8 years later: A world Go champion's reflections on AlphaGo (opens in new tab)

Computers got to being better than humans in both Go and poker in 2016. The difference is that https://www.deepstack.ai/ was solvable by academics with normal research grants. The training for the final version of AlphaGo is estimated at about $35 million. And who knows how many other versions were created?

Yes, actually solving Go and Civilization are both impossible. But I would be shocked if playing Civilization at a human level was too hard for us to solve with current machine learning techniques.

Total game states is one measure. What does it take to solve the game?

You can also look at the branching factor, how many moves are there to make on average. For chess, it's 20. For go, it's something like 300.

You can also look at how long it takes from a move to seeing its negative consequences. A bad move in chess is frequently visible in very few moves. You lose a piece. You lose an exchange. Often these sequences are virtually forced. By contrast, the consequences of a bad move in Go are usually not visible for 50 moves or more. And there is nothing forced about the sequence that gets there.

You can also look at how likely good players are to play similar games. Many chess games have been played over and over again. People sometimes play half the game out of a memorized opening sequence. By contrast, it is plausible that no Go game has ever been played twice on a 9x9 board. It is very unlikely that any Go gamme has been played twice on a 13x13 or 21x21 board.

You can also look at how big the skill gaps between humans get. In my experience, a 1 stone difference in Go is roughly a similar skill gap to 200 points of Elo in chess. A rank beginner who barely moves the pieces may have a 400 rating. No human has ever reached a 2900 rating. That's 12.5 levels. By contrast Go has 30 levels of amateur (kyo), another 9 for serious players (dan), and then the skill range among professionals is about another 3. That's 42 levels of fairly recognizable skill differences between humans. Which speaks to how much more there is to learn about Go than chess. (Even more so when you realize how much of advancing in chess is a matter of making fewer mistakes. By contrast advancing in Go is much more about integrating better principles.)

No matter how you look at it, Go is much more complex than chess.

    State-space complexity
    Game tree size
    Decision complexity
    Game-tree complexity
    Computational complexity

State count gives an upper bound, though, to how complex a game can be, for sure.

1)has infinitely many legal states/positions

2)imperfect information (another argument often used to argue that game is more complex)

And is:

3)dead simple to play optimally

It's also easy to design a board game with arbitrary number of legal positions that is dead simple to play optimally.

In the world of go, there was an obsession with finding "the perfect move". This was a significant motivation for the players.

That is now completely gone: if you want to find the perfect move, ask a computer.

My point being, we don't know where the asymptote lies. Computers have had self improving algorithms since the 60s, and people have been making the same bold claims, like you, that because an iterative process for improvement has been discovered, we're close to super human AI since the 60s too.

- The home is a very unstructured environment, whereas roads have at least _some structure_, and perhaps ~70% of the most useful roads even have clear lane markings and other signs.

- People already know that roads are dangerous, and there’s an expectation that babies won’t suddenly crawl in front of cars. This doesn’t exist in the home

- People are more comfortable being recorded on roads and highways than in their own homes, so you can get training data more easily for self driving.

- to do something useful in the home, imo you need to solve navigation _and_ complicated manipulation problems. For self driving, you only need to solve the navigation problem.

- (this is speculation on my part) Customers will happily pay 10k-20k extra for a self-driving car, and there are industries in which even more cost makes sense. Customers are less likely to pay that for a robot that does your chores

Would be very interested to hear the perspective of someone that works on self-driving

Plus there's serious questions about liability with self driving cars which are still unresolved in most of the world - if the goal is to have vehicles operate themselves with no human supervision, who goes to jail when they kill someone? Despite all of the progress that's been made with AI it's mostly been in low-stakes problems where failure isn't a big deal, so we don't have a consensus on what we're supposed to do when a neural network negligently obliterates a person because some logistics company wanted to save a few bucks on driver salaries.

It is prudent to remain cautiously optimistic that the evidence will bear out in time, but not assert unsupported claims.

Home robotics has to solve two problems: the robot and operating the robot ~perfectly. Self-driving cars already have cars, which are waldos, if you squint. What sort of sensors should be added is up for debate but the actuation mechanism is a solved problem, and a very simple one, cars have three linear inputs and two binary ones for the turn signals. Technically a few more but none of them are any less trivial.

There's less risk of a fatality when Rosie Robot knocks over the vase you inherited from your grandmother, but people are no more tolerant of that kind of failure in home robots than they are in cars.

Which small thing puts a similar burden on mankind?

Just to get ahead of anybody claiming it was not a firm promise, in 2019: “ I think we will be feature-complete full self-driving this year, meaning the car will be able to find you in a parking lot, pick you up, take you all the way to your destination without an intervention — this year. I would say that I am certain of that. That is not a question mark." [1]

See that part where he says: “I would say that I am certain of that. That is not a question mark.” That is called a firm promise.

[1] https://www.businessinsider.com/elon-musk-doubles-down-on-cl...

And we're still not really there yet. They work great during some conditions and in certain areas, but they're still nowhere close to making human drivers obsolete.

Even going back to the closest analogue, chess, there were good chess engines for a long time prior to Kasparov loosing in 97 to deep blue. Even before Kasparov lost Chess engines were pretty good, just look at the game in 96 when Kasparov won. A grand master would still need to put some thought into how he played.

In Go however even the best engines couldn't hold a candle to a professional player, let alone someone who was the equivalent of a chess grand master. Hell, even as a lowly amateur player I was able to trounce some of the most powerful AIs at the time. Looking at some of the Pro vs AI games back in the early 2010s it's almost painful how bad they were.

It's hard to communicate just how huge of a leap this was, and just how shocking to the whole Go community. It would be like a child one day being unable to speak and the literal next day reciting Shakespeare.

"AlphaZero was a reinforcement learning system that was able to master three different perfect information games - chess, shogi (Japanese chess), and Go - at superhuman levels by just learning from self-play, without using any human expert games or domain knowledge crafted by programmers.

Its predecessor AlphaGo, which defeated the world champion Go player in 2016, was revolutionary but relied on human expert games and domain-specific rules coded by the DeepMind researchers.

AlphaZero started from random play and used a general-purpose reinforcement learning algorithm to iteratively improve its gameplay through self-play, ending up with superior performance compared to the best human players and previous game-specific AI systems.

Many experts were stunned that a general algorithm could rediscover from scratch the millennia-old principles and strategies for these highly complex games, often discovering novel and counterintuitive moves along the way."

That would be pretty strange. For a trivial counterexample, you can look up the history of integrated circuits from invention to today.