Comment by canoebuilder | Hacker News Reader

Comment by canoebuilder | Hacker News Reader

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I think for most people, the research interest in games of various sorts, is not simply a desire for a better and better game contraption, a better mousetrap. But rather the thinking is, "playing games takes intelligence, what can we learn about intelligence by building machines that play games?"

Most games are also closed systems, and conveniently grokkable systems, with enumerable search spaces. Which gives us easily produceable measures of the contraptions' abilities.

Whether this is the most effective path to understanding deeper questions about intelligence is an open question.

But I don't think it's fair to say that deeper questions and problems are being foregone simply to play games.

I think most 'games researchers' are pursuing these paths because they themselves and no one else has put forth any other suggestion that makes them think, "hmm, that's a really good idea, that seems like it might be viable and there is probably something interesting we could learn from it."

Do you have any suggestions?

This is so true, I can't understand why people miss this. The games are just games. It's intelligence that is the goal.

And comparing Alpha Go Zero against those "other chess programs that existed for 30 years" is exactly missing the point also. Those programs were not constructed with zero-knowledge. They were carefully crafted by human players to achieve the result. Are we also going to count in all the brain processing power and the time spent by those researchers to learn to play chess? Alpha Go Zero did not need any of that, besides the knowledge about the basic rules of the game. Who compare compute requirements for 2 programs that have fundamentally different goals and achievements? One is carefully crafted by human intervention. The other one learns a new game without prior knowledge...

It shows something about the game, but it's clear that humans don't learn in the way that alpha zero does, do i don't think that alpha zero illuminated any aspect of human intelligence.

I think that fundamentally the goal of research is not necessarily human-like intelligence, just any high-level general intelligence. It's just that the human brain (and the rest of the body) has been a great example of an intelligent entity which we could source of a lot inspiration from. Whether the final result will share a the technical and structural similarity (and how much) to a human, the future will tell.

In principle you are right. In practice we will see. My bet is that attempts that focused on the human model will bear more fruit in the medium term because we have huge capability for observation at scale now which is v. exciting. Obviously ethics permitting!

Not sure if I am reading you correctly but to me you basically are saying "we have no idea but we believe that one day it will make sense".

Sounds more like religion and less like science to me.

I guess we could argue until the end of the world that no intelligence will emerge from more and more clever ways of brute-forcing your way out of problems in a finite space with perfect information. But that's what I think.

But humans could learn in the same way that AlphaZero does. We have the same resources and the same capabilities, just running on million-year-old hardware. Humans might not be able to replicate the performance of AlphaZero, but that does not mean it is useless in the study of intelligence.

I totally agree with you and share your confusion.

On the topic of the different algorithmic approaches, I find it so fascinating how different these two approaches actually end up looking when analyzed by a professional commentator. When you watch the new style with a chess commentator, it feels a lot like listening to the analysis of a human game. The algorithm has very clearly captured strategic concepts in its neural network. Meanwhile, with older chess engines there is a tendency to get to positions where the computer clearly doesn't know what its doing. The game reaches a strategic point and the things its supposed to do are beyond the horizon of moves it can computer by brute force. So it plays stupid. These are the positions that, even now, human players can beat better than human old style chess engines at.

The thing is that you can learn new moves/strategies that were never thought about before in these games but you still doesn't understand anything about intelligence at all.

The problem is that outside perfect information games, most areas where intelligence is required have few obvious routes to allow the computer to learn by perfectly simulating strategies and potential outcomes. Cases where "intelligence" is required typically entail handling human approximations of a lot of unknown and barely known possibilities with an inadequate dataset, and advances in approaches to perfect information games which can be entirely simulated by a machine knowing the ruleset (and possibly actually perturbed by adding inputs of human approaches to the problem) might be at best orthogonal to that particular goal. One of the takeaways from AlphaGo Zero massively outperforming AlphaGo is that even very carefully designed training sets for a problem fairly well understood by humans might actually retard system performance...

A favour work by Rodney Brooks - "elephants don't play chess"

https://people.csail.mit.edu/brooks/papers/elephants.pdf

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