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Seems coincide with this that was on HN: http://www.incompleteideas.net/IncIdeas/BitterLesson.html


> The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.

It’s nice when an author includes a sentence up top that betrays their standpoint so that I can stop reading. I’m sure this person is very nice and has lots of stuff to say, but this is the same old Scruffy v. Neat fight, except now the former side thinks that they’re empirically completely right. Which doesn’t even make sense, they’re not mutually exclusive claims, and to say that the result of 70 years of expert systems is any kind of failure is just revisionist.

For the same reason, I don’t read many papers about Realism vs Idealism, Nature vs Nurture, etc


I recommend that you continue. It's a very short and highly influential piece by a guy at the top of his field, you'll take a thing or two from the one-minute read even if you agree with nothing he says.


Why thanks for the suggestion, that’s very kind. I just read it - in fact I think I read this early on in my research on scruffies v neats.

My takeaway now is the same, sadly. It’s not so much that I disagree with his premises that I find his whole attitude and conclusion to be a preposterous artifact of ego inflation after helping found a line of research that was much more productive than people thought it would be. I get it, that’s very exciting, but I need way more evidence than that to completely abandon self-conscious structured reasoning in my conception of a good AGI, much less the human mind. Like this:

  This is a big lesson. As a field, we still have not thoroughly learned it, as we are continuing to make the same kind of mistakes.
This is just arrogance. You don’t see this among philosophers or social scientists, who recognize that rhetoric is more than the cherry on top of science, and that this sort of confidence is dangerous. To see “designing things by hand” as a categorical “mistake” is just… that’s a hot take.

But either way we’re all on the same side. Despite my harsh words I’m glad he helped pave the way LLMs, which are the biggest unexpected breakthrough in our lifetimes IMO. Which understandably makes people confident


I love story in spite of the article above.

Speech synthesis also was attempted as modeling of human biology: computer modeling of throats, vocal cords, how the air is going through mouth.

In the end computational power also won. No need of all of that.


And then there's the Voder (1939): https://www.youtube.com/watch?v=TsdOej_nC1M


Maybe we didn’t need to, but I’m glad someone did https://m.youtube.com/watch?v=qobhDJ_vEOc


The article is talking about deep learning winning, ie neural networks. Surely modelling of human biology is part of that?


Neural nets do not model human biology, they are models which are inspired by human biology.


Maybe emergent, to some extent, but not explicit.




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