计算机科学的第二个时代：从Algol到神经网络

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"The Second Age of Computer Science" 是一本由Subrata Dasgupta撰写的书籍，该书探讨了计算机科学的第二个时代，从Algol语言的影响到神经网络的发展。这本书由牛津大学出版社发行，旨在推进研究、学术和教育的卓越目标。在计算机科学的历程中，"The Second Age" 暗示了一个转变时期，这个时期可能涵盖了从早期的编程语言，如Algol，到现代计算技术，特别是神经网络的演变。Algol是20世纪60年代发展起来的一种对后续编程语言影响深远的语言，它引入了结构化编程的概念，使得程序设计更加有序和可读。Algol的基因，即其核心理念和结构，渗透到了后来的多种编程语言中，包括C、Pascal和Java等，对整个计算机科学领域产生了持久影响。而神经网络是当前人工智能领域的关键组成部分，模仿人脑神经元的工作原理来处理和学习数据。自20世纪40年代首次提出以来，神经网络经历了起伏，但随着计算能力的提升和大数据的普及，它们在机器学习、图像识别、自然语言处理等领域取得了显著成就，标志着计算机科学的新纪元。 Dasgupta的著作可能详细阐述了这两个时代的过渡，分析了技术进步如何推动了计算机科学的创新，并可能讨论了在这个过程中遇到的挑战和解决方案。书中可能还涵盖了算法的进化、数据处理方式的变化以及计算理论的发展，这些都是构成现代计算机科学基础的关键元素。此外，出版商强调了版权信息，指出未经许可，不得复制或传播该书内容，除非符合特定的法律条款或与复制权利组织达成协议。这表明作者和出版商对于保护知识产权的重视。 "The Second Age of Computer Science From Algol Genes to Neural Nets" 提供了一个深入研究计算机科学历史和发展的视角，特别是从早期的编程思想到现代人工智能技术的转变，对于理解计算机科学的过去、现在和未来具有重要意义。

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Prologue xv

e Turing machine and the stored- program computer model, both abstract

entities, are just two of the varieties of computational artifacts that were represented

in the computational paradigm as it was at the end of the ’s— and as reected in

Curriculum 68. In fact, one can place the varieties of computational artifacts into three

broad classes.



Material artifacts:ese are governed by the laws of physics and chemistry. ey

occupy physical space, and their actions consume physical time. Physical computers

and their constituent electronic circuits, input and output devices, and communica-

tion networks are the prime examples of material computational artifacts (known,

more informally, as hardware).

Abstract artifacts:ese exist as symbol structures, devoid of any materiality and

thus not subject to physico- chemical laws. e Turing machine and the stored- program

computer model are prime examples of this class, but there are others, such as pro-

gramming languages and algorithms.

Liminal artifacts: ese are inherently abstract yet their manipulation causes changes

in material artifacts such as the transmission of electrical signals across communi-

cation paths, changing the physical states of circuits, activating mechanical devices,

and so on. ey also require a material infrastructure that enables their work to be

done. Elsewhere, Ihave called such artifacts “liminal” because they straddle— are be-

tween and betwixt— the purely abstract and the purely material.



Computer programs

(“software”), microprograms (“rmware”), and computer architectures are principal

examples of liminal computational artifacts.

.      

As omas Kuhn pointed out a scientic paradigm is a dynamic thing.



ere are “holes”

in its constituent bodies of knowledge that have to be plugged. Controversies must be

resolved. Problems suggested by the overall paradigm must be solved. Solutions may

have to be revised, even dismissed. e paradigm evolves andgrows.

An articial science is driven by the invention of artifacts. New artifacts stimulated

by new social, cultural, technological, and economic needs come into being; and

older artifacts, no longer of practical relevance, recede into the background or per-

haps disappear altogether from scientic consciousness. e new artifacts give rise

to new kinds of problems never recognized before. New questions are asked. e new

problems engender new ways of thinking. Circumscribing the new artifacts, entirely

new bodies of knowledge and philosophies crystallize:ey form “subparadigms.”

Older bodies of knowledge may be discarded or forgotten, but they never disappear

altogether, for one never knows how or when they might become relevant again.

Most signicantly, however, the fundamental structure of the paradigm does not

change.

Kuhn controversially and somewhat disparagingly called this evolutionary growth

of a paradigm “normal science” because he wanted to distinguish it from radical

xvi Prologue

challenges to an existing paradigm, so radical that they may cause the existing par-

adigm to be overthrown and replaced by a new one. us his terms “extraordinary

science” and “paradigm shift”; thus his description of a scientic revolution. In the

Kuhnian framework, normal science and paradigm shifts were the twin pillars of how a

science progresses:the one incremental, constructive and evolutionary; the other icon-

oclastic, destructive or reconstructive and revolutionary.

.     

Yet the history of an actual science is rarely as well structured as philosophers of

science like to portray. Computer science may well have attained paradigmatic status

by the end of the ’s. And there was, no doubt, Kuhnian- style normal science at work

thereafter, adding bricks to the edice created in the preceding decades, and enriching

the paradigm as a whole. But computer science was still a precociously young science

at the time. e computational paradigm was anything but stable. New, unanticipated

forces, some technological, some economic, some social, some intellectual, impinged

on it. e outcome was that entirely new kinds of problem domains arose over the

next two decades that both signicantly altered existing subparadigms and created

new ones. In one or two situations, the core element of the computational paradigm—

the von Neumann model of computing— would even be challenged.

Indeed, by the beginning of the s, though there was not a paradigm shift in the

Kuhnian sense— a revolution— the structure of the computational paradigm looked very

dierent in many important respects from how it was at the end of the s. So much

so that it seems entirely reasonable to call the two decades from  to  the second

age of computer science. My aim in this book is to narrate the story of this secondage.

So what were the core themes of this newage?

.    -  

First, this was a time when many computer scientists became seriously reexive about

their discipline— not so much whether computer science was a genuine science but

about the nature of the science itself. Herbert Simon’s notion of the sciences of the

articial mentioned in Section P. obviously pointed computer scientists in a certain

direction. But outside the AI community— Simon being a seminal gure in AI— it is

not clear whether e Sciences of the Articial had much of an immediate impact on

other computer scientists.

e intellectual origins of computer science lay in logic and mathematics. As the

s ended the presence of these venerable disciplines was still very much vis-

ible in the shape of two branches of computer science. One was numerical analysis,

concerned with the approximate numerical solutions of continuous mathematical

problems— which was what began the quest for automatic computation in the rst

place and that particularly ourished in its rst age of computer science.



e other

Prologue xvii

was automata theory concerned with the study of highly abstract, mathematical

models of computation.

For some computer scientists, automata theory was the “science” in “computer

science.” is suggested, indeed assumed, that computer science was a mathematical

science. Research papers, monographs, and textbooks devoted to automata theory

were full of axioms, denitions, theorems, corollaries, and proofs in the traditions of

mathematics and mathematical logic. e problem was that not all computer scientists

were convinced that automata theory had any direct inuence on, or relevance in,

the design, implementation, and understanding of real computational artifacts. As

Maurice Wilkes, one of the pioneers of automatic computing, put it in an informal

talk delivered in , one did not need to know automata theory to build physical

computers, algorithms, or software. is can be contrasted, he remarked, to the re-

lationship between Maxwell’s equations in electromagnetic theory and radio engi-

neering. If one wanted to design a radio antenna, Wilkes said, one had better know

something about Maxwell’s equations. Automata theory had no such linkages with

much of the rest of computer science (including numerical analysis).



Moreover, there were substantial parts of the computational paradigm that at the

end of the ’s did not seem “scientic” at all. For example, a substantial part of com-

puter architecture, the branch of computer science concerned with the functional

structure and design of computers, was essentially a heuristic, craft- like discipline

having little theoretical content atall.

So also in the realm of programming:Even though there was recognition that this

entailed a whole new kind of artifact (liminal in the sense Ihave used here), deserving a

name of its own— ”software,” coined in the mid- s— there was a perceptible sense of

inadequacy about the way programs and software were being designed and implemented.

In , the term “software engineering” was invented to indicate that software had

evolved to a level of complexity that demanded the same kind of intellectual respect

that was accorded to the artifacts civil, mechanical, and other contemporary branches

of engineering dealt with. Yet it was far from clear what shape this respect shouldtake.

Programming languages— abstract artifacts— had reached a more scientically re-

spectable state by the beginning of the second age. In particular, the ’s decade was

rich in its advancement of a theory of the syntax of programming languages, con-

cerned with the rules governing grammatically correct forms of programs expressed

in programming languages. But semantic considerations— what the elements of a pro-

gramming language meant and how to characterize their meanings, how language

implementers could unambiguously and correctly translate programs in a given lan-

guage into machine- executable code, how programmers could objectively interpret the

meanings of language elements in the course of writing programs— were a dierent

matter. eories of the semantics of programming had indeed emerged in the late ’s,

but very little of these theories had been adopted by the designers and implementers

of “real- world” languages and industrial- grade software. Instead, these designers and

implementers resorted to natural language and informality in dening the semantics

of their programming languages.

xviii Prologue

Collectively, these concerns became audible as sounds of discontent and dissent.

ey represented a desire for a science of real- world computing that would play the same

role in the design, implementation, and understanding of real, utilitarian computa-

tional artifacts as, say, the engineering sciences of strength of materials and uid me-

chanics play, respectively, in structural and aeronautical engineering.



As we will see, this quest for scienticity— and how this quest elicited responses—

constituted one of the most signicant markers of the second age of computer science.

.    

is was also a time when some computer scientists developed a heightened conscious-

ness about computing as a human experience. is too did not arise de novo. e makers

of the rst age of computer science had clearly recognized that although automatic com-

putation with minimal human intervention was their fundamental mission, humans

were not thereby absolved of all responsibilities. Having built physical computers they

could not, in the manner of Pontius Pilate, wash their collective hands and look the

other way. After all, at the very least humans had to communicate their intentions to

machines. e very rst book on programming, authored by Maurice Wilkes, David

Wheeler, and Stanley Gill of Cambridge University, was called, signicantly, Preparation

of Programmes for an Electronic Digital Computer (). (In Britain, the English spelling

“programme” still prevailed to signify this artifact rather than the American spelling

that would eventually be generally adopted.)



Computer programs, reecting the users’

computational intentions, had to be prepared by human beings. e invention and de-

sign of the rst programming languages, reaching back to German engineer Konrad

Zuse’s Plankalkül in 



through David Wheeler’s invention of an assembly language

for the Cambridge EDSAC in 



to the development by the American John Backus

and his collaborators in IBM of FORTRAN



in  were clear manifestations of the

computer pioneers’ awareness of incorporating humans into the computing experience.

e remarkable transatlantic collaboration leading to the design of the Algol program-

ming language between  and 



oered further evidence of this awareness.

But with the emergence and development of the computational paradigm in the late

’s and especially through the ’s, two quite dierent and contrasting manifestations

of the human– computer connection emerged. In both cases it was not the incorpora-

tion of humans into the computing milieu but rather the opposite:incorporation of

computing into the human experience.

.    

Along one front, in the late s, the Dutch mathematician- turned- “humble

programmer” (as he would style himself



) Edsger Dijkstra articulated a vision of

programming as, fundamentally, a human activity.



Dijkstra was among the very rst

advocates of a new mentality:treating programming not only, or not necessarily, as a

Prologue xix

means for communicating tasks to computers but as an intellectual activity in its own

right regardless of the presence or absence of physical computers.

is mentality can be viewed as a provocative challenge to one of the key onto-

logical assumptions underscoring the prevailing computational paradigm in the

rst age:that it should concern itself with computing as a mechanical information

process— an assumption that, of course, was rooted in Turing’s vision of computing.

We will see that although this new human- centered view of programming was rooted

in the s it engendered an intellectual movement in the next two decades called

“structured programming.” Human- centeredness formed the second marker of a new

age of computer science.

.     

Neither Dijkstra nor the other makers of the structured programming movement

extended their idea of programming as a human activity into the realm of human

cognition itself. Envisioning programming as an intellectual activity did not imply

that the mind itself was a computational object— a natural computational object, not

an artifact. e idea of the mind as a machine has its own incredibly long history

reaching back to antiquity.



e modern notion of the mind as a computational ma-

chine, however, originated in the s, coincident with the time that digital com-

puting was “in the air.” For instance, inuenced by analog computing machines such

as the dierential analyzer (originally built by American engineer Vannevar Bush

in ),



the British psychologist Kenneth Craik suggested in  that thinking

was a kind of computational process.



is daring hypothesis was given much more

concrete form in  by Allen Newell, Cli Shaw, and Herbert Simon. In a paper

published in an inuential psychology journal they suggested just how the newly

emergent computational paradigm oered a promising, indeed powerful, model of

human problem solving.



ey backed this model by a working computer program

called Logic eorist, implemented in , which could prove theorems in mathe-

matical logic.



In eect, this paper was seminal in the emergence in the s of AI

as a subparadigm of the computational paradigm that would connect computers with

human cognition; and it would persuade psychologists, linguists, anthropologists,

and philosophers to join with AI researchers to create an interdisciplinary science of

mind they called cognitive science.

AI’s agenda in this regard was not exactly that of cognitive science:e latter’s al-

legiance was to human (and to a lesser extent, other animal) cognition; computation

was a means to that end. AI’s agenda, at its most ambitious, was to construct a uni-

ed science of intelligence that would embrace both artifacts and humans. And, in the

s and s, this project evolved in a number of surprising directions.

One was the consolidation of the idea that symbols constituted the fundamental

“stu” of thought in both machines and mind. So a unied science of intelligence

would be centered on symbol processing.

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