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I've been reading a lot of old AI papers lately -- ranging from 1950 to 2010. It's interesting how contemporary a lot of these still feel to this day. The fundamental questions are still the same, and we still don't have any solid answers.
The candidate solutions have evolved. But the complaints about their limitations are still pretty much the same today as in 1997 or 1973.
There's nothing new under the sun,just rediscovery and repackaging. Sometimes a seed idea waiting for germination with right time
The models of neurons we use in AI haven't changed over that timeframe. But we understand biological neurons a lot better now. I've written a short book about how real neurons work, and drafted some new algorithms based on that understanding. It's free on http://www.corticalcircuitry.com/
Thanks for sharing! Will read your ideas
Watch the Lighthill / McCarthy debate in 1973 or so. Amazing how little has changed...
Maybe AI researchers need first to understand human-intelligence and then implement it. I could be wrong but it seems to me that's not what they've been doing. I fear they decided long ago that the above is not possible and thereafter have been doing something quite different.
Everyone who is serious about human-level AI is also interested in understanding human intelligence. And there is in fact a lot of inspiration that can be drawn from psychology and neuroscience (perhaps more from psychology than neuroscience).
The truth of the matter is that progress has been made not because we know anything more, but because we have more hardware to throw at it. But, I do think we're acutely aware of this and I feel it gives us a sense of foreboding that we aren't breaking the wall.
Love that papers <10 years old are now old, insofar as to be lumped in with AI of the 50s :p
I like this though. There’s still a lot to learn. It’s early days. Lots of low-hanging fruit. No “standard model” (not that that is final but it is pretty darn solid theory)
May be we need a new math to tackle this challenge
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