It Began with Babbage (2 page)

This, of course, highlights the venerable philosophical distinction between
is
and
ought
. We might say that computer science is a science of the
ought
in contrast to a natural science such as evolutionary biology, which is a science of the
is
.

Computer science is not unique because of this “oughtness,” nor does its curious nature lie in this. In 1969, the polymath scientist and economics Nobel laureate Herbert Simon (1916–2001) pointed out that the main characteristic of artifacts is that they come into existence with a
purpose
and, consequently, the sciences that deal with artifacts—in his term, the “sciences of the artificial
²
”—are concerned with purpose (or goals), and in this sense they stand apart from the natural sciences. The objects of nature have no purpose. They just
are
. We don't ask about the purpose of the moon or the stars, of rocks or fossils, of oxygen or nitrogen. They just exist. It is true that anatomists and physiologists ask questions about the
function
of a particular organ or process in a living organism, but such functions are attributes that belong to some organ or life process as an outcome of natural
evolution. They do not signify some
prior
purpose originating in the mind of a creative being. Artifacts, in contrast, have prior reasons for existence, reasons that were lodged in human minds prior to the beginning of artifact making. Thus, the sciences of the artificial must concern themselves with the characteristics of artifacts as they are related to the purposes
as intended for them by their
(
earthly
)
creators
. The structure and behavior of an artifact is meaningful only in respect to its purpose. Artifacts are
imbued
with purpose, reflecting the purposes or goals imagined for them by their human creators.

This is why a material artifact can never be explained solely in terms of natural laws even though the artifact must obey such laws. To explain or understand an artifact, even something as apparently simple as a piece of pottery, one must ask: What is it for? What does it do? What was the potter's intention?

This is why a computational artifact such as one's laptop can never be explained only by the laws of physics, even though the laptop's circuits and hard drive obey such laws. A computational artifact is intended to serve some purpose, and physics has nothing to say about purpose.
Computer science is a science of the artificial
. It must, therefore, embody principles, laws, theories, models, and so forth, that allow an explanation of how its structure and behavior relate to intended goals.

Computer science, then, involves the human mind in two ways. First, as we have noted, it is concerned with how artifacts can perform the mental activity of symbol processing. Second, as a science of the artificial, it must have a place in it for the minds of the human creators of computational artifacts—and how their imagined goals and purposes are transformed into artifactual forms.

Of course, computer science is not the only science of the artificial. There are many disciplines that deal with the world of artifacts, that are concerned with changing the world to a preferred state, with pursuing the ought rather than the is. Some of them are of much earlier vintage than computer science. They include, for example, the traditional engineering disciplines—civil, mechanical, electrical, chemical, and metallurgical; they include architecture and industrial design. Others of more recent vintage include genetic engineering, biotechnology, and digital electronics design. Their concerns are, almost without exception,
material
artifacts: structures, machine tools, internal combustion engines, manufacturing and processing equipment, metals, alloys and plastics, aircraft, electronic systems, drugs, genetically modified organisms, and so forth.

Computer science stands apart because of the
peculiarity
of its artifacts. In fact, they are of three kinds.

They can be
material
objects—the physical computer system. These artifacts clearly resemble the material artifacts just mentioned because, like them, they obey the laws of physics and chemistry. They consume power, they generate heat, there is some physical
motion involved, they decay physically and chemically over time, they have material extension, they occupy physical space, and their activities consume physical time.

Computational artifacts, however, can also be completely
abstract
, existing only as symbol structures (made visible on physical media such as paper or the computer screen). They are intrinsically devoid of any physical meaning. The laws of physical nature do not apply to them. As we will see, things called algorithms and purely mathematical machines called Turing machines exemplify such artifacts. Because they are abstract, once created they exist for ever. “They neither spin nor toil” in the physical world. They occupy no physical space nor do their activities consume physical time; rather, they live in their own
abstract
space–time frame.

Abstract computational artifacts such as algorithms resemble mathematical symbol structures (for example, algebraic equations, geometric objects) except that mathematical artifacts have no space–time characteristics at all, neither physical nor abstract.

The third kind of computational artifact is arguably the most interesting and unique of all. These artifacts are
in between
the material and the abstract. They themselves are abstract, yet their existence and usefulness depend on an underlying material substrate. We will call these
liminal
artifacts.
³
Computer programs and the entities called computer architectures are prime instances of this category; they themselves are abstract, yet they must have underlying material computational artifacts as substrates to make them useful or usable (just as the mind needs the brain for its existence).

Computer science, thus, must deal with computational artifacts that straddle the material, the abstract, and the liminal. Each of these types of artifacts can be studied, analyzed, understood, explained, and created autonomously, just as the mind and the brain can be studied autonomously, but—as in the case of the mind and the brain—only up to a point, because these classes of artifacts form
symbiotic relationships
: the abstract with the liminal, the liminal with the material. In fact, as we will see, automatic computation involves the constant interplay between the abstract, the liminal, and the material.

All of this separates computer science from most other sciences of the artificial—what makes computer science so peculiar, so curious, so distinctive. Because of the abstract and liminal artifacts, the laws governing computational artifacts are, in part only, physical laws. In fact, in a certain sense, the laws of nature are almost marginal in computer science. It is the abstract and the liminal artifacts that have come to dominate computer science, and
their
laws are necessarily of an entirely different nature. This raises the issue: What
is
the nature of the
science
in computer science?

The answer, in detail, is the story this book will tell. But, we must pause here on the concept of science itself. The sciences of the artificial differ from the natural sciences because the latter is concerned with natural phenomena and the former with the world of artifacts; they differ because the former
must
factor in purpose into the discourse whereas
the latter
must not
. Yet, we call them both science. And, after all, this book is an account of the birth of computer
science
.

Throughout the centuries, especially since the 17th century—the age of Galileo and Newton, Descartes and Bacon, Kepler and Huygens—much ink has been spilled on the questions: What
is
science? What constitutes scientific knowledge? How does science differs from nonscience? How does scientific knowledge differ from other kinds of knowledge? Despite the vast, animated conversations on these issues, the debate continues. As one modern historian of physics has remarked, every attempt to “fix” the criterion of “scientificity” has failed.
⁴

Etymologically,
science
is rooted in the Latin adjective
scientificus
, used by medieval scholars to mean “referring to knowledge that is demonstrable” as opposed to intuitive knowledge. During the 17th century, we find that it appears in the name of the Académie des Sciences in Paris (1666). However, in ordinary speech, even into the early 19th century, “science” was often used to mean knowledge acquired by systematic study or a skill. In Jane Austin's novel
Pride and Prejudice
(1813), we find a character that refers to dancing as a science.

In fact, until well into the 19th century, “science” and “philosophy” (especially as “natural philosophy”) were more or less synonymous. In 1833, however, the word
scientist
was first deployed by the Englishman William Whewell (1794–1866), marking the beginning of the separation of science from philosophy.

During the 20th century, disciplines called
philosophy of science, sociology of science
, and
history of science
, and, most recently, the generic
science studies
have come into being with objects of inquiry that are the nature of the scientific enterprise. As one might expect, there has been (and continues to be) much debate, discussion, and (of course) disagreement on this matter.

Practicing scientists, however, harbor less anxiety about their trade. They broadly agree that “scientificity” has to do with the
method of inquiry
. They broadly subscribe to the idea that, in science, one gives primacy to observation and reasoning; that one seeks rational explanations of events in the world; that a highly critical mentality is exercised on a continuous basis by viewing a scientific explanation at all times as tentative—a hypothesis that must always be under public scrutiny, tested either by way of observation or experiment, and rejected if the evidence contradicts the hypothesis; and that scientific knowledge, being about the empirical world, is always incomplete. They also agree that a new piece of scientific knowledge is never an island of its own. It must cohere with other pieces of knowledge scientists have already obtained; it must fit into a network of other ideas, concepts, evidence, hypotheses, and so on.

All of this pertain to the natural sciences. What about the sciences of the artificial?

There is, of course, common ground. The sciences of the artificial, like the natural sciences, give primacy to rational explanation, they demand a critical mentality, they acknowledge
the tentativeness and impermanence of explanations, and they involve constructing hypotheses and testing them against reality by way of observation and experiment.

The crucial distinction lies in the nature of the things to be explained. For the natural sciences, these are natural objects. For the artificial sciences, these are artificial objects. Thus, the artificial sciences involve activities entirely missing in the natural sciences: the
creation of the things to be explained
—a process that (in general) involves the creation of a symbolic representation of the artifact (in some language), and then making (or
putting into effect
) that artifact in accordance with the representation.
⁵
The former is called
design
; the latter,
implementation
.

Design and implementation are thus the twins at the heart of the sciences of the artificial—activities entirely missing in the natural sciences. And the consequences are profound.

First, explanations in a science of the artificial will be of two kinds: (a) hypotheses about whether the design of an artifact satisfies the intended purpose of the artifact and (b) hypotheses about whether the implementation satisfies the design.

Second, a design is something specific. One designs a
particular
artifact (a bridge, the transmission system for a car model, a museum, a computer's operating system, and so on). So, really, the design of the artifact is a hypothesis (a theory) that says: If an artifact is built according to this design, it will satisfy the purpose intended for that artifact. A design, then, is
a theory of the individual artifact
(or a particular class of artifacts), and an implementation is
an experiment that tests the theory
.

Third, there is a consequence of this view of designs-as-theories. We noted that, within a natural science, an explanation (in the form of a law or a theory or a hypothesis or a model) does not stand on its own. It is like a piece of a jigsaw puzzle that must fit in to the jigsaw as a whole. If it does not, it is either ignored or it may lead to a radical rehaul of the overall network of knowledge, even the construction of a new jigsaw puzzle.

A science of the artificial has no such constraints. Because it deals with the design and implementation of artifacts, if the design and then the implementation result in an artifact that meets the intended purpose,
success has been achieved
. The particular artifact is what matters. The success of the artifact produces new knowledge that then enriches the network of knowledge in that science of the artificial. But, there is no obligation that this new knowledge
must
cohere with the existing network of knowledge. Thus, although a natural science seeks unified, consistent knowledge about its subject matter, an artificial science may be quite content with fragmented knowledge concerning individual artifacts or individual classes of artifacts.

One of the great glories of (natural) science is its aspiration for what scientists call
universal laws and principles
, which cover a sweeping range of phenomena that are true for all times and all places. Newton's law of gravitation, Kepler's laws of planetary motion, Harvey's explanation of the circulation of blood, the laws of chemical combination, the theory of plate tectonics in geology, Darwinian natural selection, and Planck's law are all examples of universal laws or principles.