4 ms·

This is really cool! Plus I like the ramification that the 'hard bits' of playing a game like tetris might be contained in the vision/recognition phase. My onl

This is really cool! Plus I like the ramification that the 'hard bits' of playing a game like tetris might be contained in the vision/recognition phase. My only gotcha is with calling DFS 'classical AI'. I get they have a fancy hand-tuned cost function, but if that is AI, then I guess all approximate optimization algorithms are AI? Maybe in 60s that kinda of classification would have been fine, but not now, right? Either way, that is a minor nerd-gripe with the messaging and does not, at all, detract from an otherwise rad project.

Retric 2y ago
> Maybe in 60s that kinda of classification would have been fine, but not now, right? Thus classical, as a reference to the idea that: As Soon As It Works, No One Calls It AI Anymore: https://quoteinvestigator.com/2024/06/20/not-ai/ https://quoteinvestigator.com/2024/06/20/not-ai/ It’s kind of surprising to me we still call feedforward neural networks AI.
- soganess 2y ago
  I can totally see where you are coming from and I like the wink and nod to the article! I guess...hrm... if someone asked me what classical AI was today... I would answer with something like A*, decision trees, or Markov model-style algorithms (but never DFS). All of these have 'worked' for a long time but still feel like classical (ie no back-prop) AI. To your second point, I'd be the first to admit I'm no expert (I don't do any ML professionally). In my limited understanding, it feels odd to separate MLPs from 'modern' AI. It's all still back-prop, yeah? I get that universal approximators and (idealized) Turing machine approximators like LSTMs are old hat. But Attention-based (key, value) query models just seem like the next step on the same ladder of "increasingly complex computing paradigms we've figured out a way to back-prop through." Not something uniquely separate. First, we could do functions, then we could do Turing machines, and now we can do data queries. But again, I'm probably just showing how green I am. I would, time permitting, honestly appreciate clarification/correction.
robertclaus 2y ago
A bit into the article I reframed "AI" to mean "bot".

Retric 2y ago

> Maybe in 60s that kinda of classification would have been fine, but not now, right? Thus classical, as a reference to the idea that: As Soon As It Works, No One Calls It AI Anymore: https://quoteinvestigator.com/2024/06/20/not-ai/ https://quoteinvestigator.com/2024/06/20/not-ai/ It’s kind of surprising to me we still call feedforward neural networks AI.

soganess 2y ago
I can totally see where you are coming from and I like the wink and nod to the article! I guess...hrm... if someone asked me what classical AI was today... I would answer with something like A*, decision trees, or Markov model-style algorithms (but never DFS). All of these have 'worked' for a long time but still feel like classical (ie no back-prop) AI. To your second point, I'd be the first to admit I'm no expert (I don't do any ML professionally). In my limited understanding, it feels odd to separate MLPs from 'modern' AI. It's all still back-prop, yeah? I get that universal approximators and (idealized) Turing machine approximators like LSTMs are old hat. But Attention-based (key, value) query models just seem like the next step on the same ladder of "increasingly complex computing paradigms we've figured out a way to back-prop through." Not something uniquely separate. First, we could do functions, then we could do Turing machines, and now we can do data queries. But again, I'm probably just showing how green I am. I would, time permitting, honestly appreciate clarification/correction.

robertclaus 2y ago

A bit into the article I reframed "AI" to mean "bot".