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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 th

• FrustratedMonky 3y ago
  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.
  • Jensson 3y ago
    > 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.
    • fnordpiglet 3y ago
      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.
      • Jensson 3y ago
        > 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.
        • persolb 3y ago
          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.
        • FrustratedMonky 3y ago
          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. ---------- 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. ------------ 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."
    • XenophileJKO 3y ago
      I feel like this isn't a fair characterization. This is a "general" agent where the others are single environment.
      • Jensson 3y ago
        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.
    • hackerlight 3y ago
      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.
      • yldedly 3y ago
        To your point, I believe AlphaStar had access to both the visual input and an API for most actions in the game.
    • raincole 3y ago
      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.
  • benreesman 3y ago
    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.
  • throwaway2562 3y ago
    > 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
    • FrustratedMonky 3y ago
      Dude, there are definitely reward feedbacks in the real world.