Philosophy of Computing

"Although mathematics, physics, and biology are staple specializations within the philosophy of science, it is striking that there has not yet been a correspondingly strong effort in the Philosophy of Computation.

During the first half of the 20th century, philosophical discussion about the fundamental nature of computation was largely conducted within logic and the foundations of mathematics (e.g., by Turing, Church, Kleene, Gödel, and Gandy). In the latter half of the century, philosophical analysis of the notion of computation moved into cognitive science and the philosophy of psychology, primarily as an ingredient in debates about the nature and plausibility of the computational theory of mind (Dennett, Fodor, Haugeland, Dreyfus, Searle, Penrose, etc).

What has been lacking—and notable for its omission—is an autonomous, focused, foundational inquiry into the nature of computation itself. Such an investigation should be held responsible to the requirement of underwriting discussions in cognitive science, philosophy of mind, philosophy of logic and mathematics. Its central question, however, should be about the nature of computing, treated as a subject matter in its own right. Understanding the fundamental basis of the information revolution is too important to treat as a derivative issue, en route to some other topic.

A number of computational notions are of intrinsic interest: the notion of mechanism, the nature of a symbol, the concept of digitality, the formality constraint, the metaphysical origins of the limits on computational efficacy (so-called “absolute computability” and “complexity” limits), the notion of non-deterministic computation, the idea of information processing (on syntactic, semantic, and quantitative readings of the term ‘information’), etc. In addition, computational notions have had, and continue to have, an enormous impact on and role in surrounding intellectual life, such as in cognitive science, biology, physics, economics, linguistics, and the arts. This wildfire spread of computational ideas (not just computational technology) makes the task of conducting a philosophically rigorous analysis of computing doubly urgent. " (from Brian Cantwell Smith — Research Interests)

"Addressing such questions requires knowing what computation is, and what computers are—to a depth, (..)  beyond the reach of current theories. (...) a critical examination of the conceptual foundations of computing—in an attempt to figure out what we know, what we don't know, and what a more adequate theory would look like. Overall, the motivation is to understand:

"Natural Computing is a general term referring to computing going on in nature and computing inspired by nature. When complex phenomena going on in nature are viewed as computational processes, our understanding of these phenomena and of the essence of computation is enhanced. In this way one gains valuable insights into both natural sciences and computer science.

Characteristic for man-designed computing inspired by nature is the metaphorical use of concepts, principles and mechanisms underlying natural systems. Thus, evolutionary algorithms use the concepts of mutation, recombination and natural selection from biology; neural networks are inspired by the highly interconnected neural structures in the brain and the nervous system; molecular computing is based on paradigms from molecular biology; and quantum computing based on quantum physics exploits quantum parallelism.

There are also important methodological differences between various sub-areas of natural computing. Thus, e.g., evolutionary algorithms and algorithms based on neural networks are presently implemented on conventional computers.

On the other hand, molecular computing also aims at alternatives for silicon hardware by implementing algorithms in biological hardware ("bioware"), e.g., using DNA molecules and enzymes.

 Also quantum computing aims at nontraditional hardware that would allow quantum effects to take place.

Computer science undergoes now an important transformation by trying to combine the computing carried on in computer science with the computing observed in nature all around us. Natural computing is a very important catalyst of this transformation, and holds a lot of promise for the future. " Leiden Center for Natural Computing

Let us conclude with yet another facet of (physical) computing. Computation at levels beyond storage and transmission of information appears in physical systems at phase transitions. This phenomena are studied using minimal computational models of dynamical systems that undergo a transition to chaos as a function of a nonlinearity parameter. Following questions are of interest:

How is information processing embedded in dynamical behavior?

How can we detect and then quantify structure in natural processes?

In pursuing answers to this sort of question the diverse model classes found in computation theory are key tools in being explicit about how natural information processing mechanisms can be represented and analyzed. However, contemporary notions of "computation'' and of  "useful'' information processing - colored as they are by the recent history of digital computer technology - must be extended in order to be useful within empirical science. Why? Because the processes studied by natural scientists involve systems that are continuous, stochastic, spatially extended, or some combination of these and other characteristics that fall strictly outside the purview of discrete computation theory.

Last update 2007-01-12