【诺奖得主Wilczek科普专栏】通向自我复制的机器之路
Frank Wilczek
弗兰克·维尔切克是麻省理工学院物理学教授、量子色动力学的奠基人之一。因发现了量子色动力学的渐近自由现象，他在2004年获得了诺贝尔物理学奖。
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Throughout history, creative human engineers have taken inspiration from artifacts of the biological world. Leonardo da Vinci designed flying machines, submarines and tanks with birds, fish and tortoises in mind. Today, artificial neural nets, a computer architecture directly inspired by animal nervous systems, are the cutting edge of machine learning. But none of those applications get to the deep structure of biology-likely a beacon of future creativity.
As the Nobel biologist Paul Nurse explains in his recent book “What is Life?,” the deep structure of life is the existence of physical units (cells or organisms) that can reproduce themselves, allowing small variations. Those ingredients-reproduction and variation-together drive evolution by natural selection. They generate a diverse population that can survive changes and exploit new opportunities. Those that succeed will be those that breed.
Remarkably similar tricks, working on different scales, underlie many other key biological processes. Embryos develop from single cells into mature organisms after several stages of growth (in humans, a few dozen), where each stage differs a little from the previous. Thus, the fertilized egg’s diverse progeny eventually includes heart, liver and brain cells. The “right” kind of cell emerges in response to signals in its local physical and chemical environment, in a kind of guided miniature evolution. Less specialized stem cells can re-ignite this mini-evolution in response to injury or, in the case of skin, gut and blood cells, death by wear-and-tear.
Though he worked in equations and diagrams rather than artistic renderings, John von Neumann was a visionary modern engineer on the level of da Vinci. He gave us game theory and the so-called “von Neumann architecture,” featuring stored programs and random-access memory, that is the foundation for almost all present-day computers. Some of his early ideas connecting quantum mechanics with information theory are only now becoming widely appreciated, in the “second quantum revolution.”
At the time of his death in 1957, at the age of 54, von Neumann was well into a major new project. His unfinished manuscript, edited by Arthur Burns into the book 'Theory of Self-Reproducing Automata,' is monumentally impressive. In it, he gives precise designs for mathematical models of objects he called “universal replicators.” They consist of three basic parts: a machine A that can gather resources and assemble things following a program, a program B that instructs A how to make desired products, and a master program C that instructs A how to make A + B + C.
Von Neumann provided a rigorous, detailed design for a system of this kind operating within a recognizable simplification of the real world. Technically, it would be a cellular automaton within a bath of randomly scattered pieces that it can scavenge for parts. Exploiting modern technologies, you could in principle elaborate on his designs to make a hybrid 3D printer/computer system that collects material to build something you want plus a copy of itself. It wouldn’t be hard to incorporate life’s other secret ingredient-that is, variation—either by deliberate programming or by loose quality control.
A system built with off-the-shelf 3D printers, computers, and the materials they require would be unwieldy and inefficient, to be sure. But as scientists master the art of making molecular machines according to plans encoded in DNA, von Neumann’s vision will get closer to practicality. It’s worth remembering that his early computer designs, which date from the era of vacuum tubes, likewise outstripped available technology.
Self-reproducing machines could unleash the power of exponential growth, thus enabling audacious engineering projects. They might bring the science-fiction dream of terraforming astronomical bodies within reach. Most profoundly, by embodying biology’s deep structure, they would blur the distinction between life and non-life.