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My classmate wrote back-propagating neural nets on his Amiga 1200 in 1994. We had pretty high hopes back then. I and I think many others sort of assumed we wou

My classmate wrote back-propagating neural nets on his Amiga 1200 in 1994. We had pretty high hopes back then. I and I think many others sort of assumed we would have had General AI by now. Or at least going in that direction. Another classmate said in 1992 that scientists had simulated the neural net equivalent to a worm or something. This topic came up re the neural net featured in the movie Terminator 2. The current use of "neural nets" are both under- and overwhelming. But definitely over-whelmingly boring, to me. It's good, don't get me wrong. But from my viewpoint, it looks like we have found a trick, and now try to apply this same trick to as many new fields as possible.

tomrod 6y ago
> now try to apply this same trick to as many new fields as possible. Right, this is normal and expected. While scientific growth looks like an exponential curve, the lateral application path is equally important to maintaining that curve! We may get to AGI one day (I have my doubts) but I have to remind myself that all theses clunky things we see today are v1 -- and virtually everything can improve!
- jacobush 6y ago
  It is normal, but it's kind of disappointing that from my (uneducated) vantage point, we don't seem to be at all closer to AGI than we were in 1994. It's like the difference between Kitty Hawk and a Boeing airliner. Yes, they are impressive feats of engineering, and the massive training hours tell the story. But the Boeing Airliner is still less clever than a fruit fly, to merge the metaphors. Edit: to expand - it seems to get something as smart as a dog is almost the same as something as smart as a human. 1994 neural net 2020 GPT-3 . . . . . unknown numbers of dots... . . . dog human