The most cutting-edge algorithms are known as "genetic algorithms," because they self-adapt their "recipes" through interactions with a wider environment of stimuli, thereby approximating evolutionary responses found in nature. The hardest part about explaining that to people comes from overcoming their bias toward unnatural silicon solutions, as opposed to the carbon-based pathways of discovery and adaptation through failure that define our human existence. Oddly enough, people tend to trust computers' seeming infallibility more than nature's trial and error.
But at the same time, people fear a more highly technologized future, because they assume it will be less natural. In truth, technology, including computing, will evolve more in the direction of nature than the other way around, and will fuse with it increasingly on the latter's terms. When we watch the movie, "Avatar," we see its naturally "wired" planet of Pandora through the prism of our own nostalgia for the primitive. But as a fascinating new book on computing argues, Pandora's back-to-nature alternative to our own frighteningly technologized trajectory is not the lost past, but rather the inevitable future of the path we find ourselves on.
In "
Natural Computing: DNA, Quantum Bits, and the Future of Smart Machines," Dennis Shasha and Cathy Lazere draw upon interviews with 15 leading scientists working in disparate fields to explore the outer reaches of computing. They expected to write a book about a future world dominated by thinking machines, but instead found that the common vision to have emerged across all of these fields is that "the future of computing is a synthesis with nature." The way forward is presented less as a choice and more as a necessity: In computing as in deep space, to really "go where no man has gone before," we'll need machines that can self-heal, learn and evolve -- just like humans.
Shasha and Lazere identify three strands to this vision. First, biological thinking has inspired new approaches to digital computing. The classic digital methodology is to pose a question, generate a model, code a program, and run the calculations that yield the answer. But what if the target in question contains too many "unknown unknowns," meaning it can't be directly modeled? What if you need to wander in the desert for a while, making unexpected discoveries along the way? That's where genetic algorithms come into play, in an iterative and highly competitive process: You start with a population of candidates, tweak them with random changes, and after evaluating their relative fitness, you combine the best ones into even better combinations that move you closer to the answers. Nature may abhor a vacuum, but she admires creative workarounds.
The second strand predicts that biological entities will eventually replace most silicon-based computing. We currently live in an environment dominated by a relatively small number of brand-name models that span great networks and enjoy lengthy careers. But as Shasha and Lazere note, "computers made of bacteria or viruses come by the million, have no names, and are provincial -- they communicate only with their neighbors." Yes, they often fail. But according to the authors, a complex computing machine made up of trillions of not-all-that-smart and prone-to-failure cells can nonetheless tackle the most complex challenges. Don't believe it? Just think of the human body, which can "run, think, and love, even though none of our individual cells can do those things."
Finally, the third strand of this new vision for computing says we'll invariably move past much of the binary calculations that define digital computing (answers cast in 0s and 1s) and toward the measuring of answers that come with naturally unique signatures. Unlike the industrial model of "one size fits all," in which form follows function, solutions will be customized for each organism, allowing form to follow nature. The book offers as an example a cellular computer that monitors arteries from inside the human body. When it detects a slight abnormality in its host's heartbeat, it transmits its findings to an outside computer that then designs a bacterial cleaning agent specifically for the host's body. A week later the remedy arrives in the mail.
The paradigm shift here cannot be overstated. In our current world, computer programs either work or fail, and changing software, according to Shasha and Lazere, resembles heart surgery: "You want everything in place before you wake the patient," which in this case means rebooting your PC. By contrast, in a more naturalized future computing environment, we'll expect our computers to fail more often, but to recover on their own -- in effect, fending for themselves more and more. Instead of finding them alien, argue Shasha and Lazere, we may find ourselves "feeling affection for these machines," because the fact that they tackle harder problems, sometimes make mistakes, and repair themselves will make them "more human."
Reading the book, I came away with the comforting thought that the mindset of future computers will seem far less alien to my kids than to me. I came of age in a world where science seemed more of an exogenous, uncontrollable threat (e.g., nuclear war) than an intimate trustworthy companion. But my children, having grown up totally networked from day one, seem both more comfortable with these tools -- and with the wandering, trial-and-error logic they naturally reward. In my childhood, you needed to bat .300 to make the town's Little League team, and success was measured in the most binary of fashion: Did you get on base or not? But my kids think nothing of trying 5,000 different ways to complete a role-playing game, learning from each success and failure as they move along.
That's a major theme in "Natural Computing": Most of what we call computing today seeks to approximate skills that humans have learned only in the past few hundred years. So programmers tend to err on the side of bigness, complexity and ultra-reliability, when nature says to keep it simple, distributed and fault-tolerant. In the future, we will rely less on the giant, central processing unit and more on the swarm of relatively dumb machines that operate according to a few simple rules. One man's "shallow thinking" is another man's evolutionary leap in networked intelligence.
As one scientist interviewed in the book put it, evolutionary computing "takes you to strange places." That may not sound comforting, but given all the "unknown unknowns" we're confronting thanks to globalization's continued advance, we'll need ever more capable and adaptable tools as we inexorably move toward the "undiscovered territory" that lies ahead.
Natural computing, as envisaged by Shasha and Lazere, will take on added importance as our technological society inevitably reaches a tipping point in terms of its impact on this planet. Without a doubt, we'll put Mother Earth through its most trying stress test in the decades ahead, so the more cognizant we become of her stunning complexity and resilience -- leveraging it all we can -- the better. Natural computing alone won't save the planet, but it could help us find the "unknown unknowns" we'll need to do so.
