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Comment by labrador | Hacker News Reader

To me this is a story of one AI beating another. Yes, there were humans involved on both sides of the equation.

True, one AI was used to found a weakness in another AI. But then it turned out that the weakness could easily be used to defeat that the AI by a strong amateur level player. 6 Dan amateur is about 1 Dan professional, realizing that Lee Sedol was a 9 Dan professional player. I guess that many amateur Dan players can master the weakness. It also seems to shows that the AI does not have the 'deep understanding' of Go as it was thought to have to be able to beat the top professional players. We might be still a long way away from an AI that truly understand the game. And if this is true for one game, it might also be the case for many other AIs. The road to General Intelligence might still be a long road.

Maybe because I'm a programmer but your comment mystifies me a little because I never thought the AI player had a 'deep understanding' of Go any more than I think string sort routines have a deep understanding of all the alphabets in the world.

> long way away from an AI that truly understands the game

What do people mean you mean by this? An AI that can follow the process of human reasoning that mimics human understanding or an AI that actually reasons about any topic by having human agency and awareness?

Another point: I'm assuming The AI that lost can be fixed. Maybe very soon.

The claim about AI having a deep understanding of Go comes from top tier Go players

"After humanity spent thousands of years improving our tactics, computers tell us that humans are completely wrong... I would go as far as to say not a single human has touched the edge of the truth of Go." - Ke Jie, the number #1 Go player in the world at the time, speaking after his loss to an AI

I'm not hearing Ke Jie saying computers understand Go. I'm hearing Ke Jie saying that machine enhancement of Go playing has shown there is very much more to the game of Go than we realized.

To me it is like the invention of the microscope and how it showed there was a whole world of microbes we didn't know about, but no one says microscopes understand microbes.

Microsocopes magnify images. That's only a small part of whay is interesting about microbes. Playing betters moves than anyone is a very interesting part of Go.

When as a human player, you learn to play go, there are a number of concepts you have to grasp. It starts with understanding when how a group of stones can be captured. Next you have to learn when a group of stones is dead or alive. And there are all sorts of complexities in this (like ko-fights). And the next thing you have to learn is aji (taste in Japanese), which is also a lot about potential. A group does not need to have a defined life or death state, if it has some potential to become alive or able to escape and connect to stones that are alive. At an even higher level, you need to understand learn to view the balance of the whole board, grasping what is urgent and/or important. This is something many amateur Dan players wrestle with.

Now when professional Go players were defeated by AIs, it looked like these AIs mastered all those concepts. They excelled at playing a very balanced game, came up with very creative moves that had excellent aji, avoided bad aji. But now the adversary strategy, which can be explained in a few sentences to every Dan level amateur player, reveals that the AIs do not even have a good understanding of life and death. They fall for 'trick' moves that even I, a weak amateur (about 10k) player would have how to defend myself against. If you see some of the final boards, it almost looks the kind of outcomes you would see for players at my level.

> I never thought the AI player had a 'deep understanding' of Go any more than I think string sort routines

It has a neural network trained on millions of games. Of course the model captures more than a sort routine which is non-parametric, in other words not trainable from data. The game of Go is complex enough that we can't code it up manually.

The fact that AI is implemented on matrix multiplication does not detract from it, humans are implemented in electro-chemical reactions. These reactions are all local, no single cell in our body has the big picture.

On the other hand, human understanding is also brittle - how many Go players still can't beat the AI even after this article has been posted? Blind spots/adversarial attacks exist in both humans and AI. It was AlphaGo that influenced the way Go is played at the top levels by introducing new strategies, techniques and moves that were previously considered unconventional or suboptimal by human players. AlphaGo found blind spots in our thinking first.

> I never thought the AI player had a 'deep understanding' of Go any more than I think string sort routines have a deep understanding of all the alphabets in the world.

Go AI have networks that map a board state to probability of winning. A perfect AI would correctly know whether or not any given board is a win for white or black. In general, better human understanding of the game directly allows you to better estimate if a board is a win or a loss.

Katago without tree search is plays at a professional level. I would say that this corresponds to at least professional level human intuition.

String comparison algorithms lack an objective "correct" ordering so it is hard to say what a correct sort should do. But a string comparison using utf8 has a super human (compared to any given person) understanding of alphabets because of the many people involved in ratifying the characters included in unicode.

I have a different (and I think correct) definition of 'understand'

: to have understanding : have the power of comprehension. : to achieve a grasp of the nature, significance, or explanation of something. 3. : to believe or infer something to be the case. (Miriam)

String comparison routines can sort strings like a super human, but they don't understand what they are doing. Neither does the Go AI.

I guess people are using 'understand' in a more casual sense, like my "rice cooker automatically cooks my rice, so it can be said to understand how to cook rice."

You’ll notice that the definition of “understanding” is quite vague, and in particular it is untestable. In fact, I think we understand quite a bit less than we think we do. Grandmaster chess players will identify maybe 5 or so candidate moves in a position to assess, but the actual process of pruning the game tree is hardly deliberate. I assume Go is similar. Chess tactics are often “seen” quickly and subconsciously. Players are encouraged to practice chess tactics to train their subconscious in this way. Evaluating a position is an extremely qualitative exercise as well.

It seems that MCTS rollouts are a pretty good way of “understanding” a position. It is arguably one of the most unbiased ways. That fact alone suggests to me that it is at least very hard to judge. Maybe it is not interpretable by you or I, but I can’t say that rules out understanding.

I go back and forth on this. Machines aren’t introspective enough to have the kind of behavior most would call “understanding”. But then again, people aren’t either. And I don’t mean that in the most people are stupid but I’m very smart sense. I mean we quite literally don’t know why we have thoughts, we just do. We’re able to use words to provide justifications after the fact, but that’s not the same thing. The origin of any thought is a mystery to us.

Can a submarine swim?

If you define understanding as what a human does, then of course only humans can understand. What's the point?

shift a stone one position over and the Go AI can completely change its outlook, estimation, and probabilities.

Seems like it grasps the nature & significance of things to me!

it seems that way until the AI makes a mistake that even inexperienced players would be able to spot & avoid using traditional principles

Everyone makes "what was I thinking?" mistakes, and we should not conclude, from that, a lack of relevant understanding.

everyone makes mistakes, but not everyone makes the same mistakes. depending on their mistake, players very much can and do conclude a lack of relevant understanding. this conclusion is an integral part of the learning process.

Human players make blunders as well.

right, which is why humans do not claim to "grasp the significance" of their moves.

well if you're gonna deconstruct things to that level then you could also equivalently say this is and always has been a story of one(/a group of) human(s) beating another

You could impress me more if you told me a human Go player beat an AI Go player without the aid of AI

I doubt there are any high level players in the world of games like Go that haven't studied with an AI. Or at the very least spent time practicing against one. Even if you only learned Go from humans, somewhere along the lines someone probably taught you something that was learned via AI.

Impressive that in the same thread you swung from "computers can't understand Go like humans" to "humans can't understand Go a swell as computers without computer help"

> humans can't understand Go a swell as computers without computer help

That's not what I said. What I said and what was my intended meaning was that humans can understand Go at a deeper level with a the aid of a computers. The computers don't understand go at all. They're software.

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