On Intelligence Jeff Hawkins with Sandra Blakeslee 1 Contents Prologue 1. Artificial Intelligence 2. Neural Networks 3. The Human Brain 4. Memory 5. A New Framework of Intelligence 6. How the Cortex Works 7. Consciousness and Creativity 8. The Future of Intelligence Epilogue Appendix: Testable Predictions Bibliography Acknowledgments 2 On Intelligence 3 Prologue This book and my life are animated by two passions. For twenty-five years I have been passionate about mobile computing. In the high- tech world of Silicon Valley, I am known for starting two companies, Palm Computing and Handspring, and as the architect of many handheld computers and cell phones such as the PalmPilot and the Treo. But I have a second passion that predates my interest in computers— one I view as more important. I am crazy about brains. I want to understand how the brain works, not just from a philosophical perspective, not just in a general way, but in a detailed nuts and bolts engineering way. My desire is not only to understand what intelligence is and how the brain works, but how to build machines that work the same way. I want to build truly intelligent machines. The question of intelligence is the last great terrestrial frontier of science. Most big scientific questions involve the very small, the very large, or events that occurred billions of years ago. But everyone has a brain. You are your brain. If you want to understand why you feel the way you do, how you perceive the world, why you make mistakes, how you are able to be creative, why music and art are inspiring, indeed what it is to be human, then you need to understand the brain. In addition, a successful theory of intelligence and brain function will have large societal benefits, and not just in helping us cure brain-related diseases. We will be able to build genuinely intelligent machines, although they won't be anything like the robots of popular fiction and computer science fantasy. Rather, intelligent machines will arise from a new set of principles about the nature of intelligence. As such, they will help us accelerate our knowledge of the world, help us explore the universe, and make the world safer. And along the way, a large industry will be created. Fortunately, we live at a time when the problem of understanding intelligence can be solved. Our generation has access to a mountain of data about the brain, collected over hundreds of years, and the rate at which we are gathering more data is accelerating. The United States alone has thousands of neuroscientists. Yet we have no productive theories about what intelligence is or how the brain works as a whole. Most neurobiologists don't think much about overall theories of the brain because they're engrossed in doing experiments to collect more data about the brain's many subsystems. And although legions of computer programmers have tried to make computers intelligent, they have failed. I believe they will continue to fail as long as they keep ignoring the differences between computers and brains. What then is intelligence such that brains have it but computers don't? Why can a six-year-old hop gracefully from rock to rock in a streambed while the most 4 advanced robots of our time are lumbering zombies? Why are three-year-olds already well on their way to mastering language while computers can't, despite half a century of programmers' best efforts? Why can you tell a cat from a dog in a fraction of a second while a supercomputer cannot make the distinction at all? These are great mysteries waiting for an answer. We have plenty of clues; what we need now are a few critical insights. You may be wondering why a computer designer is writing a book about brains. Or put another way, if I love brains why didn't I make a career in brain science or in artificial intelligence? The answer is I tried to, several times, but I refused to study the problem of intelligence as others have before me. I believe the best way to solve this problem is to use the detailed biology of the brain as a constraint and as a guide, yet think about intelligence as a computational problem— a position somewhere between biology and computer science. Many biologists tend to reject or ignore the idea of thinking of the brain in computational terms, and computer scientists often don't believe they have anything to learn from biology. Also, the world of science is less accepting of risk than the world of business. In technology businesses, a person who pursues a new idea with a reasoned approach can enhance his or her career regardless of whether the particular idea turns out to be successful. Many successful entrepreneurs achieved success only after earlier failures. But in academia, a couple of years spent pursuing a new idea that does not work out can permanently ruin a young career. So I pursued the two passions in my life simultaneously, believing that success in industry would help me achieve success in understanding the brain. I needed the financial resources to pursue the science I wanted, and I needed to learn how to affect change in the world, how to sell new ideas, all of which I hoped to get from working in Silicon Valley. In August 2002 I started a research center, the Redwood Neuroscience Institute (RNI), dedicated to brain theory. There are many neuroscience centers in the world, but no others are dedicated to finding an overall theoretical understanding of the neocortex— the part of the human brain responsible for intelligence. That is all we study at RNI. In many ways, RNI is like a start-up company. We are pursuing a dream that some people think is unattainable, but we are lucky to have a great group of people, and our efforts are starting to bear fruit. * * * The agenda for this book is ambitious. It describes a comprehensive theory of how the brain works. It describes what intelligence is and how your brain creates it. The theory I present is not a completely new one. Many of the individual ideas you are about to read have existed in some form or another before, but not together in a coherent fashion. This should be expected. It is said that "new ideas" are often old ideas repackaged and reinterpreted. That certainly applies to the theory proposed here, but packaging and interpretation can make a world of difference, the difference between a mass of details and a satisfying theory. I hope it strikes 5 you the way it does many people. A typical reaction I hear is, "It makes sense. I wouldn't have thought of intelligence this way, but now that you describe it to me I can see how it all fits together." With this knowledge most people start to see themselves a little differently. You start to observe your own behavior saying, "I understand what just happened in my head." Hopefully when you have finished this book, you will have new insight into why you think what you think and why you behave the way you behave. I also hope that some readers will be inspired to focus their careers on building intelligent machines based on the principles outlined in these pages. I often refer to this theory and my approach to studying intelligence as "real intelligence" to distinguish it from "artificial intelligence." AI scientists tried to program computers to act like humans without first answering what intelligence is and what it means to understand. They left out the most important part of building intelligent machines, the intelligence! "Real intelligence" makes the point that before we attempt to build intelligent machines, we have to first understand how the brain thinks, and there is nothing artificial about that. Only then can we ask how we can build intelligent machines. The book starts with some background on why previous attempts at understanding intelligence and building intelligent machines have failed. I then introduce and develop the core idea of the theory, what I call the memory-prediction framework. In chapter 6 I detail how the physical brain implements the memory-prediction model— in other words, how the brain actually works. I then discuss social and other implications of the theory, which for many readers might be the most thought-provoking section. The book ends with a discussion of intelligent machines— how we can build them and what the future will be like. I hope you find it fascinating. Here are some of the questions we will cover along the way: Can computers be intelligent? For decades, scientists in the field of artificial intelligence have claimed that computers will be intelligent when they are powerful enough. I don't think so, and I will explain why. Brains and computers do fundamentally different things. Weren't neural networks supposed to lead to intelligent machines? Of course the brain is made from a network of neurons, but without first understanding what the brain does, simple neural networks will be no more successful at creating intelligent machines than computer programs have been. Why has it been so hard to figure out how the brain works? 6 Most scientists say that because the brain is so complicated, it will take a very long time for us to understand it. I disagree. Complexity is a symptom of confusion, not a cause. Instead, I argue we have a few intuitive but incorrect assumptions that mislead us. The biggest mistake is the belief that intelligence is defined by intelligent behavior.