[visarga]

> Embodiment seems to be a branch with low hanging fruit, when it comes to advancing AGI.

If it were "low hanging" it would have been picked already. Reinforcement learning with AI agents is hard, especially in a dynamic environment with many types of objects.

I think the path towards AGI is to do simulation coupled with deep learning. Simulation would open the door to predicting non-trivial effects that cannot be learned by example because they are so rare that there are no training examples. We can generate artificial training examples to cover all the rare cases.

[rwnspace]

I'm sorry if 'low-hanging' comes off as disrespectful - I'm just guessing that aspects of embodiment, once understood, will be capable of fairly trivial description and reap large consecutive rewards. I remember your u/n from other posts, we seem to have similar interests but you are vastly more educated in the engineering of AI. What's your background, if you don't mind sharing here?

I am suspicious non-contingent aspects to cognition remain that simulation and deep learning don't necessarily grant, though they might well be sufficient. I'm not smart enough to be sure, and I'm stretching for a description: a child self-reared to adulthood in the wild won't display what we usually consider essential facets for 'humanlike' levels of intelligence or competence. We're hardly trying to build a caveman.

They lack whatever is crucial in socialisation -- the ability to make subtle differentiations between other agents' actions and motivations seems to endow self-awareness, and abstractions for successfully handling novel objects and ordering perception relevance. Successful generality to our degree seems to be better 'outsourced' rather than hard-coded into solo agents, at least in the natural examples. Though I understand that's not necessary, perhaps there are good reasons for it. I feel like the first AGI will actually look a lot more like "multiple similarly 'perspected' AIs interacting with one another leads to each carrying the G in AGI". Essentially I'm suggesting it's hard to have generality and relevance to our proficiency (or better) without a 'culture'.

What I'm thinking seems to boil down to inserting some of Piaget's ideas into the philosophy of AI, which might be a bit much, and I'm open to charges of bullshit.

[visarga]

I am just a hobbyist in machine learning. The current situation in AI is that we can only do limited aspects of perception: like vision and hearing, at a superficial level. We can recognize objects but we can't recognize relations between objects nearly as well. There is no global scene understanding yet. With text, we can do syntax and translation, but we can't do reasoning except on trivial cases. There is no power of abstraction yet, of transferring knowledge between domains, which is essential for advanced intelligence.

So, before we have an embodied agent, we need to solve the reasoning and abstraction part, and my money is on graph signal processing (a kind of neural nets) and simulators (also implemented as neural nets). We need to move from simple object recognition to reasoning and simulation on graphs of objects and relations.

[cscurmudgeon]

> We can generate artificial training examples to cover all the rare cases.

That means we have a model to start with. Chicken & egg.