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演讲简介：
神经学家Daniel Wolpert从一个出乎意料的前提谈起：大脑的进化不是思考或感觉的需要，而是为了控制身体运动。在这个有趣又不乏数据支持的演讲里，沃普特向我们简述人类大脑如何举重若轻地创造出灵活的身体运动姿态。
n. 沙文主义者；盲目的爱国者；沙文主义组织 adj.
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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.
- 所以说，我是个运动沙文主义者，我认为运动是大脑最重要的功能，不要让别人告诉你这个观点不对。
- Now if movement is so important, how well are we doing understanding how the brain controls movement?
- 那么，如果运动如此重要，我们已经能在多大程度上了解大脑如何控制动作了呢？
- And the answer is we're doing extremely poorly; it's a very hard problem.
- 答案其实是非常少，因为这是相当困难的。
- 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.
- 不过我们已经制造出一些模拟人类动作的机器人，分析这些机器的动作水平就知道我们了解大脑控制动作的研究进展如何了。
- Think about the game of chess. How well are we doing determining what piece to move where?
- 来看国际象棋这个游戏。我们让机器人决定如何走棋时候的表现如何呢？
- 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.
- 如果大家趁国际象棋冠军加里·卡斯帕罗夫还没进监狱的时候，把他请来与IBM的深蓝对战，深蓝有时会赢。
- And I think if IBM's Deep Blue played anyone in this room, it would win every time.
- 而且我觉得IBM的深蓝和在座的任何一位对战，应该每次都会赢。
- 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?
- 所以这方面完全没有问题。但是如果让机器人灵巧地拿起棋子，再放回棋盘上去呢？
- 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.
- 如果大家让五岁小孩子与当今最厉害的机器人对决，结果很简单，小孩子会赢得轻而易举。完全没有悬念。
- Now why is that top problem so easy and the bottom problem so hard?
- 那么为什么前面那个问题这么简单，后面这个问题就这么难呢？
- 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.
- 一个原因是，一个聪明点的五岁小孩子就已经能够告诉你上面那个问题的解决算法了--找出游戏结束之前所有可能的下法，选择赢面最大的一步来下。
- So it's a very simple algorithm.
- 所以其实这是个很简单的算法。
- Now of course there are other moves, but with vast computers we approximate and come close to the optimal solution.
- 当然也有其他的步法，不过用强大的计算机做近似计算就能很容易找到近似最优解。
- When it comes to being dexterous, it's not even clear what the algorithm is you have to solve to be dexterous.
- 但在灵活性这个问题上，我们甚至连让机器人变灵活的算法都找不到。
- 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，又作用于世界的话，其实是要面对很多问题的。
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