250 CATALYZING INQUIRY
- Incomplete (and sometimes even incorrect) biological understandings help to inspire different
and useful approaches to computing problems. Important and valuable insights into possible ways to
solve a current problem have been derived from biological models that were incomplete (as in the case
of evolutionary programming) or even inaccurate (as in the case of immunologically based computer
security).
On the other hand, it must be understood that the use of a biological metaphor to inspire new
approaches to computing does not necessarily imply that the biological side is well understood, whether
or not the metaphor leads to progress in computing. That is, even if a biological metaphor is applicable
and relevant to a computing problem, this does not mean that the corresponding biological phenomena
can necessarily be understood in computational terms.
For example, although researchers use the term “genetic algorithms” to describe a class of algo-
rithms using operators that have a similar flavor to evolutionary genetic operators such as mutation or
recombination to search a solution space stochastically, the definition and implementation of these
genetic operators does not imply a fundamental understanding of biological evolutionary processes.
Similarly, although the field of “artificial neural networks” is an information-processing paradigm
inspired by the parallel processing capabilities and structure of nerve tissue, and it attempts to mimic
learning in biology by learning to adjust “synaptic” connections between artificial processing elements,
the extent to which an artificial neural network reflects real neural systems may be tenuous.
8.1.3 Multiple Roles: Biology for Computing Insight
Biological inspiration can play many different roles in computing, and confusion about this multi-
plicity of meanings accounts for a wide spectrum of belief about the value of biology for developing
better computer systems and improved performance of computational tasks. One point of view is that
only a detailed “ground-up” understanding of a biological system can result in such advances, and
because such understanding is available for only a very small number of biological systems (and “very
small” is arguably zero), the potential relevance of biology for computing is small, at least in the near
term.
A more expansive view of biology’s value for computing acknowledges that detailed understand-
ing is the key for a maximal application of biology to computing, but also holds that biological meta-
phors, analogies, examples, and phenomenological insights may suggest new and interesting ways of
thinking about computational problems that might not have been imagined without the involvement of
biology.^4 From this perspective, what matters is performance of a task rather than simulation of what a
biological system actually does, though one would not necessarily expect initial performance models
of the BlueGene/L Supercomputer,” presented at Supercomputing Conference, November 2002, available at http://sc-2002.org/
paperpdfs/pap.pap207.pdf). Other obvious problems inspired by biology include computer vision and artificial intelligence. It is
also interesting to note this historical precedent of biological problems being the domain in which major suites of statistical tools
were developed. For instance, Galton invented regression analysis (correlation tests) to study the relation of phenotypes between
parents and progeny (see F. Galton, Natural Inheritance, 5th Edition, Macmillan and Company, New York, 1894). Pearson in-
vented the chi-square and other discrete tests to study the distribution of different morphs in natural populations (see K.
Pearson, “Mathematical Contributions to the Theory of Evolution, VIII. On the Inheritance of Characters Not Capable of Exact
Quantitative Measurement,” Philosophical Transactions of the Royal Society of London, Series A 195:79-150, 1900). R.A. Fisher in-
vented analysis of variance to study the partitioning of different effects in inheritance (see R. Fisher, “The Correlation Between
Relatives on the Supposition of Mendelian Inheritance,” Transactions of the Royal Society of Edinburgh 52:399-433, 1918).
(^4) An analogy might be drawn to the history of superconducting materials. A mix of quantum principles, phenomenology, and
trained experience has led to superconducting materials with ever-higher transition temperatures. (Indeed, the discovery of
superconducting materials preceded quantum mechanics by more than a decade.)