All Things Go Podcast

An In-Person Game Postponed

[00:06] Travis: The last few weeks I’ve been trying to get in touch with my friend Adam to meet in person again to play another game of Go. We had a really good time not long ago.

[00:16] Travis: Where we live we have spring break,

[00:19] Travis: which means all the kids are off for the week from school.

[00:23] Travis: So Adam let me know that he was gonna be free and available because his family was out of town. And so I finally had some time today.

[00:35] Travis: We were both, I think, pretty pumped because it’s been challenging finding a slot.

[00:41] Travis: And I get a message this morning and Adam is just under the weather.

[00:49] Travis: Whether it’s allergies from the early spring weather or just he’s coming down with something, he couldn’t do it. So I think we were both a bit bummed out.

[01:00] Travis: I had been looking forward to it because it’s just a nice way to get a change of pace, get a break on top of playing Go, which is so great.

[01:09] Travis: It is nice to connect with other people that you wouldn’t otherwise connect with and talk about other parts of life.

[01:17] Travis: So I’ll have to look forward to next time.

[01:19] Travis: Rest up, recover Adam, and we will get this episode started.

[01:23] Travis: This is All Things Go.

Introducing Rémi Coulom

[01:42] Travis: Our next conversation is with a guest that I learned about indirectly when I first started playing Go,

[01:49] Travis: because there was a period of time before I started playing where I was just reading a lot about Go. I was watching about Go. I had watched the AlphaGo documentary, but I really hadn’t started playing yet.

[01:59] Travis: I was honestly a bit nervous. I think I also felt overwhelmed by what to do on the 19×19 board.

[02:06] Travis: My first proverbial toe in the water for playing 19×19 was with an AI engine or a Go engine, and the first one I used was Crazy Stone, and I had a version of the app on my iPad and it was a great way for me to just get exposed to playing on a 19×19 board before I was able to transition to playing with people.

[02:31] Travis: Fast forward to today and the creator of Crazy Stone recently posted something on Reddit about some other new developments he’s doing around Kifu Snap, which is another product he offers and I wanted to touch base because he has a lot of background in the Go world and has provided some great contributions and so he was more than happy to chat.

[02:55] Travis: So we connected and this is my conversation with Rémi Coulom.

[02:59] Travis: Take a listen.

From Chess AI to Computer Go

[03:03] Travis: Well, Rémi, thank you so much for joining.

[03:05] Rémi Coulom: Thank you for inviting me.

[03:07] Travis: I wanted to start by understanding a little bit more about your Go ORIGIN STORY so what came first for you? Because I know your background is very much in cognitive science, AI computer programming.

[03:24] Travis: Was it the game that came into your life first or was it the other things?

[03:30] Rémi Coulom: Well, in fact, I started by playing chess when I was a kid.

[03:34] Rémi Coulom: Then I did chess AI as a hobby when I was a student.

[03:40] Rémi Coulom: Programming has always been my hobby. And I did not know about the game of Go until 1997 when Deep Blue won the match against Kasparov.

[03:52] Rémi Coulom: So in fact, in 1997, I participated in the World Computer Chess Championship that was organized in Paris by the International Computer Games Association.

[04:02] Rémi Coulom: And so at that time, the International Computer Chess association decided to diversify its activity and rename itself into the International Computer Games Association and started to organize AI competition with other games.

[04:19] Rémi Coulom: There was Shogi and there was Go. And that’s when I learned about the game of Go.

[04:24] Rémi Coulom: Yeah, excellent.

[04:25] Rémi Coulom: So I started to play Go.

[04:27] Rémi Coulom: Well, not really at that time. In fact, at first I kept focusing on chess.

[04:33] Rémi Coulom: I started working on Crazy Stone,

[04:36] Rémi Coulom: my go program in 2005.

[04:41] Rémi Coulom: So I got a job as a university professor in 2004, and at that time I was contacted by a student who was a Go player and wanted to do his master’s thesis about Go AI.

[04:53] Rémi Coulom: And so he asked me to supervise his master’s thesis and I accepted. And then I really got into Go and Go AI.

[05:03] Rémi Coulom: And so in 2005, this student left my university to go to a PhD thesis in the Netherlands and I wanted to continue. So I started Crazy Stone in 2005.

[05:17] Travis: And then it was in 2014 that you left your position as professor at the university and then really fully started Kayufu, correct?

[05:27] Rémi Coulom: Yes, that’s correct.

[05:29] Travis: So clearly you’ve worked on several engines for several board games.

[05:34] Travis: And you mentioned Shogi,

[05:37] Travis: what for you feels different about Go, both as a game that is played, but also just the technical challenge when you think about these AI systems.

[05:49] Rémi Coulom: Well, other difference, a very big difference was that Go engines were very weak when I started.

[05:54] Rémi Coulom: The game of Go is much more complex than a game of chess.

[05:58] Rémi Coulom: And at the time when I started, the best Go engine were weak, amateur level.

[06:03] Rémi Coulom: So the first thing I did when I studied Go is learn to play the game until I could beat GNU Go or the strong programs at that time, because when I played chess against my chess engine, I was so much stronger.

[06:16] Rémi Coulom: I don’t understand what’s going on. But with the game of Go at the. In the early days, it was really fun because yeah, I could see all the mistakes. The program is making.

[06:26] Travis: And yeah,

Monte Carlo Tree Search

[06:29] Travis: it was really fun for me to learn that. It was actually your 2006 paper that originally pioneered and coined this term Monte Carlo Tree Search, or MCTS, which I know from my work as a financial advisor and as a financial planner.

[06:45] Travis: We are constantly using these systems for understanding, understanding what the financial lives of our clients will look like many years out, sort of via path dependency. But that paper really laid the groundwork for you introducing this tree search, and it really dramatically improved to my understanding the performance of these systems,

[07:06] Travis: these Go systems.

[07:08] Travis: And it also became the basis for computers achieving sort of that dan level strength by around, I think it was, 2008,

[07:15] Travis: and then getting to superhuman strength with AlphaGo in 2016. Can you help us understand what is it exactly that made the Monte Carlo tree search a game changer compared to the systems that had come before it?

[07:30] Rémi Coulom: So systems that came before, a bit like the system for chess,

[07:34] Rémi Coulom: rely on heuristic evaluation of the position. And in games such as chess,

[07:41] Rémi Coulom: is rather easy to see who is ahead by just counting the number of pieces on the board. These are two armies facing each other,

[07:49] Rémi Coulom: and whoever has the most men in the army is the strongest.

[07:55] Rémi Coulom: So it’s rather easy to beat the handmade heuristic that tells who is ahead.

[08:01] Rémi Coulom: But in the game of Go, it’s a lot more complex. You cannot just count the number of stones to determine who is ahead.

[08:07] Rémi Coulom: It’s really much more difficult in Go to build a heuristic evaluation that gives a good idea of who is ahead.

[08:14] Rémi Coulom: So that’s one reason why the techniques used for chess don’t work for Go.

[08:19] Rémi Coulom: And also another reason is that there are so many more legal moves in a Go position. You can put a stone anywhere on the board,

[08:26] Rémi Coulom: which means you typically have hundreds of possibilities where in chess you only have maybe 20 or 30 moves.

[08:36] Rémi Coulom: So,

[08:36] Rémi Coulom: yes.

[08:38] Rémi Coulom: And so I did not invent everything about Monte Carlo, in fact.

[08:43] Rémi Coulom: So before myself,

[08:47] Rémi Coulom: there was some AI researchers already using Monte Carlo evaluation in go. And the idea is to estimate the position by running plenty of random games and averaging the outcome.

[09:01] Rémi Coulom: This is very simple algorithm,

[09:04] Rémi Coulom: easy to implement.

[09:05] Rémi Coulom: It can provide some rather good idea about which stone is alive or which stone is dead.

[09:12] Rémi Coulom: Although random playouts are not completely random, you have to avoid filling up eyes.

[09:17] Rémi Coulom: But it provides remarkably good heuristics to estimate who is ahead.

[09:24] Rémi Coulom: And it turned out to be,

[09:26] Rémi Coulom: yes, a good idea for the game of Go.

Crazy Stone, Crazy Sensei, and Kifu Snap

[09:29] Travis: Okay, so you had this sort of Monte Carlo system that you helped kind of further along in your research with that paper. And then 2014 comes, you sort of leave that job and say, I’m all in.

[09:42] Travis: I’m going to really focus on this work. And you specialize in AI software for board games. Not just Go, but also things like Gomoku, Renju, Mahjong, other ones.

[09:53] Travis: And you use that machine learning in the deep neural networks to create the big three integrated Go applications.

[10:03] Travis: So you have Crazy Stone, the core playing engine. Right. It’ll be your AI opponent. And I think for maybe a lot of us, that was sort of our first experience playing against AI.

[10:16] Travis: It was definitely my first experience, you know, a few years ago, and really fun to use. And then you also have, in tandem with that Crazy Sensei,

[10:25] Travis: which is used more for analysis, reviewing the game, understanding what moves were good, what moves were bad. And then finally the Kifu Snap, which I also really love to use for again, that photo capturability.

[10:39] Travis: And I do want to get into some of the new updates that you’ve provided for Kifu Snap as well.

[10:45] Travis: Maybe we can start with Crazy Stone.

[10:48] Travis: So take us back to sort of that first working version that you had. I think you said it was in 2004, 2005.

[10:56] Travis: What at that time was like your plan for that moment in your sort of career,

[11:03] Travis: and what were you trying to solve with that program?

[11:07] Rémi Coulom: Well, I did not really have plans, just it was fun for me. So I was very lucky to have this professor position at the university. So this gave me a lot of freedom to work on things I enjoyed and I did not expect at all what will come after that.

[11:24] Rémi Coulom: So it was just a fun challenge.

[11:26] Rémi Coulom: What happened is in 2005,

[11:30] Rémi Coulom: I participated in the KGS. At the time, there were computer AI tournaments on KGS every month.

[11:38] Rémi Coulom: And so In January of 2006, I entered KGS tournaments and won on 9×9.

[11:46] Rémi Coulom: That was the first victory of Crazy Stone.

[11:49] Rémi Coulom: Yeah, I was very excited because it was a completely new idea using Monte Carlo Tree Search already.

[11:56] Rémi Coulom: It was a completely new idea and I very rapidly managed to make a very strong engine.

[12:01] Rémi Coulom: And then there was a Computer Olympiad.

[12:03] Rémi Coulom: So I talked earlier about the International Computer Games Association. So the Computer Olympiad is a tournament they organize every year,

[12:11] Rémi Coulom: and that has tournaments of AI in various games that was organized in Italy. And there I won the 9×9 tournament with my Monte Carlo Tree Search engine at that time.

[12:25] Rémi Coulom: And that created a shock in the Go AI community.

[12:29] Rémi Coulom: Yeah, I remember my first game in the tournament. My opponent was Professor Chen and he asked me, how long have you been working on your program? I said it was about six months.

[12:40] Rémi Coulom: And yeah, he did not expect to lose. And he was shocked that the Monte Carlo engine could win a tournament because there were Monte Carlo engines in the past in those tournaments, but they were weak compared to the traditional approaches.

[12:59] Rémi Coulom: The way I combine Monte Carlo with tree search is really the key that allowed Monte Carlo engines to become stronger.

Episode Sponsors

[13:08] Travis: This episode is sponsored by Baduk Pop, the app that makes learning Go fun and accessible with interactive puzzles, bite sized lessons and an AI tutor to guide you step by step.

[13:18] Travis: It’s perfect for players on the move. You can download it or learn more at BadukPop.com that’s b a d u k p o p dot com this episode is also sponsored by SmartGo One, the all in one Go toolkit for Apple devices.

[13:35] Travis: Replay professional games, solve problems and keep your SGF collection organized all in one elegant app.

[13:41] Travis: Check it out at SmartGo.com or wherever you go to get your apps.

From Amateur Strength to Professional Challenges

[13:47] Travis: So you were really the first computer AI go system or computer system that really could play quite well right in go. So that sense of shock really started with your program.

[13:59] Rémi Coulom: Yeah. In 2006 it was not playing that well, especially on 19×19. It was still weak on 19×19,

[14:06] Rémi Coulom: but the IDE had a lot of potential.

[14:08] Rémi Coulom: Yeah. So at that time there was almost no machine learning in my system. And by incorporating some machine learning, we made progress and allowed using.

[14:18] Rémi Coulom: Yeah. So by the next year, I think in 2007,

[14:22] Rémi Coulom: Monte Carlo Tree Search also won on 19×19.

[14:26] Rémi Coulom: Wow.

[14:28] Rémi Coulom: I think at that time we were approaching dan level.

[14:33] Rémi Coulom: Yeah. So one big event is in 2010. I was contacted by a Japanese publisher,

[14:38] Rémi Coulom: Unbalance Corporation in Tokyo, Japan, and they asked me if I could license my engine for their commercial products.

[14:46] Rémi Coulom: I said yes, of course. And that was a great motivation for me and it’s a start of new adventure.

[14:54] Rémi Coulom: So yes, I traveled to Japan for the first time in my life. That was fantastic experience.

[15:00] Rémi Coulom: Thanks to this, I started to make money with the royalties of my software. And at some point I decided, okay, I can leave off my royalties and quit my job at the university so I can work full time on the things I enjoy.

[15:14] Rémi Coulom: So that was really great. I could do my hobby as a job and I enjoyed it very much.

[15:21] Travis: That’s wonderful. Have you been able to go back to Japan many times since?

[15:27] Rémi Coulom: Yes, yes. I usually go there almost once a year because I participated in big tournaments in Japan. So I became famous in Japan because I won the UEC Cup. So the UEC cup was a big tournament organized by the University of Electro-Communications in Tokyo.

[15:44] Rémi Coulom: It’s a tournament between Go AI.

[15:46] Rémi Coulom: But then they organized a demo game against a professional for the winner of the tournament.

[15:53] Travis: So you won the tournament against the other AI systems and they said, okay, you’re gonna play a very top level Japanese player.

[16:02] Travis: And that was a. You said it was sort of the first four handicaps stone win of a computer against a professional player.

[16:10] Travis: What was that experience like?

[16:12] Rémi Coulom: Yeah, it was a bit special. It made a lot of noise and yeah, it’s also kind of exciting to meet famous professional players. So I, I played three games in total.

[16:21] Rémi Coulom: I played Ishida Yoshio, Yoda Norimoto, and Cho Chikun.

[16:26] Travis: You played Cho Chikun? Oh my goodness.

[16:28] Rémi Coulom: Wow. Yeah, yeah. Unfortunately, they cannot speak English and I cannot speak Japanese, so the interaction was a bit limited. But yeah, we played a game of Go.

[16:41] Travis: In my research for our conversation, I was able to see some of the pictures of you and it’s really like what happened with AlphaGo before AlphaGo. It’s like you’re sitting there,

[16:50] Travis: you’re playing the moves for the computer. It looks like a very intense scene. It must have been quite an experience.

[16:57] Rémi Coulom: Yes.

Whole-History Ratings and GoRatings.org

[16:59] Travis: So that happens. And then another thing tying into the Go applications that you create is that you are also the Creator around 2009 of the whole-history rating system.

[17:14] Rémi Coulom: Oh, yes.

[17:15] Travis: And so you launched GoRatings.org which is one of the main sources people use to really understand what’s going on in terms of ranking for the top players.

[17:25] Travis: And you really maintain the the system for that. What was the impetus for creating this system?

[17:33] Travis: And in your mind,

[17:34] Travis: what gap was this rating system kind of filling in the Go world?

[17:39] Rémi Coulom: Well, at first I was focused on the rating algorithm problem and so I studied the Elo rating system.

[17:51] Rémi Coulom: I had this idea of the WHR algorithm that is an improvement over traditional Elo rating algorithms.

[18:00] Rémi Coulom: And yeah, so I tried to test it against the other rating algorithm and I found it was good.

[18:07] Rémi Coulom: And in order to test my algorithm, I tried to collect code data and test data and to run experiments. And it turns out that in the world of Go, there is no established rating list of the top professional players in chess.

[18:21] Rémi Coulom: The International Chess Federation has a list with a low ratings of everyone, but in Go, such a thing does not exist. And the rating of professional is based on not very strongly correlated with strength.

[18:35] Rémi Coulom: So I don’t understand the politics of go, but I understand that maybe the Japanese Go Federation and Chinese Go Federation and Korean Go Federation, maybe.

[18:44] Rémi Coulom: Yeah, there is no strong international Go federation that can have authority over all these people together.

[18:53] Rémi Coulom: So yeah, so my website became a kind of unofficial worldwide rating list for the top Go players.

[19:01] Rémi Coulom: And yeah, that’s great. It’s very popular. I receive thousands of visitors every day.

[19:06] Travis: Yeah, I mean it’s again, I’ve only been in this world for maybe four or five years, but it just seems like this is the source that everybody goes to in that system.

[19:16] Travis: On GoRatings.org, how often are you analyzing any of these game records? Are you analyzing them at all? Not really.

[19:26] Rémi Coulom: So I collect game result data thanks to a partnership with Go4Go.net and so Go4Go.net is another website that collects a kifu of professional games.

[19:39] Travis: Right.

[19:40] Rémi Coulom: So whenever Go4Go.net has new data, I will update the ratings. So in practice it’s once every few days.

[19:50] Rémi Coulom: Unfortunately nowadays it seems that Go4Go.net has technical problems. So most of the time his website is down.

[19:57] Rémi Coulom: Yeah, but he still managed to get some data from time to time.

[20:05] Travis: Another fun fact that I learned about you,

Working with Aja Huang Before AlphaGo

[20:08] Travis: you supervised Aja Huang’s PhD research at National Taiwan Normal University.

[20:15] Travis: And for those who don’t remember, Aja Huang is the person sitting where you were sitting playing against Lee Sedol for AlphaGo.

[20:25] Travis: So one of the key figures in the whole AlphaGo deep mind experience.

[20:30] Travis: How do you remember that collaboration working with Aja and what was it like seeing him represent AlphaGo against Lee Sedol?

[20:38] Rémi Coulom: It was really special. So I Met Aja in 2006 during the computer Olympiad in Italy when I won the 9×9 tournaments.

[20:49] Rémi Coulom: And at that time he was started his PhD thesis.

[20:54] Rémi Coulom: And so yeah, we talked a little bit during the tournament and after the tournament he started to send emails to me asking questions about my algorithms because he was of course impressed by my victory and he wanted to reimplement my algorithms.

[21:09] Rémi Coulom: So he was asking plenty of questions and I was happy to answer him.

[21:13] Rémi Coulom: And we kept exchanging emails like this for years. And so I was not his official PhD supervisor. He had a PhD supervisor in Taiwan.

[21:24] Rémi Coulom: So it was completely unofficial.

[21:27] Travis: I see.

[21:27] Rémi Coulom: But he ended up sending maybe 10 emails a day to me and it was,

[21:34] Rémi Coulom: yes, it was really interesting. And so we chatted about algorithm ID and he would implement them and it was very interesting cooperation because we have different ways of thinking. So I am very perfectionist and I will maybe spend many months thinking about an idea then, okay, that’s a good idea,

[21:53] Rémi Coulom: I must implement it.

[21:55] Rémi Coulom: And Aja was a bit the opposite. Whatever random idea I told him, he would implement during the night and the next Morning I had experiment results.

[22:04] Rémi Coulom: Yeah. So I helped him develop his engine and at the same time it helped me because he was running interesting experiments and so his engine turned out to become strong.

[22:17] Rémi Coulom: So in 2010, he won the Computer Olympiad in Kanazawa.

[22:21] Rémi Coulom: And yeah, I was so happy. I was very proud of him.

[22:25] Rémi Coulom: And. And then after his speech, he says he joined DeepMind. And. Yeah, the rest is well known.

[22:31] Travis: The rest is history, as they say.

[22:33] Rémi Coulom: Yeah.

[22:34] Travis: And it’s great because it sounds like there was almost like a complimentary nature.

[22:37] Chimin Oh: Right.

[22:38] Travis: Like you said, you have this very perfectionist, rigorous approach, but then he would just try things overnight. So it sounded like it was kind of mutually beneficial.

[22:46] Rémi Coulom: Yes, yes. And yeah, I enjoy this very much.

[22:51] Travis: Do you stay in contact with him at all?

[22:52] Rémi Coulom: Yes, yes, yes. Sometimes visits me.

[22:56] Travis: Excellent. That’s great.

[22:57] Rémi Coulom: Yeah.

Building Kifu Snap

[22:59] Travis: So I wanted to now focus on Kifu Snap and how that has been a really big tool for me to use, honestly, even just for looking at in real life, over the board situations, if I’m out of time.

[23:12] Travis: And we both agree, me and the opponent, hey, let’s take a look, see what’s happening.

[23:17] Travis: Can you tell us when you were starting to create Kifu Snap? I mean, what were some of the unique challenges that you were finding to that system? Because I see that now other developers are trying to come out with similar systems,

[23:32] Travis: but you talk about things like the camera angle and all these sorts of things. So can you just give us like a little glimpse in a way that maybe we can understand?

[23:40] Rémi Coulom: Yeah. So the problem of recognizing Go boards turned out to be considerably more difficult than I thought it would be. So I started interesting like maybe 12 years ago because it looks like a very easy problem.

[23:53] Rémi Coulom: Right.

[23:54] Rémi Coulom: For any human, looking at the Go board and recognizing where are black stones and white stones is completely obvious.

[24:02] Rémi Coulom: So when I started this, I said, okay, this is going to me take two weeks, I will try something and it’s going to work. But yeah, it took 12 years.

[24:10] Rémi Coulom: So it’s very challenging. Yeah,

[24:15] Rémi Coulom: because the human brain is wired to solve this kind of task. But turning this into code that runs on computer is difficult.

[24:26] Travis: I got a reused Go board a few months ago and I also bought new stones with it. So I said, okay, I need to check that there’s all the stones that were sent to me, that it wasn’t short any stones.

[24:36] Travis: So I poured all the stones out on a table. I think you know where I’m going with this.

[24:40] Travis: And I said, okay, Well, I know ChatGPT is not made to do this, but let me just try. I’ll take a picture of the stones. They weren’t particularly well spread out, but I tried to have a well lit area.

[24:52] Travis: The table was darker, but I took a picture of the stones and I said, can you tell me how many stones there are?

[24:59] Travis: And I’m sure this is not to your surprise, Rémi. It had a really hard time.

[25:04] Travis: It was really struggling.

[25:06] Travis: I just got a very tiny, non expert sense for the challenge.

[25:11] Rémi Coulom: Yeah. And so there are some photos that are very easy. So if the white stone are perfect, white circles and the black stone are perfect,

[25:18] Rémi Coulom: any heuristics recognize the board correctly?

[25:22] Rémi Coulom: But as soon as there are shadows or reflection on the light on the board,

[25:27] Rémi Coulom: if you have,

[25:28] Rémi Coulom: you know, light that makes a white reflection, a white spot on the board and that this is a reflection of the light. This is not a white stone.

[25:36] Rémi Coulom: And yeah, this is a shadow of the stone. This is not a black stone.

[25:40] Rémi Coulom: This is really difficult.

[25:43] Rémi Coulom: And also there are all kinds of Go boards because some people have black Go boards with white lines.

[25:51] Travis: Right?

[25:53] Rémi Coulom: Yeah.

[25:55] Travis: And you gave that update the other day, I think on the r/baduk post about, hey, here are all the new abilities.

[26:01] Travis: Live video capture for Kifu Snap, which is great.

[26:06] Travis: I think somebody posted a picture,

[26:08] Travis: will

[26:08] Rémi Coulom: it work with these stones?

[26:09] Travis: And it was like these.

[26:10] Rémi Coulom: Yeah, yeah, yeah. I don’t expect my system would be able to recognize that one.

[26:14] Travis: Well, well, somebody said which ones are the white stones? Is it the blue color or is it the tiger color?

[26:22] Rémi Coulom: Yeah.

[26:22] Travis: Wow. So it really was a process of 12 years to really.

[26:26] Rémi Coulom: Well, I was not working on this full time of course, but sure, yeah.

[26:29] Travis: But still, I mean, coming back to it, working with other things, the key

[26:33] Rémi Coulom: that allowed me to get relatively good results is when I started to use neural networks.

[26:40] Rémi Coulom: In fact, as it turns out,

[26:42] Rémi Coulom: image recognition neural networks are very similar to neural networks that play Go because the stones on the Go boards are very much like pixels in a bitmap.

[26:54] Rémi Coulom: You have two dimensional matrix.

[26:58] Rémi Coulom: And in fact I’m using the same neural networks for image recognition of Go boards as a neural network for Go AI.

[27:06] Rémi Coulom: Wow.

[27:08] Rémi Coulom: So one key to train neural network is to collect data.

[27:11] Rémi Coulom: So that’s why I made this website.

[27:14] Rémi Coulom: So I’m collecting all the photos that you are sending to me.

[27:17] Rémi Coulom: And yeah, so this website became very successful and at the moment I’m receiving between three and 4, 000 photos every day.

[27:27] Travis: Wow.

[27:28] Rémi Coulom: And so I’ve collected a database with millions of Go board photos and that’s fantastic source of data for training neural networks. So I have the, yeah, probably the,

[27:39] Rémi Coulom: yeah, huge collection. So most of the photos are boring, but I have all the difficult photos as well.

[27:45] Travis: Yeah,

[27:47] Travis: interesting.

Can AI Become a Better Go Teacher?

[27:49] Travis: And Rémi, I’ve been asking this question quite a bit and I’ve been talking about it quite a bit.

[27:55] Travis: There has been a lot of interest in the Go community for using these AI systems to try to help players learn how to improve and learn how to play the game better.

[28:07] Travis: And from some of the folks we’ve spoken to, other experts like yourself,

[28:12] Travis: they’ve talked about this challenge of gathering data. You need not only the data of different board positions, but you need a board position and then some human commentary on what the human is seeing.

[28:25] Travis: And you would need a vast amount of information for then a neural network or a large language model to really digest that.

[28:34] Travis: Do you think we’re pretty far away from being able to reach a point where a large language model could take these pictures, the pattern recognition, and say, beyond just saying,

[28:45] Travis: oh, this spot will die because this move happened? What are your thoughts there?

[28:51] Rémi Coulom: Yeah, so I, I’m not a specialist of large language models and yes, as you said,

[28:57] Rémi Coulom: having a lot of data is important and I have no idea how to tackle this problem, to be honest. I see,

[29:05] Rémi Coulom: I talk to yeah Go teachers and I ask them, what is the AI tool you would like to have? And so very often they tell me, yeah, we’d like to have a AI tool that can not just give the best move, but explain concepts and that kind of things.

[29:24] Rémi Coulom: But I have some ideas of things that could be doable. For instance,

[29:28] Rémi Coulom: have an AI look at the games you play and find the errors you make and then maybe not conceptualize twice, but give you some exercise that match the mistake you make in order to train you to avoid the mistake you make.

[29:45] Rémi Coulom: So maybe automatically generating problems that will help you make progress. This is the kind of ideas I’m thinking about.

[29:55] Rémi Coulom: Maybe more reasonably doable than having a large language model explain to you in natural language what’s happening in the game.

Live Video Capture and New Kifu Snap Features

[30:06] Travis: Okay, so you recently posted again on the r/baduk subreddit about the Kifu Snap and you had some really new and big developments, I think,

[30:16] Travis: for Kifu Snap. Could you just run through with us just some of the really key ones that you think people will be able to leverage?

[30:24] Rémi Coulom: Well, the biggest novelty is that I we have released recently, one month ago, a new app that is based on the same neural network as the Kifu Snap website, but can record a full game.

[30:38] Rémi Coulom: So we started this one year ago because I had only recognition of One picture.

[30:46] Rémi Coulom: So my Go club was organizing a tournament and they were looking for volunteers to record the games.

[30:52] Rémi Coulom: And I said to myself, oh, I could try to make a system that does this automatically with phone.

[30:59] Rémi Coulom: And I started to do this one year and a half ago.

[31:02] Rémi Coulom: Yeah, again that turned out to be more challenging than I thought it would be, but it was, yeah, really interesting.

[31:09] Rémi Coulom: So of course this cannot be done with a website like the Kifu Snap website, so it has to be run on the phone.

[31:17] Rémi Coulom: And so we have been working hard on this and I’m really happy with this application.

[31:24] Rémi Coulom: So it took some time to reach a good level of reliability, but I think now it’s very usable.

[31:30] Rémi Coulom: Last summer I was in holidays with family and I tried to prototype playing Go with my nephew and then I realized yeah, it’s really great because you know, I am a weak player.

[31:42] Rémi Coulom: Like I’m 7 kyu in a French Go Federation,

[31:46] Rémi Coulom: so is my nephew. And so usually we play an informal game and after the game we can try to make comments about the game but we both miss the mistakes we made because we are both we and with this system we could have AI analysis right after the game of the game we played on a real board and I think oh,

[32:08] Rémi Coulom: that’s great. And it makes me want to share this with all the Go players.

[32:14] Rémi Coulom: And so I contacted my Japanese publisher and for many years I’ve been developing this for 12 years and for many years I talked to them about this but they were not interested in making an app.

[32:25] Rémi Coulom: Usually I never make app by myself so I asked them permission to make it by by myself and they say okay, do it. And yeah, so we have been working on this with my employee for,

[32:37] Rémi Coulom: for quite a bit and prepared this release and yeah, so it seems our early users are happy and yeah, and so we will see how successful it will become.

[32:50] Travis: Maybe we could do some fun Lightning round questions. Do you still play Go regularly?

[32:56] Rémi Coulom: Yes, I do.

[32:57] Rémi Coulom: I’m not very serious player but yeah, a few weeks ago I participated in a tournament and I.

[33:04] Rémi Coulom: I’m a member of the Lille Go Club so I go to the Go club from time to time.

[33:09] Travis: In your perfect world situation,

[33:11] Travis: is there a type of environment or place where you would love to play a game of Go and what does that scenario look like?

[33:20] Rémi Coulom: I really love Japan. So yeah, maybe Yūgen no Ma is the best place for playing. Yeah, Yūgen no Ma is a room in the building of the Nihon Ki-in in Japan where they play the biggest title match.

[33:31] Rémi Coulom: And I actually visited that room.

[33:34] Rémi Coulom: Yeah, it’s yeah. Not as impressive in real life as in the movies, but yeah, it has a symbolic value. Yes.

[33:44] Travis: Next question. What is your thought or hope about the game of Go and whether it can get more visibility?

[33:52] Rémi Coulom: Yeah. So I’m surprised that the Go community is not following the steps of the chess community because the chess community has grown a lot in the past years.

[34:01] Rémi Coulom: One part of the success of the new kind of chess events is speed chess and online chess.

[34:10] Rémi Coulom: And there is no such thing for Go? I don’t think so. And I think Go is still very traditional,

[34:18] Rémi Coulom: you know, very long games.

[34:20] Rémi Coulom: But nowadays young people don’t want to watch a six hour game.

[34:25] Rémi Coulom: And so chess has this very spectacular speed chess tournaments that is maybe more appealing to modern audiences.

[34:33] Rémi Coulom: I think maybe the Go world could take an example on the chess world if they want to expand.

[34:40] Travis: Before we go, is there anything else that you wanted to say?

[34:44] Rémi Coulom: Well, nothing special maybe. Please everyone, try Kifu Snap. It’s really fun app and you can record your games and make progress.

[34:52] Travis: Sounds great. Well, again, Rémi, thank you so much for your time.

[34:56] Rémi Coulom: Thank you very much for inviting me.

[35:20] Travis: Next up, we’re going to actually revisit a guest who was on season four of the podcast and he had been doing an anthropological study for his research at Oxford around the effects of AI on the Go playing community in South Korea.

[35:39] Travis: And so I was able to connect back with Chimin Oh,

[35:44] Travis: and he was able to let me know about a new article he had recently published and just tell me a little bit more of some of his initial experiences and thoughts around his research.

[35:54] Travis: So we’re going to jump over to that conversation now.

A Follow-Up Conversation with Chimin Oh

[36:01] Travis: Chimin, thanks so much for joining us again.

[36:04] Chimin Oh: Yeah, thanks for another opportunity to share my findings. Yeah, it’s a real pleasure.

[36:10] Travis: Great.

[36:11] Travis: I really enjoyed the article that you wrote. So maybe we can just start and focus on that article as you’re going through your anthropological research.

How AI Has Changed South Korea’s Go Community

[36:21] Travis: The academic article is titled AI’s impact on the Go Community in South Korea A Preliminary Study of AlphaGo’s Legacy. So maybe you can just give us a refresher.

[36:32] Travis: How does your article think about the Go community as it relates to the advent of AI and its effect on society.

[36:43] Chimin Oh: Okay, so first of all, I feel lucky to be able to publish this article,

[36:49] Chimin Oh: especially just right after a decade over the Google DeepMind Challenge Match.

[36:55] Chimin Oh: As I began my PhD research,

[36:59] Chimin Oh: I came to realize that the Go community is not a simple unit, but it also consists of diverse groups of Go players, not only professional players.

[37:10] Chimin Oh: And the widespread conception is that they normally constitute the core group, which is true. But I was struck by the fact that the Go players are a minority when we compare their number with the entire number of population.

[37:29] Chimin Oh: So that’s how I began this research.

[37:32] Chimin Oh: And then I conduct collaborated with research with Dr. Daniela Trinks on the impacts of Go AI on go education. And then I came to realize that, oh, okay, there’s more groups to look at.

[37:45] Chimin Oh: And then I just figured that there are actually,

[37:48] Chimin Oh: yeah, different impacts of AI on different groups the Go community. And I chose South Korea because this is my home country where I grew up, you know, where I trained.

[38:01] Travis: Right. And so you have a lot of experience with South Korea, not only being a former Korean insei, but also knowing the culture.

[38:10] Travis: Tell us a little bit about how the article works.

[38:14] Travis: You contend that everything has really changed in the Go world since AlphaGo started 10 years ago. And we just had the anniversary.

[38:23] Travis: What effects do you think it had on that traditional role of human experts in the game? Because you talk about that quite a bit.

[38:31] Travis: Tell us about what things were expected in terms of the effect on those human experts and maybe some things that weren’t expected by you or even the community at large.

Human Authority in the Age of Go AI

[38:42] Chimin Oh: Well, first of all, as a Go teacher as still teach Go and I am experiencing some changes, especially when my students challenge my instruction. Okay, Chimin, I think AI says something contradictive.

[38:58] Chimin Oh: This is how I got really curious about my own research.

[39:03] Chimin Oh: Well, actually based on my observation at dojang, it’s happening. And as other articles suggest and argue,

[39:12] Chimin Oh: there’s a shift in the authority of human expertise in the game of Go.

[39:17] Chimin Oh: Yeah, that’s what I was expecting.

[39:20] Chimin Oh: But the thing is that even when such awkward moments, like when human instructions are found to be forced or wrong in the community, for example,

[39:31] Chimin Oh: there are tourist isn’t that much on the mind by their chances. So yeah, that was a bit actually not really expected. Yes. But I think it probably depends on social environments or the educational setting.

[39:47] Chimin Oh: So I think that was, yeah, quite interesting.

[39:50] Chimin Oh: Also another thing is that human experts are not a group that is involved in knowledge creation because it’s not them anymore. It’s, you know, AI programs and professional players are, you know, really good at learners.

[40:05] Chimin Oh: Yeah.

[40:07] Travis: I was thinking about my lesson that I had with my Go teacher last night and I have the game up and we’re reviewing it together via video conference and I’m asking him, oh, is this a good move?

[40:22] Travis: Is this not a good move?

[40:23] Travis: And then he gives his advice, but I can quickly turn on or turn off the AI.

[40:30] Travis: It’s Almost like right there immediately. And I did wonder,

[40:34] Travis: does it make my teacher feel uncomfortable? You know, that there’s the AI right there and it can quickly say, nope, that’s not a good move, or that is a good move.

[40:45] Travis: Is that what you experience as well, from your perspective as a teacher?

[40:50] Chimin Oh: Yes. A couple of days ago. Exactly. To me,

[40:54] Chimin Oh: I had an online lesson with two of my friends and there was a certain situation which was quite ambiguous.

[41:03] Chimin Oh: But anyway, there was one particular moment which I said, oh, I think it’s 90% for black.

[41:11] Chimin Oh: And then one of my friends,

[41:13] Chimin Oh: really, let’s check AI achi actually, it’s a 70% for white.

[41:21] Chimin Oh: And then, yeah, my quick response was like, oh, my.

[41:26] Travis: Right.

[41:27] Chimin Oh: But the thing is that I was embarrassed, but I think my recovery was quite quick because I witnessed the same thing. Yeah. Professional players, actually, who are now my friends, he made the same mistake.

[41:43] Chimin Oh: But I think still,

[41:44] Chimin Oh: it really doesn’t mean that teacher’s authority has been undermined because of the fact that still Go experts have a higher accuracy rate when judging certain positions. And that’s why people are willing to pay for lessons.

[42:04] Chimin Oh: And still, I think human expertise is relevant today,

[42:07] Chimin Oh: despite some changes, you know, happening now.

[42:11] Travis: I almost wonder if they’re actually validated in a way, because with the presence of AI,

[42:18] Travis: I think of myself as a Go enthusiast and I use it regularly, but I don’t often understand the rationale or the reason why, you know, the blue move or the AI move is the best.

[42:29] Travis: And then I need you, I need that expert Go player to say, okay, this is why. Right. This is why it might be making sense. Or this is something that’s happening.

[42:40] Travis: So do you think this necessarily devalues it? Then?

[42:44] Travis: What are the implications of this for AI when we think about other areas of society,

[42:51] Travis: I do think about what I do and think about implementing AI into my work. And I do wonder. It’s like, okay, does it devalue what I do? Maybe in some ways it does.

[43:04] Travis: But as sort of an expert in financial planning and things, I often am using AI systems to confirm,

[43:12] Travis: okay, this is how I’m thinking about a situation or a financial planning situation.

[43:17] Travis: And then it will validate it. Sometimes it will hallucinate and not get it right. And then I’m saying, wait a second, that’s not right. And I’ll say, oh, you’re right, that is wrong.

Can Go AI Still Be Wrong?

[43:26] Travis: Do we have situations in the Go community where the AI prediction will actually get it wrong or incorrect? Or is that not really something that happens?

[43:38] Travis: The only reason I Asked that is because I think I heard of a situation where, you know, it was a top level game. I think it was like Park Junghwan against Shin Jin-seo.

[43:47] Travis: Or something like this some years ago and one of them played a move. And at that time, I don’t know if they’re using KataGo. I don’t know what AI system they were using for the commentating and the viewing of the game,

[44:00] Travis: but I think it was showing that one of the opponents played a move and the win lose graph started in one position.

[44:13] Travis: And then after one or two or three minutes,

[44:16] Travis: no more moves had been played.

[44:19] Travis: But the graph started changing quite dramatically.

[44:23] Travis: It just makes me wonder like what’s happening in that situation.

[44:26] Chimin Oh: So.

[44:26] Chimin Oh: Oh yes,

[44:28] Chimin Oh: that’s interesting.

[44:29] Chimin Oh: There was one case in which Shin Jin-seo defeated Park Junghwan quite easily because he found a specific move.

[44:39] Chimin Oh: Shin Jin-seo had to wait for some time and then the AI discovered them. Move.

[44:48] Chimin Oh: Yeah,

[44:49] Chimin Oh: so I mean, still it takes time until someone gets the optimal move. Also, it could mean that we are also exposed to suboptimal moves, but still very good. And yeah, now the competition to find like optimal moves has begun and it’s kind of open ended competition in my opinion.

Returning to the Dojang as a Researcher

[45:16] Travis: So the last thing I wanted to ask you about is you’re starting to go back into the dojang that you went to when you were going through your experience as a Korean insei.

[45:28] Travis: I know it’s not finished yet, so you’re still kind of going to publish that data and that information, but can you just give us any sort of sense, like, how is that going?

[45:37] Travis: What is it like?

[45:40] Chimin Oh: Yeah, it was a fantastic adventure. I feel extremely lucky and honored to go back to place where I considered as my homeland.

[45:53] Chimin Oh: You know, I was like 1920 when I left my dojang, and then my age has doubled.

[46:01] Chimin Oh: There are all, you know, lots of kids, you know, looking at me, oh, who is this guy?

[46:08] Chimin Oh: Yeah, so it took me quite some time to really familiarize myself with this new environment. And there were some unfamiliar things in the classroom that were computers, you know,

[46:23] Chimin Oh: AI. And then,

[46:24] Chimin Oh: yeah, that experience was excellent.

[46:28] Chimin Oh: And then at the beginning I just felt that, oh, what if I got crushed by all these kids?

[46:34] Chimin Oh: But at the beginning I played fairly well.

[46:38] Chimin Oh: I managed to defeat some top insei. I was very surprised by myself.

[46:44] Rémi Coulom: Interesting.

[46:47] Chimin Oh: Yeah, at the beginning, oh, just that made me think, oh, maybe can I do it? Can I make pro?

[46:54] Travis: Well again though, Chimin, like you’re ranked fourth in Europe, so I mean, you’re a very high level amateur player. So, I mean. But you’re still surprised even still.

[47:03] Chimin Oh: Yeah, but actually it didn’t take long until I realized that I’m a little bit far from making it.

[47:10] Chimin Oh: Yeah, because the kids. Yeah, like 14, 15 year old kids.

[47:14] Chimin Oh: You know, extremely strong.

[47:16] Chimin Oh: You know, I lost quite some games even, you know, seven games in a row.

[47:21] Chimin Oh: Yeah.

[47:22] Chimin Oh: That would probably surprise many of my European friends. But it didn’t. Yeah,

[47:26] Chimin Oh: but it was a great experience overall. Yes. I really cannot complain about it. I’m really excited to document all my experiences, you know, in a form of a dissertation or other research papers in the future.

What Comes Next for the Fieldwork

[47:42] Travis: Oh, it’s exciting. And Chimin, I think we’re all very excited to see, you know, what your findings are and what you come out with. So what is next for you in terms of conducting your research?

[47:52] Travis: Is there anything you can tell us? Is it really just sort of documenting all of your different experiences?

[47:58] Travis: How long will this process take?

[48:02] Chimin Oh: Yeah,

[48:03] Chimin Oh: my field work is still ongoing. I think it will last until September or October this year. I just began visiting a school park where there were a lot of elderly men.

[48:16] Chimin Oh: Yeah. So I just feel like I’m a black swan there.

[48:21] Chimin Oh: Extremely difficult to approach them and you know, strike just start a conversation.

[48:27] Chimin Oh: But you know, they still played their Go in the era of AI.

[48:34] Chimin Oh: So there’s something I found and there’s quite a lot of people, more than 100 people playing in one go park.

[48:41] Chimin Oh: So. Oh, looking, looking at them.

[48:43] Travis: Oh.

[48:44] Chimin Oh: So just that kind of makes me think that oh, is it go really declining or not? Yeah, but I don’t know yet. I will just say.

[48:52] Travis: Right, right.

[48:53] Chimin Oh: Yeah.

[48:54] Rémi Coulom: Wow.

[48:54] Travis: Very interesting. Well,

[48:56] Travis: Chimin, thank you again for the time. This is really great to have a follow up conversation and we wish you the best. So thank you so much.

[49:05] Chimin Oh: Thank you very much. And also again, yeah, thank you for having me in this great conversation.

[49:16] Travis: That is what I have for you today. So I hope you enjoyed the conversations. Thanks for listening and catch you next time.

Closing Sponsors

[49:39] Travis: On a final note, I just want to thank again our sponsors for the episode. SmartGo One is your all in one Go toolkit for iPhone, iPad and Mac.

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