In computer scientist Leslie Valiant's recent book, Probably Approximately Correct, we discover one human behavior that computers have a hard time emulating: our ability to cope in new environments. But Valiant believes one day computers will master our coping mechanisms. How? We've got an excerpt.
By Leslie Valiant
Excerpt from Chapter 1
In 1947 John von Neumann, the famously gifted mathematician, was keynote speaker at the first annual meeting of the Association for Computing Machinery. In his address he said that future computers would get along with just a dozen instruction types, a number known to be adequate for expressing all of mathematics. He went on to say that one need not be surprised at this small number, since 1,000 words were known to be adequate for most situations in real life, and mathematics was only a small part of life, and a very simple part at that. The audience reacted with hilarity. This provoked von Neumann to respond: “If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is.”
Though counterintuitive, von Neumann’s quip contains an obvious truth. Einstein’s theory of general relativity is simple in the sense that one can write the essential content on one line as a single equation. Understanding its meaning, derivation, and consequences requires more extensive study and effort. However, this formal simplicity is striking and powerful. The power comes from the implied generality, that knowledge of one equation alone will allow one to make accurate predictions about a host of situations not even conceived when the equation was first written down.
Most aspects of life are not so simple. If you want to succeed in a job interview, or in making an investment, or in choosing a life partner, you can be quite sure that there is no equation that will guarantee you success. In these endeavors it will not be possible to limit the pieces of knowledge that might be relevant to any one definable source. And even if you had all the relevant knowledge, there may be no surefire way of combining it to yield the best decision.
This book is predicated on taking this distinction seriously. Those aspects of knowledge for which there is a good predictive theory, typically a mathematical or scientific one, will be called theoryful. The rest will be called theoryless. I use the term theory here in the same sense as it is used in science, to denote a “good, effective, and useful theory” rather than the negative sense of “only a theory.” Predicting the orbit of a planet based on Newton’s laws is theoryful, since the predictor uses an explicit model that can accurately predict everything about orbits. A card player is equally theoryful in predicting an opponent’s hand, if this is done using a principled calculation of probabilities, as is a chemist who uses the principles of chemistry to predict the outcome of mixing two chemicals.
In contrast, the vast majority of human behaviors look theoryless. Nevertheless, these behaviors are often highly effective. These abundant theoryless but effective behaviors still lack a scientific account, and it is these that this book addresses.
The notions of the theoryful and the theoryless as used here are relative, relative to the knowledge of the decision maker in question. While gravity and mechanics may be theoryful to a physicist, they will not be to a fish or a bird, which still have to cope with the physical world, but do so, we presume, without following a theory. Worms can burrow through the ground without apparently any understanding of the physical laws to which they are subject. Most humans manage their finances adequately in an economic world they don’t fully understand. They can often muddle through even at times when experts stumble. Humans can also competently navigate social situations that are quite complex, without being able to articulate how.
In each of these examples the entity manages to cope somehow, without having the tenets of a theory or a scientific law to follow. Almost any biological or human behavior may be viewed as some such coping. Many instances of effective coping have aspects both of the mundane and also of the grand and mysterious. In each case the behavior is highly effective, yet if we try to spell out exactly how the behavior operates, or why it is successful, we are often stumped. How can such behavior be effective in a world that is too complex to offer a clear scientific theory to be followed as a guide? Even more puzzling, how can a capability for such effective coping be acquired in the first place?
Science books generally restrict their subject matter to the theoryful. However, I am impressed with how effectively life forms “cope” with the theoryless in this complex world. Surely these many forms of coping have some commonality. Perhaps behind them all is a single basic phenomenon that is itself subject to scientific laws.
This book is based on two central tenets. The first is that the coping mechanisms with which life abounds are all the result of learning from the environment. The second is that this learning is done by concrete mechanisms that can be understood by the methods of computer science.
On the surface, any connection between coping and computation may seem jarring. Computers have traditionally been most effective when they follow a predictive science, such as the physics of fluid flow. However, computers also have their softer side. Contrary to common perception, computer science has always been more about humans than about machines. The many things that computers can do, such as search the Web, correct our spelling, solve mathematical equations, play chess, or translate from one language to another, all emulate capabilities that humans possess and have some interest in exercising. Depending on the task, the performance of present-day computers will be better or worse than humans. But in regarding computers merely as our slaves for getting things done, we may be missing the point. The overlap between what computers and humans do every day is already vast and diverse. Even without any extrapolation into the future, we have to ask what computers already teach us about ourselves.
The variety of applications of computation to domains of human interest is a totally unexpected discovery of the last century. There is no trace of anyone a hundred years ago having anticipated it. It is a truly awesome phenomenon. Each of us can identify our own different way of being impacted by the range of applications that computers now offer. A few years ago I was interested in the capabilities of a certain model of the brain. In a short, hermit-like span of a few weeks I ran a simulation of this model on my laptop and wrote up a paper based on the calculations performed by my laptop. I used a word processor on the same laptop to write and edit the article. I then emailed it off to a journal again from that laptop. This may sound unremarkable to the present-day reader, but a few generations ago, who would have thought that one device could perform such a variety of tasks? Indeed, while for most ideas some long and complex history can be traced, the modern notion of computation emerged remarkably suddenly, and in a most complete form, in a single paper published by Alan Turing in 1936.
Science prior to that time made no mention of abstract machines. Turing’s theory did. He defined the mathematical notion of computation that our all-pervasive information technology now follows. But in offering his work, he made it clear that his goal went beyond understanding only machines: “We may compare a man in the process of computing a real number to a machine which is only capable of a finite number of conditions.” With these words he was declaring that he was aiming to formalize the process of computation where a human mechanically follows some rules. He was seeking to capture the limits of what could be regarded as mechanical intellectual work, where no appeal to other capabilities such as intuition or creativity was being made.
Turing succeeded so well that the word computation is now used in exactly the sense in which he defined it. We forget that a “computer” in the 1930s referred to a human being who made a living doing routine calculations. Speculations that philosophers or psychologists entertained in earlier times as to the nature of mechanical mental capabilities equally dim in the memory. Turing had discovered a precise and fundamental law that both living and inert things must obey, but which only humans had been observed to exhibit up to that time. His notion is now being realized in billions of pieces of technology that have transformed our lives. But if we are blinded by this technological success, we may miss the more important point that Turing’s concept may enable us to understand human activity itself.
This may seem paradoxical. Humans clearly existed before Turing, but Turing’s notion of computation was not noticed before his time. So how can his theory be so fundamental to humans if little trace of it had even been suspected before?
My answer to this is that even in the pre-Turing era, in fact since the beginning of life, the dominating force on Earth within all its life forms was computation. But the computations were of a very special kind. These computations were weak in almost every respect when compared with the capabilities of our laptops. They were exceedingly good, however, at one enterprise: adaptation. These are the computations that I call ecorithms—algorithms that derive their power by learning from whatever environment they inhabit, so as to be able to behave effectively in it. To understand these we need to understand computations in the Turing sense. But we also need to refine his definitions to capture the more particular phenomena of learning, adaptation, and evolution.
Excerpted with permission from Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World, by Leslie Valiant. Available from Basic Books, a member of The Perseus Books Group. Copyright © 2013.