我们对NN的兴趣在于，有朝一日NN能够 go far beyond such basic pattern recognition 问题。
我们能否造出能够match or surpass human intelligence的machine？
作者认为 an intelligent computer is possible - although it may be extremely complicated。
让我们再简化问题，是否存在 a simple set of principles which can be used to explain intelligence？
即，is there a simple algorithm for intelligence?
当然这个问题看起来很复杂，但不是不可能啊。
历史上有好多类似的例子。
一开始人们认为行星运动很难去用一些简单的原则来解释，后来牛顿搞出了万有引力定律。
世间万物这么纷杂，都能被门捷列夫的元素周期表解释。
这么看来，虽然存在这么多 optimistic examples，但也有可能intelligence can only be explained by a large number of fundamentally distinct mechanisms.
就拿brain来说，很多大脑机制是为了应对 selection pressures in our species’ evolutionary history.
这样，intelligence involves considerable irreducible complexity, and no simple algorithm for intelligence is possible.
关于 whether there’s a simple explanation of how human brains work.有两种解释
第一个：从连接的角度讲，the brain contains on the order of 100 billion neurons, 100 billion glial cells, and 100 trillion connections between neurons.很复杂。
第二个：从molecular biology的角度，即dna，人和大猩猩只有4%的差别。
我们的染色体不可能包含了neural connections的详细描述。只需要指定the broad architecture and basic principles underlying the brain. 这就能保证 we humans will grow up to be intelligent。 the information in our genes contains the essence of how we think.
还有其他证据：不同文化的人都develop出了相似的技能，如 音乐舞蹈，词汇结构等。这就表明many very specialized behaviours are hardwired into particular parts of our brains.
作者倾向于认为there being a simple algorithm for intelligence.
因为， an unjustified optimism is often more productive than a seemingly better justified pessimism,
A pessimist may be more “correct” in some narrow sense, but will discover less than the optimist.
we can accept that no-one yet knows, and instead work hard on developing the big, bold idea, in the understanding that while we have no guarantee of success, it is only thus that our understanding advances.
The key to artificial intelligence is simple, powerful ideas, and we can and should search optimistically for those ideas. But we’re going to need many such ideas, and we’ve still got a long way to go!