John Long: Darwin’s Devices

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John Long Darwin’s Devices
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Darwin’s Devices: краткое содержание, описание и аннотация

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The challenge of studying evolution is that the history of life is buried in the past—we can’t witness the dramatic events that shaped the adaptations we see today. But biorobotics expert John Long has found an ingenious way to overcome this problem: he creates robots that look and behave like extinct animals, subjects them to evolutionary pressures, lets them compete for mates and resources, and mutates their ‘genes’. In short, he lets robots play the game of life. In Darwin’s Devices, Long tells the story of these evolving biorobots—how they came to be, and what they can teach us about the biology of living and extinct species. Evolving biorobots can replicate creatures that disappeared from the earth long ago, showing us in real time what happens in the face of unexpected environmental challenges. Biomechanically correct models of backbones functioning as part of an autonomous robot, for example, can help us understand why the first vertebrates evolved them. But the most impressive feature of these robots, as Long shows, is their ability to illustrate the power of evolution to solve difficult technological challenges autonomously—without human input regarding what a workable solution might be. Even a simple robot can create complex behavior, often learning or evolving greater intelligence than humans could possibly program. This remarkable idea could forever alter the face of engineering, design, and even warfare. An amazing tour through the workings of a fertile mind, Darwin’s Devices will make you rethink everything you thought you knew about evolution, robot intelligence, and life itself.

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Thinking fish, fish, fish, Koob and I overlaid on this pattern of convergent vertebral evolution the pattern of changes in swimming behavior. Because we really know so little about swimming speeds and accelerations in vertebrates—which is the same problem that plagued us in the biomechanical analysis—the correlation was weak and, therefore, disappointing. First off, we had to leave out the land-based tetrapods because few adult tetrapods have retained their ancestral fish-like bodies and swimming behaviors. Second, with only elasmobranchs and ray-finned fishes to compare, we only have two large points on the map. Given those caveats, what we think we see on the tree is that vertebrae are correlated with faster swimming. Observations of single species appear to bear this out: frilled sharks with notochords are slow and plodding; mako sharks with vertebrae are some of the fastest fish in the sea; paddlefish with notochords cruise along but are not acrobatic; salmon with vertebrae leap over waterfalls. We were left with the same expectation our biomechanical analysis generated: stiffer backbones would allow their possessors to swim faster than those with floppier backbones.

But this expectation—this prediction—even though it is based on biomechanical and phylogenetic data, isn’t satisfying because it leaves so many questions unanswered. Are the proxies for stiffness and speed reasonable? Is the phylogenetic tree accurate? What other parts of the body, like muscles and shape, influence stiffness and speed? Do we find only a weak correlation because other parts of the species are different too? Would the correlation hold up if we could measure top speeds seen in the wild? Might stiffness also impact other parts of swimming performance, like acceleration and turning? What are the trade-offs in performance with increased speed? And worst of all, these questions don’t even speak to the evolutionary question of the dynamic process of adaptation.

When we ask why vertebral columns evolved from notochords, we are asking about adaptation. For biologists adaptation is the process by which natural selection acts over generational time to alter—to evolve—the characteristics of a population of organisms. Evolution by natural selection—as proposed by Darwin and supported since his time by thousands of experimental and observational tests—happens when the following conditions are met: (1) a feature, like the backbone, varies from individual to individual; (2) genes, at least in part, code the feature and its variations; and (3) the feature’s variations impact how individual organisms behave, survive, and reproduce relative to others in that population. When these three conditions are in place, what we see as we watch a population over time is that some individuals are better at making babies than are others. Because of these individual differences in reproductive output, as individuals and generations die, the population looks different, physically and genetically, from what it once looked like. This change over time is what Darwin called “descent with modification” and what we now call “evolution by natural selection.”

FIGURE 1.1. Evolving robots. Three autonomous, fish-like robots compete with each other for food. Because the swimming mode, sensory system, and brain of these robots are based on the tadpole-shaped larvae of sea squirt chordates, we call them “Tadros,” short for “tadpole robots.” Each Tadro has for its axial skeleton a notochord of differing stiffness. The stiffness of the notochord controls the swimming performance of the Tadro. Stiffness of the notochord is genetically coded and can, therefore, evolve from one generation to the next.

Like a clumsy criminal, adaptation leaves behind many clues in the DNA and anatomy of extinct and living species. But adaptation never leaves behind witnesses or a surveillance tape. Biologists inevitably have to guess at the process of evolution. The best guesses about what went on come from reconstructing the events. Using the clues—the physical evidence—good investigators can piece together a step-by-step sequence of places, agents, and interactions that most likely caused the outcome.

And what can we do to test this sequence? We can build models, let them run, and see if their behavior matches our predictions based on our evolutionary reconstruction. But we can also do one better: let the models evolve. This idea is what would ultimately lead us to invent something my students, collaborators, and I came to call Tadros (Figure 1.1). Starting with those little autonomous robots—not much more than a small computer in a bowl—we were about to embark on a journey of considerable discovery that would help us understand not just what a backbone does for a marlin, but what evolution can do for technology, and what technology can do for our knowledge of the history of life. Which is to say, Tadros themselves would be the best answer to the question: what do robots have to do with biology?

Chapter 2


“GREAT IS THE POWER OF STEADY MISINTERPRETATION.” This lament by Charles Darwin, from his sixth and final edition of The Origin of Species in 1872, summed up years of simmering frustration. Many of his critics and even some of his well-meaning champions had oversimplified his particular theory (other theories existed at the time) of descent with modification, what we now call evolution. The oversimplification was this: descent with modification has a single cause, natural selection.

Although natural selection was Darwin’s most important insight, he recognized and stated repeatedly in print that while it was the primary mechanism of change, it was not the only one. “Evolution by natural selection” was the phrase that I used in the previous chapter to define “adaptation.” Though that may be true, it’s only part of the evolutionary picture. Oops. Because I didn’t talk about other kinds of causal mechanisms—like mutation, recombination, genetic drift, and assortative mating—I’m one of the oversimplifiers. Let me make amends here to get you ready for the lifelike complexities of evolving robots.

I think that Darwin, a keen observer, would’ve loved watching our evolving robots. With them we can show what evolution looks like when selection is dominant, on the one hand, and when it takes a backseat to other evolutionary mechanisms, on the other. We can use robots to look at evolutionary processes, those ongoing, real-time, cause-and-effect interactions of autonomous agents with their environment—at any specific place and time. That is, we can become spectators at the greatest game on Earth: the game of life.

Think of it this way: life is a game, a never-ending contest played on the world’s stage. But the players are not often locked in open combat. Although a great white shark hunting a California sea lion makes for dramatic theater on Animal Planet’s “Shark Week,” in the real game of life most of the players never meet. Instead, each player is more like a plodding decathlete, doing ten different sports in quick succession and often at the same time. Each mobile, autonomous animal navigates its landscape, finds food or hunts for it, figures out how or if to eat what it’s found, detects and escapes threats, seeks and selects mates, finds shelter if it can, and makes offspring. Winners are those who survive long enough to reproduce. Among the winners, the champions are those who have the most children. The game of life is called evolution, and robots are allowed to play.

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