The Ravenous Brain: How the New Science of Consciousness Explains Our Insatiable Search for Meaning by Daniel Bor
Authors: Daniel Bor
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give up on such a fiendish task before we’d even begun. But because of the parallel, probabilistic nature of our brains, our processing is far more fluid and nuanced than any silicon computer currently in existence. We are exquisitely subject to biases, influences, and idiosyncrasies. For instance, if you read the following words: “artichoke artichoke artichoke artichoke artichoke,” you will spend the rest of the day (or even somewhat longer) recognizing the word “artichoke” a little bit quicker than before (and you might even be a little more likely to buy one the next time you go to the supermarket). My word-processing program simply produces angry red lines to punish me for my ungrammatical repetitions of “artichoke.” It does not learn to insert those red lines any quicker by the fifth repetition of the word, compared to the first.
This continuous, subtle updating of our inner mental world means that we can also learn virtually anything very effectively. For instance, we would consider the task of distinguishing between a dog or cat in a picture to be a simple and trivial matter. But computers are still cripplingly impaired at such processes. The reason is that although recognizing different animals appears basic to us, such skills are, in fact, behind the veil of consciousness, fiendishly complex, and ideally they require an immensely parallel computational architecture—such as the human brain. Of course, it makes no sense for evolution to have shaped our brains to be highly skilled at accurately calculating square roots. But, from a survival perspective, having a general-purpose information-processing device, which can learn to recognize any single critical danger or benefit in a moment, and then appropriately respond, is highly advantageous.
Therefore, over a few seconds, serial deterministic processing is best suited to performing huge quantities of simple tasks, whereas parallel probabilistic processing is only effective at carrying out a handful of tasks. But these tasks can be very complex indeed.
In order to capture the scale of the challenge ahead for anyone wanting to make a book or computer that could speak the Chinese language, we need to delve further into the details of our human probabilistic parallel computer and understand precisely how it differs from a standard PC. Assume for the moment that a single neuron is capable of one rudimentary calculation. There are roughly 85 billion neurons in a human brain. An average PC processor has around 100 million components: so, about 850 times fewer components than a human brain has in neurons—an impressive win for humans, but not staggeringly so, and indeed there are some supercomputers today that have more components than a human brain has neurons. But this is only the beginning of the story. There is another critical feature of human brains that, in a race, would leave any computer in the world stumbling along, choking pitiably on the dust of our own supercharged biological computational device. While each component on a central processing unit may be connected to only one or a handful of others, each neuron in the human brain is connected to, on average, 7,000 others. This means there are some 600 trillion connections in the brain, which is about 3,000 times more than the number of stars in our galaxy. In every young adult human brain these microcables, extended end to end, would run about 165,000 kilometers—enough to wrap around the earth four times over! The complexity of the human brain is utterly staggering.
To begin to understand the sheer vastness of the parallelism of human brain activity, imagine that the human population is around 85 billion people, about 12 times what it is now. You’ve suddenly discovered an earth-shattering revelation, and you simply have to tell everyone. You e-mail every single person in your contact list, all 100 people, and tell them to pass this wondrous insight on to 100 new friends. They do so, then the
This continuous, subtle updating of our inner mental world means that we can also learn virtually anything very effectively. For instance, we would consider the task of distinguishing between a dog or cat in a picture to be a simple and trivial matter. But computers are still cripplingly impaired at such processes. The reason is that although recognizing different animals appears basic to us, such skills are, in fact, behind the veil of consciousness, fiendishly complex, and ideally they require an immensely parallel computational architecture—such as the human brain. Of course, it makes no sense for evolution to have shaped our brains to be highly skilled at accurately calculating square roots. But, from a survival perspective, having a general-purpose information-processing device, which can learn to recognize any single critical danger or benefit in a moment, and then appropriately respond, is highly advantageous.
Therefore, over a few seconds, serial deterministic processing is best suited to performing huge quantities of simple tasks, whereas parallel probabilistic processing is only effective at carrying out a handful of tasks. But these tasks can be very complex indeed.
In order to capture the scale of the challenge ahead for anyone wanting to make a book or computer that could speak the Chinese language, we need to delve further into the details of our human probabilistic parallel computer and understand precisely how it differs from a standard PC. Assume for the moment that a single neuron is capable of one rudimentary calculation. There are roughly 85 billion neurons in a human brain. An average PC processor has around 100 million components: so, about 850 times fewer components than a human brain has in neurons—an impressive win for humans, but not staggeringly so, and indeed there are some supercomputers today that have more components than a human brain has neurons. But this is only the beginning of the story. There is another critical feature of human brains that, in a race, would leave any computer in the world stumbling along, choking pitiably on the dust of our own supercharged biological computational device. While each component on a central processing unit may be connected to only one or a handful of others, each neuron in the human brain is connected to, on average, 7,000 others. This means there are some 600 trillion connections in the brain, which is about 3,000 times more than the number of stars in our galaxy. In every young adult human brain these microcables, extended end to end, would run about 165,000 kilometers—enough to wrap around the earth four times over! The complexity of the human brain is utterly staggering.
To begin to understand the sheer vastness of the parallelism of human brain activity, imagine that the human population is around 85 billion people, about 12 times what it is now. You’ve suddenly discovered an earth-shattering revelation, and you simply have to tell everyone. You e-mail every single person in your contact list, all 100 people, and tell them to pass this wondrous insight on to 100 new friends. They do so, then the
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