ual neurons and connecting the simulations together.

While there’s a difference between “should be possible” and “is actually practical,” this is the general idea behind the claim that we can understand the brain computationally. Assuming it’s right, it has some important consequences. If computers can simulate brains (and by similar reasoning, humans in general), then they can solve any problem humans can solve, if only by simulating a human. But since we know that computers can’t solve certain problems (the uncomputable problems), that would mean that humans can’t solve uncomputable problems either. In other words, it imposes a fundamental limit on human knowledge.

Although we’re a long way from being able to fully simulate a brain, computational neuroscience—in which scientists try to understand neural systems as computational processes—is an important and growing area of biological research. By modeling neural systems as computational systems, we can better understand their function. And in some experimental treatments, such as cochlear implants, we can actually replace damaged components with computational systems that are behaviorally equivalent to the biological tissue.

SO WHAT IS COMPU TATION?

Computation is an idea in flux. Our culture is in the process of renegotiating what it thinks the concepts computation and computer really mean. Computation lies on a conceptual fault line where small changes can have major consequences to how we view the world. That makes it a very interesting field to follow right now.

It’s an interesting exercise to type “define computation” into Google and look at the definitions you get. Search results include “determining something by mathematical or logical methods,” which doesn’t apply well to the idea of DNA being a computer program, or “finding a solution to a problem from given inputs by means of an algorithm,” which is essentially the functional model that, as we saw, doesn’t apply well to things like computer games or anti-lock brake systems. Another possible answer is that computation is “ information processing,” although that simply

Turing argued if
a computer could
fool humans into
thinking it was
human, then it
would have to be
considered to be
intelligent even
though it wasn’t
actually human.

begs the question of what we mean by information.

A pragmatic definition would be that computation is what (modern, digital) computers do, so a system is “computational” if ideas from computers are useful for understanding it. This is probably the closest definition to how real people use the term in practice. As we’ve seen, there are a lot of different kinds of systems that are computational under this view. Computation is then a set of metaphors, a lens through which we can view and describe other phenomena.

But that isn’t a very satisfying definition. Another view would be that computation is wrapped up with the study of behavioral equivalence. Under this view, computation is the process of producing some desired behavior without prejudice as to whether it is implemented through silicon, neurons, or clock work.

Whatever definition we adopt, it’s clear that our society is undergoing profound changes as a result of both computational technology and computational metaphors. These raise a number of psychological and social issues we need to think about:

 

• What kinds of computational systems are easy for people to use?

• How can we design systems that people find more rewarding and engaging?

• How can we design large systems that are easy for humans to understand?

• What are the risks of information
technology? Do we really want to
make airplanes that are controlled
by computers, so that if program
crashes, the plane does too? Do
databases and surveillance tech-
nology give governments and cor-
porations too much power?

 

Computation is a broad, rich field. It has had deep influences on our lives and culture. If your PC, cell phone, and Web access suddenly disappeared, you would probably have to radically recon-figure your life, even though these have only been widely available in the last 15 years. It’s unlikely that the changes to society are going to stop any time soon. By better understanding computation, we can help make sure those changes are for the better.

 

Biography

Ian Hors will is an associate professor of computer science at North western University. He is a member of the Department of Electrical Engineering and Computer Science, where he is Director of the Division of Graphics and Interactive Media, and was cofounder of Northwestern’s Animate Arts Program. His research interests include interactive entertainment technologies, and cognitive modeling for virtual characters, particularly modeling of emotion and personality. He received his Ph. D. in computer science from the Massachusetts Institute of Technology in 1993. He has been the chair of the standing committee of the Association for the Advancement of Artificial Intelligence’s Fall and Spring Symposium Series, as well as the 2009 International Conference on the Foundations of Digital Games.

References

[ 1] Turing, A. On computable numbers, with an application to the Entscheidungsproblem. In Proceedings of the London Mathematical Society, Series 2, 42 (1936), 230-265. Note that Turing machines were more a conceptual design than a practical engineering design. Although Turing did work on the design of actual working hardware, his work on Turing machines came before the construction of real computers was practical.

[ 2] Minsky and Blum proved in the 1960s that a “ two-counter machine” was Turing complete. A two-counter machine is a machine whose only data representation is a pair of numbers (non-negative integers, in fact), and whose only commands are to add or subtract 1 from one of the numbers and to check whether one of them was zero.

[ 3] Turing, A. Computing machinery and intelligence.
Originally published by Oxford University Press on
behalf of MIND (the Journal of the Mind Association)
59, 236 (1959), 433-60; http:// www.abelard.org/
turpap/ turpap.htm
[ 4] Although widely accepted, the computational vie w is
not universally accepted. See for example, Searle, J.
R. Minds, brains, and programs. Behavioral and Brain
Sciences 3, 3 (1980), 417-457; http:// www.bbsonline.
org/Preprints/OldArchive/ bbs.searle2.html
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