<< preface

this blog is nina wenhart's collection of resources on the various histories of new media art. it consists mainly of non or very little edited material i found flaneuring on the net, sometimes with my own annotations and comments, sometimes it's also textparts i retyped from books that are out of print.

it is also meant to be an additional resource of information and recommended reading for my students of the prehystories of new media class that i teach at the school of the art institute of chicago in fall 2008.

the focus is on the time period from the beginning of the 20th century up to today.

>> search this blog

2009-09-30

>> Larry Cuba, computer animated scene from star wars

2009-09-24

>> "Mother of all Demos", Douglas Engelbart, 09.12.1968



presentation of the projects of the Augmentation Research Center (ARC), founded by Douglas Engelbart of the Stanford Research Institute (SRI), held @ Fall Joint Computer Conference (FJCC), December 9th, 1968, which became known as "the mother of all demos":
-- demo of NLS (= oNLine System)
-- "X-Y position indicator for a display system" = computer mouse (developed together with Bill English, 1967)
-- video/teleconference
-- hypertext

>> "Hyperland", Douglas Adams, 1990

50min documentary about hypertext, internet,...
written by Douglas Adams, produced by BBC2 in 1990

playlist in 5 parts on youtube:
http://www.youtube.com/view_play_list?p=E090E024E3E8E7A3



Douglas Adam's website for the project:
http://www.douglasadams.com/creations/hype.html
where it says:
"In this one-hour documentary produced by the BBC in 1990, Douglas falls asleep in front of a television and dreams about future time when he may be allowed to play a more active role in the information he chooses to digest. A software agent, Tom (played by Tom Baker), guides Douglas around a multimedia information landscape, examining (then) cuttting-edge research by the SF Multimedia Lab and NASA Ames research center, and encountering hypermedia visionaries such as Vannevar Bush and Ted Nelson. Looking back now, it's interesting to see how much he got right and how much he didn't: these days, no one's heard of the SF Multimedia Lab, and his super-high-tech portrayal of VR in 2005 could be outdone by a modern PC with a 3D card. However, these are just minor niggles when you consider how much more popular the technologies in question have become than anyone could have predicted - for while Douglas was creating Hyperland, a student at CERN in Switzerland was working on a little hypertext project he called the World Wide Web..."

>> brief history of the internet (excerpt)

from: http://www.isoc.org/internet/history/brief.shtml#Origins

"Origins of the Internet

The first recorded description of the social interactions that could be enabled through networking was a series of memos written by J.C.R. Licklider of MIT in August 1962 discussing his "Galactic Network" concept. He envisioned a globally interconnected set of computers through which everyone could quickly access data and programs from any site. In spirit, the concept was very much like the Internet of today. Licklider was the first head of the computer research program at DARPA, 4 starting in October 1962. While at DARPA he convinced his successors at DARPA, Ivan Sutherland, Bob Taylor, and MIT researcher Lawrence G. Roberts, of the importance of this networking concept.

Leonard Kleinrock at MIT published the first paper on packet switching theory in July 1961 and the first book on the subject in 1964. Kleinrock convinced Roberts of the theoretical feasibility of communications using packets rather than circuits, which was a major step along the path towards computer networking. The other key step was to make the computers talk together. To explore this, in 1965 working with Thomas Merrill, Roberts connected the TX-2 computer in Mass. to the Q-32 in California with a low speed dial-up telephone line creating the first (however small) wide-area computer network ever built. The result of this experiment was the realization that the time-shared computers could work well together, running programs and retrieving data as necessary on the remote machine, but that the circuit switched telephone system was totally inadequate for the job. Kleinrock's conviction of the need for packet switching was confirmed.

In late 1966 Roberts went to DARPA to develop the computer network concept and quickly put together his plan for the "ARPANET", publishing it in 1967. At the conference where he presented the paper, there was also a paper on a packet network concept from the UK by Donald Davies and Roger Scantlebury of NPL. Scantlebury told Roberts about the NPL work as well as that of Paul Baran and others at RAND. The RAND group had written a paper on packet switching networks for secure voice in the military in 1964. It happened that the work at MIT (1961-1967), at RAND (1962-1965), and at NPL (1964-1967) had all proceeded in parallel without any of the researchers knowing about the other work. The word "packet" was adopted from the work at NPL and the proposed line speed to be used in the ARPANET design was upgraded from 2.4 kbps to 50 kbps. 5

In August 1968, after Roberts and the DARPA funded community had refined the overall structure and specifications for the ARPANET, an RFQ was released by DARPA for the development of one of the key components, the packet switches called Interface Message Processors (IMP's). The RFQ was won in December 1968 by a group headed by Frank Heart at Bolt Beranek and Newman (BBN). As the BBN team worked on the IMP's with Bob Kahn playing a major role in the overall ARPANET architectural design, the network topology and economics were designed and optimized by Roberts working with Howard Frank and his team at Network Analysis Corporation, and the network measurement system was prepared by Kleinrock's team at UCLA. 6

Due to Kleinrock's early development of packet switching theory and his focus on analysis, design and measurement, his Network Measurement Center at UCLA was selected to be the first node on the ARPANET. All this came together in September 1969 when BBN installed the first IMP at UCLA and the first host computer was connected. Doug Engelbart's project on "Augmentation of Human Intellect" (which included NLS, an early hypertext system) at Stanford Research Institute (SRI) provided a second node. SRI supported the Network Information Center, led by Elizabeth (Jake) Feinler and including functions such as maintaining tables of host name to address mapping as well as a directory of the RFC's. One month later, when SRI was connected to the ARPANET, the first host-to-host message was sent from Kleinrock's laboratory to SRI. Two more nodes were added at UC Santa Barbara and University of Utah. These last two nodes incorporated application visualization projects, with Glen Culler and Burton Fried at UCSB investigating methods for display of mathematical functions using storage displays to deal with the problem of refresh over the net, and Robert Taylor and Ivan Sutherland at Utah investigating methods of 3-D representations over the net. Thus, by the end of 1969, four host computers were connected together into the initial ARPANET, and the budding Internet was off the ground. Even at this early stage, it should be noted that the networking research incorporated both work on the underlying network and work on how to utilize the network. This tradition continues to this day.

Computers were added quickly to the ARPANET during the following years, and work proceeded on completing a functionally complete Host-to-Host protocol and other network software. In December 1970 the Network Working Group (NWG) working under S. Crocker finished the initial ARPANET Host-to-Host protocol, called the Network Control Protocol (NCP). As the ARPANET sites completed implementing NCP during the period 1971-1972, the network users finally could begin to develop applications.

In October 1972 Kahn organized a large, very successful demonstration of the ARPANET at the International Computer Communication Conference (ICCC). This was the first public demonstration of this new network technology to the public. It was also in 1972 that the initial "hot" application, electronic mail, was introduced. In March Ray Tomlinson at BBN wrote the basic email message send and read software, motivated by the need of the ARPANET developers for an easy coordination mechanism. In July, Roberts expanded its utility by writing the first email utility program to list, selectively read, file, forward, and respond to messages. From there email took off as the largest network application for over a decade. This was a harbinger of the kind of activity we see on the World Wide Web today, namely, the enormous growth of all kinds of "people-to-people" traffic."

2009-09-21

>> "Man-Computer Symbiosis", J.C.R. Licklider, 1960

from: http://groups.csail.mit.edu/medg/people/psz/Licklider.html


Man-Computer Symbiosis


J. C. R. Licklider
IRE Transactions on Human Factors in Electronics,
volume HFE-1, pages 4-11, March 1960
Summary

Man-computer symbiosis is an expected development in cooperative interaction between men and electronic computers. It will involve very close coupling between the human and the electronic members of the partnership. The main aims are 1) to let computers facilitate formulative thinking as they now facilitate the solution of formulated problems, and 2) to enable men and computers to cooperate in making decisions and controlling complex situations without inflexible dependence on predetermined programs. In the anticipated symbiotic partnership, men will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations. Computing machines will do the routinizable work that must be done to prepare the way for insights and decisions in technical and scientific thinking. Preliminary analyses indicate that the symbiotic partnership will perform intellectual operations much more effectively than man alone can perform them. Prerequisites for the achievement of the effective, cooperative association include developments in computer time sharing, in memory components, in memory organization, in programming languages, and in input and output equipment.

1 Introduction
1.1 Symbiosis

The fig tree is pollinated only by the insect Blastophaga grossorun. The larva of the insect lives in the ovary of the fig tree, and there it gets its food. The tree and the insect are thus heavily interdependent: the tree cannot reproduce wit bout the insect; the insect cannot eat wit bout the tree; together, they constitute not only a viable but a productive and thriving partnership. This cooperative "living together in intimate association, or even close union, of two dissimilar organisms" is called symbiosis [27].

"Man-computer symbiosis is a subclass of man-machine systems. There are many man-machine systems. At present, however, there are no man-computer symbioses. The purposes of this paper are to present the concept and, hopefully, to foster the development of man-computer symbiosis by analyzing some problems of interaction between men and computing machines, calling attention to applicable principles of man-machine engineering, and pointing out a few questions to which research answers are needed. The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.
1.2 Between "Mechanically Extended Man" and "Artificial Intelligence"

As a concept, man-computer symbiosis is different in an important way from what North [21] has called "mechanically extended man." In the man-machine systems of the past, the human operator supplied the initiative, the direction, the integration, and the criterion. The mechanical parts of the systems were mere extensions, first of the human arm, then of the human eye. These systems certainly did not consist of "dissimilar organisms living together..." There was only one kind of organism-man-and the rest was there only to help him.

In one sense of course, any man-made system is intended to help man, to help a man or men outside the system. If we focus upon the human operator within the system, however, we see that, in some areas of technology, a fantastic change has taken place during the last few years. "Mechanical extension" has given way to replacement of men, to automation, and the men who remain are there more to help than to be helped. In some instances, particularly in large computer-centered information and control systems, the human operators are responsible mainly for functions that it proved infeasible to automate. Such systems ("humanly extended machines," North might call them) are not symbiotic systems. They are "semi-automatic" systems, systems that started out to be fully automatic but fell short of the goal.

Man-computer symbiosis is probably not the ultimate paradigm for complex technological systems. It seems entirely possible that, in due course, electronic or chemical "machines" will outdo the human brain in most of the functions we now consider exclusively within its province. Even now, Gelernter's IBM-704 program for proving theorems in plane geometry proceeds at about the same pace as Brooklyn high school students, and makes similar errors.[12] There are, in fact, several theorem-proving, problem-solving, chess-playing, and pattern-recognizing programs (too many for complete reference [1, 2, 5, 8, 11, 13, 17, 18, 19, 22, 23, 25]) capable of rivaling human intellectual performance in restricted areas; and Newell, Simon, and Shaw's [20] "general problem solver" may remove some of the restrictions. In short, it seems worthwhile to avoid argument with (other) enthusiasts for artificial intelligence by conceding dominance in the distant future of cerebration to machines alone. There will nevertheless be a fairly long interim during which the main intellectual advances will be made by men and computers working together in intimate association. A multidisciplinary study group, examining future research and development problems of the Air Force, estimated that it would be 1980 before developments in artificial intelligence make it possible for machines alone to do much thinking or problem solving of military significance. That would leave, say, five years to develop man-computer symbiosis and 15 years to use it. The 15 may be 10 or 500, but those years should be intellectually the most creative and exciting in the history of mankind.
2 Aims of Man-Computer Symbiosis

Present-day computers are designed primarily to solve preformulated problems or to process data according to predetermined procedures. The course of the computation may be conditional upon results obtained during the computation, but all the alternatives must be foreseen in advance. (If an unforeseen alternative arises, the whole process comes to a halt and awaits the necessary extension of the program.) The requirement for preformulation or predetermination is sometimes no great disadvantage. It is often said that programming for a computing machine forces one to think clearly, that it disciplines the thought process. If the user can think his problem through in advance, symbiotic association with a computing machine is not necessary.

However, many problems that can be thought through in advance are very difficult to think through in advance. They would be easier to solve, and they could be solved faster, through an intuitively guided trial-and-error procedure in which the computer cooperated, turning up flaws in the reasoning or revealing unexpected turns in the solution. Other problems simply cannot be formulated without computing-machine aid. Poincare anticipated the frustration of an important group of would-be computer users when he said, "The question is not, 'What is the answer?' The question is, 'What is the question?'" One of the main aims of man-computer symbiosis is to bring the computing machine effectively into the formulative parts of technical problems.

The other main aim is closely related. It is to bring computing machines effectively into processes of thinking that must go on in "real time," time that moves too fast to permit using computers in conventional ways. Imagine trying, for example, to direct a battle with the aid of a computer on such a schedule as this. You formulate your problem today. Tomorrow you spend with a programmer. Next week the computer devotes 5 minutes to assembling your program and 47 seconds to calculating the answer to your problem. You get a sheet of paper 20 feet long, full of numbers that, instead of providing a final solution, only suggest a tactic that should be explored by simulation. Obviously, the battle would be over before the second step in its planning was begun. To think in interaction with a computer in the same way that you think with a colleague whose competence supplements your own will require much tighter coupling between man and machine than is suggested by the example and than is possible today.
3 Need for Computer Participation in Formulative and Real-Time Thinking

The preceding paragraphs tacitly made the assumption that, if they could be introduced effectively into the thought process, the functions that can be performed by data-processing machines would improve or facilitate thinking and problem solving in an important way. That assumption may require justification.
3.1 A Preliminary and Informal Time-and-Motion Analysis of Technical Thinking

Despite the fact that there is a voluminous literature on thinking and problem solving, including intensive case-history studies of the process of invention, I could find nothing comparable to a time-and-motion-study analysis of the mental work of a person engaged in a scientific or technical enterprise. In the spring and summer of 1957, therefore, I tried to keep track of what one moderately technical person actually did during the hours he regarded as devoted to work. Although I was aware of the inadequacy of the sampling, I served as my own subject.

It soon became apparent that the main thing I did was to keep records, and the project would have become an infinite regress if the keeping of records had been carried through in the detail envisaged in the initial plan. It was not. Nevertheless, I obtained a picture of my activities that gave me pause. Perhaps my spectrum is not typical--I hope it is not, but I fear it is.

About 85 per cent of my "thinking" time was spent getting into a position to think, to make a decision, to learn something I needed to know. Much more time went into finding or obtaining information than into digesting it. Hours went into the plotting of graphs, and other hours into instructing an assistant how to plot. When the graphs were finished, the relations were obvious at once, but the plotting had to be done in order to make them so. At one point, it was necessary to compare six experimental determinations of a function relating speech-intelligibility to speech-to-noise ratio. No two experimenters had used the same definition or measure of speech-to-noise ratio. Several hours of calculating were required to get the data into comparable form. When they were in comparable form, it took only a few seconds to determine what I needed to know.

Throughout the period I examined, in short, my "thinking" time was devoted mainly to activities that were essentially clerical or mechanical: searching, calculating, plotting, transforming, determining the logical or dynamic consequences of a set of assumptions or hypotheses, preparing the way for a decision or an insight. Moreover, my choices of what to attempt and what not to attempt were determined to an embarrassingly great extent by considerations of clerical feasibility, not intellectual capability.

The main suggestion conveyed by the findings just described is that the operations that fill most of the time allegedly devoted to technical thinking are operations that can be performed more effectively by machines than by men. Severe problems are posed by the fact that these operations have to be performed upon diverse variables and in unforeseen and continually changing sequences. If those problems can be solved in such a way as to create a symbiotic relation between a man and a fast information-retrieval and data-processing machine, however, it seems evident that the cooperative interaction would greatly improve the thinking process.

It may be appropriate to acknowledge, at this point, that we are using the term "computer" to cover a wide class of calculating, data-processing, and information-storage-and-retrieval machines. The capabilities of machines in this class are increasing almost daily. It is therefore hazardous to make general statements about capabilities of the class. Perhaps it is equally hazardous to make general statements about the capabilities of men. Nevertheless, certain genotypic differences in capability between men and computers do stand out, and they have a bearing on the nature of possible man-computer symbiosis and the potential value of achieving it.

As has been said in various ways, men are noisy, narrow-band devices, but their nervous systems have very many parallel and simultaneously active channels. Relative to men, computing machines are very fast and very accurate, but they are constrained to perform only one or a few elementary operations at a time. Men are flexible, capable of "programming themselves contingently" on the basis of newly received information. Computing machines are single-minded, constrained by their " pre-programming." Men naturally speak redundant languages organized around unitary objects and coherent actions and employing 20 to 60 elementary symbols. Computers "naturally" speak nonredundant languages, usually with only two elementary symbols and no inherent appreciation either of unitary objects or of coherent actions.

To be rigorously correct, those characterizations would have to include many qualifiers. Nevertheless, the picture of dissimilarity (and therefore p0tential supplementation) that they present is essentially valid. Computing machines can do readily, well, and rapidly many things that are difficult or impossible for man, and men can do readily and well, though not rapidly, many things that are difficult or impossible for computers. That suggests that a symbiotic cooperation, if successful in integrating the positive characteristics of men and computers, would be of great value. The differences in speed and in language, of course, pose difficulties that must be overcome.
4 Separable Functions of Men and Computers in the Anticipated Symbiotic Association

It seems likely that the contributions of human operators and equipment will blend together so completely in many operations that it will be difficult to separate them neatly in analysis. That would be the case it; in gathering data on which to base a decision, for example, both the man and the computer came up with relevant precedents from experience and if the computer then suggested a course of action that agreed with the man's intuitive judgment. (In theorem-proving programs, computers find precedents in experience, and in the SAGE System, they suggest courses of action. The foregoing is not a far-fetched example. ) In other operations, however, the contributions of men and equipment will be to some extent separable.

Men will set the goals and supply the motivations, of course, at least in the early years. They will formulate hypotheses. They will ask questions. They will think of mechanisms, procedures, and models. They will remember that such-and-such a person did some possibly relevant work on a topic of interest back in 1947, or at any rate shortly after World War II, and they will have an idea in what journals it might have been published. In general, they will make approximate and fallible, but leading, contributions, and they will define criteria and serve as evaluators, judging the contributions of the equipment and guiding the general line of thought.

In addition, men will handle the very-low-probability situations when such situations do actually arise. (In current man-machine systems, that is one of the human operator's most important functions. The sum of the probabilities of very-low-probability alternatives is often much too large to neglect. ) Men will fill in the gaps, either in the problem solution or in the computer program, when the computer has no mode or routine that is applicable in a particular circumstance.

The information-processing equipment, for its part, will convert hypotheses into testable models and then test the models against data (which the human operator may designate roughly and identify as relevant when the computer presents them for his approval). The equipment will answer questions. It will simulate the mechanisms and models, carry out the procedures, and display the results to the operator. It will transform data, plot graphs ("cutting the cake" in whatever way the human operator specifies, or in several alternative ways if the human operator is not sure what he wants). The equipment will interpolate, extrapolate, and transform. It will convert static equations or logical statements into dynamic models so the human operator can examine their behavior. In general, it will carry out the routinizable, clerical operations that fill the intervals between decisions.

In addition, the computer will serve as a statistical-inference, decision-theory, or game-theory machine to make elementary evaluations of suggested courses of action whenever there is enough basis to support a formal statistical analysis. Finally, it will do as much diagnosis, pattern-matching, and relevance-recognizing as it profitably can, but it will accept a clearly secondary status in those areas.
5 Prerequisites for Realization of Man-Computer Symbiosis

The data-processing equipment tacitly postulated in the preceding section is not available. The computer programs have not been written. There are in fact several hurdles that stand between the nonsymbiotic present and the anticipated symbiotic future. Let us examine some of them to see more clearly what is needed and what the chances are of achieving it.
5.1 Speed Mismatch Between Men and Computers

Any present-day large-scale computer is too fast and too costly for real-time cooperative thinking with one man. Clearly, for the sake of efficiency and economy, the computer must divide its time among many users. Timesharing systems are currently under active development. There are even arrangements to keep users from "clobbering" anything but their own personal programs.

It seems reasonable to envision, for a time 10 or 15 years hence, a "thinking center" that will incorporate the functions of present-day libraries together with anticipated advances in information storage and retrieval and the symbiotic functions suggested earlier in this paper. The picture readily enlarges itself into a network of such centers, connected to one another by wide-band communication lines and to individual users by leased-wire services. In such a system, the speed of the computers would be balanced, and the cost of the gigantic memories and the sophisticated programs would be divided by the number of users.
5.2 Memory Hardware Requirements

When we start to think of storing any appreciable fraction of a technical literature in computer memory, we run into billions of bits and, unless things change markedly, billions of dollars.

The first thing to face is that we shall not store all the technical and scientific papers in computer memory. We may store the parts that can be summarized most succinctly-the quantitative parts and the reference citations-but not the whole. Books are among the most beautifully engineered, and human-engineered, components in existence, and they will continue to be functionally important within the context of man-computer symbiosis. (Hopefully, the computer will expedite the finding, delivering, and returning of books.)

The second point is that a very important section of memory will be permanent: part indelible memory and part published memory. The computer will be able to write once into indelible memory, and then read back indefinitely, but the computer will not be able to erase indelible memory. (It may also over-write, turning all the 0's into l's, as though marking over what was written earlier.) Published memory will be "read-only" memory. It will be introduced into the computer already structured. The computer will be able to refer to it repeatedly, but not to change it. These types of memory will become more and more important as computers grow larger. They can be made more compact than core, thin-film, or even tape memory, and they will be much less expensive. The main engineering problems will concern selection circuitry.

In so far as other aspects of memory requirement are concerned, we may count upon the continuing development of ordinary scientific and business computing machines There is some prospect that memory elements will become as fast as processing (logic) elements. That development would have a revolutionary effect upon the design of computers.
5.3 Memory Organization Requirements

Implicit in the idea of man-computer symbiosis are the requirements that information be retrievable both by name and by pattern and that it be accessible through procedure much faster than serial search. At least half of the problem of memory organization appears to reside in the storage procedure. Most of the remainder seems to be wrapped up in the problem of pattern recognition within the storage mechanism or medium. Detailed discussion of these problems is beyond the present scope. However, a brief outline of one promising idea, "trie memory," may serve to indicate the general nature of anticipated developments.

Trie memory is so called by its originator, Fredkin [10], because it is designed to facilitate retrieval of information and because the branching storage structure, when developed, resembles a tree. Most common memory systems store functions of arguments at locations designated by the arguments. (In one sense, they do not store the arguments at all. In another and more realistic sense, they store all the possible arguments in the framework structure of the memory.) The trie memory system, on the other hand, stores both the functions and the arguments. The argument is introduced into the memory first, one character at a time, starting at a standard initial register. Each argument register has one cell for each character of the ensemble (e.g., two for information encoded in binary form) and each character cell has within it storage space for the address of the next register. The argument is stored by writing a series of addresses, each one of which tells where to find the next. At the end of the argument is a special "end-of-argument" marker. Then follow directions to the function, which is stored in one or another of several ways, either further trie structure or "list structure" often being most effective.

The trie memory scheme is inefficient for small memories, but it becomes increasingly efficient in using available storage space as memory size increases. The attractive features of the scheme are these: 1) The retrieval process is extremely simple. Given the argument, enter the standard initial register with the first character, and pick up the address of the second. Then go to the second register, and pick up the address of the third, etc. 2) If two arguments have initial characters in common, they use the same storage space for those characters. 3) The lengths of the arguments need not be the same, and need not be specified in advance. 4) No room in storage is reserved for or used by any argument until it is actually stored. The trie structure is created as the items are introduced into the memory. 5) A function can be used as an argument for another function, and that function as an argument for the next. Thus, for example, by entering with the argument, "matrix multiplication," one might retrieve the entire program for performing a matrix multiplication on the computer. 6) By examining the storage at a given level, one can determine what thus-far similar items have been stored. For example, if there is no citation for Egan, J. P., it is but a step or two backward to pick up the trail of Egan, James ... .

The properties just described do not include all the desired ones, but they bring computer storage into resonance with human operators and their predilection to designate things by naming or pointing.
5.4 The Language Problem

The basic dissimilarity between human languages and computer languages may be the most serious obstacle to true symbiosis. It is reassuring, however, to note what great strides have already been made, through interpretive programs and particularly through assembly or compiling programs such as FORTRAN, to adapt computers to human language forms. The "Information Processing Language" of Shaw, Newell, Simon, and Ellis [24] represents another line of rapprochement. And, in ALGOL and related systems, men are proving their flexibility by adopting standard formulas of representation and expression that are readily translatable into machine language.

For the purposes of real-time cooperation between men and computers, it will be necessary, however, to make use of an additional and rather different principle of communication and control. The idea may be highlighted by comparing instructions ordinarily addressed to intelligent human beings with instructions ordinarily used with computers. The latter specify precisely the individual steps to take and the sequence in which to take them. The former present or imply something about incentive or motivation, and they supply a criterion by which the human executor of the instructions will know when he has accomplished his task. In short: instructions directed to computers specify courses; instructions-directed to human beings specify goals.

Men appear to think more naturally and easily in terms of goals than in terms of courses. True, they usually know something about directions in which to travel or lines along which to work, but few start out with precisely formulated itineraries. Who, for example, would depart from Boston for Los Angeles with a detailed specification of the route? Instead, to paraphrase Wiener, men bound for Los Angeles try continually to decrease the amount by which they are not yet in the smog.

Computer instruction through specification of goals is being approached along two paths. The first involves problem-solving, hill-climbing, self-organizing programs. The second involves real-time concatenation of preprogrammed segments and closed subroutines which the human operator can designate and call into action simply by name.

Along the first of these paths, there has been promising exploratory work. It is clear that, working within the loose constraints of predetermined strategies, computers will in due course be able to devise and simplify their own procedures for achieving stated goals. Thus far, the achievements have not been substantively important; they have constituted only "demonstration in principle." Nevertheless, the implications are far-reaching.

Although the second path is simpler and apparently capable of earlier realization, it has been relatively neglected. Fredkin's trie memory provides a promising paradigm. We may in due course see a serious effort to develop computer programs that can be connected together like the words and phrases of speech to do whatever computation or control is required at the moment. The consideration that holds back such an effort, apparently, is that the effort would produce nothing that would be of great value in the context of existing computers. It would be unrewarding to develop the language before there are any computing machines capable of responding meaningfully to it.
5.5 Input and Output Equipment

The department of data processing that seems least advanced, in so far as the requirements of man-computer symbiosis are concerned, is the one that deals with input and output equipment or, as it is seen from the human operator's point of view, displays and controls. Immediately after saying that, it is essential to make qualifying comments, because the engineering of equipment for high-speed introduction and extraction of information has been excellent, and because some very sophisticated display and control techniques have been developed in such research laboratories as the Lincoln Laboratory. By and large, in generally available computers, however, there is almost no provision for any more effective, immediate man-machine communication than can be achieved with an electric typewriter.

Displays seem to be in a somewhat better state than controls. Many computers plot graphs on oscilloscope screens, and a few take advantage of the remarkable capabilities, graphical and symbolic, of the charactron display tube. Nowhere, to my knowledge, however, is there anything approaching the flexibility and convenience of the pencil and doodle pad or the chalk and blackboard used by men in technical discussion.

1) Desk-Surface Display and Control: Certainly, for effective man-computer interaction, it will be necessary for the man and the computer to draw graphs and pictures and to write notes and equations to each other on the same display surface. The man should be able to present a function to the computer, in a rough but rapid fashion, by drawing a graph. The computer should read the man's writing, perhaps on the condition that it be in clear block capitals, and it should immediately post, at the location of each hand-drawn symbol, the corresponding character as interpreted and put into precise type-face. With such an input-output device, the operator would quickly learn to write or print in a manner legible to the machine. He could compose instructions and subroutines, set them into proper format, and check them over before introducing them finally into the computer's main memory. He could even define new symbols, as Gilmore and Savell [14] have done at the Lincoln Laboratory, and present them directly to the computer. He could sketch out the format of a table roughly and let the computer shape it up with precision. He could correct the computer's data, instruct the machine via flow diagrams, and in general interact with it very much as he would with another engineer, except that the "other engineer" would be a precise draftsman, a lightning calculator, a mnemonic wizard, and many other valuable partners all in one.

2) Computer-Posted Wall Display: In some technological systems, several men share responsibility for controlling vehicles whose behaviors interact. Some information must be presented simultaneously to all the men, preferably on a common grid, to coordinate their actions. Other information is of relevance only to one or two operators. There would be only a confusion of uninterpretable clutter if all the information were presented on one display to all of them. The information must be posted by a computer, since manual plotting is too slow to keep it up to date.

The problem just outlined is even now a critical one, and it seems certain to become more and more critical as time goes by. Several designers are convinced that displays with the desired characteristics can be constructed with the aid of flashing lights and time-sharing viewing screens based on the light-valve principle.

The large display should be supplemented, according to most of those who have thought about the problem, by individual display-control units. The latter would permit the operators to modify the wall display without leaving their locations. For some purposes, it would be desirable for the operators to be able to communicate with the computer through the supplementary displays and perhaps even through the wall display. At least one scheme for providing such communication seems feasible.

The large wall display and its associated system are relevant, of course, to symbiotic cooperation between a computer and a team of men. Laboratory experiments have indicated repeatedly that informal, parallel arrangements of operators, coordinating their activities through reference to a large situation display, have important advantages over the arrangement, more widely used, that locates the operators at individual consoles and attempts to correlate their actions through the agency of a computer. This is one of several operator-team problems in need of careful study.

3) Automatic Speech Production and Recognition: How desirable and how feasible is speech communication between human operators and computing machines? That compound question is asked whenever sophisticated data-processing systems are discussed. Engineers who work and live with computers take a conservative attitude toward the desirability. Engineers who have had experience in the field of automatic speech recognition take a conservative attitude toward the feasibility. Yet there is continuing interest in the idea of talking with computing machines. In large part, the interest stems from realization that one can hardly take a military commander or a corporation president away from his work to teach him to type. If computing machines are ever to be used directly by top-level decision makers, it may be worthwhile to provide communication via the most natural means, even at considerable cost.

Preliminary analysis of his problems and time scales suggests that a corporation president would be interested in a symbiotic association with a computer only as an avocation. Business situations usually move slowly enough that there is time for briefings and conferences. It seems reasonable, therefore, for computer specialists to be the ones who interact directly with computers in business offices.

The military commander, on the other hand, faces a greater probability of having to make critical decisions in short intervals of time. It is easy to overdramatize the notion of the ten-minute war, but it would be dangerous to count on having more than ten minutes in which to make a critical decision. As military system ground environments and control centers grow in capability and complexity, therefore, a real requirement for automatic speech production and recognition in computers seems likely to develop. Certainly, if the equipment were already developed, reliable, and available, it would be used.

In so far as feasibility is concerned, speech production poses less severe problems of a technical nature than does automatic recognition of speech sounds. A commercial electronic digital voltmeter now reads aloud its indications, digit by digit. For eight or ten years, at the Bell Telephone Laboratories, the Royal Institute of Technology (Stockholm), the Signals Research and Development Establishment (Christchurch), the Haskins Laboratory, and the Massachusetts Institute of Technology, Dunn [6], Fant [7], Lawrence [15], Cooper [3], Stevens [26], and their co-workers, have demonstrated successive generations of intelligible automatic talkers. Recent work at the Haskins Laboratory has led to the development of a digital code, suitable for use by computing machines, that makes an automatic voice utter intelligible connected discourse [16].

The feasibility of automatic speech recognition depends heavily upon the size of the vocabulary of words to be recognized and upon the diversity of talkers and accents with which it must work. Ninety-eight per cent correct recognition of naturally spoken decimal digits was demonstrated several years ago at the Bell Telephone Laboratories and at the Lincoln Laboratory [4], [9]. To go a step up the scale of vocabulary size, we may say that an automatic recognizer of clearly spoken alpha-numerical characters can almost surely be developed now on the basis of existing knowledge. Since untrained operators can read at least as rapidly as trained ones can type, such a device would be a convenient tool in almost any computer installation.

For real-time interaction on a truly symbiotic level, however, a vocabulary of about 2000 words, e.g., 1000 words of something like basic English and 1000 technical terms, would probably be required. That constitutes a challenging problem. In the consensus of acousticians and linguists, construction of a recognizer of 2000 words cannot be accomplished now. However, there are several organizations that would happily undertake to develop an automatic recognize for such a vocabulary on a five-year basis. They would stipulate that the speech be clear speech, dictation style, without unusual accent.

Although detailed discussion of techniques of automatic speech recognition is beyond the present scope, it is fitting to note that computing machines are playing a dominant role in the development of automatic speech recognizers. They have contributed the impetus that accounts for the present optimism, or rather for the optimism presently found in some quarters. Two or three years ago, it appeared that automatic recognition of sizeable vocabularies would not be achieved for ten or fifteen years; that it would have to await much further, gradual accumulation of knowledge of acoustic, phonetic, linguistic, and psychological processes in speech communication. Now, however, many see a prospect of accelerating the acquisition of that knowledge with the aid of computer processing of speech signals, and not a few workers have the feeling that sophisticated computer programs will be able to perform well as speech-pattern recognizes even without the aid of much substantive knowledge of speech signals and processes. Putting those two considerations together brings the estimate of the time required to achieve practically significant speech recognition down to perhaps five years, the five years just mentioned.


References

[1] A. Bernstein and M. deV. Roberts, "Computer versus chess-player," Scientific American, vol. 198, pp. 96-98; June, 1958.

[2] W. W. Bledsoe and I. Browning, "Pattern Recognition and Reading by Machine," presented at the Eastern Joint Computer Conf, Boston, Mass., December, 1959.

[3] F. S. Cooper, et al., "Some experiments on the perception of synthetic speech sounds," J. Acoust Soc. Amer., vol.24, pp.597-606; November, 1952.

[4] K. H. Davis, R. Biddulph, and S. Balashek, "Automatic recognition of spoken digits," in W. Jackson, Communication Theory, Butterworths Scientific Publications, London, Eng., pp. 433-441; 1953.

[5] G. P. Dinneen, "Programming pattern recognition," Proc. WJCC, pp. 94-100; March, 1955.

[6] H. K. Dunn, "The calculation of vowel resonances, and an electrical vocal tract," J. Acoust Soc. Amer., vol. 22, pp.740-753; November, 1950.

[7] G. Fant, "On the Acoustics of Speech," paper presented at the Third Internatl. Congress on Acoustics, Stuttgart, Ger.; September, 1959.

[8] B. G. Farley and W. A. Clark, "Simulation of self-organizing systems by digital computers." IRE Trans. on Information Theory, vol. IT-4, pp.76-84; September, 1954

[9] J. W. Forgie and C. D. Forgie, "Results obtained from a vowel recognition computer program," J. Acoust Soc. Amer., vol. 31, pp. 1480-1489; November, 1959

[10] E. Fredkin, "Trie memory," Communications of the ACM, Sept. 1960, pp. 490-499

[11] R. M. Friedberg, "A learning machine: Part I," IBM J. Res. & Dev., vol.2, pp.2-13; January, 1958.

[12] H. Gelernter, "Realization of a Geometry Theorem Proving Machine." Unesco, NS, ICIP, 1.6.6, Internatl. Conf. on Information Processing, Paris, France; June, 1959.

[13] P. C. Gilmore, "A Program for the Production of Proofs for Theorems Derivable Within the First Order Predicate Calculus from Axioms," Unesco, NS, ICIP, 1.6.14, Internatl. Conf. on Information Processing, Paris, France; June, 1959.

[14] J. T. Gilmore and R. E. Savell, "The Lincoln Writer," Lincoln Laboratory, M. I. T., Lexington, Mass., Rept. 51-8; October, 1959.

[15] W. Lawrence, et al., "Methods and Purposes of Speech Synthesis," Signals Res. and Dev. Estab., Ministry of Supply, Christchurch, Hants, England, Rept. 56/1457; March, 1956.

[16] A. M. Liberman, F. Ingemann, L. Lisker, P. Delattre, and F. S. Cooper, "Minimal rules for synthesizing speech," J. Acoust Soc. Amer., vol. 31, pp. 1490-1499; November, 1959.

[17] A. Newell, "The chess machine: an example of dealing with a complex task by adaptation," Proc. WJCC, pp. 101-108; March, 1955.

[18] A. Newell and J. C. Shaw, "Programming the logic theory machine." Proc. WJCC, pp. 230-240; March, 1957.

[19] A. Newell, J. C. Shaw, and H. A. Simon, "Chess-playing programs and the problem of complexity," IBM J. Res & Dev., vol.2, pp. 320-33.5; October, 1958.

[20] A. Newell, H. A. Simon, and J. C. Shaw, "Report on a general problem-solving program," Unesco, NS, ICIP, 1.6.8, Internatl. Conf. on Information Processing, Paris, France; June, 1959.

[21] J. D. North, "The rational behavior of mechanically extended man", Boulton Paul Aircraft Ltd., Wolverhampton, Eng.; September, 1954.

[22] 0. G. Selfridge, "Pandemonium, a paradigm for learning," Proc. Symp. Mechanisation of Thought Processes, Natl. Physical Lab., Teddington, Eng.; November, 1958.

[23] C. E. Shannon, "Programming a computer for playing chess," Phil. Mag., vol.41, pp.256-75; March, 1950.

[24] J. C. Shaw, A. Newell, H. A. Simon, and T. O. Ellis, "A command structure for complex information processing," Proc. WJCC, pp. 119-128; May, 1958.

[25] H. Sherman, "A Quasi-Topological Method for Recognition of Line Patterns," Unesco, NS, ICIP, H.L.5, Internatl. Conf. on Information Processing, Paris, France; June, 1959

[26] K. N. Stevens, S. Kasowski, and C. G. Fant, "Electric analog of the vocal tract," J. Acoust. Soc. Amer., vol. 25, pp. 734-742; July, 1953.

[27] Webster's New International Dictionary, 2nd e., G. and C. Merriam Co., Springfield, Mass., p. 2555; 1958.

2009-09-17

>> "On Computable Numbers...", Alan Turing, 1936

from: http://plms.oxfordjournals.org/cgi/reprint/s2-42/1/230

originally published in:
A. M. Turing
On Computable Numbers, with an Application to the Entscheidungsproblem
Proc. London Math. Soc. 1937 s2-42: 230-265; doi:10.1112/plms/s2-42.1.230


Turing_OnComputableNumbers_1936

2009-09-16

>> video playlists on computer histories



and

>> UNIVAC, 1951







plus two 1950s advertisements:





and a prediction for the 1956 presidential election:

>> ENIAC, 1946 (raw footage)

>> "Whole Earth Catalogue", Stewart Brand et al, 1968 ff


the complete archive of the Whole Earth Catalogue is accessible online:
http://www.wholeearth.com/history-whole-earth-catalog.php


use the flipbook-option, it is free

2009-09-15

>> "Computer Programming as an Art", Donald E. Knuth, 1974

from: http://www.paulgraham.com/knuth.html

originally published in: CACM, December 1974


"When Communications of the ACM began publication in 1959, the members of ACM'S Editorial Board made the following remark as they described the purposes of ACM'S periodicals [2]:

"If computer programming is to become an important part of computer research and development, a transition of programming from an art to a disciplined science must be effected."
Such a goal has been a continually recurring theme during the ensuing years; for example, we read in 1970 of the "first steps toward transforming the art of programming into a science" [26]. Meanwhile we have actually succeeded in making our discipline a science, and in a remarkably simple way: merely by deciding to call it "computer science."

Implicit in these remarks is the notion that there is something undesirable about an area of human activity that is classified as an "art"; it has to be a Science before it has any real stature. On the other hand, I have been working for more than 12 years on a series of books called "The Art of Computer Programming." People frequently ask me why I picked such a title; and in fact some people apparently don't believe that I really did so, since I've seen at least one bibliographic reference to some books called "The Act of Computer Programming."

In this talk I shall try to explain why I think "Art" is the appropriate word. I will discuss what it means for something to be an art, in contrast to being a science; I will try to examine whether arts are good things or bad things; and I will try to show that a proper viewpoint of the subject will help us all to improve the quality of what we are now doing.

One of the first times I was ever asked about the title of my books was in 1966, during the last previous ACM national meeting held in Southern California. This was before any of the books were published, and I recall having lunch with a friend at the convention hotel. He knew how conceited I was, already at that time, so he asked if I was going to call my books "An Introduction to Don Knuth." I replied that, on the contrary, I was naming the books after him. His name: Art Evans. (The Art of Computer Programming, in person.)

From this story we can conclude that the word "art" has more than one meaning. In fact, one of the nicest things about the word is that it is used in many different senses, each of which is quite appropriate in connection with computer programming. While preparing this talk, I went to the library to find out what people have written about the word "art" through the years; and after spending several fascinating days in the stacks, I came to the conclusion that "art" must be one of the most interesting words in the English language.

The Arts of Old

If we go back to Latin roots, we find ars, artis meaning "skill." It is perhaps significant that the corresponding Greek word was τεχνη, the root of both "technology" and "technique."

Nowadays when someone speaks of "art" you probably think first of "fine arts" such as painting and sculpture, but before the twentieth century the word was generally used in quite a different sense. Since this older meaning of "art" still survives in many idioms, especially when we are contrasting art with science, I would like to spend the next few minutes talking about art in its classical sense.

In medieval times, the first universities were established to teach the seven so-called "liberal arts," namely grammar, rhetoric, logic, arithmetic, geometry, music, and astronomy. Note that this is quite different from the curriculum of today's liberal arts colleges, and that at least three of the original seven liberal arts are important components of computer science. At that time, an "art" meant something devised by man's intellect, as opposed to activities derived from nature or instinct; "liberal" arts were liberated or free, in contrast to manual arts such as plowing (cf. [6]). During the middle ages the word "art" by itself usually meant logic [4], which usually meant the study of syllogisms.

Science vs. Art

The word "science" seems to have been used for many years in about the same sense as "art"; for example, people spoke also of the seven liberal sciences, which were the same as the seven liberal arts [1]. Duns Scotus in the thirteenth century called logic "the Science of Sciences, and the Art of Arts" (cf. [12, p. 34f]). As civilization and learning developed, the words took on more and more independent meanings, "science" being used to stand for knowledge, and "art" for the application of knowledge. Thus, the science of astronomy was the basis for the art of navigation. The situation was almost exactly like the way in which we now distinguish between "science" and "engineering."

Many authors wrote about the relationship between art and science in the nineteenth century, and I believe the best discussion was given by John Stuart Mill. He said the following things, among others, in 1843 [28]:
Several sciences are often necessary to form the groundwork of a single art. Such is the complication of human affairs, that to enable one thing to be done, it is often requisite to know the nature and properties of many things... Art in general consists of the truths of Science, arranged in the most convenient order for practice, instead of the order which is the most convenient for thought. Science groups and arranges its truths so as to enable us to take in at one view as much as possible of the general order of the universe. Art... brings together from parts of the field of science most remote from one another, the truths relating to the production of the different and heterogeneous conditions necessary to each effect which the exigencies of practical life require.
As I was looking up these things about the meanings of "art," I found that authors have been calling for a transition from art to science for at least two centuries. For example, the preface to a textbook on mineralogy, written in 1784, said the following [17]: "Previous to the year 1780, mineralogy, though tolerably understood by many as an Art, could scarce be deemed a Science."

According to most dictionaries "science" means knowledge that has been logically arranged and systematized in the form of general "laws." The advantage of science is that it saves us from the need to think things through in each individual case; we can turn our thoughts to higher-level concepts. As John Ruskin wrote in 1853 [32]: "The work of science is to substitute facts for appearances, and demonstrations for impressions."

It seems to me that if the authors I studied were writing today, they would agree with the following characterization: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it. Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.

Artificial intelligence has been making significant progress, yet there is a huge gap between what computers can do in the foreseeable future and what ordinary people can do. The mysterious insights that people have when speaking, listening, creating, and even when they are programming, are still beyond the reach of science; nearly everything we do is still an art.

From this standpoint it is certainly desirable to make computer programming a science, and we have indeed come a long way in the 15 years since the publication ot the remarks I quoted at the beginning of this talk. Fifteen years ago computer programming was so badly understood that hardly anyone even thought about proving programs correct; we just fiddled with a program until we "knew" it worked. At that time we didn't even know how to express the concept that a program was correct, in any rigorous way. It is only in recent years that we have been learning about the processes of abstraction by which programs are written and understood; and this new knowledge about programming is currently producing great payoffs in practice, even though few programs are actually proved correct with complete rigor, since we are beginning to understand the principles of program structure. The point is that when we write programs today, we know that we could in principle construct formal proofs of their correctness if we really wanted to, now that we understand how such proofs are formulated. This scientific basis is resulting in programs that are significantly more reliable than those we wrote in former days when intuition was the only basis of correctness.

The field of "automatic programming" is one of the major areas of artificial intelligence research today. Its proponents would love to be able to give a lecture entitled "Computer Programming as an Artifact" (meaning that programming has become merely a relic of bygone days), because their aim is to create machines that write programs better than we can, given only the problem specification. Personally I don't think such a goal will ever be completely attained, but I do think that their research is extremely important, because everything we learn about programming helps us to improve our own artistry. In this sense we should continually be striving to transform every art into a science: in the process, we advance the art.

Science and Art

Our discussion indicates that computer programming is by now both a science and an art, and that the two aspects nicely complement each other. Apparently most authors who examine such a question come to this same conclusion, that their subject is both a science and an art, whatever their subject is (cf. [25]). I found a book about elementary photography, written in 1893, which stated that "the development of the photographic image is both an art and a science" [13]. In fact, when I first picked up a dictionary in order to study the words "art" and "science," I happened to glance at the editor's preface, which began by saying, "The making of a dictionary is both a science and an art." The editor of Funk & Wagnall's dictionary [27] observed that the painstaking accumulation and classification of data about words has a scientific character, while a well-chosen phrasing of definitions demands the ability to write with economy and precision: "The science without the art is likely to be ineffective; the art without the science is certain to be inaccurate."

When preparing this talk I looked through the card catalog at Stanford library to see how other people have been using the words "art" and "science" in the titles of their books. This turned out to be quite interesting.

For example, I found two books entitled The Art of Playing the Piano [5, 15], and others called The Science of Pianoforte Technique [10], The Science of Pianoforte Practice [30]. There is also a book called The Art of Piano Playing: A Scientific Approach [22].

Then I found a nice little book entitled The Gentle Art of Mathematics [31], which made me somewhat sad that I can't honestly describe computer programming as a "gentle art." I had known for several years about a book called The Art of Computation, published in San Francisco, 1879, by a man named C. Frusher Howard [14]. This was a book on practical business arithmetic that had sold over 400,000 copies in various editions by 1890. I was amused to read the preface, since it shows that Howard's philosophy and the intent of his title were quite different from mine; he wrote: "A knowledge of the Science of Number is of minor importance; skill in the Art of Reckoning is absolutely indispensible."

Several books mention both science and art in their titles, notably The Science of Being and Art of Living by Maharishi Mahesh Yogi [24]. There is also a book called The Art of Scientific Discovery [11], which analyzes how some of the great discoveries of science were made.

So much for the word "art" in its classical meaning. Actually when I chose the title of my books, I wasn't thinking primarily of art in this sense, I was thinking more of its current connotations. Probably the most interesting book which turned up in my search was a fairly recent work by Robert E. Mueller called The Science of Art [29]. Of all the books I've mentioned, Mueller's comes closest to expressing what I want to make the central theme of my talk today, in terms of real artistry as we now understand the term. He observes: "It was once thought that the imaginative outlook of the artist was death for the scientist. And the logic of science seemed to spell doom to all possible artistic flights of fancy." He goes on to explore the advantages which actually do result from a synthesis of science and art.

A scientific approach is generally characterized by the words logical, systematic, impersonal, calm, rational, while an artistic approach is characterized by the words aesthetic, creative, humanitarian, anxious, irrational. It seems to me that both of these apparently contradictory approaches have great value with respect to computer programming.

Emma Lehmer wrote in 1956 that she had found coding to be "an exacting science as well as an intriguing art" [23]. H.S.M. Coxeter remarked in 1957 that he sometimes felt "more like an artist than a scientist" [7]. This was at the time C.P. Snow was beginning to voice his alarm at the growing polarization between "two cultures" of educated people [34, 35]. He pointed out that we need to combine scientific and artistic values if we are to make real progress.

Works of Art

When I'm sitting in an audience listening to a long lecture, my attention usually starts to wane at about this point in the hour. So I wonder, are you getting a little tired of my harangue about "science" and "art"? I really hope that you'll be able to listen carefully to the rest of this, anyway, because now comes the part about which I feel most deeply.

When I speak about computer programming as an art, I am thinking primarily of it as an art form, in an aesthetic sense. The chief goal of my work as educator and author is to help people learn how to write beautiful programs. It is for this reason I was especially pleased to learn recently [32] that my books actually appear in the Fine Arts Library at Cornell University. (However, the three volumes apparently sit there neatly on the shelf, without being used, so I'm afraid the librarians may have made a mistake by interpreting my title literally.)

My feeling is that when we prepare a program, it can be like composing poetry or music; as Andrei Ershov has said [9], programming can give us both intellectual and emotional satisfaction, because it is a real achievement to master complexity and to establish a system of consistent rules.

Furthermore when we read other people's programs, we can recognize some of them as genuine works of art. I can still remember the great thrill it was for me to read the listing of Stan Poley's SOAP II assembly program in 1958; you probably think I'm crazy, and styles have certainly changed greatly since then, but at the time it meant a great deal to me to see how elegant a system program could be, especially by comparison with the heavy-handed coding found in other listings I had been studying at the same time. The possibility of writing beautiful programs, even in assembly language, is what got me hooked on programming in the first place.

Some programs are elegant, some are exquisite, some are sparkling. My claim is that it is possible to write grand programs, noble programs, truly magnificent ones!

Taste and Style

The idea of style in programming is now coming to the forefront at last, and I hope that most of you have seen the excellent little book on Elements of Programming Style by Kernighan and Plauger [16]. In this connection it is most important for us all to remember that there is no one "best" style; everybody has his own preferences, and it is a mistake to try to force people into an unnatural mold. We often hear the saying, "I don't know anything about art, but I know what I like." The important thing is that you really like the style you are using; it should be the best way you prefer to express yourself.

Edsger Dijkstra stressed this point in the preface to his Short Introduction to the Art of Programming [8]:
It is my purpose to transmit the importance of good taste and style in programming, [but] the specific elements of style presented serve only to illustrate what benefits can be derived from "style" in general. In this respect I feel akin to the teacher of composition at a conservatory: He does not teach his pupils how to compose a particular symphony, he must help his pupils to find their own style and must explain to them what is implied by this. (It has been this analogy that made me talk about "The Art of Programming.")
Now we must ask ourselves, What is good style, and what is bad style? We should not be too rigid about this in judging other people's work. The early nineteenth-century philosopher Jeremy Bentham put it this way [3, Bk. 3, Ch. 1]:
Judges of elegance and taste consider themselves as benefactors to the human race, whilst they are really only the interrupters of their pleasure... There is no taste which deserves the epithet good, unless it be the taste for such employments which, to the pleasure actually produced by them, conjoin some contingent or future utility: there is no taste which deserves to be characterized as bad, unless it be a taste for some occupation which has a mischievous tendency.
When we apply our own prejudices to "reform" someone else's taste, we may be unconsciously denying him some entirely legitimate pleasure. That's why I don't condemn a lot of things programmers do, even though I would never enjoy doing them myself. The important thing is that they are creating something they feel is beautiful.

In the passage I just quoted, Bentham does give us some advice about certain principles of aesthetics which are better than others, namely the "utility" of the result. We have some freedom in setting up our personal standards of beauty, but it is especially nice when the things we regard as beautiful are also regarded by other people as useful. I must confess that I really enjoy writing computer programs; and I especially enjoy writing programs which do the greatest good, in some sense.

There are many senses in which a program can be "good," of course. In the first place, it's especially good to have a program that works correctly. Secondly it is often good to have a program that won't be hard to change, when the time for adaptation arises. Both of these goals are achieved when the program is easily readable and understandable to a person who knows the appropriate language.

Another important way for a production program to be good is for it to interact gracefully with its users, especially when recovering from human errors in the input data. It's a real art to compose meaningful error messages or to design flexible input formats which are not error-prone.

Another important aspect of program quality is the efficiency with which the computer's resources are actually being used. I am sorry to say that many people nowadays are condemning program efficiency, telling us that it is in bad taste. The reason for this is that we are now experiencing a reaction from the time when efficiency was the only reputable criterion of goodness, and programmers in the past have tended to be so preoccupied with efficiency that they have produced needlessly complicated code; the result of this unnecessary complexity has been that net efficiency has gone down, due to difficulties of debugging and maintenance.

The real problem is that programmers have spent far too much time worrying about efficiency in the wrong places and at the wrong times; premature optimization is the root of all evil (or at least most of it) in programming.

We shouldn't be penny wise and pound foolish, nor should we always think of efficiency in terms of so many percent gained or lost in total running time or space. When we buy a car, many of us are almost oblivious to a difference of $50 or $100 in its price, while we might make a special trip to a particular store in order to buy a 50 cent item for only 25 cents. My point is that there is a time and place for efficiency; I have discussed its proper role in my paper on structured programming, which appears in the current issue of Computing Surveys [21].

Less Facilities: More Enjoyment

One rather curious thing I've noticed about aesthetic satisfaction is that our pleasure is significantly enhanced when we accomplish something with limited tools. For example, the program of which I personally am most pleased and proud is a compiler I once wrote for a primitive minicomputer which had only 4096 words of memory, 16 bits per word. It makes a person feel like a real virtuoso to achieve something under such severe restrictions.

A similar phenomenon occurs in many other contexts. For example, people often seem to fall in love with their Volkswagens but rarely with their Lincoln Continentals (which presumably run much better). When I learned programming, it was a popular pastime to do as much as possible with programs that fit on only a single punched card. I suppose it's this same phenomenon that makes APL enthusiasts relish their "one-liners." When we teach programming nowadays, it is a curious fact that we rarely capture the heart of a student for computer science until he has taken a course which allows "hands on" experience with a minicomputer. The use of our large-scale machines with their fancy operating systems and languages doesn't really seem to engender any love for programming, at least not at first.

It's not obvious how to apply this principle to increase programmers' enjoyment of their work. Surely programmers would groan if their manager suddenly announced that the new machine will have only half as much memory as the old. And I don't think anybody, even the most dedicated "programming artists," can be expected to welcome such a prospect, since nobody likes to lose facilities unnecessarily. Another example may help to clarify the situation: Film-makers strongly resisted the introduction of talking pictures in the 1920's because they were justly proud of the way they could convey words without sound. Similarly, a true programming artist might well resent the introduction of more powerful equipment; today's mass storage devices tend to spoil much of the beauty of our old tape sorting methods. But today's film makers don't want to go back to silent films, not because they're lazy but because they know it is quite possible to make beautiful movies using the improved technology. The form of their art has changed, but there is still plenty of room for artistry.

How did they develop their skill? The best film makers through the years usually seem to have learned their art in comparatively primitive circumstances, often in other countries with a limited movie industry. And in recent years the most important things we have been learning about programming seem to have originated with people who did not have access to very large computers. The moral of this story, it seems to me, is that we should make use of the idea of limited resources in our own education. We can all benefit by doing occasional "toy" programs, when artificial restrictions are set up, so that we are forced to push our abilities to the limit. We shouldn't live in the lap of luxury all the time, since that tends to make us lethargic. The art of tackling miniproblems with all our energy will sharpen our talents for the real problems, and the experience will help us to get more pleasure from our accomplishments on less restricted equipment.

In a similar vein, we shouldn't shy away from "art for art's sake"; we shouldn't feel guilty about programs that are just for fun. I once got a great kick out of writing a one-statement ALGOL program that invoked an innerproduct procedure in such an unusual way that it calculated the mth prime number, instead of an innerproduct [19]. Some years ago the students at Stanford were excited about finding the shortest FORTRAN program which prints itself out, in the sense that the program's output is identical to its own source text. The same problem was considered for many other languages. I don't think it was a waste of time for them to work on this; nor would Jeremy Bentham, whom I quoted earlier, deny the "utility" of such pastimes [3, Bk. 3, Ch. 1]. "On the contrary," he wrote, "there is nothing, the utility of which is more incontestable. To what shall the character of utility be ascribed, if not to that which is a source of pleasure?"

Providing Beautiful Tools

Another characteristic of modern art is its emphasis on creativity. It seems that many artists these days couldn't care less about creating beautiful things; only the novelty of an idea is important. I'm not recommending that computer programming should be like modern art in this sense, but it does lead me to an observation that I think is important. Sometimes we are assigned to a programming task which is almost hopelessly dull, giving us no outlet whatsoever for any creativity; and at such times a person might well come to me and say, "So programming is beautiful? It's all very well for you to declaim that I should take pleasure in creating elegant and charming programs, but how am I supposed to make this mess into a work of art?"

Well, it's true, not all programming tasks are going to be fun. Consider the "trapped housewife," who has to clean off the same table every day: there's not room for creativity or artistry in every situation. But even in such cases, there is a way to make a big improvement: it is still a pleasure to do routine jobs if we have beautiful things to work with. For example, a person will really enjoy wiping off the dining room table, day after day, if it is a beautifully designed table made from some fine quality hardwood.

Therefore I want to address my closing remarks to the system programmers and the machine designers who produce the systems that the rest of us must work with. Please, give us tools that are a pleasure to use, especially for our routine assignments, instead of providing something we have to fight with. Please, give us tools that encourage us to write better programs, by enhancing our pleasure when we do so.

It's very hard for me to convince college freshmen that programming is beautiful, when the first thing I have to tell them is how to punch "slash slash JoB equals so-and-so." Even job control languages can be designed so that they are a pleasure to use, instead of being strictly functional.

Computer hardware designers can make their machines much more pleasant to use, for example by providing floating-point arithmetic which satisfies simple mathematical laws. The facilities presently available on most machines make the job of rigorous error analysis hopelessly difficult, but properly designed operations would encourage numerical analysts to provide better subroutines which have certified accuracy (cf. [20, p. 204]).

Let's consider also what software designers can do. One of the best ways to keep up the spirits of a system user is to provide routines that he can interact with. We shouldn't make systems too automatic, so that the action always goes on behind the scenes; we ought to give the programmer-user a chance to direct his creativity into useful channels. One thing all programmers have in common is that they enjoy working with machines; so let's keep them in the loop. Some tasks are best done by machine, while others are best done by human insight; and a properly designed system will find the right balance. (I have been trying to avoid misdirected automation for many years, cf. [18].)

Program measurement tools make a good case in point. For years, programmers have been unaware of how the real costs of computing are distributed in their programs. Experience indicates that nearly everybody has the wrong idea about the real bottlenecks in his programs; it is no wonder that attempts at efficiency go awry so often, when a programmer is never given a breakdown of costs according to the lines of code he has written. His job is something like that of a newly married couple who try to plan a balanced budget without knowing how much the individual items like food, shelter, and clothing will cost. All that we have been giving programmers is an optimizing compiler, which mysteriously does something to the programs it translates but which never explains what it does. Fortunately we are now finally seeing the appearance of systems which give the user credit for some intelligence; they automatically provide instrumentation of programs and appropriate feedback about the real costs. These experimental systems have been a huge success, because they produce measurable improvements, and especially because they are fun to use, so I am confident that it is only a matter of time before the use of such systems is standard operating procedure. My paper in Computing Surveys [21] discusses this further, and presents some ideas for other ways in which an appropriate interactive routine can enhance the satisfaction of user programmers.

Language designers also have an obligation to provide languages that encourage good style, since we all know that style is strongly influenced by the language in which it is expressed. The present surge of interest in structured programming has revealed that none of our existing languages is really ideal for dealing with program and data structure, nor is it clear what an ideal language should be. Therefore I look forward to many careful experiments in language design during the next few years.

Summary

To summarize: We have seen that computer programming is an art, because it applies accumulated knowledge to the world, because it requires skill and ingenuity, and especially because it produces objects of beauty. A programmer who subconsciously views himself as an artist will enjoy what he does and will do it better. Therefore we can be glad that people who lecture at computer conferences speak about the state of the Art.



References

1. Bailey, Nathan. The Universal Etymological English Dictionary. T. Cox, London, 1727. See "Art," "Liberal," and "Science."

2. Bauer, Walter F., Juncosa, Mario L., and Perlis, Alan J. ACM publication policies and plans. J. ACM 6 (Apr. 1959), 121-122.

3. Bentham, Jeremy. The Rationale of Reward. Trans. from Theorie des peines et des recompenses, 1811, by Richard Smith, J. & H. L. Hunt, London, 1825.

4. The Century Dictionary and Cyclopedia 1. The Century Co., New York, 1889.

5. Clementi, Muzio. The Art of Playing the Piano. Trans. from L'art de jouer le pianoforte by Max Vogrich. Schirmer, New York, 1898.

6. Colvin, Sidney. "Art." Encyclopaedia Britannica, eds 9, 11, 12, 13, 1875-1926.

7. Coxeter, H. S. M. Convocation address, Proc. 4th Canadian Math. Congress, 1957, pp. 8-10.

8. Dijkstra, Edsger W. EWD316: A Short Introduction to the Art of Programming. T. H. Eindhoven, The Netherlands, Aug. 1971.

9. Ershov, A. P. Aesthetics and the human factor in programming. Comm. ACM 15 (July 1972), 501-505.

10. Fielden, Thomas. The Science of Pianoforte Technique. Macmillan, London, 927.

11. Gore, George. The Art of Scientific Discovery. Longmans, Green, London, 1878.

12. Hamilton, William. Lectures on Logic 1. Win. Blackwood, Edinburgh, 1874.

13. Hodges, John A. Elementary Photography: The "Amateur Photographer" Library 7. London, 1893. Sixth ed, revised and enlarged, 1907, p. 58.

14. Howard, C. Frusher. Howard's Art of Computation and golden rule for equation of payments for schools, business colleges and self-culture .... C.F. Howard, San Francisco, 1879.

15. Hummel, J.N. The Art of Playing the Piano Forte. Boosey, London, 1827.

16. Kernighan B.W., and Plauger, P.J. The Elements of Programming Style. McGraw-Hill, New York, 1974.

17. Kirwan, Richard. Elements of Mineralogy. Elmsly, London, 1784.

18. Knuth, Donald E. Minimizing drum latency time. J. ACM 8 (Apr. 1961), 119-150.

19. Knuth, Donald E., and Merner, J.N. ALGOL 60 confidential. Comm. ACM 4 (June 1961), 268-272.

20. Knuth, Donald E. Seminumerical Algorithms: The Art of Computer Programming 2. Addison-Wesley, Reading, Mass., 1969.

21. Knuth, Donald E. Structured programming with go to statements. Computing Surveys 6 (Dec. 1974), pages in makeup.

22. Kochevitsky, George. The Art of Piano Playing: A Scientific Approach. Summy-Birchard, Evanston, II1., 1967.

23. Lehmer, Emma. Number theory on the SWAC. Proc. Syrup. Applied Math. 6, Amer. Math. Soc. (1956), 103-108.

24. Mahesh Yogi, Maharishi. The Science of Being and Art of Living. Allen & Unwin, London, 1963.

25. Malevinsky, Moses L. The Science of Playwriting. Brentano's, New York, 1925.

26. Manna, Zohar, and Pnueli, Amir. Formalization of properties of functional programs. J. ACM 17 (July 1970), 555-569.

27. Marckwardt, Albert H, Preface to Funk and Wagnall's Standard College Dictionary. Harcourt, Brace & World, New York, 1963, vii.

28. Mill, John Stuart. A System Of Logic, Ratiocinative and Inductive. London, 1843. The quotations are from the introduction, S 2, and from Book 6, Chap. 11 (12 in later editions), S 5.

29. Mueller, Robert E. The Science of Art. John Day, New York, 1967.

30. Parsons, Albert Ross. The Science of Pianoforte Practice. Schirmer, New York, 1886.

31. Pedoe, Daniel. The Gentle Art of Mathematics. English U. Press, London, 1953.

32. Ruskin, John. The Stones of Venice 3. London, 1853.

33. Salton, G.A. Personal communication, June 21, 1974.

34. Snow, C.P. The two cultures. The New Statesman and Nation 52 (Oct. 6, 1956), 413-414.

35. Snow, C.P. The Two Cultures: and a Second Look. Cambridge University Press, 1964.

Copyright 1974, Association for Computing Machinery, Inc. General permission to republish, but not for profit, all or part of this material is granted provided that ACM's copyright notice is given and that reference is made to the publication, to its date of issue, and to the fact that reprinting privileges were granted by permission of the Association for Computing Machinery."

2009-09-14

>> "hackers and painters", paul graham, 2004


chapter 10, programming languages explained, pp 146

"the net", lutz dammbeck, 2004

http://www.youtube.com/view_play_list?p=FE019426C78CD603




http://www.t-h-e-n-e-t.com/start_html.htm

2009-09-11

>> "from here to ear", céleste boursier mougenot, 2007

>> "birds on wires", jarbas agnelli, 2009

Birds on the Wires from Jarbas Agnelli on Vimeo.

>> "unauthorized access", annaliza savage, 1994

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>> timetravel

>> cloudy with a chance of tags


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... is a Media Art historian and independent researcher. She is currently writing on "speculative archiving && experimental preservation of Media Art" and graduated from Prof. Oliver Grau's Media Art Histories program at the Danube University in Krems, Austria with a Master Thesis on Descriptive Metadata for Media Arts. For many years, she has been working in the field of archiving/documenting Media Art, recently at the Ludwig Boltzmann Institute for Media.Art.Research and before as the head of the Ars Electronica Futurelab's videostudio, where she created their archives and primarily worked with the archival material. She was teaching the Prehystories of New Media Class at the School of the Art Institute of Chicago (SAIC) and in the Media Art Histories program at the Danube University Krems.