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Comment by avree | Hacker News Reader

There were 112 heroes available at that time. It's also worth noting that two of the heros chosen for OpenAI to use, Viper and Sniper, are considered some of the mechanically 'easier' heroes, as they rely primarily on autoattacks to do damage, as oppose to decision-making around when to use spells. Crystal Maiden, Lich, and Necrophos, the other 3 of the 5 OpenAI heros, are similarly considered 'easier' as they have spammable, very forgiving abilities that can be used almost indiscriminately.

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baby steps. Still impressive.

AlphaGo was strait up same.

AlhaStar did have some limits placed to narrow it down for the AI. But was still imperfect information, and wildly complicated.

And those were all 3+ years ago.

Games are a lower resolution representation of the 'real' world. And we haven't seen any slowing down of AI scaling up for more and more complex world views.

Eventually the 'map' will be the 'real', as real as the human brains internal map of reality.

> 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.

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.

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.

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."

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.

> 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.

I apologize in advance if this comes off as critical of you personally, you’re not saying anything that isn’t said constantly and I certainly don’t mean to single you out.

With that said, we’ve got to strangle this meme. ML/AI moves forward in unpredictable fits and starts, it doesn’t follow e.g. Moore’s law in exponential formulation.

When they’re doing research and not PR, researchers talk about “performance” on “tasks”, and define those terms rigorously.

People have been trying to improve performance, as measured by some metric or metrics, on any number of tasks, since at least the 1950s.

Certain periods of time generated breakthrough after breakthrough and a bunch of “well we’ll just scale it up and it’ll be a thinking machine” sentiment amongst the lay or semi-technical public, and similar grandiosity from experts when PR and/or funding are the objective. The world we live in I guess, but not a fire we on HN should be pouring fuel on.

During other periods of time, we’ve hit the effective asymptote on the techniques thus far invented, the scaling dimensions flattened out. Then it’s all “AI was a fad, it’s hype, this is AI Winter”.

There’s no robust consensus on when these summers and winters happen, how long they last, how much performance on one task is amenable to “transfer learning” regarding another task. It seems pretty random, the constant being the PR/funding talk.

The years since AlexNet in 2011, word2vec in 2013, ResNet in 2016, Attention is All you Need in 2017, the GA on GPT-3 series just over a year ago, and countless other interesting things have been wildly fruitful, we’ve been on a hot streak.

This is generally good news! The human race has new capabilities, win! But it’s a nearly impossible claim to defend that modern attention transformers are the final word on this area of endeavor, progress since then has been substantially brute-forced via unprecedented budgets achieved through subsidy of one kind or another, and there will continue to be periods of rapid progress unlocked by key insights, and there will continue to be less explosive periods of progress, and it serves no one with a plan more noble than “cash out while the spice flows” to tee up another collapse in interest, funding, and attention by pulling a Yud: a log scale and a ruler are never the complete toolkit on forecasting novel research.

This stuff is incredibly cool stated as flat, consensus, rigorous science, it’s incredibly exciting to practitioners and laypeople alike without any breathless hyperventilation at all. The story thus far needs no grandiose embellishment to be thrilling.

But the absolute best case in terms of research we currently know about as hyped by those seeking funding would be a nightmare end-state if it landed there (it won’t, but this disaster comes in degrees): right now the off-the wall exhilarating tech demos are so expensive that the public is effectively a spectator. There’s talk of multi-trillion dollar buildouts under complete, utterly unaccountable, ethically dubious control of people who crossed the “yikes is that even legal” line some time ago.

A trillion dollars in 2024 is give or take thirty Manhattan Projects, the idea of handing that kind of scope to people who answer to no one, hold strong minority worldviews, and give the public the finger in print by calling the bribery department “OpenPhilanthropy”?

Who the fuck thinks this isn’t a dystopian horror movie outcome in an already hyper fragile world?

It’s time to squeeze the water out of these bloated, money is no object models, make them run on reasonable power budgets in the hands of John Q. Taxpayer (who along with a bunch of helpless civilians and service men and women, ultimately foots the tab when Nadella or Riyadh write blank checks one way or another), reform copyright law so that the commons isn’t vacuumed up, compressed, and copyrighted, and take a few whacks at shit like Jenson and Lisa Su being literally cousins while partitioning the market and gouging via API lock-in.

The hyper, hyper-elite stand to gain even more immunity from all scrutiny, consequence, accountability, and even bad press if “AGI” turns out to be a mere 1-3 trillion in de facto blood money away from being locked in a vault somewhere.

Literally everyone else stands to find out that slavery isn’t a strong enough word for what this would mean for them.

> Jenson and Lisa Su being literally cousins

I know the word literally doesn't mean anything anymore, but Jenson and Lisa Su are first cousins once removed. Jensen's grandfather and Lisa's great-grandfather are the same person.

https://www.businessinsider.com/nvidia-jensen-huang-amd-lisa...

Their family tree according to Jean Wu, a former Taiwanese journalist : https://www.facebook.com/photo.php?fbid=350633737719840&set=...

I know what the word "literally" means, there's a great Sorkin bit on it [1] that's eerily prescient given that show is like, 15 years old. Via your own citation it's apparently Lisa Su who doesn't know what "literally" means, as she asserts that they are "second cousins", which is literally false according if your second citation says "first cousins, once-removed", which Quora [2] says means they're closely blood-related.

I don't know how their family works, but in mine and most people from my neighborhood, a first-cousin once-removed is fucking family, they're blood. Not being a securities lawyer myself I'm not sure which definition, statue, or regulation would apply here, [3] seems close (and has a creepy rush-job feel about it that smells vaguely like Kushner shit of one kind or another, Feb 2020 on an accelerated basis?).

But whether this squeaks above the line of regulations and laws and whatnot getting midnight "lgtm" stamps in an election year is, I'd argue, substantially missing the point.

When I recently said:

"Now did Lisa Su decide to "concentrate on the supercomputing market with the MI300XYZ" and Jensen decided to "concentrate on AI with Hopper" independently to a degree where the market is perfectly partitioned? Who knows, I certainly don't have proof one way or the other. But if someone made a call being like "I'm thinking of focusing on X but don't really see our differentiation in Y. How's Cathy?", it wouldn't be the fucking first time." [4]

I thought at the time I was kinda pushing it with how flip that sounded, but lo and behold, I was insufficiently cynical.

So when I say that I literally don't understand why anyone is defending this trivially dubious cartel behavior complete with a 55.58% Net Profit Margin in an ostensibly competitive market both directly and indirectly subsidized by the taxpayer (TSMC isn't going to fight off the PLA with their next process node) [5], I think Leona Lansing knows that the public will burn the building down with this shit in it before they let this shit get much ickier.

[1] https://www.youtube.com/watch?v=_1kETLlGn-8

[2] https://www.quora.com/What-is-the-difference-between-a-secon...

[3] https://www.winston.com/en/blogs-and-podcasts/capital-market...

[4] https://news.ycombinator.com/item?id=39362196

[5] https://www.businessinsider.com/nvidia-ai-chip-semiconductor...

So I'm downvoted for over-hyping?

You went from:

Start:

"we’ve got to strangle this meme. ML/AI moves forward in unpredictable fits and starts, it doesn’t follow e.g. Moore’s law in exponential formulation."

At End:

"Who the fuck thinks this isn’t a dystopian horror movie outcome in an already hyper fragile world?"

Isn't that hype? By the end of the post you are doubling down on the over-hyped meme's.

Hey I'd like to apologize for that (and I didn't downvote you FWIW, I've now upvoted both the GP and parent to offset whoever did, your comment didn't merit a downvote).

You're exactly right that my comment veers from high-quality to low-quality linearly with character count: I had an ambient distraction burst into my office in the middle of writing it and I was over-multitasking and failed to clean up the second half within the edit window.

The second half of my comment has important signal but it's too high-noise to be a good comment, as my grandmother used to say: "A barrel of wine and a spoonful of sewage makes a barrel of sewage".

If anyone deserved a downvote it's me, please know that it was unintentional.

Wow. Thank You for that feedback.

It is funny because as you degraded, I agreed more. The dystopian hell scape is on the way. So hard to tell where people are coming from, from posts.

Generally, I think part of problem, we are becoming desensitized to progress.

https://www.youtube.com/watch?v=nUBtKNzoKZ4

> Games are a lower resolution representation of the 'real' world.

Are they though? In the real world you’re playing at many unbounded activities at the same time, with no reward counter

Dude, there are definitely reward feedbacks in the real world.

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