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Will we ever understand how our brains work?
CULTURELAB
Making sense of our minds Reverse-engineering the brain may be the way to unravel its riddles, finds Laura Spinney To understand the brain we need to look beyond isolated functions
How to Create a Mind: The secret of human thought revealed by Ray Kurzweil, Viking, $27.95
Thomas Gray/Millennium Images, UK
WHEN it comes to the human brain, many scientists believe that we are incapable of understanding how it works because we lack the tools and intelligence to measure its mind-blowing complexity. Others are starting to question that notion, and to subtly redefine the task. In How to Create a Mind, futurist Ray Kurzweil has ridden into battle for the challengers. He starts by asking what it means to be complex. If you consider that a forest is made up of trees, each of whose branches is different, then you might conclude that the forest’s complexity is impenetrable. But if you realise that the forest grows according to certain rules and contains repeating patterns, then become increasingly complex and the problem becomes tractable. abstract as you move up it. You don’t have to measure every There are now a handful of last gnarly twig; you can make efforts afoot to describe both the predictions instead. As Kurzweil units and the rules, including the says, you can reverse-engineer it. Human Brain Project at the Swiss All you need to get started are Federal Institute of Technology the rules, and an understanding in Lausanne, in which, in the of the basic neural building blocks whose configuration they guide. “The brain’s outer layer, the neocortex, is made up Neuroscientists noticed long ago of repeating units, like that the structure of the brain’s transistors in a microchip” outer layer, the neocortex, is remarkably uniform, made up of repeating units, like transistors interests of full disclosure, in a microchip. Estimates of what I should say my husband is this basic unit might be vary – involved. What they have in from a single neuron up to tens common is the idea that the brain of thousands – but all are relevant is an integrated system, with as they are likely to fit into a properties that emerge only when hierarchy whose functions its elements are combined. 50 | NewScientist | 10 November 2012
This means that traditional neuroscientific research, which seeks to explain increasingly specialised aspects of brain function in isolation, may have limited scope. The reductionist approach has to be supplemented by a constructivist one – putting the pieces back together again to explain the whole. Modern tools, including supercomputers and the mathematics of complexity, make that possible. You can see why, on learning about this new line of research, Kurzweil felt he had to write this book. For years he has been talking about what he calls the law of accelerating returns, according to which both biological and technological evolution are
speeding up. In his 2005 book, The Singularity is Near, he argued that we are approaching a point where humans and machines will merge, producing a leap in intelligence. Reverse-engineering the human brain could open the door to all sorts of significant innovations, such as the design of a computer that thinks more like us. This could be the springboard from which to make that leap. There is good evidence that neither biological nor technological evolution happens in the smoothly exponential way he says they do, and even that the blistering pace of technological progress of the last few centuries might be slowing. On the whole, though, this book is a breath of fresh air. Scientists tend to be cautious in their predictions – they certainly were about the Human Genome Project, which was completed in 15 years against all expectations – and Kurzweil makes an argument for optimism. There are plenty of challenges ahead, not least because reverseengineering the brain is only the first step. The result of that process will then have to forwardengineer itself – that is, learn. There are ethical concerns about what this new intelligence will be capable of, but for now at least they seem to be dwarfed by its promise, not least in helping us to understand and treat the rising tide of brain disease. It is debatable whether this will happen as early as the 2020s, as Kurzweil predicts. But if, as seems increasingly clear, this is where the science must go, does it matter if he is off by a decade or two? n
Making sense of our minds Reverse-engineering the brain may be the way to unravel its riddles, finds Laura Spinney To understand the brain we need to look beyond isolated functions
How to Create a Mind: The secret of human thought revealed by Ray Kurzweil, Viking, $27.95
Thomas Gray/Millennium Images, UK
WHEN it comes to the human brain, many scientists believe that we are incapable of understanding how it works because we lack the tools and intelligence to measure its mind-blowing complexity. Others are starting to question that notion, and to subtly redefine the task. In How to Create a Mind, futurist Ray Kurzweil has ridden into battle for the challengers. He starts by asking what it means to be complex. If you consider that a forest is made up of trees, each of whose branches is different, then you might conclude that the forest’s complexity is impenetrable. But if you realise that the forest grows according to certain rules and contains repeating patterns, then become increasingly complex and the problem becomes tractable. abstract as you move up it. You don’t have to measure every There are now a handful of last gnarly twig; you can make efforts afoot to describe both the predictions instead. As Kurzweil units and the rules, including the says, you can reverse-engineer it. Human Brain Project at the Swiss All you need to get started are Federal Institute of Technology the rules, and an understanding in Lausanne, in which, in the of the basic neural building blocks whose configuration they guide. “The brain’s outer layer, the neocortex, is made up Neuroscientists noticed long ago of repeating units, like that the structure of the brain’s transistors in a microchip” outer layer, the neocortex, is remarkably uniform, made up of repeating units, like transistors interests of full disclosure, in a microchip. Estimates of what I should say my husband is this basic unit might be vary – involved. What they have in from a single neuron up to tens common is the idea that the brain of thousands – but all are relevant is an integrated system, with as they are likely to fit into a properties that emerge only when hierarchy whose functions its elements are combined. 50 | NewScientist | 10 November 2012
This means that traditional neuroscientific research, which seeks to explain increasingly specialised aspects of brain function in isolation, may have limited scope. The reductionist approach has to be supplemented by a constructivist one – putting the pieces back together again to explain the whole. Modern tools, including supercomputers and the mathematics of complexity, make that possible. You can see why, on learning about this new line of research, Kurzweil felt he had to write this book. For years he has been talking about what he calls the law of accelerating returns, according to which both biological and technological evolution are
speeding up. In his 2005 book, The Singularity is Near, he argued that we are approaching a point where humans and machines will merge, producing a leap in intelligence. Reverse-engineering the human brain could open the door to all sorts of significant innovations, such as the design of a computer that thinks more like us. This could be the springboard from which to make that leap. There is good evidence that neither biological nor technological evolution happens in the smoothly exponential way he says they do, and even that the blistering pace of technological progress of the last few centuries might be slowing. On the whole, though, this book is a breath of fresh air. Scientists tend to be cautious in their predictions – they certainly were about the Human Genome Project, which was completed in 15 years against all expectations – and Kurzweil makes an argument for optimism. There are plenty of challenges ahead, not least because reverseengineering the brain is only the first step. The result of that process will then have to forwardengineer itself – that is, learn. There are ethical concerns about what this new intelligence will be capable of, but for now at least they seem to be dwarfed by its promise, not least in helping us to understand and treat the rising tide of brain disease. It is debatable whether this will happen as early as the 2020s, as Kurzweil predicts. But if, as seems increasingly clear, this is where the science must go, does it matter if he is off by a decade or two? n