solve the problems you want and so on. Since when they don't they fail spectacularly. These big companies will continue to add new s…
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Every day I wake up and open HN. “LLM has made legitimate mathematical discoveries” —> Wow the rate of progress is amazing. Highly upvoted. “LLM does something not good” -> Does everyone else not realize LLMs are just dumb next token predictors? Highly upvoted. So tired of this discourse and this site.
The rate of progress can be high and they can also be dumb next token predictors. Not sure why that is hard to understand. These models can do a lot of things but they also can't do a lot of things. In order to use these models effectively you have to understand that they are next token predictors and how that allows it to do what they do.
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> but being able to generate probabilistic outputs based on inputs is not what makes us intelligent. ??? Of course it is. The brain is mechanically not capable of doing anything other than that. Do you believe the brain is something other than a bundle of probabilistic physical interactions? Or are brains not the source of what we call intelligence?
Why is the brain probabilistic instead of deterministic?
The brain is deterministic at the level of specific interactions, which process inputs in a highly chaotic (but still deterministic) manner into probabilistic outcomes.
The opposite of deterministic is random, i.e. in the quantum sense of truly no relationship between input and output.
There are probably some quantum effects in the brain here and there, but the vast majority of it is just traditional deterministic interactions networked together in such a complex system that the resulting behavior is much, much easier to predict in probabilistic terms than otherwise.
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OK. A more pointed question. What do you know about intelligence that allows you to exclude LLMs with CoT from the category of intelligent systems with certainty?
How do you know there's not a teapot orbiting the sun?
We can chalk this up as another example of over-exhuberance by what folks believe LLMs can accomplish vs. what they actually are. LLM-based “AI” is able to use its vast corpus of inputs and calculate the most statistically likely output in a given situation. It is probabilistic, and when you are dealing with probabilities in a situation where certainties, not probabilities, matter, you’re going to get dinged on credi…
This line of critique is pernicious because it is both technically correct, as description, and profoundly misleading.
Saying that outputs are a product of inputs is not interesting and to the point it is not explanatory.
What is interesting, is how they do what they do. What is the "statistically likely* next token? To answer that you can do exactly one thing, run the LLM. That's because what they are doing is interesting and not reducible.
What is more interesting is that in order to do what they do, given the architectures we apply and the training strategies we use and the harnesses we situate them in, LLM are recapitulating in their deep layers strategies observed in the animal brain. This is still suggestive, interpretibility is nascent: but it is also more than a little interesting. In some respects, for cognitive scientists interested in the manner in which mind merges from computational substrates, it is profoundly interesting.
One can incorporate this, and, still be viciously critical of bother the success and failure of LLM in the applications we have put them to, and of how we (as individuals and as institutions such as corporations) are integrating them into our work.
There is a lot to criticize!
But criticism can be taken more seriously when it is not obscured by misunderstanding or misrepresentation (intentional, or not) of what LLM are and why they are not remotely "parrots" in the pejorative sense.
The technology, as technology, at the scale we are architecting it, is doing things we did not imagine would be witnessed in our lifetime, if ever. Dismissing that and denying it because of the career, industry, society, and civilization challenges that technology brings are existential, is bad argumentation or bad faith.
Both can be true at once.
Seriously, the hubris on these "AI" sloppy clowns needs consequences. =3