4

Memory

As you read this book, walk down a crowded street, hear a symphony, or comfort
a crying child, your brain is being flooded with the spatial and temporal patterns
from all of your senses. The world is an ocean of constantly changing patterns that
come lapping and crashing into your brain. How do you manage to make sense of
the onslaught? Patterns stream in, pass through various parts of the old brain, and
eventually arrive at the neocortex. But what happens to them when they enter the
cortex?

From the dawn of the industrial revolution, people have viewed the brain as some
sort of machine. They knew there weren't gears and cogs in the head, but it was
the best metaphor they had. Somehow information entered the brain and the
brain-machine determined how the body should react. During the computer age,
the brain has been viewed as a particular type of machine, the programmable
computer. And as we saw in chapter 1, AI researchers have stuck with this view,
arguing that their lack of progress is only due to how small and slow computers
remain compared to the human brain. Today's computers may be equivalent only
to a cockroach brain, they say, but when we make bigger and faster computers
they will be as intelligent as humans.

There is a largely ignored problem with this brain-as-computer analogy. Neurons
are quite slow compared to the transistors in a computer. A neuron collects inputs
from its synapses, and combines these inputs together to decide when to output a
spike to other neurons. A typical neuron can do this and reset itself in about five
milliseconds (5 ms), or around two hundred times per second. This may seem fast,
but a modern silicon-based computer can do one billion operations in a second.
This means a basic computer operation is five million times faster than the basic
operation in your brain! That is a very, very big difference. So how is it possible
that a brain could be faster and more powerful than our fastest digital computers?
"No problem," say the brain-as-computer people. "The brain is a parallel computer.
It has billions of cells all computing at the same time. This parallelism vastly
multiplies the processing power of the biological brain."

I always felt this argument was a fallacy, and a simple thought experiment shows
why. It is called the "one hundred-step rule." A human can perform significant
tasks in much less time than a second. For example, I could show you a
photograph and ask you to determine if there is cat in the image. Your job would
be to push a button if there is a cat, but not if you see a bear or a warthog or a
turnip. This task is difficult or impossible for a computer to perform today, yet a
human can do it reliably in half a second or less. But neurons are slow, so in that
half a second, the information entering your brain can only traverse a chain one
hundred neurons long. That is, the brain "computes" solutions to problems like this

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in one hundred steps or fewer, regardless of how many total neurons might be
involved. From the time light enters your eye to the time you press the button, a
chain no longer than one hundred neurons could be involved. A digital computer
attempting to solve the same problem would take billions of steps. One hundred
computer instructions are barely enough to move a single character on the
computer's display, let alone do something interesting.

But if I have many millions of neurons working together, isn't that like a parallel
computer? Not really. Brains operate in parallel and parallel computers operate in
parallel, but that's the only thing they have in common. Parallel computers
combine many fast computers to work on large problems such as computing
tomorrow's weather. To predict the weather you have to compute the physical
conditions at many points on the planet. Each computer can work on a different
location at the same time. But even though there may be hundreds or even
thousands of computers working in parallel, the individual computers still need to
perform billions or trillions of steps to accomplish their task. The largest
conceivable parallel computer can't do anything useful in one hundred steps, no