诺奖得主Wilczek：通向自我复制的机器之路
The following article is from 蔻享学术 Author Frank Wilczek
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撰文 | Frank Wilczek
翻译 | 胡风、梁丁当
中文版
或许很快，我们就能模仿生命体的功能，制造出能自我复制的机器。
纵观历史长河，富有创造力的人类工程师不断从生物世界中汲取灵感。莱昂纳多·达芬奇（Leonardo da Vinci）受到鸟类、鱼类和乌龟的启发，分别设计了飞行器、潜艇和坦克。如今，受动物神经系统启发而研发的计算机构架——人工神经网络，已成为机器学习的前沿技术。但这些应用都未触及生物学的深层结构。而这，或将成为未来创造的灯塔。
诺 贝 尔 生 理 学 或 医 学 奖 获 得 者 保 罗 · 纳 斯（Paul Nurse）在他的新书《生命是什么？》（What is Life?）中指出，生命的深层结构指的是细胞或有机体这样的基本单元，它们能够自我繁殖，并允许微小的变异。繁殖与变异共同通过自然选择推动物种的演化，从而形成多样化的生物种群。它们不仅能够在变化的环境中存活，还能够利用新的机会。而那些成功适应环境的单元就能继续繁殖后代。
类似的机制在不同的尺度上都发挥着作用，构成了众多关键生物过程的基础。胚胎从单细胞发育为成熟有机体的过程中，会经历好几个生长阶段（人类有几十个），每个都与前一个略有不同。最终，受精卵繁衍出各种不同的细胞，包括心脏、肝脏和脑部的细胞。在一个微型的演化过程中，局部物理、化学环境中的信号会诱导“正确”的细胞形成。当干细胞在应对损伤，或者是皮肤、肠道和血液细胞由于磨损而死亡时，这样的微型演化机制也会被激活。
约翰·冯·诺依曼（John von Neumann）和达芬奇一样，也是一位有远见的工程师，只不过他表达的方式不是通过艺术，而是方程式与图表。他创建了博弈论，以及以程序和随机存储器为基本特征的“冯诺依曼体系”——这几乎是所有现代计算机的基础。他早期将量子力学与信息理论联系起来的一些观点直到“第二次量子革命”时才被广泛认可。
冯·诺依曼于1957年去世。去世前，他正在研究一个新项目，其未完成的手稿后来被收入了自我复制自动机理 论 》（Theory of Self-Reproducing Automata）一书。其中，他精确地设计了一个被称为“通用复制器”的数学模型。它包括三个基本组成部分：机器A是一台可以根据指令整合资源并进行组装的机器 ；程序B能够指挥机器A；主程序C可以指挥A来制造A+B+C。
对于这种能在简单化的现实世界中运行的复制系统，冯·诺依曼做了严格、详细的设计。从技术上讲，它是一台元胞自动机，可以从周围随机散落的碎片中获取零件。原则上，根据他的设计，你可以用现代技术造出一个3D打印与计算机的混合系统，它能够收集材料来制作你想要的东西或复制其自身。通过精心设计故意犯错的程序或宽松的质量控制，我们也能解锁生命的另一个秘密——变异。
用现成的3D打印机、计算机和原材料所构建的系统肯定是笨拙和低效的。如果有一天，科学家们能够从生物中学到如何根据编码在DNA上的指令来制造分子机器，那么冯 · 诺依曼的愿景将更接近实用。要记住，他早期的电脑模型是在真空电子管时代提出的，这同样超越了当时的技术。
能自我复制的机器可以释放指数增长的魔力，或许能让一些大胆的工程成为现实。它们或许能让将其他天体地球化的科幻梦想变得触手可及。而最深刻的或许是，通过呈现生物学的深层结构，生命与非生命的界限终将变得模糊。
英文版
Advances in technology will soon allow us to build machines that replicate themselves and evolve like living beings.
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.
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。
本文经授权转载自微信公众号“蔻享学术”。本文纸质版发表在《环球科学》2021年10月刊，编辑：黄琦。
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