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Comment by Jensson | Hacker News Reader

> And we haven't seen any slowing down of AI scaling up for more and more complex world views.

We absolutely have. We have superhuman performance on Go, we have human expect level performance at Starcraft, and now we get human baby level performance at 3d games. The more complex the task/game the worse the AI gets relative humans it seems, I don't see how this shows the AI scaling up, to me this is all moving horizontally.

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I feel like the dimensional complexity of the problem space is disproportionately larger at each level than the gap in capability over that evolution. Each level of capability you described was science fiction stuff before they were achieved. They’re not in any way horizontal achievements.

> I feel like the dimensional complexity of the problem space is disproportionately larger at each level than the gap in capability over that evolution

But you acknowledge that things slowed down as we moved into more complex domains? Then you agree with my comment, the person I responded to said that things didn't slow down as we moved towards more complex domains, but there is no way you can say they haven't. AlphaStar and AlphaGo quickly competed and could beat top humans, the domains they worked with after that went way slower and still can't compete with top humans.

> Each level of capability you described was science fiction stuff before they were achieved. They’re not in any way horizontal achievements.

The second statement doesn't follow from the first, moving horizontally by applying the same things to a new domain can still unlock massive capabilities that we didn't have before.

I think your definition of ‘slow’ differs from theirs. An analogy to make it clear:

A self driving car 20 years ago could be in a drag race and do 100mph.

Now self driving goes most everywhere, but at the posted speed limit.

I believe the point in 'slowing'/'not-slowing', is that while we are still talking about 'games' and beating humans, the games that are being tackled are getting more complex in each iteration, solving new problems.

And DeepMind did go off and tackle increasingly complex areas in other fields.

Not sure how anyone can argue AI slowed down. Maybe advancements in one particular game slowed down, but was that because they hit a wall? or because they shifted company resources after a game demo.

I'm not sure how you are measuring time, do you think that because AlphaStar was a few years ago, that means AI advancement slowed down? Because there wasn't another breakthrough in AlphaStar? Because they didn't keep going and fully build out every race and unit?

Do you think DeepMind has been throwing resources at Star Craft and just hitting a brick wall?

It was a proof of concept, they beat some humans, and moved on to other things like Protein Folding. Was the Protein Folding not impressive enough to think AI was still advancing?

AI is continuing to advance, because for each iteration it is tackling bigger, more complex, problems.

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The time between each breakthrough does seem to be a down line, less time between each plateau.

Chess : Board with a lot of possible moves, but 'manageable', the AI could just calculate every move.

GO: More possible moves than atoms in the universe or something. So the AI had to use some form of 'intuition', it could no longer brute force calculate every move. (it was only few months after AlphaGO won that they turned the same engine on Chess and it 'learned' from scratch to be a Master in only a few hours.)

SC2: There are no 'moves' it is all real time movement, and most important, there as imperfect information. The AI had to scout and keep track of un-known positions, to remember and anticipate.

Dota: Honestly, I'm not sure what the big breakthrough is for Dota. But quibbling over how many 'hero's the AI had access to seems pedantic. Wasn't this years ago. This isn't a knock on AI, the Dota work was years ago. We're arguing about an AI that is 3+ years old now. I really don't think that you can say AI research slowed because a company stopped throwing money at a game demo.

Protein Folding: Hey, lets stop just focusing on games and do something to help the world.

Poker: Wasn't Poker also conquered in this time frame, in last 2 years? Showing ability to bluff?

3D Virtual Environment: Was in discussion in another thread where everyone's main argument was AI isn't 'embodied' in the 'world', doesn't 'live' in the 'world'. And boom, same day, another breakthrough covering that. Giving machine what we would call 'vision', to understand objects in the world.

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Now slap this into a robot, give it a gun, and tell it the world is a 3d game.

LOL.

"We haven't had a miracle in the last 6 months, oh no, AI advancement is slowing down."

No, I don’t agree. It appears to slow down simply because the complexity is growing so fast that even though we are accelerating our ability the relative sophistication of play is worse than the prior. But we aren’t slowing down in the least, we are tackling extremely difficult challenges with advances that are accelerating rapidly in their capability.

> Each level of capability you described was science fiction stuff before they were achieved

Machine learning has been a thing almost since discrete electrical circuits have existed, just wildly impractical to make generalizable versions of until recently

https://en.wikipedia.org/wiki/Perceptron

It's not about complexity in the sense we're familiar with. Minecraft isn't more complex than Starcraft from a human intelligence POV. Kids can play Minecraft. It's about the difficulty of fitting it into current methods. We can solve Go because we have a symbolic-neuro planning approach (monte carlo tree search over an evaluation net) that almost perfectly models the correct way to reason about the game at an expert level. This is an incredibly strong inductive bias that gives AlphaGo an unfair head start over AlphaStar. Starcraft is harder to solve because we don't have such a symbolic approach figured out, and we need the net to learn visual representations and connect those visual representations to actions, and we have a continuous action space. So good luck with that! Minecraft is even harder to model because the rewards are so sparse.

To your point, I believe AlphaStar had access to both the visual input and an API for most actions in the game.

I believe it says more about humans than about AI.

Humans are evolved in 3D world. Our ancestors didn't decide who can have food or sex through chess competitions. The human brains have been "trained" and optimized in 3D world intensively.

I feel like this isn't a fair characterization. This is a "general" agent where the others are single environment.

Yes, this is more complex, that is cool. But we did see the slowing down as otherwise we would have maintained superhuman performance at every step, AlphaGo and Alphastar saw much quicker progress past human skill levels.

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