No, not even close. First, AlphaZero started with the rules of chess, chess pieces, and a chess board. Second, the possible moves are several orders of magnitude fewer than the steps needed to build a working car out of parts.
Closer would be: Here's a car. Here are all the tuneable parameters. Make it as fast or as efficient as possible. But that would still be inordinately more complex then groking chess.
> rules of chess
Just like "AlphaEngine" would have the rules of reality: physics.
> several orders of magnitude
Sharpshooting. Go (the other game that it plays) has more positions than there are atoms in the universe. Not having to evaluate every position is precisely the improvement that AlphaGo brings to the table.
For the record I don't think that AlphaGo could build a car and, even if it somehow could, the universe would likely experience heat death before it arrived at a workable solution.
Reminds me of BoxCar2D from 2011: http://www.boxcar2d.com/
Aren't the number of chess moves, for practical purposes, near infinite? A car can be made only in so many ways, but a chess game can be won under scenarios that outnumber the atoms in the universe.
There are around 100 legal moves for any chess position, and only a handful of rules. The possible actions and rules for building cars is literally all of physics and engineering.
This is a correct upper bound, but the state space is much smaller than that, as the only position that two pieces can share is "captured".
Edit: it's not a correct upper bound (or at least, it may be larger than the state space, but only by coincidence), in that while there are at most 16 pieces, the pawns may be in an officer state rather than a pawn state.
The way I read it was, you have a robot and it:
1. knows it has to build a ferrari (implying it knows what it looks like at least)
2. knows nothing about building, parts, etc
3. can engage in some sort of learning behavior
Once a robot knows it can only connect two parts by screwing them together, it will only try to screw them together, and it might even transfer that behavior over to other parts.
The possible scenarios become much more limited as the robot gets smarter.
Why would learning to screw invalidate other means of connecting things? Why not use an adhesive? Or weld the parts together? Or shape the materials so they can be joined without a separate fastener, like a dovetail or something. Even if it learns those, how does it know there isn't some way of connecting two parts by a chemical means that humans haven't discovered?
With this consideration, there are many more ways to go wrong in the car scenario.
Chess moves are limited by the chess piece selection and board.
Building something IRL isn’t really limited by anything; For example, you could combine just two pieces in hundreds of ways (tape, staple, weld, etc), in unlimited number of positions (off by 1mm, off by 2mm, off by 3mm, etc).
Examples of games that have been solved include reversi, gomoku, connect four and draughts.
Now that Go and Chess are efficiently solved for AI...what's next? Are there any other interesting complete information games remaining? What's the next milestone for incomplete information games?
I'd like to see an AI that can learn things related to what it already knows with very little training, like humans can do.
Take chess. There's a chess variant that is popular between rounds at tournaments, called "bughouse". It's played by two teams of two, using two chess sets and two clocks. One member of the team plays white on one of the boards, and the other plays black on the other board.
For the most part, the game on each board follows the normal rules of chess. Whichever individual game ends first determines the outcome of the combined game. The teammates may talk to each other and collaborate during the game.
The big difference between the game on each board and a regular game of chess is that on your turn you can, instead of making a move on the board, pick up any piece that your partner has captured, and drop it anywhere on your board (with some restrictions, such as pawn cannot be dropped on the first or eight rank).
If you take a human who has learned to play chess to a certain level, and who has never played (or even heard of) bughouse, and explain the rules to them and then have them start playing it only takes them a little while to get about as good as they are at normal chess.
The human quickly figures out what chess knowledge transfers directly to bughouse, which needs tweaking, and which needs to be thrown out. As far as I know, current AI cannot do that--to it bughouse is a completely new game.
It seems like one approach is to use markov networks to establish statistical relationships between models.
How about axioms of logic as legal moves, and asking it to go from a set of axioms to open mathematical problems?
Or chemical procedures and components as moves, and asking it to tackle a disease with a known molecular structure?
It is not as straightforward as I make it sound, but these are complete information problems.
>Or chemical procedures and components as moves, and asking it to tackle a disease with a known molecular structure?
>It is not as straightforward as I make it sound, but these are complete information problems.
However they are not adversarial games. Self-play only works for adversarial games. Also each mathematical problem is different and needs to be solved only once, and likewise for molecular structures. Additionally, they only need to be solved once, so there is no "gradient of skill" that we know to climb.
You can extend this idea to full first-order intuitionistic logic and probably also to higher-order logics, as well as many different modal logics. There are also formulations of classical logic as a single player game, but that doesn't seem to be very useful here.
While chess if very complicated to play, the state can be represented by (at worst) an 8x8x6 boolean array (board, one of 6 possible pieces), for go a 17x17x2 boolean array. There is nothing similar for logic and and deep learning breaks down (in my experience) once you don't have that nice regular input space.
I'm not sure how you could train it against itself. The branching factor would be infinite as well so I don't know how you'd constrain the legal moves. For example, maybe rewriting (a * 2) to (a + a) or (a * 4 - a * 2) and so on for every theorem you know would be useful in a proof, plus you can invent your own theorems as intermediate steps.
Chess is not "solved." Solving chess would mean knowing with absolute certainty the best move in any position. It would also mean knowing the best first move. We do not have technology with the computational capability to "solve chess." We have technology that can play chess better than any human, but that's not the same as solving it.
I think the best current Poker AI (which can beat good humans) doesn't use neural networks, but expect that combing counterfactual risk minimization with neural networks shouldn't bee too hard.
RTS games should be the next bigger challenge. Not only do they have incomplete information, you also have to handle widely different scales (both space and time) and have a large number of units. I expect this to require an interesting new approach to integrate large scale strategies with small scale tactics without getting stuck in local strategy optima.
Cleaning toilets? I would love to see AI doing degrading but useful work. Sadly, all we get is computers playing board games.
EDIT: YC is doing UBI experiments now. How about funding startups building menial labor robots, then passing the savings on to the humans who used to do the work?
Imperfect information games seem like a much more interesting challenge though.
> pip install pysc2
StarCraft BroodWar also has a more mature AI scene.
Facebook even tried their hands at a bot this year ending 6/28. Only 2 teams made the top 10, with 15 entries being from independent developers.
Starcraft 2 state is much bigger than Go's and requires real time action. Also, actions have to be performed in parallel in real time, which probably requires different techniques and probably puts a way lower bound on number of iterations doable in given time.
When AlphaZero plays Go with itself, it can basically go as fast as it can calculate next move. With Starcraft that's not the case - both networks have to work in sync, probably need some temporal awareness and probably will have some limit of actions per time fraction, which basically requires a whole new approach. Of course, I can be gravely mistaken, but I would like to now how they can circumvent this.
> state is much bigger.
Can AlphaGo not care about it and just play better and faster?
I think AI does have an advantage once it starts to be competent, especially if it's interacting through APIs exclusively and not the interface, which means that it's actions per minute could be astronomically higher than a human player, with unheard of levels of micro. At the same time, I think machine learning is almost an idea solution to figuring out build orders. It's gonna be fast and smart. Question just is how long?
For AI vs AI it could be interesting to see what strategies develop with two opponents with the ability to perfectly microcontrol their units.
Zerglings vs Siege Tanks when controlled by AI with perfect micro.
Otherwise, you make a fair point and that video is amazing. AI vs AI strategy with unlimited APM would be very exciting to watch.
For example the "All ravens, all the time" Terran player Ketrok just responded to the surge in popularity of Cannon Rushes by making a tiny tweak to his opening worker movement. The revised opening spots the Cannon Rush in time to adequately defend and thus of course win.
There's no way they'll compete under unlimited APM rules, it wouldn't even be remotely interesting. We're trying to match wits with the AI, not the inertia and momentum of super slow fingers, keys, and mouse.
I'm sure they'll come up with an "effective APM" heuristic which compares similarly to top pros, and feed it as a constraint to the AI.
I'd be interested to see if it can "plan ahead". Maybe a Chess variant where you have to submit your next move before the current player moves, or something like that.
As far as I understand (and I am no expert at all), AlphaGo basically creates a heuristic of what move to play in a given situation (which heurisitic is created by playing against itself many, many times). Instead of trying to "break" the game, they just decided to simulate playing and results were good enough to outmatch humans, but we have no idea how close to the "perfect game" AlphaGo actually got.
But - whole input to a network is 19x19 array with 3 possible states per cell, plus maybe turn count and one bit for determining whose next move is. S2 network should process graphic stream (lets say 1280/720), needs spatial awareness(minimap), priority setting and computational resource management. And it has to be fast enough in the first place just to follow the game.
I'm not saying that won't happen (who predicted Go breakthrough?), but it at least seem like a much bigger challenge.
It will probably lead to interesting challenges.
I would love to see it tackle my favorite RTS (Supreme Commander) but really, what would be interesting would be to have it attack 'real life' problems :
-optimize the energy efficiency of a building.
(just armchair opinion, I don't know how well suited AZ would be to these problems. I do know that AI has already helped with some of these though)
Not really, given that they already tried Shogi too. Personally I would love to see AlphaZero playing Arimaa, but it is probably not as interesting because of its short history.
I actually prefer "expert iteration" and "tree-policy target" term instead of "AlphaZero algorithm", because they emphasize what is novel about it among the entire system. Terms are from https://arxiv.org/abs/1705.08439
The game models for each Alpha Zero (Chess, Shogi, and Go) look to have been created by a human, as well as the input and output translation (e.g., a human would need to intervene to help AZ Chess to play and win Atari 2600 Video Chess).
 Intervene by doing the translation by the human or by writing a program to convert AZ I/O to Atari 2600 I/O.
Checkers is solved.
mmm, this gives me a idea. Do anybody know of a procedurally generated rts.
Something like Starcraft 2; multiplayer action-rts, massive army coordination, with mikro, makro and meta game mechanics. On a randomly generated map?
So you will still have a good idea where the guy spawn. But your now forced to scout to see where to take engagements and not.
The players even get to choose which maps they'll play (by eliminating some map if they feel it's unfair) and in which order...
With that said, whether it's fair or not, I am curious to see how Stockfish with its opening book would compare to AlphaZero in its current state.
These engines are built for results, not as technical demonstrators. You are testing the engine in a scenario that it was not built to cover. Opening books are not optional add ons for these engines, they mean that the heuristics are not tuned for early game, and no work is put into optimizing the evaluation in that phase of the game.
If they could have beaten Stockfish in its default configuration (using book), they wouldn't have artificially weakened it, right?
Tablebases are a different story, but in every one of the games they released, where Stockfish lost, it was far behind by its own estimation well before tablebases would have had meaningful utility.
Neither opening books nor tablebases would have helped because Stockfish was handily losing the middlegames, not openings nor endgames.
A critique is that the game model (rules, pieces, movements, legal moves) is still bespoke and painstakingly created by a human being. One next step would be for an algorithm that develops the game model as well as the strategy and the I/O translation. E.g., use Atari 2600 Video Chess frame grabs as the input and the Atari controller as the output. After experimentation the algorithm creates everything: the game model (chess, checkers, shogi, go), the strategy for the game, and the I/O processing needed to effect the strategy with the available inputs and outputs.
The key is that you give the kid a computer version of the game, that doesn't allow rule breaking and makes it clear when a reward or penalty is applied (YOU WIN!).
We have this, state of the art, AI which can turn the screws and hone in on some underlying reality about “how to win at Chess”, a formal game. Great.
How does this then extend into the social domain, where AGI would be operating? Like, how does AlphaZero optimize for “how to slow Climate Change”?
I can’t even fathom how it would even understand climate change without an army of scientists publishing new work for it to consume. And then on top of that it will need to understand how it’s adversary, Putin, will try to optimize for the opposite, “ensure global warming to open up our shipping routes and arable land”.
It just seems like a non-starter to me. Saying you could win at chess, so winning at geopolitics is just a scaling problem to me is like saying I can drink a bowl of miso soup so drinking the ocean is just a scaling problem.
It would seem to me that intelligence at the highest levels isn’t constrained by foreknowledge, it’s constrained by the consequences of past decisions made during the (inevitably ongoing) interactive learning phase.
> It would seem to me that intelligence at the highest levels isn’t constrained by foreknowledge, it’s constrained by the consequences of past decisions made during the (inevitably ongoing) interactive learning phase.
This on the other hand does not seem like a particularly big obstacle. AIs can learn from automated systems such as chatbots, alexa-style gadgets, by observing human interactions, omitting half of the conversation and trying to reconstruct it, etc.
Humans are constrained by a limited lifetime and not being able to parallelize their experience, AIs could gobble up billions of hours of human-human, human-AI interaction and AI-AI interaction data and generate more when needed.
It doesn't, AlphaZero is a domain specific AI. That's like asking: "Like, how does differential equation solver slow climate change?". AlphaZero can learn to play a narrow class of games given the rules, that's all it does.
But I agree with your sentiment obviously. We're n technological breakthroughs away from AGI, where n is unknown. We have approximately zero idea how to move from DNNs to AGI - or weather DNNs are the right approach.
I think that's a bit ambitious. GAI is not obliged to solve all the world's problems. But I would ask a more modest question: how could AlphaZero approach problems like web service integration? These problems are definitely solvable, but converting them to optimization problems is the really difficult part that requires genuine intelligence.
> Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can.
It is far from clear that humans can answer "how to slow climate change", so it seems like an artificially high bar to set for AGI.
Do you want AIs exterminating humans? Because that's how you get AIs exterminating humans.
I believe every developer has found themselves in a situation where they'd be describing their problem in a StackOverflow question, only to realize half way through what the answer is. Just because they forced themselves to phrase the problem clearly and looked at it from a fresh perspective. That's the "rubber duck" effect at work.
Who knows if we won't bump into this phenomenon pretty often when it comes to high level AI :) Merely setting the whole thing up will often provide such quality research and a solid answer before actually submitting the problem to the AI
Update: took a look at settings used for TCEC. Looks like they used 16 GB in season 7, 64GB in season 8, 32 GB in season 9, and 16 GB in season 10. Two observations: (1) interesting that they've decreased hash sizes in recent years (2) definitely seems like 1 GB is not reflective of how an engine would be configured for TCEC.
For example, on my 4-core machine I just loaded up two instances of Stockfish, one with 32MB hash and one with 512MB, and assigned 2 cores to each. I loaded up a few random middlegame positions, and after analyzing for 1 minute, the evaluations and main lines were generally the same (within the margin of error for repeated runs of the same engine). when analyzing the Kasparov's immortal game, it was a toss-up which engine would find the famous rook sac first.
1GB is probably suboptimal on the hardware they used, but the difference is probably minimal.
I guess we will have to wait for next year's TCEC for the final verdict though. Hope they change the hardware specs in some way that would allow AlphaZero to compete.
The Go/Baduk community has experienced the similar thing early last year.
- Jan 2016 (AlphaGo beat Fan Hui): Fan Hui was only a 2p and European champion, he's no way near the top
- Mar 2016 (AlphaGo beats Lee Sedol): AlphaGo still lost 1 game. The #1 rank player can probably still beat it
- Jan 2017 (AlphaGo Master beats 60 pros): Ok, AlphaGo is strong, but those are only online games with short time control.
- May 2017 (AlphaGo Master beats #1 ranking, Ke Jie): ...
- Oct 2017 (AlphaGo Zero beats AlphaGo Master): Ok, nothing we have right now can probably beat it.
Google showed up to play Lee Sedol with a ~3,750 elo network. 600 elo in 6 months is basically unprecedented growth in Go AI strength, and took AlphaGo from "it can play a good professional game" to "gee, maybe the No. 1 human can take a game off it".
I'm getting the number from the AlphaGo wiki page.
There is plenty of "they didn't give stockfish enough memory, the hardware isn't comparable, the time controls were too short, etc etc" in the chess community so there is certainly some denial. But nobody thinks any human would stand a chance.
But I agree it's nowhere near the Go denial since the chess community gave up on humans remaining competitive with computers long ago.
So it's not clear if the algorithm is better or the algorithm was just run faster.
But even that isn't the important part in my opinion.
They basically created an ML solution that can learn to play these various games, in very little time (hours), while beating other previous engines (by decent to ridiculous margins). They've created a more generalised solution to their previous AlphaGo engine, which may be very useful in future.
As I understand it, we don't really know what AZ does when it evaluates a position. As it was not explicitly programmed. It could do something that is similar to evaluating more positions.
In AlphaGo Zero paper, they show that selectivity is so important that playing solely from selectivity (that is, ask neural network which move one should search first, and play that move without searching at all) results in professional level, see Figure 6b. Fan Hui level, not Lee Sedol level, but still.
Also, changing the heuristic you use to chose candidates (selectivity) doesn't mean you're not doing search anymore!
It's difficult to attribute some of these wins to a simple superiority in computing power.
Would the grandmasters look at the games and say "Yes, it won every time but it's style is rather clumsy"?
It's important to state, though, that calling an engine's play "romantic" is not a value judgement. Stockfish and other engines play in a way that give a clear concrete advantage after X moves, whereas AlphaZero relatively prefers move that are "creative" or "beautiful" in the sense that there's less of a clear definite benefit, but instead some sort of slight positional advantage or more attacking chances or something similar. It's harder to prove that it was a good move, but it just "feels" good. In that sense humans enjoy it either since it's surprising, or maybe closer to how we're able to think.
As an example, there is one game I saw analyzed where AlphaZero gradually gives up more and more material with little clear compensation, but gradually its position gets better and better, until Stockfish is completely out of good moves. It then turns the corner, snatches up more material than it gave up, and converts into a winning end-game. Such imbalanced style of play is much more interesting to watch than previous computer slogs of slowly jockying for slight advantages until one side can grab a pawn without giving up much and build into a win from there.
The difference in style is likely influenced by the insertion of historical boards as input to the neural network.
The sequence of moves are therefore more likely to look related to one another.
That is similar to how humans play.
The player with most strident objections to the conditions of the match was GM Hikaru Nakamura. While a heated discussion is taking place online about processing power of the two sides, Nakamura thought that was a secondary issue.
The American called the match "dishonest" and pointed out that Stockfish's methodology requires it to have an openings book for optimal performance. While he doesn't think the ultimate winner would have changed, Nakamura thought the size of the winning score would be mitigated.
One problem is that in a sense AlphaZero has an "opening book" encoded in its neural network weights. But just like it is unclear how to construct "Lee Sedol without an opening book" at all, it is unclear how to construct "AlphaZero without an opening book in such sense". So indeed, while unusual for engine evaluation, it probably is best to play against Stockfish with an opening book.
While there is no doubt Stockfish can play stronger with good opening book, keeping a book up to date with engine changes is really a full time job. So Stockfish project does not have any official opening book.
Personally, I think using publicly available Komodo book would have been enough, but obviously Komodo book is tuned for Komodo and everybody would complain about any book problem. In a sense, "no opening book" is the official upstream supported configuration, so it is entirely a defensible choice.
Lots of AI problems aren't solved with current techniques just by throwing more CPU power at them so it would still be impressive even if it required a lot of computing power.
Some code or weight is changed, then they have it play to see if it leads to better performance.
An exhaustive, sort of manual work. On the other hand Deepmind's bot is fully automated and they have it just running day and night improving itself on a large hardware configuration.
Shades of WOPR. "This is a game nobody can win..."
And, when the legal moves are replaced with the moves of other games (like Go for which it was actually written, or Shogi), it did exactly the same thing. Redevelop millennia of human knowledge and go beyond, all within 8 hours of compute.
Makes you wonder what will happen when instead of the rules of chess, you put in the axioms of logic and natural numbers. And give it 8 months of compute.
How do you score this computation? What's your goal? There's no checkmate here.
For example, give it a number and tell it to factor it into the prime factors. The score might be the solution with the smallest time or storage requirements. Or maybe find a way to generate a hash where the last n digits are zero.
I also remember a RadioLab episode where they talked about electronic theorem solvers and they would do something like show it a video of a double pendulum and it would come up with equations to model the behavior of that system.
Maybe give it a list of problems of varying degree of hardness, and tell it to either prove or disprove as many as possible?
But it's not really misleading at all. The ability to produce massive amount of power and product is the pinnacle of our society. The fact is we can make thousand, if not millions or even billions of TPUs if we desired, and it is a relatively easy engineering problem at that. And that these things may solve all kinds of problems mankind has had for millennia in hours should be a wakeup call to a future that will be hard to predict.
Humans take 9 months to gestate, no amount of parallelism will speed that up, after that it takes 18 years for them not to be completely stupid all the time attempting to hammer an education in them. Even after that it takes more years to become a specialist.
(The operations the TPU can run are far simpler than what supercomputers can do, but just for the sake of comparison, the current top supercomputer in the world can do 1.25×10^17 floating point operations per second)
If you're talking about formal proofs or maths, I'm not sure how this would apply in general as the branching factor for each 'move' in a proof is efficiently infinite. It would be interesting to see it applied to more constrained proof domains though.
> of course it goes so much further
> The ramifications for such an inventive way of learning are of course not limited to games.
>But obviously the implications are wonderful far beyond chess and other games. The ability of a machine to replicate and surpass centuries of human knowledge in complex closed systems is a world-changing tool
Okay then. Let's go beyond games already!
> Training proceeded [...] using 5,000 first-generation TPUs
> We evaluated the fully trained instances of AlphaZero against Stockfish, Elmo and the previous
version of AlphaGo Zero [...]. AlphaZero and the
previous AlphaGo Zero used a single machine with 4 TPUs.
So the 5000 TPUs processing power or energy consumption should be compared with those spent in devising Stockfish, not running it.
This absurd comparison would raise my eyebrows coming from an English tabloid.
Having said that I've looked the author's profile and was appalled to learn he's a chess prodigy. Then I also seen he's a Chess Journalist. Apparently he became much more a journalist than a chess master...
It's interesting that AlphaZero was finally applied to a different game, though. I wonder what architectural changes they had to make. I've read that pure MCTS isn't that good at playing Chess. How true is that?
Draws are common in high-level chess. There's a belief that perfect play by both sides will result in a draw.
They changed input format and amount of noise (which facilitates exploration) to account for different branching factor.
As it looks, for move times < 0.2s, Stockfish is stronger, anything above that, AlphaZero is stronger.
Is it still easy to find positions that alphazero totally misunderstands?
If a Hold'em AI would never choose to bet 72 off it doesn't need to have an opinion of what to do when that bet is raised by the opponent.
> pointed out that Stockfish's methodology requires it to have an openings book for optimal performance.
I went from amazed, utter shock like "What!! No way. This is unreal. This is absolutely unreal. What? What? What?" to a total feeling that I've been reading 1,000 words of fake news.
I feel cheated by this write-up and flagged it for this reason. They need to mention it in the first couple of words, not after selling that it has deduced 1600 years of human chess knowledge in 4 hours.
Suppose at every turn there are n possible future states of the game based on the rules. To avoid "brute force" the AI must be able to ignore many of those states as irrelevant. In effect, the AI is learning what to pay attention to, not just considering what might happen, thereby conserving computational resources.
Chess and Go are interesting for two nearly opposite reasons: 1) because they are too large for humans to consider the reasoning obvious, and 2) because the input to the reasoning is simply a small (and easily perceived by humans) grid of rule-constrained pieces.
But when you think of AI in an information theoretic way, so that given representative training data the system (if large enough) will always "learn" perfectly, it's not really all that remarkable. It's just a different computational way of doing the same transformation from input states to moves. Given a problem (chess, go, etc.) the researchers must simply learn what network structure and training regimen will do the job with the least computational cost.
To see why this is relevant, consider a deep learning model that could continually generate successive digits of pi (or primes) without having the concept baked in already. Would the result be computationally cheaper than highly optimized brute force algorithm? No, because what it would "learn" would be something already known by humans. Perfect chess is simply a function from input states to moves that humans do not already know the definition of. Most humans do know the definition of this function for the game of tic tac toe by the time they reach middle school.
I'd argue that while this is useful it's ultimately not hard. Comparing it with Stockfish mainly demonstrates how chess is hard for humans to reason about and hence hard for humans to write non-brute-force algorithms to solve.
Thus, I think this is an example of "weak AI" even though humans associate chess with high degrees of exceptional human cognition. Chess data contains no noise, so the algorithm is dealing only with signals of varying degrees of utility.
I'm looking forward to AI that can be useful in the midst of lots of noise, such as AI that analyzes peoples' body language to predict interesting things about them, analyzes speech in real time for deception, roulette wheels for biases, and office environments for emotional toxicity.
Chess is interesting because we can't introspect to understand what makes humans good at chess (other than practice). So many human insights and intuitions are similarly opaque yet the data is noisy enough that it will take significantly better AI to be able to do anything that truly seems super-human.
Number of humans that can beat Stockfish: 0
We detached this subthread from https://news.ycombinator.com/item?id=15870384 and marked it off-topic.
Number of cats that can beat Stockfish: 0
Number of humans that can create AlphaZero:At least the number of humans on the Deep mind team.
Number of computers that can independently create AlphaZero: 0
Bascically a function
winAt(“chess”) and another function with no knowledge of chess that can implement the first.
What are you modeling that suggests something like that in 10 years?
going to need stimulants to work at 100% or your company AI will cut your pay.
Your processes don't scale well, beyond the biological scaling that your body has developed via evolution in the last 4 billion years. Digital processes do.
This is a real time strategy video game. The number of decisions you need to make in this game are mind boggling. It takes most people many months, if not longer just to get to the point where you're not clueless.
A recap video is at: https://www.youtube.com/watch?v=jAu1ZsTCA64
I've played dota-like games in the past (at a medium-high level). The skill gap in this game is remarkably huge.
It's a type of game where a new human player would get absolutely destroyed with a 0% chance of possibly ever winning, even in a minimal scenario where only 1 hero were involved.