> I don't think biological precedent is the only or even most valuable heuristic for deciding where to research intelligence...
Good point, we wouldn't have AlphaZero now if we only relied on biological inspiration. Nature hardly ever performs Monte Carlo Tree Search (though I'm not sure this is entirely true, see slime mold searching for food: https://thumbs.gfycat.com/IdealisticThirdCalf-size_restricte...).
I don't understand this fixation on symbolic reasoning. Do any other animals practice this? If the answer is no, then it is probably not the most important milestone to AGI or at least not the one we should be currently aiming for. Right now we can not replicate the cognition of a mouse. Feels like we want to go to Mars before figuring out how to build a rocket.
If this was true, wouldn't everybody be programming in assembly? E.g. developing a website in assembly would keep more people occupied than HTML/JS/CSS.
Interestingly though the failures of image generation are often of the missing narrative kind: eg. windowless bedrooms, eyes/ears at wrong places on animals. It feels like that deep learning is good at local but fails at global coherence.
h3h3Productions became famous by exposing stupid trends on YouTube (e.g. social experiments) but they also cover current events sometimes: https://www.youtube.com/user/h3h3Productions
I think this is a very good point. Years ago people were worried about gradient descent getting stuck at a local minima, plausibly because this problem is very obvious in a 3 dimensional space. In higher dimensions however this problem seems to go away more or less and a lot of worrying about the issue seems to be the result of lower dimensional intuitions wrongly extrapolated to higher dimensions.
> And so do chimpanzees. Evolution must have provided us with something additional, which would be our rather more developed cognitive abilities to employ abstract reasoning and metaphor.
Note that the current capabilities of AI systems are nowhere near the general capabilities of a chimpanzee. It seems reasonable to assume that the hard task is to come up with the prior of the mammalian brain. The "easy" part is to discover the parameter space on top of that prior, be it chimpanzee or human.
You can define 'intelligent life' however you want. No matter how high you set the bar though, an algorithm that reproduces the intelligence of the average human and can be run in parallel at speeds unhinged by biological constraints would provide immense power, wouldn't it?
