Humans, not so much, as in all top-level competitions, human abilities improve minimally at the top, because we have millions of humans competing against each other until the plateau of human performance is reached. Then you can push that a bit more with drugs (see doping in sports). And after that, you are pretty much done.
So it's only a matter of time and effort until AIs are fully unbeatable.
And if watching the screen, do you want it to have bad eyes like we do too (good resolution only in the center)?
Yes, it is. Also, “seeing” the screen rather than being able to directly introspect the game world digitally. Orders of magnitude harder. This is known as Moravec’s Paradox.
I mean, "human vs AI" matchups are ostensibly about strategy - machines already win at timing and twitch, there's nothing to test. But esports games aren't pure strategy, they all involve various amounts of timing, twitch skills, the ability to monitor lots of details at once, etc. Those are all things that AI opponents can (trivially) do perfectly, which gives the AI a huge advantage. It then follows that an AI player should be able to win even with an inferior strategy (which makes you wonder if these games are really suited to AI research in the first place?).
[1] For an entertaining case-study, check out Day[9]'s learns DotA2 series.
It occurs to me that for a really even playing field, the humans should probably be allowed to make and install UI mods if they want to. E.g. if there's an advantage to using an ability precisely when your hit points hit 50% (or whatever), an AI can easily do that reliably so the human should probably be able to if they want to.
(Of course, for heavily twitch games like Counterstrike, being allowed to use UI mods (i.e. aimbots) would break things. But then, I suppose that the extent to which UI mods break a game is more or less the extent to which that game favors twitch over strategy.)
Like, imagine if this was a chess AI, and we were trying to determine who was better at chess, humans or AI. Would you make the AI use robotic hands to move the pieces? No, because thats not the interesting part of chess. The interesting part of chess is the strategy.
And consider a game like Quake where mechanical skill is even more important (even though mechanical skill matters, Dota 2 is still primarily a game about strategy and team coordination).
What if we could pit humans and ai bots at the speed of human imagination?
One could imagine a much better way of testing hand eye coordination, through a serious of mazes or puzzles or reaction tests.
It would be like trying to test hand key coordination by having a robot play physical chess against a person.
Another interesting part will be creating an AI / neural network that can utilize inputs that are closer to human level inputs (e.g., using the frame buffer and audio out as input to the neural network and passing the outputs of the neural network to a keyboard and mouse driver). Just let the network train itself without having a human laboriously determine the topology of the neural network. Such a neural network can then be applied to several different types of games / problems much more quickly than at present where significant human labor is required to generate deeply customized neural networks for each game / problem.
I hope this little koan illustrates that this sentence is impossible to execute. The human always has to specify something.
--
In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6.
"What are you doing?", asked Minsky.
"I am training a randomly wired neural net to play Tic-tac-toe", Sussman replied.
"Why is the net wired randomly?", asked Minsky.
"I do not want it to have any preconceptions of how to play", Sussman said.
Minsky then shut his eyes.
"Why do you close your eyes?" Sussman asked his teacher.
"So that the room will be empty."
At that moment, Sussman was enlightened.
The exercise then becomes one of finding the minimal constraints needed to achieve the desired results. Please correct if needed, but looking at the Dota 2 neural network [1], it boils down to generating an input State vector from the Dota 2 bot output interface, running the state Vector through an lstm (of sufficient length) to generate an output State vector, and generating the inputs for the Dota 2 bot input interface from the output State vector. Update this network (1) to have the input State Vector generated from a convolutional network that feeds a fully connected Network and uses the frame buffer as input and (2) to have the final outputs of the neural network be keyboard and mouse commands instead of dota 2 bot input interface commands, then let the network train itself. The number of elements in the state vector, the number of convolutional layers, the number of lstm layers, and the number of layers and elements in each fully connected hidden layer could each also be determined by a recurrent neural network.
[1] https://towardsdatascience.com/the-science-behind-openai-fiv... (see the image under "The Architecture")
[ random capitalization powered by Google speech dictation ]
I agree it'll be even cooler when it all justworkstm end to end, but in terms of incremental 'holyshiticantbelievethatworked' this is at least as big a step as it will be when they add in direct visual input.
One of the next significant moments could be taking the current Dota 2 algorithm and massaging it to use human style inputs and outputs. Please correct if needed, but the current Dota 2 algorithm boils down to (1) a fully connected network that generates an input state vector from the Dota 2 bot output interface, (2) an LSTM of sufficient length that generates an output state vector from the input state vector, and (3) another fully connected network that generates the Dota 2 bot interface inputs from the output state vector. This could be updated to have (1a) a convolutional network that feeds into a fully connected network, where the input to the convolutional network is the frame buffer (and perhaps the audio output) and the output of the fully connected network is the input state vector, (2) the same or similar LSTM network, and (3a) a fully connected network that outputs keyboard and mouse commands instead of DotA 2 bot interface inputs.
It is an open question as to whether current compute power is sufficient for this massage.
Am I missing something, or does that set consist of Checkers, Chess, and Go so far? (presumably with analogous misc games of comparable complexity)
Discounting the reaction time wins, I'd say the sample size is too limited to generalize to eventual AI behavior in more complex / open-ended games.
Extrapolation was the cause of the last AI winter.
So that's a pretty different game, it's got a big luck factor and has asymmetrical information and still the AI just kept getting better and the humans... didn't
The reason I point out the unfathomable numeric complexity is that it makes the games, from the perspective of an AI, effectively infinite. AIs are calculating, but to an extremely superficial degree relative to the depth of the game. E.g. - when a chess program says it's calculated to 30 ply (15 moves for both sides) what it really says is that it's seen up to 15 moves deep after intentionally ignoring or pruning 99.9999999999% of moves which it thinks probably aren't good -- something it still often gets wrong, but its 'understanding' of what is 'not wrong' is strong enough that it still results in a phenomenally strong level of play, compared to humans. There's no doubt that perfect play in chess would still go 1 billion - 0 against something like AlphaZero.
So what matters is not the number of decisions to be made but the individual complexity of the decisions to be made. And in most games we consider complex the individual decisions are not really that complex, and complex systems can often be broken down into very simple games. For instance a great example of this is a 4x game. Taken as a whole they seem complex, but they're really just a large number of relatively simple components that are mostly independent. E.g. - Given this state, where do you explore next? Given this state, what do you research next? Etc. Another benefit for AIs in that in games we consider more complex, the value of any given mistake often becomes diminished. If you make a single bad move in chess, it's enough to lose the game. In a 4x game the weight of individual decisions is not so high, it's all about the big picture. But as perhaps computer success in Go shows most clearly, actually seeing the big picture is not really necessary to produce play like you do.
This, I think, is why research has moved more onto real time competitive games. Crushing humans at chess, go, and now poker as well is a pretty solid proof of concept for computers beating humans at any turn based game. When you start adding bunches of different layers to games I think it's more likely to handicap the human than the computer. Imagine playing some sort of 100x100 chess. We can only speculate, but I imagine the distance between the top AIs and humans would be far greater than it is in 8x8 chess.
I would disagree with this characterization. I believe at the time, it was (a) a problem that a machine had not yet conquered, (b) a problem that it seemed feasible that a machine might conquer, and (c) a problem that, once conquered, would point the way to general artificial intelligence.
I would point at (c) as the assumption that proved to be erroneous. Deep Blue was clever algorithmic and hardware engineering (with a healthy budget) but led to... what?
AlphaGo is a fundamentally different approach, which shows signs of being more adaptable.
Point being, that winning a game is not sufficient evidence that a given approach will scale to winning all games, much less generalized intelligence.
To put it in terms of the fallacy I read in an article linked on HN (paraphrased), 'The public assumes that if a machine can perform a task that humans can perform, the machine must be human-like, and therefore able to perform all tasks that humans can perform.'
But in the same way that we use rendering tricks to go beyond-state-of-hardware-art in graphics rendering (by abusing hidden limitations), so do we often build ml systems.
I believe the most optimistic point against me was the slide in this year's GTC keynote pointing to the "Cambrian explosion" in the diversity of ml approaches this time around.
I can see following hypotheses (in no particular order):
1. Human brain is the optimal solution in the space of all computational devices capable of playing games, and we can only approach it.
2. To do computation human brain employs some physical processes, we will not be able to replicate in the foreseeable future.
3. Human brain do not produce general intelligence, so we will not be able to replicate it as such a task is outside of the scope of our limited intelligence (while playing games isn't).
4. Human brain uses metaphysical abilities to do cognition, we will not be to replicate them at all.
5. Human brain is a local optimum, but the space of potential AIs' constructions is too huge to explore in the lifetime of our civilization, so we will be stuck at this local optimum with marginal improvements.
I don't see any of them as sufficiently likely, but your mileage may vary.
I believe there exists a combination of hardware and software capable of beating humans in all games. However, I also believe victory in a single game gives us minimal information on whether or not the system generalizes to many games (to say nothing of non-game, e.g. more complex, ruleless systems).
There will be a time where we learn from the AI and the AI learns from us, where we trade victories and defeats as we adapt to each other.
Don't discount the ability of humans. They figured out how to exploit the 1v1 bot in a few days and soon humans had a 100% win rate using that strategy.
That's just not true in doto. The player base improves quite a bit over time. The top pro plays from only a few years ago are not impressive anymore.