If one feeds GPT-4 a novel problem that does not require multi-step reasoning or very high precision to solve, it can often solve it.
In many cases, we humans have structured our language such that it encapsulates reality very closely. For these cases, when an LLM learns the language it will by construction appear to have a model of the world. Because we humans already spent thousands of years and billions of actually intelligent minds building the language to be the world model.
But in a sense when YOU learned language YOU also learned a world model. For instance when your teacher explains to you the difference between the tenses (had, have, will have) you realize that time is a thing that you need to think about. Even if you already had some sense of this, you now have it made explicit.
Why should we say the LLM hasn't learned a world model when it's done what a kid has done, and everyone agrees the kid understands things?
From what I see, there are some things it hasn't learned correctly. Notably with limbs, it doesn't know how fingers and elbows work, for some reason. But it does know something about what they should look like, and so we get these hilarious images. But I also don't see why it shouldn't overcome this eventually, since it's come pretty far as it is.
By doing this you'd glue the words to sights, sounds, smells, etc.
But it also seems like this is already someone has thought of and is being explored.
You can very easily give "evidence" of gpt4 being anywhere between emerging super-intelligence and a naked emperor depending what you ask it to solve. They do not learn models of the world, they learn models of some class of our models of the world, which are very specific and already very restricted in how they represent the world.
Of course they are, they haven't been trained on anything spatial, they've only been trained on text that only vaguely describes spatial relations. A world model built from an anemic description of the world will be anemic.
In my experience, things outside coding quickly devolve into something more like "technobabble" (and in coding there is always a lot of made-up stuff that doesn't exists in terms of functions etc.).
It's almost like we need our AI's to have two brain parts. A fast one, for intuition, and a slow one, for correctness. ;-)
For some industries where I understand the cost stacks with lower and higher skilled workers, I'd say it only takes out the "cheap" part and thereby not taking out a large chunk of costs (more like 10% cost out prior to paying for the AI). That is still a lot of cost reduction, but something that also will potentially be relatively quickly be "arbitraged away", i.e., will bleed into lower prices.
An example that exists today would be the combination of ChatGPT and Wolfram [1], in which ChatGPT can provide the method and Wolfram can provide the execution. This approach can be used with other systems for other domains, and we've only just started scratching the surface.
Once we have AI's running around with the creativity of artists, and the precision of logicians, ... Well, time to read some Iain M. Banks novels.
You're glossing over a shocking about of information here. The problems we'd like to use AI for are hard to find correct answers for. If we knew how to do this, we wouldn't need the AI.
Incredibly poor compared to ours, but thousands of times better than what "AI" we had before.
At night when he is awake (he sleeps in our room in a covered cage) he knows not to vocalize anything more "Dear" when my wife gets up - he says nothing when I do this as he is not bonded to me.
When I sit at my computer and put on my headset he switches to using English words and starts having his own Teams meetings.
When the garage door opens or we walk out he the back door he starts saying Goodbye - Seeya later and then does the sound of the creaky outside gate.