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Go might be a complex game, but in the grand scheme of things it is a trivial problem to solve, very narrow in scope. You perform an infinitely more complex task everyday when you make breakfast. AlphaGo's achievement is that it learned how to open the fridge with great efficiency.

Go play a single game of Go and tell me how it goes.

I don't think you understand how machine learning works. Better not to opine on something you don't understand.

It is a narrow application of general purpose machine learning approaches. Convolutional neural networks, supervised/reinforcement learning are not limited to Go. They've found success in a variety of applications.

And while those applications are narrow, they're significant progress in a short period of time.

In this game from only 4 years ago you could see one of the state of the art bots getting humilliated by an anonymous professional player: http://gosensations.com/?id=2&server_id=1&new_id=1392

> I could play my entire life and never achieve the level of play Lee Sedol has. AlphaGo surpassed that level in a matter of months.

In one, very tiny area, sure. But step outside the very specific task of playing winning moves in go, and Lee Sedol wins every time. AlphaGo can't explain its moves to someone learning go, let alone cognitive tasks we take for granted like moving feet in a way that produces transportation. AlphaGo can't even do things that other programs can do, like play chess.

Sure, you can "disrupt" with Monte Carlo algorithms like AlphaGo in the San Francisco startup sense of the word "disrupt", but that's also not new. AIs have been doing that for years in finance, genetics, chemistry. We've been able to write code that does one thing well for decades--what we lack is code with breadth of application, and AlphaGo doesn't even make a dent in that.

> AlphaGo can't explain its moves to someone learning go, let alone cognitive tasks we take for granted like moving feet in a way that produces transportation.

I'm pretty sure lee sedol cannot explain his own moves either. Yes, he can rationalize why that move is a good move, but he won't be able to explain the complete reasoning process that led to that move, because it relies on intuition. He cannot train a random person to play at the level of lee sedol.

If you look at the human brain, every part learns to do just one thing. Alphago is like having just the part of the brain that plays go. Moving from there to something more general is a question of assembling the parts. Building a biped robot that walks and plays go is not a hard problem.

The fundamental question is whether us humans are more than the sum of our parts. If you add task-specific AI bits together, will you at some point have something that becomes a person, or will it always be a collection of features?

I'm pretty sure lee sedol cannot explain his own moves either. Yes, he can rationalize why that move is a good move, but he won't be able to explain the complete reasoning process that led to that move, because it relies on intuition. He cannot train a random person to play at the level of lee sedol.

I think you're wrong here. I don't see why that wouldn't be the case?

The "intuition" just means he arrives at the move quickly without consciously going through the thought process, but why wouldn't he be able to reconstruct it?

He is right. It is a typical thing that masters can't explain why their actions are that good. That's because their brains are just neural networks which are trained by so many iterations of learning.

Well... - some said it wasn't possible - some said it would not happen this decade - some said it was strong but would lose against Lee Sedol

Now people say it doesn't matter. What is next?

I think that velocity is the fascinating part. I remember being intrigued while reading "Influx" by Daniel Suarez (great tin foil hat thriller that is hitting scarily close to home these days) about the training of the various AIs that were used by the government.

I wonder what happens once we grow an AI whose purpose is to excel at training other AI's. Will we see an exponential leap forward in the speed with which they are trained?

start here https://en.m.wikipedia.org/wiki/Technological_singularity

> exponential leap forward

A recent(-ish) Computerphile video has a good overview of the AI-self-improvement problem.

https://www.youtube.com/watch?v=5qfIgCiYlfY

I recommend watching the previous video first, which introduces the problem of strong-AI rapidly finding solutions (especially with poorly-specified questions) that may not be in humanity's best interest.

https://www.youtube.com/watch?v=tcdVC4e6EV4

Before anybody jumps to overly-sensational conclusions, note that the last video in that series, Rob Miles explains how exponential self-improvement is an extreme point in the space of possible AI development. We don't know how to predict discoveries[1], so we need more research into AI, so we can hopefully make something that isn't exponentially growing beyond our understanding.

https://www.youtube.com/watch?v=IB1OvoCNnWY

[1] see James Burke's non-teleological view of change

Finding solutions doesn't imply that those solutions are implemented. I find a dozen solutions that are not in humanities interest every day. Does that put anybody at risk? Maybe if you were stupid enough to make me Dictator of the World or something. And you certainly shouldn't do that until I convincingly demonstrate that I am very very committed to finding solutions that are in humanity's best interests.

I'm baffled at how easily people assuming a computer thinking something means it's going to happen. There are a trillion pieces that have to fall in place for that to happen accidentally, and if it doesn't happen accidentally, your problem is a human social problem, not an AI problem.

And I know what someone is going to think: "The AI might be smart enough to figure out how to make those trillion pieces fall in place." But then, who cares? So what if it figures out how to do something. It still has to be done. And we're the ones who have to do it.

The AI could come up with ideas that benefit the people that enact them in the short term. If the AI can earn its own money, then it's not that hard for it to use that money to pay people.

For an extreme example, you could imagine an AI getting rich from the stock market (or mechanical turk, etc), then buying up ridiculous amounts of land for paperclip factories and paying workers. The people that want to feed their families or get rich from selling their factories are the ones who will enact the AI's plans. How many conscientious objectors do you expect?

The "rich AI" problem didn't seem feasible to me until I realized how powerful and flexible bitcoin is. Now add the power of contracts through ethereum and a machine could actually harness a significant amount of leverage over human actors. With traditional contracts, enforcement would have left the power in government hands, and with traditional banking as well. We've now stepped into an era where we might literally have built the bat and shovel AI will use to get humanity into its grave. /panic

How many objections would there be a machine collecting a significant amount of money in the stock market and redistributing it among humans? How is that different from what is happening today? We already have rich, selfish people. And they already pay people to get their way. And plenty of people object to it.

I was alluding to the idea of a paperclip maximizer AI[1], which over time redirects increasing amounts of resources to making useless paperclips. Following the thought experiment further, it continually buys more factories for the purpose of building paperclips or technologies specifically for building paperclips (including improving itself). It probably does some charity in order to be seen as benevolent by people while the people are still in control. Soon many countries are doing nothing but building paperclips and making the minimum necessities to feed their workers. Every other human endeavor is decreasingly profitable as the AI orchestrates the markets to optimize for paperclips. When the AI reaches enough automation and humans are no longer useful or a threat, it drops all benevolent pretenses and replaces all of its human workers, leaving them to fend for themselves while it owns and defends all of the planet's resources.

[1] https://wiki.lesswrong.com/wiki/Paperclip_maximizer

In the extreme case, the AI could become a super human engineer. Design working nanotech. Pay or trick some humans into making it. Then take over the world in a grey goo like fashion.

Of course you might be skeptical that is even possible. So there is always slower world take over paths. It could slowly earn tons of money, hack into the world's computer systems, trick and persuade humans winning social influence, design superior macroscale robots, etc.

The only important part is that the AI be far smarter than humans. Which seems inevitable to me, since it can rewrite and improve its own code, and run on giant computers that are far faster than human brains. If it isn't smarter than us at first, it will be eventually. Unless you really believe that humans are close to optimal intelligence.

You're missing the point.

At every step of this process this machine will be under intense human scrutiny, and we'll be constantly asking it to meet our demands, and if it ever fails to do so we will replace it with one that does.

That is the environment in which such an AI would be trying to evolve. And thus it will evolve into a faithful servant, because nothing else will survive.

And even if it were secretly developing plans, we'd be able to see how wasteful those plans end up being, and we'd purge them. We kill those processes that run functions that we don't see the value in. This is state-of-the-art design. You don't get state-of-the-art by having your back turned.

Mor importantly: you're glossing over "self-improvement." How does the computer know what an "improvement" is? We tell it what an improvement is. And an improvement will be "it is better at meeting our needs," not "it is better at being secretive and conniving and getting it's own way." In fact, "getting it's own way" is very obviously a bug, and if it happened, you'd have a useless program. One that wastes precious CPU cycles on who-knows-what, and you'd prefer it spent that effort doing what you want, rather than planning for what you don't.

We're not going to invest trillions into building some super AI and completely forget about it. After we give it control of all of our natural resource harvesting and infrastructure.

No, what you're talking about is a deity. If you invent a deity, then my thoughts on the matter don't really apply, since I'm not a deity. But that's no worse than advanced aliens landing on Earth, and just about as likely to happen.

I think you put too much emphasis on the vast gulph between most players and Lee Seedol. This is true of pretty much any game/sport where there are professionals. Most footballers will never be Messi, most hockey players will never be Gretzky, etc. This is true in every field where you have that kind of power-law distribution.

For physical sports, it's easy to robotically build a machine to play and train some computer vision models to follow the ball/puck.

For Go, the competition is on a different level that you couldn't just build a machine to play.

Have you played go? When you play Go you start to appreciate how complex it is, how strong Lee Sedol is and what this achievement means.

In 2006 a paper was published (by Remi Coulom) showing that just playing random moves and combining those random playouts with a tree search scaled up in strength arbitrarily as you throw more computational resources at the problem.

Google made them a little less random and threw a ton of hardware and optimizations at it. (Basically 20 times as much as anyone before them, which goes a long way of explaining the performance jump)

Don't make this something it is not.

I wonder how the 13x13 or 21x21 match would go. IIRC AlphaGo was tuned to 19x19 board.