In or around 2015, Google’s AlphaGo was the original impetus that started a sort of evolutionary “Cambrian explosion” in the world of Computer Go AI. At this point it is fair to say the genie is out of the bottle and there is no going back. Go has existed in some form or another for roughly 5000 years, and it was only in the last year and a half that computers have suddenly reached a level that surpassed top pros. So while AlphaGo was the first, it will certainly not be the last. In a few years, more and more AI programs will proliferation their way to besting and beating the top pros at this ancient game. From the grand scheme of things, a few years compared to a few thousand years is but a blink of an eye. AI has won, forever, and there is no going back.
But that is not to say that in the future Human vs Computer Go games would be futile or meaningless. We must merely adapt and evolve the nature of the competition and the structure of the dynamic to make things more interesting and to put things on more common ground without giving a handicap or a surplus of moves or stones.
One way to naturally do this is to compete on energy parity. Kudos that the AG version that has won against Kie Je is reported to only use 10% of the total power requirement compared to the AG19 version of Lee Sedol games a year or so back. This is a huge step forwards but still it uses far more power than the human brain or even the entire human body. So as computers get stronger and stronger at Go, ultimately and eventually one metric to target would be the first computer AI Go program that can beat a top pro while using same equivalent to the top pro himself. This can be in the form of scaling down the AI to use less and less resources, or to artificially cap the thinking time of the AI, to give it less time and thus forcing it to use less total energy consumption, in order to match that of the human, so that at the end of the game the AI used no more total power or energy than its human counterpart. This would be an interesting goal post and qualitative challenge.
Another way to make human vs computer matches more intriguing is to give the human a limited number of redos or undos. To essentially allow the human to go back to a particular move, to play out a different branch to see if that new path or new route might entail a better end result or maybe even end up in a winning game. While this may seem like “cheating” at first glance, it is really not. Let me explain.
Computers have always been better than humans at brute force calculations. This has been decided since the days of the very first pocket calculator. Your mobile phone can calculate arithmetic operations orders of magnitude faster than all the top mathematicians in the world added together could do in terms of team mental math or group pencil and paper. But we should not confuse brute force calculations as “artificial intelligence” any more than we would say that a rudimentary autopilot program capable of holding an airplanes altitude without deviation is more “intellectual” than the human pilot. Likewise we would not attribute “artificial intelligence” to a fast calculator.
Everything from brains to computers are actually higher level emulators, at least second order or higher computers. It is the laws of physics and the individual quantum effects, the atoms and molecules and forces of the universe that combine together in such and such a way as to actually do the “calculation” and the “computer”. Everything else is a virtualization and symbolic computing layer on top of that. So be it an abacus or pocket calculator or slide ruler or human brain or silicon processor, these are all second/higher order computers within a computer, virtual emulators of processing, if you will. The reason Intel processors are faster than the abacus is because the integrated circuit is far smaller and thus more efficient than the larger macro-sized beads, but make no mistake, these are all symbolic representations of calculations, the smaller we can make these representations the more powerful of a processor we can have. But at the end of the day, the universe is the only true computer, and everything else is a higher level emulator, the only question is how efficient is the emulation and the symbolic emulation.
Humans are better than computers at some things while computers are much better than humans at a lot of other things. The question is fundamentally why?
It is akin to building a virtual CPU inside of Minecraft. It would be much more efficient not to have to have these higher order virtualizations which actually add to the symbolic unnecessities by artificially increasing computational overhead by orders of magnitudes and for no good reasons. This is why the human brain cannot compute arithmetic faster than a crude calculator. Because the brain was not hardwired to directly manipulate so closely with efficiency level the way a silicon processor can symbolically reduce numbers to more atomic bits of binary zeros and ones and thus calculate them much faster, and without error. It is not because the calculator is more advanced than the human brain, but that the human brain has to visualize and add symbolic layers to concept such as numbers (and cannot directly manipulate them on the molecular level) and thus just like in the earlier virtual CPU inside of Minecraft example, this is why humans cannot outdo computers when it comes to things like brute force arithmetic calculations.
Coming back to Go games, MCTS is akin to brute force calculation and not the sort of “intuitive artificial Intelligence” people think of when they think of AI outsmarting human intuition. Traditionally humans have had the advantage against computer Go programs of the past due to the large search tree of the Go game made it not possible to do an exhaustive brute force search. Therefore to a large degree this thing we call “human intuition” came into play, and without computers being able to emulate that intuition, there was no way computers were ever going to catch up by sheer scaling up of brute force searches. Modern AI Go programs have changed all of that because for the very time in human history, computer programs are now starting to catch up with human intuition when it comes to the game of Go. And with the advantageousness of brute force calculations that are not afforded to humans, the MCTS and other “brute force” simple algorithmic methods help carry Alpha Go and DeepZen and others the “last miles” to the “finish line”, finally boosting it above superhuman levels of overall effective play.
So the question is one of how do we highlight the true “intelligence” aspect of artificial intelligence and bring that real AI to the forefront and diminish the whole brute force aspect of modern “GO AI”, which in truth is actually a hybrid of AI and that of good old fashioned brute force calculations which humans were never good at and never had a chance or a hope against computers anyway.
A way to do that would be to give human players “redo” attempts. Since by definition Go is not a ‘brute force-able’ game anyway, (ironically, otherwise computers would have beat humans at Go back in the 1960’s without need for DCNN etc) a 25kyu player could have unlimited redo’s and could spend the rest of his life and would never win a single game against something like AlphaGo 2017. But on the other hand it is fair to say that someone like Ke Jie and Lee Sedol, given sufficient redos, might find a particular branch in which they win out against AG and the likes of top AI Go programs in the future.
Since humans are by definition fallible and machines are by definition do not get tired or sleepy and do not slip up and make “miscalculations” etc, it would only actually be “fair” for a human vs computer competition to allow the humans to have a set number of attempts per game.
Or if that is not acceptable, then a more natural way to level the playing field to be fundamentally more equitable would be for AI programs to rely ONLY on the neural network component for play and not use any MCTS or anything remotely like it. Another possibility would be to allow the human to have a real board to do analysis, to let him put down stones and to analyze the different variations in a manner that does not prone to victimize him to the fallibility of his own mind and memory and visualization which cannot ever compare to the brute force ‘speed of light’ search that a computer could instantly perform.
Finally, once GO AI programs have cast aside the MCTS and other functional equals to the MCTS methods and the brute force components that humans could never compete against and was never really ‘artificial intelligence’ anyway and then to rely solely and exclusively on the emulation of neural networks instead, then the next step would be to reduce the dataset the AI has access to in a way that is quantitatively fair to the human counterpart. Train an AI from scratch and only give it access to play a very limited number of games, on par with what a human would be exposed to in his lifetime.
What is intuition? It is ability to extrapolate and learn from a subset of data. AlphaGo has played hundreds of millions of games within the span of a year or two. Is this true intuition when a top pro could only ever hope to play a tiny fraction of a fraction of that in his entire lifetime?
So the ultimate goal in AI would be to reach energy parity with human brain itself, to only use Nueral Network and no other brute force searching and whatnot, and exposing it to a limited dataset of games, comparable to what a human would be exposed to.
In human vs computer games, humans should be given a generous number of redo’s. The human should be given much more time than the computer, the computer should be capped to only a few seconds thinking per move, and not allowed to think on human time, no komi should be given to the computer, human should be allowed to pick which color, and finally the human should be given access and ability to consult with other humans during the game, and as well as ability to use a real go board in live match to physically play out and experiment with variations.