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So I am a movement chauvinist. I believe movement is the most important function of the brain -- don't let anyone tell you that it's not true.
所以說，我是個運動沙文主義者，我認為運動是大腦最重要的功能，不要讓別人告訴你這個觀點不對。
Now if movement is so important, how well are we doing understanding how the brain controls movement?
那么，如果運動如此重要，我們已經能在多大程度上了解大腦如何控制動作了呢？
And the answer is we're doing extremely poorly; it's a very hard problem.
答案其實是非常少，因為這是相當困難的。
But we can look at how well we're doing by thinking about how well we're doing building machines which can do what humans can do.
不過我們已經制造出一些模擬人類動作的機器人，分析這些機器的動作水平就知道我們了解大腦控制動作的研究進展如何了。
Think about the game of chess. How well are we doing determining what piece to move where?
來看國際象棋這個游戲。我們讓機器人決定如何走棋時候的表現如何呢？
If you pit Garry Kasparov here, when he's not in jail, against IBM's Deep Blue, well the answer is IBM's Deep Blue will occasionally win.
如果大家趁國際象棋冠軍加里·卡斯帕羅夫還沒進監獄的時候，把他請來與IBM的深藍對戰，深藍有時會贏。
And I think if IBM's Deep Blue played anyone in this room, it would win every time.
而且我覺得IBM的深藍和在座的任何一位對戰，應該每次都會贏。
That problem is solved. What about the problem of picking up a chess piece, dexterously manipulating it and putting it back down on the board?
所以這方面完全沒有問題。但是如果讓機器人靈巧地拿起棋子，再放回棋盤上去呢？
If you put a five year-old child's dexterity against the best robots of today, the answer is simple: the child wins easily. There's no competition at all.
如果大家讓五歲小孩子與當今最厲害的機器人對決，結果很簡單，小孩子會贏得輕而易舉。完全沒有懸念。
Now why is that top problem so easy and the bottom problem so hard?
那么為什么前面那個問題這么簡單，后面這個問題就這么難呢？
One reason is a very smart five year-old could tell you the algorithm for that top problem -- look at all possible moves to the end of the game and choose the one that makes you win.
一個原因是，一個聰明點的五歲小孩子就已經能夠告訴你上面那個問題的解決算法了--找出游戲結束之前所有可能的下法，選擇贏面最大的一步來下。
So it's a very simple algorithm.
所以其實這是個很簡單的算法。
Now of course there are other moves, but with vast computers we approximate and come close to the optimal solution.
當然也有其他的步法，不過用強大的計算機做近似計算就能很容易找到近似最優解。
When it comes to being dexterous, it's not even clear what the algorithm is you have to solve to be dexterous.
但在靈活性這個問題上，我們甚至連讓機器人變靈活的算法都找不到。
And we'll see you have to both perceive and act on the world, which has a lot of problems.
可以看到，如果既感知世界，又作用于世界的話，其實是要面對很多問題的。
