[EAS]Artificial Biology

pjk pjk at design.eng.yale.edu
Fri Nov 7 01:54:42 EST 2003

Subject:   Artificial Biology

(from INNOVATION, 5 November 2003)

The metaphor for computing has long been the idea of "artificial 
intelligence," but a growing number of researchers are now
envisioning something more akin to "artificial biology." Rather
than striving for bug-free software, companies should aim for
software that's designed to survive the bugs, using the biological
properties of redundancy and regeneration. This new way of thinking
is transforming research at top universities like Stanford,
UC-Berkeley and the University of Virginia, and is also influencing
the development of commercial products, such as IBM Web servers.
"You make it possible to reboot a system easily and automatically,"
says Steve White, who heads up autonomic-computing research at
IBM's Watson Research Center. But to create self-healing software, a
whole new approach to programming will be required, says U. of
Virginia's David Evans. "We need to move towards a programming
philosophy where we look at the global system and understand what
properties it needs to have, rather than thinking about programming
as a sequence of instructions. It's really a different way of
approaching problems." Rather than writing code in a linear
fashion, more robust software would include many independent 
components, enabling it to continue functioning even if some of its 
components failed. But anticipating all the things that might go
wrong with  a software program is still problematic, says software
consultant Tom  DeMarco. "We haven't developed anything that is very
persuasive yet for  healing unanticipated conditions. You have to
remember that software  doesn't break. It is flawed to begin with.
So for software to self-heal, you have to find a way to have the
program create things that were not there when the program was
written. We'll get there someday." (Technology  Review Nov 2003)

Another thing one can do with subtly buggy software is to simulate
complex (e.g. biological) systems where it is really impossible to
tell the right answer amidst the many fascinating ones.  --PJK

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