You can accurately describe any AGI or reasoning problem as an open domain sequence modeling problem. It is not an unreasonable hypothesis that brains evolved to solve a similar sequence modeling problem.
The real world is random, requires making decisions on incomplete information in situations that have never happened before. The real world is not a sequence of tokens.
Consciousness requires instincts in order to prioritize the endless streams of information. One thing people dont want to accept about any AI is that humans always have to tell it WHAT to think about. Our base reptilian brains are the core driver behind all behavior. AI cannot learn that
What if "instinct" is also just (pretrained) model weight?
The human brain is very complex and far from understood and definitely does NOT work like a LLM. But it likely shares some core concepts. Neuronal networks were inspired by brain synapses after all.
We are clearly a product of our past experience (in LLMs this is called our datasets). If you go back to the beginning of our experiences, there is little identity, consciousness, or ability to reason. These things are learned indirectly, (in LLMs this is called an emergent property). We don't learn indiscriminately, evolved instinct, social pressure and culture guide and bias our data consumption (in LLMs this is called our weights).
I can't think of any other way our minds could work, on some level they must function like a LLM, Language perhaps supplemented with general Data, but the principle being the same. Every new idea has been an abstraction or supposition of someones current dataset, which is why technological and general societal advancement has not been linear but closer to exponential.
> If you go back to the beginning of our experiences, there is little identity, consciousness, or ability to reason.
That is because babies brains aren't properly developed. There is nothing preventing a fully conscious being from being born, you see that among animals etc. A newborn foal is a fully functional animal for example. Genes encode the ability to move around, identify objects, follow other beings, collision avoidance etc.
I'm not ignoring that, I'm just saying that in LLMs we call these things weights. And i don't want to downplay the importance of weights, its probably a significant difference between us and other hominids.
But even if you considered some behaviors to be more akin to the server or interface or preprocess in LLMs it still wouldn't detract from the fact that the vast majority of the things that make us autonomous logical sentient beings come about through a process that is very similar to the core workings of LLMs. I'm also not saying that all animal brains function like LLMs, though that's an interesting thought to consider.
I admit that humans don’t progress much behaviorally, outside of intellect, past our teen years; we’re very instinct driven.
But still, I think even very young children have a spark that’s something far beyond rote token generation.
I think it’s typical human hubris (and clever marketing) to believe that we can invent AGI in less than 100 years when it took nature millions of years to develop.
Until we understand consciousness, we won’t be able to replicate it and we’re a very long way from that leap.
What LLMs learn is exactly the diff between primitive humans and us. It's such a huge jump a human alone can't make it. If we were smarter we should have figured out the germ theory of disease sooner, as we were dying from infections.
So don't praise the learning abilities of little children, without language and social support they would not develop very much. We develop not just by our DNA and direct experiences but also by assimilating past experiences through language. It's a huge cache of crystallized intelligence from the past, without which we would not rule this planet.
That's also why I agree LLMs are stalling because we can't quickly scale a few more orders of magnitude the organic text inputs. So there must the a different way to learn, and that is by putting AI in contact with environments and letting it do its own actions and learn from its mistakes just like us.
I believe humans are "just" contextual language and action models. We apply language to understand, reason and direct our actions. We are GPTs with better feedback from outside, and optimized for surviving in this environment. That explains why we need so few samples to learn, the hard work has been done by many previous generations, brains are fit for their own culture.
So the path forward will imply creating synthetic data, and then somehow evaluating the good from the bad. This will be task specific. For coding, we can execute tests. For math, we can use theorem provers to validate. But for chemistry we need simulations or labs. For physics, we need the particle accelerator to get feedback. But for games - we can just use the score - that's super easy, and already led to super-human level players like AlphaZero.
Each topic has its own slowness and cost. It will be a slow grind ahead. And it can't be any other way, AI and AGI are not magic. They must use the scientific method to make progress just like us.
