Name: 深度學習與AlphaGo--計算機戰勝人類#2》。 (Deep Learning and AlphaGo - Computers Beating Humans #2)
Uploaded: 2021-01-14T06:43:12.000Z
Duration: 6 min 26 s
Description: 【看影片學英語】數萬部 YouTube 影片，搭配英漢字典即點即查，輕鬆掌握單字發音與用法，長久累積看電影不必再看字幕。

So...computers get really good at beating us at  chess.

程度副詞

And then they get really good at beating us at a lot of things.

We're so used to getting beat by computers that when I play Greece in deity mode, I don't

'Kay, when I play Greece in any mode, I don't cry when it stomps me.

But humanity, crafty devils that we are has long had one secret weapon...Go!

And it was just a matter of time before those computers came for that too.

Welcome to CompChomp...the only show on the internets where we can talk about atari without

having to mention any video game crashes.

From the beginning of AI work, GO was considered the impossible dream.

There were games like tic-tac-toe that could be completely solved with math.

And if you can solve it with math, computers are gonna dominate.

There were games like chess that were a bit harder, but with a little bit of tweaking

of the algorithms, you could also make computers dominate there too.

Hey...uhh...didn't I just talk about this?

You play it by taking stones and placing it on a 19 by 19 grid.

And the goal is to surround more territory than your opponent does.

There are more possible game combinations than there are atoms in the entire known universe.

And every single piece has the exact same value.

So that chess approach...it ain't gonna work.

As recently as 2015, experts in AI were predicting that it would be at least...oh...a decade

before any computers were able to beat top Go players.

Then, in January of 2016, the journal Nature came out with a little article talking about

the algorithms that the Google-backed AI had used to defeat the European champion.

And, BTWs, that exact same AI was going to be facing off against one of the top players

So what had changed between those expert predictions of at least a decade and that article in Nature?

SInce Go has so many possible moves during any game, that uh whole more game combinations

than atoms in the universe thing, you can't have the computer go through every possible

move to determine which move is the best.

Instead, computers use an algorithm called the Monte Carlo Tree Search.

This is where they take a random sampling of possible moves, calculate out to see which

one of those is the best, and then select that move.

AlphaGo is no different than any of the other Go-playing computers on this front.

What sets AlphaGo apart is it uses Deep Learning to prune the unwanted combinations before

Deep Learning is a kind of artificial neural network inspired by the fancy little brains

Brains have neuron and artificial neural networks have simple processors.

Those neurons in brains can be connected.

Brains have a way of getting input from the outside world.

And artificial neural networks have that too.

There are processors that can take in data from the world around it.

Similar to how the brain passes messages from neuron to neuron in order to make decisions,

data gets passed through the layers of an artificial neural network with each layer

transforming it in different ways based on what it's "learned" in previous layers - until

So AlphaGo's training started out with an initial data set of 30 million training moves.

It worked with this data until it could correctly identify the next human move 57% of the time.

So, just a little over half of the time, it could look at what a human was playing and

Then, they matched it up with a slightly modified version of itself and it just played thousands

And, after that, the European champion played hundreds of games against it so it got used

After alllllll of these games, AlphaGo was ready.

Game 1 starts and AlphaGo and Lee Sedol trade moves.

Trying to get a sense for how the other one played.

And at the end, even though he had led the entire game, Lee Sedol lost to AlphaGo.

The second game got off to a very similar start.

Then, on move 37, AlphaGo's piece gets plunked down....*pop*...on a random part of the board.

It's called a shoulder hit, and everyone there that knows anything about Go was just like,

The people that built AlphaGo thought it made a mistake.

They looked at the control room data afterwards and this was not even one of the moves that

30 million moves by top players, and, not a single one of them was this move that AlphaGo