This is tough to calculate. We can easily come up with benchmark scores for various types of computers, but how do you measure the instructions per second of, say, the chip in a Furby?
这个计算有些困难。我们可以很容易地给各种各样计算机的性能打分，但你如何衡量——比方说——“菲比精灵”玩具中的芯片每秒能够执行多少个指令呢？
Most of the transistors in the world are in microchips not designed to run these tests. If we 8217;re assuming that all humans are being modified (trained) to carry out the benchmark calculations, how much effort should we spend to modify each computer chip so it can run the benchmark? To avoid this problem, we can instead estimate the aggregate power of all the world 8217;s computing devices by counting transistors. It turns out that processors from the 1980s and processors from today have a roughly similar ratio of transistors to MIPS-about 30 transistors per instruction per second, give or take an order of magnitude. A paper by Gordon Moore (of Moore 8217;s law fame) gives figures for the total number of transistors manufactured per year since the 1950s. It looks something like this: Using our ratio, we can convert the number of transistors to a total amount of computing power. This tells us that a typical modern laptop, which has a benchmark score in the tens of thousands of MIPS, has more computing power than existed in the entire world in 1965. By that measure, the year when the combined power of computers finally pulled ahead of the combined computing power of humans was 1977.
世界上绝大多数晶体管都封装在并非专门用于这种测试的芯片里，如果假设所有的人类都经过训练能够进行基准计算的话，那么需要花多少功夫才能修改每一台计算机的芯片以使它们能够进行基准测试呢？为了避免这种问题，我们可以通过数晶体管的数目来粗略估计全球所有计算设备的总计算能力。结果我发现20世纪80年代的处理器和今天的处理器的晶体管数目与MIPS的比值大致相同——这个比值大约为每秒每条指令需要30个晶体管，数据可能误差一个数量级。戈登•摩尔（著名的摩尔定律的发现者）发表的一篇论文中给出了自20世纪50年代以来每年生产的晶体管总量。这些数字画成图表之后长这样：有了这些比值，我们就能把晶体管总数折算成总计算能力。这意味着一台基准测试结果为几万MIPS的现代普通笔记本电脑的计算能力超过1965年全球总人口的计算能力。按照这种算法，计算机的总运算能力超越全人类的总计算能力应该发生在1977年。
The complexity of neurons
神经的复杂度
Again, making people do pencil-and-paper CPU benchmarks is a phenomenally silly way to measure human computing power. Measured by complexity, our brains are more sophisticated than any supercomputer. Right? There are projects that attempt to use supercomputers to fully simulate a brain at the level of individual synapses.5 If we look at how many processors and how much time these simulations require, we can come up with a figure for the number of transistors required to equal the complexity of the human brain. The numbers from a 2013 run of the Japanese K supercomputer suggest a figure of 1015 transistors per human brain.6 By this measure, it wasn 8217;t until the year 1988 that all the logic circuits in the world added up to the complexity of a single brain . . . and the total complexity of all our circuits is still dwarfed by the total complexity of all brains. Under Moore 8217;s law–based projections, and using these simulation figures, computers won 8217;t pull ahead of humans until the year 2036.7
我想再次重申一下，让人类拿纸笔做CPU基准测试来得出人类的计算能力是一个很愚蠢的方法。从复杂度上来看，我们的大脑比任何一台超级计算机都要复杂，没错吧？绝大多数时这是没错的。现在有些项目致力于用超级计算机来完整模拟大脑单独一个突触的功能。6如果我们能看到这些实验动用了多少处理器和时间，我们就能大致猜测出要媲美人类全脑复杂度需要多少个晶体管。2013年日本“京”超级计算机经过测试得出的结果是，每个人脑相当于1015个晶体管。7这样算来，直到1988年全世界所有的逻辑电路加在一起才能抵得上一个人类大脑的复杂度……而与所有人脑加在一起的复杂度比起来，这些电路的总复杂度根本不值一提。如果摩尔定律预测的趋势持续保持下去的话，根据这些模拟结果，计算机要在2036年才能超过人类。