4 ms·

All the efforts to get computers to play chess mostly like humans do were made to look insufficient by a large enough neural network that took zero knowledge of

All the efforts to get computers to play chess mostly like humans do were made to look insufficient by a large enough neural network that took zero knowledge of human position evaluation, which just played against itself an outrageous number of games. The plane is in many ways lesser than birds, and we barely understood aerodynamics when the wright brothers gave us a working plane: While the bird is efficient, we had a whole lot more thrust. Our planes are far better than they were back then, but not because we understand bird better, but because we focused on efficiency of the simpler designs we could build. The Formula 1 car doesn't come from deep understanding of the efficient, graceful running movements of the cheetah. Our results in medicine have been far and ahead of our understanding of biochemistry, DNA, and in general, how the human body works. The discovery of penicilin didn't require a lot of understanding: It required luck. Ozempic and Viagra didn't come from deep understanding of the human body: Resarchers were looking for one thing, and ended up with something useful that was straight out unintended. We might be able to build AGI with what we know of brains, we might not. But either way, it's not because we need to understand the human mind better: Do we have enough compute for a very crude systems that we know how to build to overcome our relative lack of understanding? So if you ask me, what is the height of hubris is to think that working engineering solutions have to come from deep understanding of how nature solves the problem. The only reasonable bet, given the massive improvements in AI in the last decade, is to assume that the error bars of any prediction here are just huge. Will we get stuck, the way self-driving cars seem to have gotten stuck for a bit? Possibly. Will be go from a stochastic parrot into something better than humans, the same way that Go AIs went from pretty weak to better than any human in the blink of an eye? I'd not discount it either. I expect to be surprised either way.

galaxyLogic 3y ago
> height of hubris is to think that working engineering solutions have to come from deep understanding of how nature solves the problem. It is basically understood how "nature solves the problem". It does that by evolution. And what is machine learning and neural networks but artificial evolution?
- valval 3y ago
  Nothing of the sort -- it's best characterized as learning, like it says in the name.
  - pixl97 3y ago
    Something like a GAN is closer to what we'd consider evolution.
  - throwuwu 3y ago
    You only save the changes that improve the loss values no? Those are the survivors whose progeny go on to the next iteration.
- TeMPOraL 3y ago
  > It is basically understood how "nature solves the problem". It does that by evolution. Also known as throwing shit at a wall to see what sticks. Evolution is dumb, but it brute-forces its way down the optimization gradients by sheer fucking scale of having turned the entire surface of this planet into tiny, imperfectly self-replicating machines.
PeterStuer 3y ago
In AI research there are different branches with different goals and motivations. You have applied research, where the goal is to engineer systems that are better at soving certain needs. You also have scientific endeavours, usually interdisciplinary, typically crossed with biology, psychology and philosophy, where AI models are created and used as models of understanding human or animal intelligence. The former is where as you describe where better understanding of biology might help but is not a prerequisite for progress, but in the latter it is not just needed but the goal. Now I know this is a bit of a caricature as both of these disciplines are in practice poorly deliniated and often intermixed. It's easy to find hubristic examples to mock on both sides, but there's value and brilliance to be found in each respectively as well.
mike_hearn 3y ago
> All the efforts to get computers to play chess You say Go later so I think you know this and it was just a thinko, but chess fell easily to pre-neural techniques that are fully logic based and understandable. It was Go that required a big neural network trained through self play.
- bananaflag 3y ago
  I think what they meant was that even logic-based superhuman chess was overtaken by AlphaZero.

galaxyLogic 3y ago

> height of hubris is to think that working engineering solutions have to come from deep understanding of how nature solves the problem. It is basically understood how "nature solves the problem". It does that by evolution. And what is machine learning and neural networks but artificial evolution?

valval 3y ago
Nothing of the sort -- it's best characterized as learning, like it says in the name.
- pixl97 3y ago
  Something like a GAN is closer to what we'd consider evolution.
- throwuwu 3y ago
  You only save the changes that improve the loss values no? Those are the survivors whose progeny go on to the next iteration.
TeMPOraL 3y ago
> It is basically understood how "nature solves the problem". It does that by evolution. Also known as throwing shit at a wall to see what sticks. Evolution is dumb, but it brute-forces its way down the optimization gradients by sheer fucking scale of having turned the entire surface of this planet into tiny, imperfectly self-replicating machines.

PeterStuer 3y ago

In AI research there are different branches with different goals and motivations. You have applied research, where the goal is to engineer systems that are better at soving certain needs. You also have scientific endeavours, usually interdisciplinary, typically crossed with biology, psychology and philosophy, where AI models are created and used as models of understanding human or animal intelligence. The former is where as you describe where better understanding of biology might help but is not a prerequisite for progress, but in the latter it is not just needed but the goal. Now I know this is a bit of a caricature as both of these disciplines are in practice poorly deliniated and often intermixed. It's easy to find hubristic examples to mock on both sides, but there's value and brilliance to be found in each respectively as well.

mike_hearn 3y ago

> All the efforts to get computers to play chess You say Go later so I think you know this and it was just a thinko, but chess fell easily to pre-neural techniques that are fully logic based and understandable. It was Go that required a big neural network trained through self play.

bananaflag 3y ago
I think what they meant was that even logic-based superhuman chess was overtaken by AlphaZero.