Jeff Hawkins is an inventor and neuroscientist. In 1979 he received his bachelor's degree in electrical engineering from Cornell. He joined GRiD Systems in 1982, where he developed rapid application development (RAD) software called GRiDtask. He enrolled in the biophysics program at the UC Berkeley in 1986. While there he patented a "pattern classifier" for handwritten text, but his PhD proposal on developing a theory of the neocortex was rejected, apparently because none of the professors there were working on anything similar. The setback led him back to GRiD, where, as vice president of research from 1988 to 1992, he developed GRiD's pen-based computing initiative. Hawkins founded Palm Inc. in January 1992. There, he invented the Palm Pilot, a personal digital assistant (PDA). In 1998 he left Palm along with Palm co-founders Donna Dubinsky and Ed Colligan to start Handspring. There they developed the Treo family of PDA and cell phone, which was later acquired by Palm as the Palm Treo. In 2002, after two decades of finding little interest from neuroscience institutions that he did not have a stake in, Hawkins founded the Redwood Neuroscience Institute in Menlo Park, California. In 2003, Hawkins was elected as a member of the National Academy of Engineering "for the creation of the hand-held computing paradigm and the creation of the first commercially successful example of a hand-held computing device." In 2004 Hawkins wrote On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines. The book was written with Sarah Blakeslee, a science writer at the New York Times specializing in neuroscience. In the book, Hawkins described "the core idea of his theory," what he calls the "memory prediction framework." (p.5). It explains "how the brain remembers things and uses its memories to make predictions." (p.20) While his first passion was mobile computing, Hawkins says his second passion is figuring out how the human brain works to apply its design to the creation of artificially intelligent machines.Normal | Teacher | Scholar
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.In March 2005, Hawkins, together with Donna Dubinsky (Palm's original CEO) and Dileep George, founded Numenta, Inc., to specialize in artificial intelligence. In July, 2005 The Redwood Center for Theoretical Neuroscience became a part of the Helen Wills Neuroscience Institute at UC Berkeley. For 15 years, Hawkins headed a research team at Numenta studying the brain's neocortex. After reading Vernon Mountcastle's 1978 article "An Organizing Principle for Cerebral Function," Hawkins became interested in the cortical columns in the neocortex, groups of neurons forming a cylindrical structure through the cerebral cortex of the brain perpendicular to the cortical surface. He hypothesized that cortical columns did not capture just a sensation, but also the relative location of that sensation, in three dimensions rather than two, in relation to what was around it. Hawkins said "When the brain builds a model of the world, everything has a location relative to everything else" In his 2022 book A Thousand Brains: A New Theory of Intelligence, Hawkins describes his theory of each cortical column as a brain arranged in what he calls "reference frames," forming a framework for intelligence and what he calls "cortical computation". The book details the advances he and the Numenta team made in the development of their theory of how the brain understands the world and what it means to be intelligent. It also details how the "thousand brains" theory can affect machine intelligence, and how an understanding of the brain impacts the threats and opportunities facing humanity.
I have divided the book into three parts. In the first part, I describe our theory of reference frames, which we call the Thousand Brains Theory. The theory is partly based on logical deduction, so I will take you through the steps we took to reach our conclusions. I will also give you a bit of historical background to help you see how the theory relates to the history of thinking about the brain. By the end of the first part of the book, I hope you will have an understanding of what is going on in your head as you think and act within the world, and what it means to be intelligent. The second part of the book is about machine intelligence. The twenty-first century will be transformed by intelligent machines in the same way that the twentieth century was transformed by computers. The Thousand Brains Theory explains why today's AI is not yet intelligent and what we need to do to make truly intelligent machines. I describe what intelligent machines in the future will look like and how we might use them. I explain why some machines will be conscious and what, if anything, we should do about it. Finally, many people are worried that intelligent machines are an existential risk, that we are about to create a technology that will destroy humanity. I disagree. Our discoveries illustrate why machine intelligence, on its own, is benign. But, as a powerful technology, the risk lies in the ways humans might use it.Hawkins says he no longer describes his main thesis as the "memory prediction framework," which now has its own Wikipedia page.
To make predictions, the brain has to learn what is normal - that is, what should be expected based on past experience. My previous book, On Intelligence, explored this idea of learning and prediction. In the book, I used the phrase "memory prediction framework" to describe the overall idea, and I wrote about the implications of thinking about the brain this way. I argued that by studying how the brain makes predictions, we would be able to unravel how the cortex works. Today I no longer use the phrase "the memory prediction framework." Instead, I describe the same idea by saying that the neocortex learns a model of the world, and it makes predictions based on its model.The information philosopher wants to know where the information for Hawkins' model of the world is stored in the brain? He tells us "the brain has to learn what should be expected based on past experience." The information philosopher has proposed a model of how the mind records experiences and then plays them back any experiences which resemble the current experience, to provide context and meaning to the current experience. See our Experience Recorder and Reproducer.