[BITList] Computers vs Brains

[John Feltham]

Guest Column: Computers vs. Brains

Update | Sam Wang responds to readers’ comments here.
By Sandra Aamodt and Sam Wang


Inventor Ray Kurzweil, in his 2005 futurist manifesto “The Singularity  
Is Near,” extrapolates current trends in computer technology to  
conclude that machines will be able to out-think people within a few  
decades. In his eagerness to salute our robotic overlords, he neglects  
some key differences between brains and computers that make his  
prediction unlikely to come true.

Brains have long been compared to the most advanced existing  
technology — including, at one point, telephone switchboards. Today  
people talk about brains as if they were a sort of biological  
computer, with pink mushy “hardware” and “software” generated by life  
experiences.

However, any comparison with computers misses a messy truth. Because  
the brain arose through natural selection, it contains layers of  
systems that arose for one function and then were adopted for another,  
even though they don’t work perfectly. An engineer with time to get it  
right would have started over, but it’s easier for evolution to adapt  
an old system to a new purpose than to come up with an entirely new  
structure. Our colleague David Linden has compared the evolutionary  
history of the brain to the task of building a modern car by adding  
parts to a 1925 Model T that never stops running. As a result, brains  
differ from computers in many ways, from their highly efficient use of  
energy to their tremendous adaptability.

One striking feature of brain tissue is its compactness. In the  
brain’s wiring, space is at a premium, and is more tightly packed than  
even the most condensed computer architecture. One cubic centimeter of  
human brain tissue, which would fill a thimble, contains 50 million  
neurons; several hundred miles of axons, the wires over which neurons  
send signals; and close to a trillion (that’s a million million)  
synapses, the connections between neurons.

The memory capacity in this small volume is potentially immense.  
Electrical impulses that arrive at a synapse give the recipient neuron  
a small chemical kick that can vary in size. Variation in synaptic  
strength is thought to be a means of memory formation. Sam’s lab has  
shown that synaptic strength flips between extreme high and low  
states, a flip that is reminiscent of a computer storing a “one” or a  
“zero” — a single bit of information.

But unlike a computer, connections between neurons can form and break  
too, a process that continues throughout life and can store even more  
information because of the potential for creating new paths for  
activity. Although we’re forced to guess because the neural basis of  
memory isn’t understood at this level, let’s say that one movable  
synapse could store one byte (8 bits) of memory. That thimble would  
then contain 1,000 gigabytes (1 terabyte) of information. A thousand  
thimblefuls make up a whole brain, giving us a million gigabytes — a  
petabyte — of information. To put this in perspective, the entire  
archived contents of the Internet fill just three petabytes.

To address this challenge, Kurzweil invokes Moore’s Law, the principle  
that for the last four decades, engineers have managed to double the  
capacity of chips (and hard drives) every year or two. If we imagine  
that the trend will continue, it’s possible to guess when a single  
computer the size of a brain could contain a petabyte. That would be  
about 2025 to 2030, just 15 or 20 years from now.

This projection overlooks the dark, hot underbelly of Moore’s law:  
power consumption per chip, which has also exploded since 1985. By  
2025, the memory of an artificial brain would use nearly a gigawatt of  
power, the amount currently consumed by all of Washington, D.C. So  
brute-force escalation of current computer technology would give us an  
artificial brain that is far too costly to operate.

Compare this with your brain, which uses about 12 watts, an amount  
that supports not only memory but all your thought processes. This is  
less than the energy consumed by a typical refrigerator light, and  
half the typical needs of a laptop computer. Cutting power consumption  
by half while increasing computing power many times over is a pretty  
challenging design standard. As smart as we are, in this sense we are  
all dim bulbs.

A persistent problem in artificial computing is the sensitivity of the  
system to component failure. Yet biological synapses are remarkably  
flaky devices even in normal, healthy conditions. They release  
neurotransmitter only a small fraction of the time when their parent  
neuron fires an electrical impulse. This unreliability may arise  
because individual synapses are so small that they contain barely  
enough machinery to function. This may be a trade-off that stuffs the  
most function into the smallest possible space.

In any case, a brain’s success is not measured by its ability to  
process information in precisely repeatable ways. Instead, it has  
evolved to guide behaviors that allow us to survive and reproduce,  
which often requires fast responses to complex situations. As a  
result, we constantly make approximations and find “good-enough”  
solutions. This leads to mistakes and biases. We think that when two  
events occur at the same time, one must have caused the other. We make  
inaccurate snap judgments such as racial prejudice. We fail to plan  
rationally for the future, as explored in the field of neuroeconomics.

Still, engineers could learn a thing or two from brain strategies. For  
example, even the most advanced computers have difficulty telling a  
dog from a cat, something that can be done at a glance by a toddler —  
or a cat. We use emotions, the brain’s steersman, to assign value to  
our experiences and to future possibilities, often allowing us to  
evaluate potential outcomes efficiently and rapidly when information  
is uncertain. In general, we bring an extraordinary amount of  
background information to bear on seemingly simple tasks, allowing us  
to make inferences that are difficult for machines.

If engineers can understand how to apply these shortcuts and tricks,  
computer performance could begin to emulate some of the more  
impressive feats of human brains. However, this route may lead to  
computers that share our imperfections. This may not be exactly what  
we want from robot overlords, but it could lead to better “soft”  
judgments from our computers.

This gets us to the deepest point: why bother building an artificial  
brain?

As neuroscientists, we’re excited about the potential of using  
computational models to test our understanding of how the brain works.  
On the other hand, although it eventually may be possible to design  
sophisticated computing devices that imitate what we do, the  
capability to make such a device is already here. All you need is a  
fertile man and woman with the resources to nurture their child to  
adulthood. With luck, by 2030 you’ll have a full-grown, college- 
educated, walking petabyte. A drawback is that it may be difficult to  
get this computing device to do what you ask.




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