5 ms·

The caveat being that the scope of the game was significantly pared down for the sake of the AI. Specifically the team compositions were pre-determined, meaning

The caveat being that the scope of the game was significantly pared down for the sake of the AI. Specifically the team compositions were pre-determined, meaning the AI only had to understand 10 heroes in two specific arrangements of 5, when there's normally >100* heroes which can be chosen in any permutation, and certain game mechanics were also declared off-limits for the human players because the AI wasn't able to understand them. Beating pros in that subset of the game was an impressive achievement but there was a huge gulf between what they did and doing it in the full version of the game, which they quickly gave up on trying to do after collecting their marketing trophy of beating Dota pros in something resembling Dota. * I'm not sure exactly how many heroes there were at the time, it was less than the 124 there are today, but it was certainly a lot more than 10.

avree 3y ago
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.
- 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.
        3y ago
  - 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.
msp26 3y ago
One of the constraints ended up being implemented into the main game: separate couriers for each player. But generally, agree with your point. But it's very cool how the OpenAI matches ended up making mid players reevaluate how they used consumable regen.
- nindalf 3y ago
  Explanation - health potions cost a small amount of in game money and have to be ferried by a courier to the player. Most pros (and good players copying the pros) didn’t do this because it wasn’t considered cost effective. They would rather save up for a larger purchase. Until they repeatedly lost to OpenAI bots spending absurd amounts of money on health potions. The AI didn't follow "best practice" because it wasn't trained on human games, found a better way and that was quickly adopted by all, becoming the new best practice.
  - murderfs 3y ago
    League of Legends players discovered this like 15 years ago (the "13 health pot start"), I wonder why this didn't cross over. I suppose the player bases don't actually intersect very much?
    - evandale 3y ago
      I think maybe the games are a bit different and it wasn't viable? I was pretty into the original WC3 Dota and starting with tangos for healing was a pretty popular strategy for supports and solo lane players. caveat: my Dota 2 knowledge is lacking because I haven't followed the game for about a decade now and I have essentially 0 experience with League.
    - cptn_badass 3y ago
      It's mostly because it's was a different scarce resource at that time that was seen as non-optimal use by the players, the courier. It can ferry item to you, in a normal game there was only one of them for your whole team, which mean using it would take that ability away for your teammate during the ferry time. One constraint to those showmatches at the time was that every heroes had their own courier, and player at that point were not accustomed to using it for "low value" travel, unlike the AI that was using it liberally. In a later patch, the 1 courier per hero feature was added, and now pro players are much better at managing it, but at that time it was truly a heavy opportunity cost.
Etherlord87 3y ago
Very interesting! Shows how much AI is over-hyped (even though, as you say, it was very impressive anyway). It was even worse in case of Starcraft 2, where the AI had a much wider view than humans, and while the AI was supposed to show its strategic superiority, by limiting the APM (actions per minute), the limit was still very high, inspired by the max APM achieved by humans - whereas this max is achieved only for a very short period of time (a single minute), and consists mostly of insignificant click spam (had APM been limited to half that for humans, the effect would probably be negligible, and very minor for a quarter...). So as a result the AI would win by being able to micro-manage more units, rather than having a better strategy. But again, it was very impressive anyway.
- FrustratedMonky 3y ago
  How do you have 'over-hyped' and 'very impressive' in same sentence. Which is it? I think you are not giving AlphaStar the correct spin. They came back and changed it to only have the same viewport as the human, it could not see all of its units simultaneously, it had to move the cameras like a human. BUT importantly, it NEVER had perfect information. It could only see exactly the same as the human, just at one point they were letting it see the whole map without changing the camera, but it still could not see enemy units without sending a probe. And. Little unsure on what the argument about APM is saying. It was slowed down to match the human speed, but somehow that makes it less impressive? That is just making it more 'human-like'. Kind of like people today want to put guardrails on AI, but if it was unleashed, it beats them easily. That isn't a knock on the AI. The AI would still have to think about every move, and form a strategy. They slowed it down to human level inputs, handicapped it, to make it playable to a human. But to your point, if AI could make 400 APM and human had 400 APM (both limited to same), then that is better measure about the 'thought' behind each individual move. I still remember watching one match where the human was winning, the AI was down, and the AI really did fight back very aggressively from a loosing position, like a human. by expanding and adapting, and it looked very scary.
  - Etherlord87 3y ago
    > How do you have 'over-hyped' and 'very impressive' in same sentence. Which is it? I'm stunned; how would you think they are contradictory? Imagine a transportation that moves with the speed of 1000 km/h. Very impressive, right? Now imagine media everywhere say it moves with the speed of light. Wouldn't this be over-hyping? > BUT importantly, it NEVER had perfect information. It could only see exactly the same as the human Maybe we're speaking about different events... In the one I'm commenting on, the AI had some zoom-out, I think 2x (meaning it would see 4 times more at once). Yes it had fog of war, but a zoom out like this is a very significant advantage. > And. Little unsure on what the argument about APM is saying. It was slowed down to match the human speed, No it wasn't, not exactly. Imagine that you measure a human racer speed in km/minute, every minute. Then you take the highest measured "average per minute", and program AI to move with that speed at all times. Then you praise AI for its pathfinding algorithm, because using that speed, it beats the human racers. Yes, if a human racer has to slow down, because e.g. the human is unable to avoid obstacles at maximum speed, it does make the AI being able to move faster, impressive. But few people here would be impressed by a high reflex of a computer, because we all are used to the fact computers can react much faster than humans. It is misleading, however, to allow AI to move faster, and then give it the "spin", as you say, that the AI has won because it was smart, as opposed to being fast. BTW, I think the AI was either only using one race, or was playing only against one race. This one thing was actually mentioned in the event (once). The APM was mentioned too, I think, but the nuance I describe unfortunately wasn't mentioned. It makes me sad, because as I said, it is a very impressive technology. But it's hard to fully appreciate something when it is so blatantly over-hyped and when you see so many people around you being mislead and praising AI for achievements that it didn't exactly accomplish.
chse_cake 3y ago
That's a fair assessment but it was also 6 years ago. Back then transformers had recently come out. Tricks to do DL at scale were still brewing. Even achieving what they did for 10 heroes showed DL could work in "non-deterministic-ish" problem settings. The more interesting question is: can we train a Dota model that plays with all 124 heroes today?

avree 3y ago

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.

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.
      - 3y ago
- 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.

msp26 3y ago

One of the constraints ended up being implemented into the main game: separate couriers for each player. But generally, agree with your point. But it's very cool how the OpenAI matches ended up making mid players reevaluate how they used consumable regen.

nindalf 3y ago
Explanation - health potions cost a small amount of in game money and have to be ferried by a courier to the player. Most pros (and good players copying the pros) didn’t do this because it wasn’t considered cost effective. They would rather save up for a larger purchase. Until they repeatedly lost to OpenAI bots spending absurd amounts of money on health potions. The AI didn't follow "best practice" because it wasn't trained on human games, found a better way and that was quickly adopted by all, becoming the new best practice.
- murderfs 3y ago
  League of Legends players discovered this like 15 years ago (the "13 health pot start"), I wonder why this didn't cross over. I suppose the player bases don't actually intersect very much?
  - evandale 3y ago
    I think maybe the games are a bit different and it wasn't viable? I was pretty into the original WC3 Dota and starting with tangos for healing was a pretty popular strategy for supports and solo lane players. caveat: my Dota 2 knowledge is lacking because I haven't followed the game for about a decade now and I have essentially 0 experience with League.
  - cptn_badass 3y ago
    It's mostly because it's was a different scarce resource at that time that was seen as non-optimal use by the players, the courier. It can ferry item to you, in a normal game there was only one of them for your whole team, which mean using it would take that ability away for your teammate during the ferry time. One constraint to those showmatches at the time was that every heroes had their own courier, and player at that point were not accustomed to using it for "low value" travel, unlike the AI that was using it liberally. In a later patch, the 1 courier per hero feature was added, and now pro players are much better at managing it, but at that time it was truly a heavy opportunity cost.

Etherlord87 3y ago

Very interesting! Shows how much AI is over-hyped (even though, as you say, it was very impressive anyway). It was even worse in case of Starcraft 2, where the AI had a much wider view than humans, and while the AI was supposed to show its strategic superiority, by limiting the APM (actions per minute), the limit was still very high, inspired by the max APM achieved by humans - whereas this max is achieved only for a very short period of time (a single minute), and consists mostly of insignificant click spam (had APM been limited to half that for humans, the effect would probably be negligible, and very minor for a quarter...). So as a result the AI would win by being able to micro-manage more units, rather than having a better strategy. But again, it was very impressive anyway.

FrustratedMonky 3y ago
How do you have 'over-hyped' and 'very impressive' in same sentence. Which is it? I think you are not giving AlphaStar the correct spin. They came back and changed it to only have the same viewport as the human, it could not see all of its units simultaneously, it had to move the cameras like a human. BUT importantly, it NEVER had perfect information. It could only see exactly the same as the human, just at one point they were letting it see the whole map without changing the camera, but it still could not see enemy units without sending a probe. And. Little unsure on what the argument about APM is saying. It was slowed down to match the human speed, but somehow that makes it less impressive? That is just making it more 'human-like'. Kind of like people today want to put guardrails on AI, but if it was unleashed, it beats them easily. That isn't a knock on the AI. The AI would still have to think about every move, and form a strategy. They slowed it down to human level inputs, handicapped it, to make it playable to a human. But to your point, if AI could make 400 APM and human had 400 APM (both limited to same), then that is better measure about the 'thought' behind each individual move. I still remember watching one match where the human was winning, the AI was down, and the AI really did fight back very aggressively from a loosing position, like a human. by expanding and adapting, and it looked very scary.
- Etherlord87 3y ago
  > How do you have 'over-hyped' and 'very impressive' in same sentence. Which is it? I'm stunned; how would you think they are contradictory? Imagine a transportation that moves with the speed of 1000 km/h. Very impressive, right? Now imagine media everywhere say it moves with the speed of light. Wouldn't this be over-hyping? > BUT importantly, it NEVER had perfect information. It could only see exactly the same as the human Maybe we're speaking about different events... In the one I'm commenting on, the AI had some zoom-out, I think 2x (meaning it would see 4 times more at once). Yes it had fog of war, but a zoom out like this is a very significant advantage. > And. Little unsure on what the argument about APM is saying. It was slowed down to match the human speed, No it wasn't, not exactly. Imagine that you measure a human racer speed in km/minute, every minute. Then you take the highest measured "average per minute", and program AI to move with that speed at all times. Then you praise AI for its pathfinding algorithm, because using that speed, it beats the human racers. Yes, if a human racer has to slow down, because e.g. the human is unable to avoid obstacles at maximum speed, it does make the AI being able to move faster, impressive. But few people here would be impressed by a high reflex of a computer, because we all are used to the fact computers can react much faster than humans. It is misleading, however, to allow AI to move faster, and then give it the "spin", as you say, that the AI has won because it was smart, as opposed to being fast. BTW, I think the AI was either only using one race, or was playing only against one race. This one thing was actually mentioned in the event (once). The APM was mentioned too, I think, but the nuance I describe unfortunately wasn't mentioned. It makes me sad, because as I said, it is a very impressive technology. But it's hard to fully appreciate something when it is so blatantly over-hyped and when you see so many people around you being mislead and praising AI for achievements that it didn't exactly accomplish.

chse_cake 3y ago

That's a fair assessment but it was also 6 years ago. Back then transformers had recently come out. Tricks to do DL at scale were still brewing. Even achieving what they did for 10 heroes showed DL could work in "non-deterministic-ish" problem settings. The more interesting question is: can we train a Dota model that plays with all 124 heroes today?