This is going to elevate your thinking on this no end, if you're interested.
We know that the brain is a probabilistic input → output machine because the universe is a probabilistic input → output machine. The brain is made of universe. There are deterministic relationships (which at high sensitivity or complexity become easier to describe as probabilistic), and quantum relationships. That's it. The brain, like every other thing comprised of "universe" is comprised of those two types of relationships.
If Romain's book provides evidence of relationships in the brain that are neither quantum (therefore random) NOR classical (therefore deterministic), then 1) he would have already won at least one Nobel prize, and 2) anyone in this thread would be able to at least gesture toward what relationship that is.
No quantum bullshittery in there I promise.
Equating "classical" with "deterministic" is however pushing it a bit too far, when no one and nothing can ever do even a very small fraction of said determination...
That's why the brain cannot possibly be anything other than an input → output machine, which is functionally deterministic (with maybe some fully random components), but is easiest to describe as probabilistic.
In the same way that LLMs are functionally deterministic, but easiest to describe as probabilistic.
The brain is an object in the universe.
The universe has quantum behaviors (fully random, not a source of intelligence) and it has deterministic behaviors (fully non-random). Many of those deterministic behaviors are so complex that they're easier to analyze and describe as probabilistic, which is where most brain input → output relationships land.
Please point to any evidence whatsoever that the brain has some third type of interaction going on that has never been observed anywhere in the entire universe, then we can have a discussion about it.
The brain is a (very complex, incredible) input → output machine. That's it! It's incredible!
I don't understand why people are so afraid of this that they will believe otherwise with literally zero evidence whatsoever.
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.
You cannot say an AI model cannot be intelligent because it's a probability machine, when all available evidence points toward natural intelligence also being generated by probability machines (much more complex ones, called brains).
So, LLM are just next token predictors, brains are next token predictors + many other things in addition, and that makes people still feel LLM are dumb even when they solve a lot of problems using tokens.
Of the vast uncertainties and philosophical exercises that we must face to bridge the chasm between where we are now, and where we will be when we understand intelligence, I can take comfort in claiming, with 100% accuracy, that our biology is not based on technology invented by Google in 2017.
Like what?
What specific biological structure in the brain could be doing anything other than producing output as a function of 1) current electrical/chemical/thermal inputs and 2) previous electrical/chemical/thermal inputs?
The way they make LLM solve problems is by adding a lot of logical jumps into its data, or break down different problems etc, and then as it predicts the text it predicts these logical jumps and then solves the problem. That is very different from how humans learn to solve problems, you don't feed them a billion different state transitions they have to encode to be able to navigate math, they learn to become proficient at math from a few hundred to a few thousand examples, that is fundamentally different from how LLM can learn.
That LLM are so slow learners that requires massive amount of data is a big reason its hard to make them smarter, and its caused by them being next token predictors. And the reason humans can learn with so little data is because we are not just next token predictors.
You changed the definition there, for it to be like an LLM it should be:
> transforming an input into an output trying to mimic inputs that part of the brain has previously been exposed to
Anyone can see how that limits you a lot, and why that makes it so much harder for LLM to learn things properly than it is for humans.
Pre-training is just direct mimicry. A pre-trained LLM is very stupid and mostly useless. To become useful they are post-trained with a reward function.
The only general part of this training is the text prediction. All the other steps add specific skills to it and try to not hurt the generality as they do so, but we have no way to add general skill in the second step. So the general part is still the text predictor part. We know that training a neural net on specific tasks doesn't produce generality, we have tried that before, the text predictor is what let us get a glimpse of generality.
So then the text predictor try to solve problems by moving into the different specific skills there.
But yeah, you are right, without the CoT the text predictor wouldn't have any reliable skills to map to, so its still an important part of it. But the CoT doesn't add to the generality of the model, it just adds special skills it can utilize.
But, I agree what I am arguing now is a weaker statement. But I still believe its true, but I probably wont convince you of it.
On functional grounds my bike has not a lot of distinction from a horse, but just, like, saying that doesn't tell me much about either. Or at least, it seems to leave out a lot of otherwise crucial details and differences..
What does it mean to you, this point of view? Are you truly coming from like a 20th century pragmatism point of view? Where what is most useful is what is right? Or are your trying to make a larger claim about nature? I think being clear about that would help focus your critique here.
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?