Author Peter J. Bentley “is interested in more than just building the next algorithm. He wants to understand the deep meaning of digital biology — what common principle ties together projects as disparate as computer immune systems, neural networks and virtual ant colonies…”
“The rules of a system actually matter more than the stuff the system is made of. In fact, the stuff matters so little that Bentley sees no real difference between digital biology and biology outside of a computer. To him, there is nothing artificial about artificial life: ‘The first person to hold a conversation with an alien intelligence will not be an astronaut, it will be a computer scientist or computational neuroscientist, talking to an evolved digital neural network.'”
‘Digital Biology’: Is This Chip Educable?
By CARL ZIMMER
Biologists tolerate a level of mystery in their work that would drive your average engineer or computer programmer crazy. They’ve put together a complete rough draft of the human genome but they have little understanding of how those 40,000 or so genes work together to make a human. They’ve mapped every muscle and nerve in a fly’s wings, yet still struggle to explain how it keeps from crashing into a wall. No engineer would build a DVD player without knowing what every circuit was for; no programmer would let a computer write its own code. Or at least that’s how things used to be. As Peter J. Bentley demonstrates in ”Digital Biology,” the cool, rational temple of technology is becoming infested with biology’s weedy enigmas.
Microchips, for example, can now evolve. Bentley describes how Adrian Thompson, a British engineer, came up with a few dozen random arrangements of transistors and programmed a computer to test how well they did various jobs, like distinguishing between high-pitched and low-pitched tones. The first generation of chips always performed miserably, but some of them a little less miserably than the rest. The computer saved the less miserable designs and combined them into hybrids. In the process, it also sprinkled a few random changes into the designs, mutations if you will. A few offspring could distinguish between the tones slightly better than their parents — and they produced a third generation. By mimicking evolution for a few thousand rounds, the computer produced chips that did their job exquisitely well. But Thompson doesn’t quite know how they work. To understand them, he resorts to measuring the temperature of parts of the chips, like a neurologist using an M.R.I. scanner to probe a brain.
People have been exploring digital biology since the 1970’s, and Bentley’s book is not the first history. Its predecessors include Steven Levy’s ”Artificial Life” (1993), Kevin Kelly’s ”Out of Control” (1994) and ”Emergence,” by Steven Johnson, published last year. In some ways, ”Digital Biology” suffers by comparison. Some of Bentley’s case studies have been written about before, and he doesn’t try hard to explain how digital biology may transform culture. He promises it will change our lives, but backs up the claim only with lists of coming appliances: washing machines with chaos-theory-driven spin cycles! Carpet-cleaning microrobots! But more stuff tends to clutter life, not change it. To observe people leading an utterly conventional lifestyle, just watch ”The Jetsons.”
Yet Bentley has an important advantage over previous chroniclers: he is a digital biologist himself. (He teaches at University College, London, and specializes in evolutionary computing.) Digital biology is at a crucial point in its history; it is quickly changing from thought experiments into a real science, and Bentley is part of the experience. His book is fascinating because it gives us a sense of what it’s like to be overwhelmed the way Bentley is these days — he calls it ”riding a tornado.” He has also become a discerning student of biology. He demonstrates a good sense of what biologists know about how life works and what they don’t. And he shows how biology is essential to the work he does. The strategy ants use to follow scent trails becomes a method for laying out networks of cellphone towers. The way embryos develop becomes a method for turning a small program into a complex one without any intervention from a programmer.
Bentley is interested in more than just building the next algorithm. He wants to understand the deep meaning of digital biology — what common principle ties together projects as disparate as computer immune systems, neural networks and virtual ant colonies. He believes complexity can emerge spontaneously in any system in which many parts interact according to certain rules. The rules can be simple, but it’s crucial that each part, be it a neuron or a chunk of programming code, can affect the behavior of other parts, creating a complex pattern of feedbacks.
The rules of a system actually matter more than the stuff the system is made of. In fact, the stuff matters so little that Bentley sees no real difference between digital biology and biology outside of a computer. To him, there is nothing artificial about artificial life: ”The first person to hold a conversation with an alien intelligence will not be an astronaut, it will be a computer scientist or computational neuroscientist, talking to an evolved digital neural network.”
In a sense Bentley is right, but in a sense that is nearly meaningless. Computers don’t replicate nature; they replicate what we think we know about nature. And biologists are the first to tell you their models of the brain, the immune system or the network of proteins in a cell are pretty crude. Computer programs modeled after these models are even cruder. The most complex digital ”brain” consists of a few thousand simulated neurons — a far cry from the human brain, which consists of 100 billion neurons, each of which is connected to thousands of its neighbors and uses dozens of neurotransmitters to communicate with them. To treat them as the same thing is a bit like treating four notes played in a thousand combinations as the same thing as Mahler’s Ninth Symphony. They share some things, but not the things that really matter.
A biological concept doesn’t even have to be true to make for good software. Bentley describes how computer scientists invented a way to destroy computer viruses based on a 1970’s model of the immune system. But immunologists now consider the model a failure. The fact that computer programmers can turn a failed biological idea into a powerful program is proof that life and machinery are not interchangeable. Instead, they both draw their strength from a common source — the murky depths of complexity.
The relationship works both ways. Just as computers can be lifelike, biologists realize that in a lot of ways life acts like a computer. Bentley doesn’t spend much time on this, but it is an astonishing development in biology. Neuroscientists build neural networks to understand how different parts of the brain work; researchers who study insect navigation build robots to test their ideas; biochemists now treat genes as if they were lines of code in a piece of software. Thinking of life as a computer doesn’t drain the majesty from life. In fact, its grandeur only deepens. And that kind of insight is worth more than all the carpet-cleaning robots in the world.