It's even worse than that. Of course there is a mechanism in LLMs that could explain intelligence. That is the entire point of neural networks, from the 1950's! They were designed from the start as a model of brain computation.
Neural networks are not literally brains - just computational models - but if you are not a dualist, then computation is what the human brain does. Modeling that computation can explain something about intelligence.
Specifically: when scientists look inside a human brain, it seems it does its work using large numbers of highly-interconnected but simple units. The neural network model of brain computation begins there and tries to produce intelligent behavior. If it succeeds, then perhaps the model is right.
And it has succeeded: after 75 years, neural networks produce complex behavior that is arguably intelligent. Nobel Prizes were awarded. This does not prove the neural network model of intelligence is accurate, but it is a significant point in its favor, at least.
Also, you have to have a lot of confidence in the reliability of these systems to say, "If only OpenAI prompted 'do not hack outside systems' then the agents would not have hacked outside systems".
It would be great if they were so reliable, but I don't think they are!
Judging by the slow speed of node here, I wonder if it was run without forcing optimizations. It doesn't do OSR (on-stack replacement), meaning that a benchmark script doesn't get a chance to tier up from the baseline compiler to the fully optimized tier.
Running with
node --no-liftoff
avoids this problem.
(This is not an issue on the Web, where code must return to the event loop anyhow, allowing tiering up to work, and node is optimized for that kind of workflow.)
> Type confusion in V8 in Google Chrome prior to 152.0.7977.82 allowed a remote attacker to execute arbitrary code inside the sandbox via a crafted HTML page.
So it was fixed in 152.0.7977.82 (before .83), if I read that right.
But neurotransmitters and action potentials don't help in modeling symbolic structure.
That is, yes, ANNs are not brains. There are countless differences. But are there differences at the computational level? ANNs are meant to model brain computation, not brain biology.
(There is still a lot to debate there, I'm not saying "ANNs are perfect computational models for the brain")
>But neurotransmitters and action potentials don't help in modeling symbolic structure.
This is exactly why Fodor argues that psychology should be explained on its on level with symbols rather than appealing to neurology. But if you're interested in modeling symbols, there's much better options than ANNs (see nearly any programming language ever).
>ANNs are meant to model brain computation
But we don't really know how that works! So if you know if you're not modelling the low-level behaviour right, you can't assume that there's a correspondence of the higher level computation when you don't really know what that higher level computations are.
We don't know all the details about how the brain computes, you are right.
But we do have a hypothesis: that it is done by a large number of simple units with very high connectivity and in deep layers. This is what neural networks model.
Personally I was skeptical of this model of the brain, but they have achieved remarkable success in practice, as well as Nobel prizes. The neural networks people may have been onto something all along (I say that grudgingly).
But the models learn in a very different way than humans, they're certainly vastly more sample inefficient. I'm not saying it isn't impressive or that it isn't necessarily a kind of intelligence, I'm just sceptical as to how much it really tells us about the brain.
> LLMs can detect patterns at a scale that no human ever could, but patterns only get you so far.
This is asserted without evidence, and from a scientific standpoint, unjustified.
First, "detect patterns" makes it sound like a classification task, "is this a picture of a cat". But LLMs transform the input.
For example, an LLM can translate text between two languages while properly handling the names of the people described, no matter what those names are. That shows they are representing the text in a somewhat abstract way, that they can perform operations on that representation, and also convert it to useful output.
And, what I just described is the most general form of information processing algorithm. Science is not aware of any limitations in principle on such systems.
I am not saying LLMs have no limits, but "they only recognize patterns, and that is a true limit" is not a good argument.
If you want a more concrete example, then LLMs are also trained on visual data these days, which means they do have access to the world in an important way. This directly contradicts the blogpost's claim that LLMs have
> no way to associate the text vectors they manipulate with real-world phenomena.
Historically, that LLMs were text-only used to be a major argument for why they "lack access to meaning", see the Stochastic Parrot paper and the Octopus paper that it references. But even the authors of those papers have (grudgingly) conceded that the argument no longer holds due to multimodality.
AI-generated data might not always be that useful, but at least in this case it obviously is.