[aoeusnth1]

I think the author is projecting significantly when he says the goal of AI researchers is to understand and replicate how humans think. If you start from that wrong assumption of course it looks silly for them to be doing anything other than neuroscience research, the author's field.

It's like saying the stockfish developers should stop researching mixed NN and search methods because they don't understand how humans play chess yet.

[Wonderfall]

This is mainly a misunderstanding due to the way I phrased it. This is what I think. I know for a fact that is the case for other AI researchers having watched many conferences - "all of them" is not what I meant (I wrote "many other") and we certainly need people to approach problems from different perspectives and backgrounds, since they will benefit from each other in the end. Not going to lie I'm a bit disappointed to see these kind of comments.

[aoeusnth1]

Fair enough about your motivation, however you also to further in saying that the best way to achieve and exceed human intelligence is to first understand it. That didn't pan out for chess, it hasn't contributed much to our current SOTA approaches to many other problems where LLMs are king, and I'm not sure why neuroscientists are so confident in some future where their field is the key to intelligence when their track record of breakthroughs is so poor.

[Wonderfall]

I admit my phrasing was poor there, and I got too excited. I will clarify since I don't really disagree with you or what others said (claiming it's the best way is an overstatement).

Well, one could say that neural networks pioneers modeled their ideas on simplified brain structures representations. Modern neural networks have little in common with an actual biological brain, however, the inspiration remains there (even for modern NNs like CNNs). I recall the intent was there too, originally: providing a framework to study biological cognition in the 50s. Then it evolved to become a new paradigm in computer science so that we have programs able to learn and adapt for problems that are formally too complicated for deterministic solutions.