| This is kind of ignoring other NN's. You're very focused on LLM's as the example. AlphaGo learned by playing itself. And is able to anticipate multiple moves ahead. Then, that same 'engine', was able to be applied to Chess, and learned how to beat a master from scratch, by playing itself, in just a few hours. There was no lookups, or zip'ing of aggregated data. A lot of what you are postulating as cognition, humans don't do either. Humans didn't figure out gravity from photos of the sky (unless you mean tracing stars and planets, and then yes an NN probably can figure it out). Many humans go through their whole day without analyzing propositions and inferring reality and what to do next. Humans are similarly un-conscious. A lot of AI news all the time. This link is from today. A little more in the theme of 'world building models', than this current post about LLM's. https://news.ycombinator.com/item?id=39692387 There was another on also about an NN that could pass some international Geometry competition. It was based on propositions, and reasoning. |
The way we build explanations is largely by reasoning-by-analogy. We build physical models with our hands, to resolve ambiguities in our environment, ever more complexly -- and then, eventually, land upon the right analogy that then falls away.
Prior to gravity we had crystal spheres -- reasoning by ananlogy with such things.
Since machines arent in the world, as in my robot example, they can never build explantory conceptualisations of it.
Chess et al. are not worlds in any relevant sense. No one cares, or doubts, that a system with a fully mathematically specific "world" can be "learnt" by a computer.
The full specification of a "world" in mathematical terms doesnt require intelligence. At that point you can use the dumb strategies of alphago.
Intelligence is what you do when you don't know what you're doing. The "World Model"s we're interested in are those that arent already specified to the machine.
All these formal games are just outcomes spaces where every event is known a priori.
As I said, the AI people arent even operating in the category of intelligence. It just the profound lack of background knowledge of any field outside pytorch.lol() that their meglomania plausible
Go look at every paper in AI or ML that purports to build a model of anything: can you find a single one where the outcome space cannot be fully specified either formally (as with chess, etc.) or empirically (as with data samples)?
This has nothing to do with intelligence. We do not either start from the answers, or samples of the answers. We have no answers.
The resolution to this problem requires having a body: you have to move in order to think, in direct causal contact with the world beyond the capacities of clay, to think about it.