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On the Impact of the Computer on Society 1

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• 1 Reproduction de l’article de Joseph Weizenbaum (1972), « On the Impact of the Computer on Society  (...)

1 The structure of the typical essay on “The impact of computers on society” is as follows: First there is an “on the one-hand” statement. It tells all the good things computers have already done for society and often even attempts to argue that the social order would already have collapsed were it not for the “computer revolution.” This is usually followed by an “on the other hand” caution which tells of certain problems the introduction of computers brings in its wake. The threat posed to individual privacy by large data banks and the danger of large­scale unemployment induced by industrial automation are usually mentioned. Finally, the glorious present and prospective achievements of the computer are applauded, while the dangers alluded to in the second part are shown to be capable of being alleviated by sophisticated technological fixes. The closing paragraph consists of a plea for generous societal support for more, and more large-scale, computer research and development. This is usually coupled to the more or less subtle assertion that only computer science, hence only the computer scientist, can guard the world against the admittedly hazardous fallout of applied computer technology.

2 In fact, the computer has had very considerably less societal impact than the mass media would lead us to believe. Certainly, there are enterprises like space travel that could not have been undertaken without computers. Certainly the computer industry, and with it the computer education industry, has grown to enormous proportions. But much of the industry is self-serving. It is rather like an island economy in which the natives make a living by taking in each other's laundry. The part that is not self-serving is largely supported by government agencies and other gigantic enterprises that know the value of everything but the price of nothing, that is, that know the short-range utility of computer systems but have no idea of their ultimate social cost. In any case, airline reservation systems and computerized hospitals serve only a tiny, largely the most affluent, fraction of society. Such things cannot be said to have an impact on society generally.

3 The more important reason that I dismiss the argument which I have caricatured is that the direct societal effects of any pervasive new technology are as nothing compared to its much more subtle and ultimately much more important side effects. In that sense, the societal impact of the computer has not yet been felt.

4 To help firmly fix the idea of the importance of subtle indirect effects of technology, consider the impact on society of the invention of the microscope. When it was invented in the middle of the 17th century, the dominant commonsense theory of disease was fundamentally that disease was a punishment visited upon an individual by God. The sinner's body was thought to be inhabited by various so-called humors brought into disequilibrium in accordance with divine justice. The cure for disease was therefore to be found first in penance and second in the balancing of humors as, for example, by bleeding. Bleeding was, after all, both painful, hence punishment and penance, and potentially balancing in that it actually removed substance from the body. The microscope enabled man to see microorganisms and thus paved the way for the germ theory of disease. The enormously surprising discovery of extremely small living organisms also induced the idea of a continuous chain of life which, in turn, was a necessary intellectual precondition for the emergence of Darwinism. Both the germ theory of disease and the theory of evolution profoundly altered man's conception of his contract with God and consequently his self-image. Politically these ideas served to help diminish the power of the Church and, more generally, to legitimize the questioning of the basis of hitherto unchallenged authority. I do not say that the microscope alone was responsible for the enormous social changes that followed its invention. Only that it made possible the kind of paradigm shift, even on the commonsense level, without which these changes might have been impossible.

5 Is it reasonable to ask whether the computer will induce similar changes in man's image of himself and whether that influence will prove to be its most important effect on society? I think so, although I hasten to add that I don't believe the computer has yet told us much about man and his nature. To come to grips with the question, we must first ask in what way the computer is different from man's many other machines. Man has built two fundamentally different kinds of machines, nonautonomous and autonomous. An autonomous machine is one that operates for long periods of time, not on the basis of inputs from the real world, for example from sensors or from human drivers, but on the basis of internalized models of some aspect of the real world. Clocks are examples of autonomous machines in that they operate on the basis of an internalized model of the planetary system. The computer is, of course, the example par excellence. It is able to internalize models of essentially unlimited complexity and of a fidelity limited only by the genius of man.

2 B. F. Skinner, Beyond Freedom and Dignity (Knopf, New York, 1971).

6 It is the autonomy of the computer we value. When, for example, we speak of the power of computers as increasing with each new hardware and software development, we mean that, because of their increasing speed and storage capacity, and possibly thanks to new programming tricks, the new computers can internalize ever more complex and ever more faithful models of ever larger slices of reality. It seems strange then that, just when we exhibit virtually an idolatry of autonomy with respect to machines, serious thinkers in respected academies [I have in mind B. F. Skinner of Harvard University 2 ] can rise to question autonomy as a fact for man. I do not think that the appearance of this paradox at this time is accidental. To understand it, we must realize that man's commitment to science has always had a masochistic component.

7 Time after time science has led us to insights that, at least when seen superficially, diminish man. Thus Galileo removed man from the center of the universe, Darwin removed him from his place separate from the animals, and Freud showed his rationality to be an illusion. Yet man pushes his inquiries further and deeper. I cannot help but think that there is an analogy between man's pursuit of scientific knowledge, and an individual's commitment to psychoanalytic therapy. Both are undertaken in the full realization that what the inquirer may find may well damage his self-esteem. Both may reflect his determination to find meaning in his existence through struggle in truth, however painful that may be, rather than to live without meaning in a world of ill-disguised illusion. However, I am also aware that sometimes people enter psychoanalysis unwilling to put their illusions at risk, not searching for a deeper reality but in order to convert the insights they hope to gain to personal power. The analogy to man's pursuit of science does not break down with that observation.

8 Each time a scientific discovery shatters a hitherto fundamental cornerstone of the edifice on which man's self-esteem is built, there is an enormous reaction, just as is the case under similar circumstances in psychoanalytic therapy. Powerful defense mechanisms, beginning with denial and usually terminating in rationalization, are brought to bear. Indeed, the psychoanalyst suspects that, when a patient appears to accept a soul-shattering insight without resistance, his very casualness may well mask his refusal to allow that insight truly operational status in his self-image. But what is the psychoanalyst to think about the patient who positively embraces tentatively proffered, profoundly humiliating self-knowledge, when he embraces it and instantly converts it to a new foundation of his life? Surely such an event is symptomatic of a major crisis in the mental life of the patient.

9 I believe we are now at the beginning of just such a crisis in the mental life of our civilization. The microscope, I have argued, brought in its train a revision of man's image of himself. But no one in the mid-l7th century could have foreseen that. The possibility that the computer will, one way or another, demonstrate that, in the inimitable phrase of one of my esteemed colleagues, “the brain is merely a meat machine” is one that engages academicians, industrialists, and journalists in the here and now. How has the computer contributed to bringing about this very sad state of affairs? It must be said right away that the computer alone is not the chief causative agent. It is merely an extreme extrapolation of technology. When seen as an inducer of philosophical dogma, it is merely the reductio ad absurdum of a technological ideology. But how does it come to be regarded as a source of philosophic dogma?

3 K. M. Colby, S. Weber, F. D. Hi1f, Artif. Intel. 1, 1 (1971).

10 We must be dear about the fact that a computer is nothing without a program. A program is fundamentally a transformation of one computer into another that has autonomy and that, in a very real sense, behaves. Programming languages describe dynamic processes. And, most importantly, the processes they describe can be actually carried out. Thus we can build models of any aspect of the real world that interests us and that we understand. And we can make our models work. But we must be careful to remember that a computer model is a description that works. Ordinarily, when we speak of A being a model of B, we mean that a theory about some aspects of the behavior of B is also a theory of the same aspects of the behavior of A. It follows that when, for example, we consider a computer model of paranoia, like that published by Colby et al. 3 , we must not be persuaded that it tells us anything about paranoia on the ground that it, in some sense, mirrors the behavior of a paranoiac. After all, a plain typewriter in some sense mirrors the behavior of an autistic child (one types a question and gets no response whatever), but it does not help us to understand autism. A model must be made to stand or fall on the basis of its theory. Thus, while programming languages may have put a new power in the hands of social scientists in that this new notation may have freed them from the vagueness of discursive descriptions, their obligation to build defensible theories is in no way diminished. Even errors can be pronounced with utmost formality and eloquence. But they are not thereby transmuted to truth.

• 4 N. Chomsky, Aspects of the Theory of Syntax (M.I.T. Press, Cambridge, Mass., 1965); N. Chomsky and (...)

11 The failure to make distinctions between descriptions, even those that “work,” and theories accounts in large part for the fact that those who refuse to accept the view of man as machine have been put on the defensive. Recent advances in computer understanding of natural language offer an excellent case in point. Halle and Chomsky, to mention only the two with whom I am most familiar, have long labored on a theory of language which any model of language behavior must satisfy 4 . Their aim is like that of the physicist who writes a set of differential equations that anyone riding a bicycle must satisfy. No physicist claims that a person need know, let alone be able to solve, such differential equations in order to become a competent cyclist. Neither do Halle and Chomsky claim that humans know or knowingly obey the rules they believe to govern language behavior. Halle and Chomsky also strive, as do physical theorists, to identify the constants and parameters of their theories with components of reality. They hypothesize that their rules constitute a kind of projective description of certain aspects of the structure of the human mind. Their problem is thus not merely to discover economical rules to account for language behavior, but also to infer economic mechanisms which determine that precisely those rules are to be preferred over all others. Since they are in this way forced to attend to the human mind, not only that of speakers of English, they must necessarily be concerned with all human language behavior -not just that related to the understanding of English.

12 The enormous scope of their task is illustrated by their observation that in all human languages declarative sentences are often transformed into questions by a permutation of two of their words. (John is here → Is John here?) It is one thing to describe rules that transform declarative sentences into questions -a simple permutation rule is clearly insufficient- but another thing to describe a “machine” that necessitates those rules when others would, all else being equal, be simpler. Why, for example, is it not so that declarative sentences read backward transform those sentences into questions? The answer must be that other constraints on the “machine” combine against this local simplicity in favor of a more nearly global economy. Such examples illustrate the depth of the level of explanation that Halle and Chomsky are trying to achieve. No wonder that they stand in awe of their subject matter.

13 Workers in computer comprehension of natural language operate in what is usually called performance mode. It is as if they are building machines that can ride bicycles by following heuristics like “if you feel a displacement to the left, move your weight to the left.” There can be, and often is, a strong interaction between the development of theory and the empirical task of engineering systems whose theory is not yet thoroughly understood. Witness the synergistic cooperation between aerodynamics and aircraft design in the first quarter of the present century. Still, what counts in performance mode is not the elaboration of theory but the performance of systems. And the systems being hammered together by the new crop of computer semanticists are beginning (just beginning) to perform.

14 Since computer scientists have recognized the importance of the interplay of syntax, semantics, and pragmatics, and with it the importance of computer-manipulable knowledge, they have made progress. Perhaps by the end of the present decade, computer systems will exist with which specialists, such as physicians and chemists and mathematicians, will converse in natural language. And surely some part of such achievements will have been based on other successes in, for example, computer simulation of cognitive processes. It is understandable that any success in this area, even if won empirically and without accompanying enrichments of theory, can easily lead to certain delusions being planted. Is it, after all, not terribly tempting to believe that a computer that understands natural language at all, however narrow the context, has captured something of the essence of man? Descartes himself might have believed it. Indeed, by way of this very understandable seduction, the computer comes to be a source of philosophical dogma.

15 I am tempted to recite how performance programs are composed and how things that don't work quite correctly are made to work via all sorts of stratagems which do not even pretend to have any theoretical foundation. But the very asking of the question, “Has the computer captured the essence of man?” is a diversion and, in that sense, a trap. For the real question “Does man understand the essence of man?” cannot be answered by technology and hence certainly not by any technological instrument.

16 I asked earlier what the psychoanalyst is to think when a patient grasps a tentatively proffered deeply humiliating interpretation and attempts to convert it immediately to a new foundation of his life. I now think I phrased that question too weakly. What if the psychoanalyst merely coughed and the cough entrained the consequences of which I speak? That is our situation today. Computer science, particularly its artificial intelligence branch, has coughed. Perhaps the press has unduly amplified that cough -but it is only a cough nevertheless. I cannot help but think that the eagerness to believe that man's whole nature has suddenly been exposed by that cough, and that it has been shown to be a clockwork, is a symptom of something terribly wrong.

5 L. Mumford, The Pentagon of Power (Harcourt, Brace, Jovanovich, New York, 1970).

6 H. A. Simon, The Sciences of the Artificial (M.I.T. Press, Cambridge, Mass., 1969), p. 22-25.

17 What is wrong, I think, is that we have permitted technological metaphors, what Mumford 5 calls the “Myth of the Machine,” and technique itself to so thoroughly pervade our thought processes that we have finally abdicated to technology the very duty to formulate questions. Thus sensible men correctly perceive that large data banks and enormous networks of computers threaten man. But they leave it to technology to formulate the corresponding question. Where a simple man might ask: “Do we need these things?”, technology asks “what electronic wizardry will make them safe?” Where a simple man will ask “is it good?”, technology asks “will it work?” Thus science, even wisdom, becomes what technology and most of all computers can handle. Lest this be thought to be an exaggeration, I quote from the work of H. A. Simon, one of the most senior of American computer scientists 6 :

As we succeed in broadening and deepening our knowledge-theoretical and empirical-about computers, we shall discover that in large part their behavior is governed by simple general laws, that what appeared as complexity in the computer program was, to a considerable extent, complexity of the environment to which the program was seeking to adapt its behavior.

To the extent that this prospect can be realized, it opens up an exceedingly important role for computer simulation as a tool for achieving a deeper understanding of human behavior. For if it is the organization of components, and not their physical properties, that largely determines behavior, and if computers are organized somewhat in the image of man, then the computer becomes an obvious device for exploring the consequences of alternative organizational assumptions for human behavior.

A man, viewed as a behaving system, is quite simple. The apparent complexity of his behavior over lime is largely a reflection of the complexity of the environment in which he finds himself.

... I believe that this hypothesis holds even for the whole man.

19 We already know that those aspects of the behavior of computers which cannot be attributed to the complexity of their programs is governed by simple general laws -ultimately by the laws of Boolean algebra. And of course the physical properties of the computer's components are nearly irrelevant to its behavior. Mechanical relays are logically equivalent to tubes and to transistors and to artificial neurons. And of course the complexity of computer programs is due to the complexity of the environments, including the computing environments themselves, with which they were designed to deal. To what else could it possibly be due? So, what Simon sees as prospective is already realized. But does this collection of obvious and simple facts lead to the conclusion that man is as simple as are computers? When Simon leaps to that conclusion and then formulates the issue as he has done here, that is, when he suggests that the behavior of the whole man may be understood in terms of the behavior of computers as governed by simple general laws, then the very possibility of understanding man as an autonomous being, as an individual with deeply internalized values, that very possibility is excluded. How does one insult a machine?

20 The question “Is the brain merely a meat machine?”, which Simon puts in a so much more sophisticated form, is typical of the kind of question formulated by, indeed formulatable only by, a technological mentality. Once it is accepted as legitimate, arguments as to what a computer can or cannot do “in principle” begin to rage and themselves become legitimate. But the legitimacy of the technological question ­for example, is human behavior to be understood either in terms of the organization or of the physical properties of “components”- need not be admitted in the first instance. A human question can be asked instead. Indeed, we might begin by asking what has already become of “the whole man” when he can conceive of computers organized in his own image.

21 The success of technique and of some technological explanations has, as I've suggested, tricked us into permitting technology to formulate important questions for us -questions whose very forms severely diminish the number of degrees of freedom in our range of decision-making. Whoever dictates the questions in large part determines the answers. In that sense, technology, and especially computer technology, has become a self-fulfilling nightmare reminiscent of that of the lady who dreams of being raped and begs her attacker to be kind to her. He answers “it's your dream, lady.” We must come to see that technology is our dream and that we must ultimately decide how it is to end.

22 I have suggested that the computer revolution need not and ought not to call man's dignity and autonomy into question, that it is a kind of pathology that moves men to wring from it unwarranted, enormously damaging interpretations. Is then the computer less threatening that we might have thought? Once we realize that our visions, possibly nightmarish visions, determine the effect of our own creations on us and on our society, their threat to us is surely diminished. But that is not to say that this realization alone will wipe out all danger. For example, apart from the erosive effect of a technological mentality on man's self-image, there are practical attacks on the freedom and dignity of man in which computer technology plays a critical role.

• 7 B. Buchanan, G. Sutherland, E. A. Feigenbaum, in Machine Intelligence, B. Meltzer, Ed. (American E (...)
• 8 W. A. Martin and R. J. Fateman, “The Macsyma system,” in Proceedings of the 2nd Symposium on Symbo (...)

23 I mentioned earlier that computer science has come to recognize the importance of building knowledge into machines. We already have a machine -Dendral- 7 that commands more chemistry than do many Ph.D. chemists, and another -Mathlab- 8 that commands more applied mathematics than do many applied mathematicians. Both Dendral and Mathlab contain knowledge that can be evaluated in terms of the explicit theories from which it was derived. If the user believes that a result Mathlab delivers is wrong, then, apart from possible program errors, he must be in disagreement, not with the machine or its programmer, but with a specific mathematical theory. But what about the many programs on which management, most particularly the government and the military, rely, programs which can in no sense be said to rest on explicable theories but are instead enormous patchworks of programming techniques strung together to make them work?

9 N. Wiener, Science 131, 1355 (1960).

24 In our eagerness to exploit every advance in technique we quickly incorporate the lessons learned from machine manipulation of knowledge in theory-based systems into such patch­ works. They then “work” better. I have in mind systems like target selection systems used in Vietnam and war games used in the Pentagon, and so on. These often gigantic systems are put together by teams of programmers, often working over a time span of many years. But by the time the systems come into use, most of the original programmers have left or turned their attention to other pursuits. It is precisely when gigantic systems begin to be used that their inner workings can no longer be understood by any single person or by a small team of individuals. Norbert Wiener, the father of cybernetics, foretold this phenomenon in a remarkably prescient article 9 published more than a decade ago.

25 He said there:

It may well be that in principle we cannot make any machine the elements of whose behavior we cannot comprehend sooner or later. This does not mean in any way that we shall be able to comprehend these elements in substantially less time than the time required for operation of the machine, or even within any given number of years or generations.

An intelligent understanding of [machines'] mode of performance may be delayed until long after the task which they have been set has been completed. This means that though machines are theoretically subject to human criticism, such criticism may be ineffective until long after it is relevant.

26 This situation, which is now upon us, has two consequences: first that decisions are made on the basis of rules and criteria no one knows explicitly, and second that the system of rules and criteria becomes immune to change. This is so because, in the absence of detailed understanding of the inner workings of a system, any substantial modification is very likely to render the system altogether inoperable. The threshold of complexity beyond which this phenomenon occurs has already been crossed by many existing systems, including some compiling and computer operating systems. For example, no one likes the operating systems for certain large computers, but they cannot be substantially changed nor can they be done away with. Too many people have become dependent on them.

27 An awkward operating system is inconvenient. That is not too bad. But the growing reliance on supersystems that were perhaps designed to help people make analyses and decisions, but which have since surpassed the understanding of their users while at the same time becoming indispensable to them, is another matter. In modern war it is common for the soldier, say the bomber pilot, to operate at an enormous psychological distance from his victims. He is not responsible for burned children because he never sees their village, his bombs, and certainly not the flaming children themselves. Modern technological rationalizations of war, diplomacy, politics, and commerce such as computer games have an even more insidious effect on the making of policy. Not only have policy makers abdicated their decision-making responsibility to a technology they don't understand, all the while maintaining the illusion that they, the policy makers, are formulating policy questions and answering them, but responsibility has altogether evaporated. No human is any longer responsible for “what the machine says.” Thus there can be neither right nor wrong, no question of justice, no theory with which one can agree or disagree, and finally no basis on which one can challenge “what the machine says.” My father used to invoke the ultimate authority by saying to me, “it is written.” But then I could read what was written, imagine a human author, infer his values, and finally agree or disagree. The systems in the Pentagon, and their counterparts elsewhere in our culture, have in a very real sense no authors. They therefore do not admit of exercises of imagination that may ultimately lead to human judgment. No wonder that men who live day in and out with such machines and become dependent on them begin to believe that men are merely machines. They are reflecting what they themselves have become.

28 The potentially tragic impact on society that may ensue from the use of systems such as I have just discussed is greater than might at first be imagined. Again it is side effects, not direct effects, that matter most. First, of course, there is the psychological impact on individuals living in a society in which anonymous, hence irresponsible, forces formulate the large questions of the day and circumscribe the range of possible answers. It cannot be surprising that large numbers of perceptive individuals living in such a society experience a kind of impotence and fall victim to the mindless rage that often accompanies such experiences. But even worse, since computer­based knowledge systems become essentially unmodifiable except in that they can grow, and since they induce dependence and cannot, after a certain threshold is crossed, be abandoned, there is an enormous risk that they will be passed from one generation to another, always growing. Man too passes knowledge from one generation to another. But because man is mortal, his transmission of knowledge over the generations is at once a process of filtering and accrual. Man doesn't merely pass knowledge, he rather regenerates it continuously. Much as we may mourn the crumb1ing of ancient civilizations, we know nevertheless that the glory of man resides as much in the evolution of his cultures as in that of his brain. The unwise use of ever larger and ever more complex computer systems may well bring this process to a halt. It could well replace the ebb and flow of culture with a world without values, a world in which what counts for a fact has long ago been determined and forever fixed.

29 I’ve spoken of some potentially dangerous effects of present computing trends. Is there nothing positive to be said? Yes, but it must be said with caution. Again, side effects are more important than direct effects. In particular, the idea of computation and of programming languages is beginning to become an important metaphor which, in the long run, may well prove to be responsible for paradigm shifts in many fields. Most of the commonsense paradigms in terms of which much of mankind interprets the phenomena of the everyday world, both physical and social, are still deeply rooted in fundamentally mechanistic metaphors. Marx's dynamics as well as those of Freud are, for example, basically equilibrium systems. Any hydrodynamicist could come to understand them without leaving the jargon of his field. Languages capable of describing ongoing processes, particularly in terms of modular subprocesses, have already had an enormous effect on the way computer people think of every aspect of their worlds, not merely those directly related to their work. The information-processing view of the world so engendered qualifies as a genuine metaphor. This is attested to by the fact that it (i) constitutes an intellectual framework that permits new questions to be asked about a wide-ranging set of phenomena, and (ii) that it itself provides criteria for the adequacy of proffered answers. A new metaphor is important not in that it may be better than existing ones, but rather in that it may enlarge man's vision by giving him yet another perspective on his world. Indeed, the very effectiveness of a new metaphor may seduce lazy minds to adopt it as a basis for universal explanations and as a source of panaceas. Computer simulation of social processes has already been advanced by single-minded generalists as leading to general solutions of all of mankind's problems.

30 The metaphors given us by religion, the poets, and by thinkers like Darwin, Newton, Freud, and Einstein have rather quickly penetrated to the language of ordinary people. These metaphors have thus been instrumental in shaping our entire civilization's imaginative reconstruction of our world. The computing metaphor is as yet available to only an extremely small set of people. Its acquisition and internalization, hopefully as only one of many ways to see the world, seems to require experience in program composition, a kind of computing literacy. Perhaps such literacy will become very widespread in the advanced societal sectors of the advanced countries. But, should it become a dominant mode of thinking and be restricted to certain social classes, it will prove not merely repressive in the ordinary sense, but an enormously divisive societal force. For then classes which do and do not have access to the metaphor will, in an important sense, lose their ability to communicate with one another. We know already how difficult it is for the poor and the oppressed to communicate with the rest of the society in which they are embedded. We know how difficult it is for the world of science to communicate with that of the arts and of the humanities. ln both instances the communication difficulties, which have grave consequences, are very largely due to the fact that the respective communities have unsharable experiences out of which unsharable metaphors have grown.

31 Given these dismal possibilities, what is the responsibility of the computer scientist? First I should say that most of the harm computers can potentially entrain is much more a function of properties people attribute to computers than of what a computer can or cannot actually be made to do. The nonprofessional has little choice but to make his attributions of properties to computers on the basis of the propaganda emanating from the computer community and amplified by the press. The computer professional therefore has an enormously important responsibility to be modest in his claims. This advice would not even have to be voiced if computer science had a tradition of scholarship and of self-criticism such as that which characterizes the established sciences. The mature scientist stands in awe before the depth of his subject matter. His very humility is the wellspring of his strength. I regard the instilling of just this kind of humility, chiefly by the example set by teachers, to be one of the most important missions of every university department of computer science.

10 R. Gillette, ibid. 174, 477 (1971).

32 The computer scientist must be aware constantly that his instruments are capable of having gigantic direct and indirect amplifying effects. An error in a program, for example, could have grievous direct results, including most certainly the loss of much human life. On 11 September 1971, to cite just one example, a computer programming error caused the simultaneous destruction of 117 high-altitude weather balloons whose instruments were being monitored by an earth satellite 10 . A similar error in a military command and control system could launch a fleet of nuclear tipped missiles. Only censorship prevents us from knowing how many such events involving non­nuclear weapons have already occurred. Clearly then, the computer scientist has a heavy responsibility to make the fallibility and limitations of the systems he is capable of designing brilliantly clear. The very power of his systems should serve to inhibit the advice he is ready to give and to constrain the range of work he is willing to undertake.

33 Of course, the computer scientist, like everyone else, is responsible for his actions and their consequences. Sometimes that responsibility is hard to accept because the corresponding authority to decide what is and what is not to be done appears to rest with distant and anonymous forces. That technology itself determines what is to be done by a process of extrapolation and that individuals are powerless to intervene in that determination is precisely the kind of self-fulfilling dream from which we must awaken.

34 Consider gigantic computer systems. They are, of course, natural extrapolations of the large systems we already have. Computer networks are another point on the same curve extrapolated once more. One may ask whether such systems can be used by anybody except by governments and very large corporations and whether such organizations will not use them mainly for antihuman purposes. Or consider speech recognition systems. Will they not be used primarily to spy on private communications? To answer such questions by saying that big computer systems, computer networks, and speech recognition systems are inevitable is to surrender one's humanity. For such an answer must be based either on one's profound conviction that society has already lost control over its technology or on the thoroughly immoral position that “if I don't do it, someone else will.”

35 I don't say that systems such as I have mentioned are necessarily evil­ only that they may be and, what is most important, that their inevitability cannot be accepted by individuals claiming autonomy, freedom, and dignity. The individual computer scientist can and must decide. The determination of what the impact of computers on society is to be is, at least in part, in his bands.

36 Finally, the fundamental question the computer scientist must ask himself is the one that every scientist, indeed every human, must ask. It is not “what shall I do?” but rather “what shall I be?” I cannot answer that for anyone save myself. But I will say again that if technology is a nightmare that appears to have its own inevitable logic, it is our nightmare. It is possible, given courage and insight, for man to deny technology the prerogative to formulate man's questions. It is possible to ask human questions and to find humane answers.

1 Reproduction de l’article de Joseph Weizenbaum (1972), « On the Impact of the Computer on Society », Science , New Series, 176/4035, p. 609-614. En ligne : https://www.jstor.org/stable/1734465

4 N. Chomsky, Aspects of the Theory of Syntax (M.I.T. Press, Cambridge, Mass., 1965); N. Chomsky and M. Halle, The Sound Pattern of English (Harper & Row, New York, 1968).

7 B. Buchanan, G. Sutherland, E. A. Feigenbaum, in Machine Intelligence, B. Meltzer, Ed. (American Elsevier, New York, 1969).

8 W. A. Martin and R. J. Fateman, “The Macsyma system,” in Proceedings of the 2nd Symposium on Symbolic and Algebraic Manipulation (Association for Computer Machines, New York, 1971); J. Moses, Commun. Assoc. Computer Mach. 14 (No. 8), 548 (1971).

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Joseph Weizenbaum , « On the Impact of the Computer on Society » ,  Socio-anthropologie , 47 | 2023, 193-206.

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Joseph Weizenbaum , « On the Impact of the Computer on Society » ,  Socio-anthropologie [En ligne], 47 | 2023, mis en ligne le 31 mai 2023 , consulté le 18 avril 2024 . URL  : http://journals.openedition.org/socio-anthropologie/13631 ; DOI  : https://doi.org/10.4000/socio-anthropologie.13631

Joseph Weizenbaum

The author is professor of computer science, Massachusetts Institute of Technology, 545 Technology Square, Cambridge 02139

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Title: data-driven computational social science: a survey.

Abstract: Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.

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A comprehensive survey of data mining

• Original Research
• Published: 06 February 2020
• Volume 12 , pages 1243–1257, ( 2020 )

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• Manoj Kumar Gupta   ORCID: orcid.org/0000-0002-4481-8432 1 &
• Pravin Chandra 1  

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Data mining plays an important role in various human activities because it extracts the unknown useful patterns (or knowledge). Due to its capabilities, data mining become an essential task in large number of application domains such as banking, retail, medical, insurance, bioinformatics, etc. To take a holistic view of the research trends in the area of data mining, a comprehensive survey is presented in this paper. This paper presents a systematic and comprehensive survey of various data mining tasks and techniques. Further, various real-life applications of data mining are presented in this paper. The challenges and issues in area of data mining research are also presented in this paper.

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Fayadd U, Piatesky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AAAI Press/The MIT Press, Massachusetts Institute of Technology. ISBN 0–262 56097–6 Fayap

Fayadd U, Piatesky-Shapiro G, Smyth P (1996) Knowledge discovery and data mining: towards a unifying framework. In: Proceedings of the 2nd ACM international conference on knowledge discovery and data mining (KDD), Portland, pp 82–88

Heikki M (1996) Data mining: machine learning, statistics, and databases. In: SSDBM ’96: proceedings of the eighth international conference on scientific and statistical database management, June 1996, pp 2–9

Arora RK, Gupta MK (2017) e-Governance using data warehousing and data mining. Int J Comput Appl 169(8):28–31

Google Scholar  

Morik K, Bhaduri K, Kargupta H (2011) Introduction to data mining for sustainability. Data Min Knowl Discov 24(2):311–324

Han J, Kamber M, Pei J (2012) Data mining concepts and techniques, 3rd edn. Elsevier, Netherlands

MATH   Google Scholar  

Friedman JH (1997) Data mining and statistics: What is the connection? in: Keynote Speech of the 29th Symposium on the Interface: Computing Science and Statistics, Houston, TX, 1997

Turban E, Aronson JE, Liang TP, Sharda R (2007) Decision support and business intelligence systems. 8 th edn, Pearson Education, UK

Gheware SD, Kejkar AS, Tondare SM (2014) Data mining: tasks, tools, techniques and applications. Int J Adv Res Comput Commun Eng 3(10):8095–8098

Kiranmai B, Damodaram A (2014) A review on evaluation measures for data mining tasks. Int J Eng Comput Sci 3(7):7217–7220

Sharma M (2014) Data mining: a literature survey. Int J Emerg Res Manag Technol 3(2):1–4

Venkatadri M, Reddy LC (2011) A review on data mining from past to the future. Int J Comput Appl 15(7):19–22

Chen M, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883

Gupta MK, Chandra P (2019) A comparative study of clustering algorithms. In: Proceedings of the 13th INDIACom-2019; IEEE Conference ID: 461816; 6th International Conference on “Computing for Sustainable Global Development”

Ponniah P (2001) Data warehousing fundamentals. Wiley, USA

Chandra P, Gupta MK (2018) Comprehensive survey on data warehousing research. Int J Inform Technol 10(2):217–224

Weiss SH, Indurkhya N (1998) Predictive data mining: a practical guide. Morgan Kaufmann Publishers, San Francisco

Fu Y (1997) Data mining: tasks, techniques, and applications. IEEE Potentials 16(4):18–20

Abuaiadah D (2015) Using bisect k-means clustering technique in the analysis of arabic documents. ACM Trans Asian Low-Resour Lang Inf Process 15(3):1–17

Algergawy A, Mesiti M, Nayak R, Saake G (2011) XML data clustering: an overview. ACM Comput Surv 43(4):1–25

Angiulli F, Fassetti F (2013) Exploiting domain knowledge to detect outliers. Data Min Knowl Discov 28(2):519–568

MathSciNet   MATH   Google Scholar  

Angiulli F, Fassetti F (2016) Toward generalizing the unification with statistical outliers: the gradient outlier factor measure. ACM Trans Knowl Discov Data 10(3):1–26

Bhatnagar V, Ahuja S, Kaur S (2015) Discriminant analysis-based cluster ensemble. Int J Data Min Modell Manag 7(2):83–107

Bouguessa M (2013) Clustering categorical data in projected spaces. Data Min Knowl Discov 29(1):3–38

MathSciNet   Google Scholar  

Campello RJGB, Moulavi D, Zimek A, Sander J (2015) Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Trans Knowl Discov Data 10(1):1–51

Carpineto C, Osinski S, Romano G, Weiss D (2009) A survey of web clustering engines. ACM Comput. Surv. 41(3):1–38

Ceglar A, Roddick JF (2006) Association mining. ACM Comput Surv 38(2):1–42

Chen YL, Weng CH (2009) Mining fuzzy association rules from questionnaire data. Knowl Based Syst 22(1):46–56

Fan Chin-Yuan, Fan Pei-Shu, Chan Te-Yi, Chang Shu-Hao (2012) Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals. Expert Syst Appl 39:8844–8851

Das R, Kalita J, Bhattacharya (2011) A pattern matching approach for clustering gene expression data. Int J Data Min Model Manag 3(2):130–149

Dincer E (2006) The k-means algorithm in data mining and an application in medicine. Kocaeli Univesity, Kocaeli

Geng L, Hamilton HJ (2006) Interestingness measures for data mining: a survey. ACM Comput Surv 38(3):1–32

Gupta MK, Chandra P (2019) P-k-means: k-means using partition based cluster initialization method. In: Proceedings of the international conference on advancements in computing and management (ICACM 2019), Elsevier SSRN, pp 567–573

Gupta MK, Chandra P (2019) An empirical evaluation of k-means clustering algorithm using different distance/similarity metrics. In: Proceedings of the international conference on emerging trends in information technology (ICETIT-2019), emerging trends in information technology, LNEE 605 pp 884–892 DOI: https://doi.org/10.1007/978-3-030-30577-2_79

Hea Z, Xua X, Huangb JZ, Denga S (2004) Mining class outliers: concepts, algorithms and applications in CRM. Expert Syst Appl 27(4):681e97

Hung LN, Thu TNT, Nguyen GC (2015) An efficient algorithm in mining frequent itemsets with weights over data stream using tree data structure. IJ Intell Syst Appl 12:23–31

Hung LN, Thu TNT (2016) Mining frequent itemsets with weights over data stream using inverted matrix. IJ Inf Technol Comput Sci 10:63–71

Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput. Surv 31(3):1–60

Jin H, Wang S, Zhou Q, Li Y (2014) An improved method for density-based clustering. Int J Data Min Model Manag 6(4):347–368

Khandare A, Alvi AS (2017) Performance analysis of improved clustering algorithm on real and synthetic data. IJ Comput Netw Inf Secur 10:57–65

Koh YS, Ravana SD (2016) Unsupervised rare pattern mining: a survey. ACM Trans Knowl Discov Data 10(4):1–29

Kosina P, Gama J (2015) Very fast decision rules for classification in data streams. Data Min Knowl Discov 29(1):168–202

Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268

Kumar D, Bezdek JC, Rajasegarar S, Palaniswami M, Leckie C, Chan J, Gubbi J (2016) Adaptive cluster tendency visualization and anomaly detection for streaming data. ACM Trans Knowl Discov Data 11(2):1–24

Lee G, Yun U (2017) A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives. Future Gener Comput Syst 68:89–110

Li G, Zaki MJ (2015) Sampling frequent and minimal boolean patterns: theory and application in classification. Data Min Knowl Discov 30(1):181–225. https://doi.org/10.1007/s10618-015-0409-y

Article   MathSciNet   MATH   Google Scholar  

Liao TW, Triantaphyllou E (2007) Recent advances in data mining of enterprise data: algorithms and applications. World Scientific Publishing, Singapore, pp 111–145

Mabroukeh NR, Ezeife CI (2010) A taxonomy of sequential pattern mining algorithms. ACM Comput Surv 43:1

Mampaey M, Vreeken J (2011) Summarizing categorical data by clustering attributes. Data Min Knowl Discov 26(1):130–173

Menardi G, Torelli N (2012) Training and assessing classification rules with imbalanced data. Data Min Knowl Discov 28(1):4–28. https://doi.org/10.1007/s10618-012-0295-5

Mukhopadhyay A, Maulik U, Bandyopadhyay S (2015) A survey of multiobjective evolutionary clustering. ACM Comput Surv 47(4):1–46

Pei Y, Fern XZ, Tjahja TV, Rosales R (2016) ‘Comparing clustering with pairwise and relative constraints: a unified framework. ACM Trans Knowl Discov Data 11:2

Rafalak M, Deja M, Wierzbicki A, Nielek R, Kakol M (2016) Web content classification using distributions of subjective quality evaluations. ACM Trans Web 10:4

Reddy D, Jana PK (2014) A new clustering algorithm based on Voronoi diagram. Int J Data Min Model Manag 6(1):49–64

Rustogi S, Sharma M, Morwal S (2017) Improved Parallel Apriori Algorithm for Multi-cores. IJ Inf Technol Comput Sci 4:18–23

Shah-Hosseini H (2013) Improving K-means clustering algorithm with the intelligent water drops (IWD) algorithm. Int J Data Min Model Manag 5(4):301–317

Silva JA, Faria ER, Barros RC, Hruschka ER, de Carvalho ACPLF, Gama J (2013) Data stream clustering: a survey. ACM Comput Surv 46(1):1–31

Silva A, Antunes C (2014) Multi-relational pattern mining over data streams. Data Min Knowl Discov 29(6):1783–1814. https://doi.org/10.1007/s10618-014-0394-6

Sim K, Gopalkrishnan V, Zimek A, Cong G (2012) A survey on enhanced subspace clustering. Data Min Knowl Discov 26(2):332–397

Sohrabi MK, Roshani R (2017) Frequent itemset mining using cellular learning automata. Comput Hum Behav 68:244–253

Craw Susan, Wiratunga Nirmalie, Rowe Ray C (2006) Learning adaptation knowledge to improve case-based reasoning. Artif Intell 170:1175–1192

Tan KC, Teoh EJ, Yua Q, Goh KC (2009) A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Appl 36(4):8616–8630

Tew C, Giraud-Carrier C, Tanner K, Burton S (2013) Behavior-based clustering and analysis of interestingness measures for association rule mining. Data Min Knowl Discov 28(4):1004–1045

Wang L, Dong M (2015) Exemplar-based low-rank matrix decomposition for data clustering. Data Min Knowl Discov 29:324–357

Wang F, Sun J (2014) Survey on distance metric learning and dimensionality reduction in data mining. Data Min Knowl Discov 29:534–564

Wang B, Rahal I, Dong A (2011) Parallel hierarchical clustering using weighted confidence affinity. Int J Data Min Model Manag 3(2):110–129

Zacharis NZ (2018) Classification and regression trees (CART) for predictive modeling in blended learning. IJ Intell Syst Appl 3:1–9

Zhang W, Li R, Feng D, Chernikov A, Chrisochoides N, Osgood C, Ji S (2015) Evolutionary soft co-clustering: formulations, algorithms, and applications. Data Min Knowl Discov 29:765–791

Han J, Fu Y (1996) Exploration of the power of attribute-oriented induction in data mining. Adv Knowl Discov Data Min. AAAI/MIT Press, pp 399-421

Gupta A, Mumick IS (1995) Maintenance of materialized views: problems, techniques, and applications. IEEE Data Eng Bull 18(2):3

Sawant V, Shah K (2013) A review of distributed data mining using agents. Int J Adv Technol Eng Res 3(5):27–33

Gupta MK, Chandra P (2019) An efficient approach for selection of initial cluster centroids for k-means clustering algorithm. In: Proceedings international conference on recent developments in science engineering and technology (REDSET-2019), November 15–16 2019

Gupta MK, Chandra P (2019) MP-K-means: modified partition based cluster initialization method for k-means algorithm. Int J Recent Technol Eng 8(4):1140–1148

Gupta MK, Chandra P (2019) HYBCIM: hypercube based cluster initialization method for k-means. IJ Innov Technol Explor Eng 8(10):3584–3587. https://doi.org/10.35940/ijitee.j9774.0881019

Article   Google Scholar  

Enke David, Thawornwong Suraphan (2005) The use of data mining and neural networks for forecasting stock market returns. Expert Syst Appl 29:927–940

Mezyk Edward, Unold Olgierd (2011) Machine learning approach to model sport training. Comput Hum Behav 27:1499–1506

Esling P, Agon C (2012) Time-series data mining. ACM Comput Surv 45(1):1–34

Hüllermeier Eyke (2005) Fuzzy methods in machine learning and data mining: status and prospects. Fuzzy Sets Syst 156:387–406

Hullermeier Eyke (2011) Fuzzy sets in machine learning and data mining. Appl Soft Comput 11:1493–1505

Gengshen Du, Ruhe Guenther (2014) Two machine-learning techniques for mining solutions of the ReleasePlanner™ decision support system. Inf Sci 259:474–489

Smith Kate A, Gupta Jatinder ND (2000) Neural networks in business: techniques and applications for the operations researcher. Comput Oper Res 27:1023–1044

Huang Mu-Jung, Tsou Yee-Lin, Lee Show-Chin (2006) Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowl Based Syst 19:396–403

Padhraic S (2000) Data mining: analysis on grand scale. Stat Method Med Res 9(4):309–327. https://doi.org/10.1191/096228000701555181

Article   MATH   Google Scholar  

Saeed S, Ali M (2012) Privacy-preserving back-propagation and extreme learning machine algorithms. Data Knowl Eng 79–80:40–61

Singh Y, Bhatia PK, Sangwan OP (2007) A review of studies on machine learning techniques. Int J Comput Sci Secur 1(1):70–84

Yahia ME, El-taher ME (2010) A new approach for evaluation of data mining techniques. Int J Comput Sci Issues 7(5):181–186

Jackson J (2002) Data mining: a conceptual overview. Commun Assoc Inf Syst 8:267–296

Heckerman D (1998) A tutorial on learning with Bayesian networks. Learning in graphical models. Springer, Netherlands, pp 301–354

Politano PM, Walton RO (2017) Statistics & research methodol. Lulu. com

Wetherill GB (1987) Regression analysis with application. Chapman & Hall Ltd, UK

Anderberg MR (2014) Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks, vol 19. Academic Press, USA

Mihoci A (2017) Modelling limit order book volume covariance structures. In: Hokimoto T (ed) Advances in statistical methodologies and their application to real problems. IntechOpen, Croatia. https://doi.org/10.5772/66152

Chapter   Google Scholar  

Thompson B (2004) Exploratory and confirmatory factor analysis: understanding concepts and applications. American Psychological Association, Washington, DC (ISBN:1-59147-093-5)

Kuzey C, Uyar A, Delen (2014) The impact of multinationality on firm value: a comparative analysis of machine learning techniques. Decis Support Syst 59:127–142

Chan Philip K, Salvatore JS (1997) On the accuracy of meta-learning for scalable data mining. J Intell Inf Syst 8:5–28

Tsai Chih-Fong, Hsu Yu-Feng, Lin Chia-Ying, Lin Wei-Yang (2009) Intrusion detection by machine learning: a review. Expert Syst Appl 36:11994–12000

Liao SH, Chu PH, Hsiao PY (2012) Data mining techniques and applications—a decade review from 2000 to 2011. Expert Syst Appl 39:11303–11311

Kanevski M, Parkin R, Pozdnukhov A, Timonin V, Maignan M, Demyanov V, Canu S (2004) Environmental data mining and modelling based on machine learning algorithms and geostatistics. Environ Model Softw 19:845–855

Jain N, Srivastava V (2013) Data mining techniques: a survey paper. Int J Res Eng Technol 2(11):116–119

Baker RSJ (2010) Data mining for education. In: McGaw B, Peterson P, Baker E (eds) International encyclopedia of education, 3rd edn. Elsevier, Oxford, UK

Lew A, Mauch H (2006) Introduction to data mining and its applications. Springer, Berlin

Mukherjee S, Shaw R, Haldar N, Changdar S (2015) A survey of data mining applications and techniques. Int J Comput Sci Inf Technol 6(5):4663–4666

Data mining examples: most common applications of data mining (2019). https://www.softwaretestinghelp.com/data-mining-examples/ . Accessed 27 Dec 2019

Devi SVSG (2013) Applications and trends in data mining. Orient J Comput Sci Technol 6(4):413–419

Data mining—applications & trends. https://www.tutorialspoint.com/data_mining/dm_applications_trends.htm

Keleş MK (2017) An overview: the impact of data mining applications on various sectors. Tech J 11(3):128–132

Top 14 useful applications for data mining. https://bigdata-madesimple.com/14-useful-applications-of-data-mining/ . Accessed 20 Aug 2014

Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Making 5(4):597–604

Padhy N, Mishra P, Panigrahi R (2012) A survey of data mining applications and future scope. Int J Comput Sci Eng Inf Technol 2(3):43–58

Gibert K, Sanchez-Marre M, Codina V (2010) Choosing the right data mining technique: classification of methods and intelligent recommendation. In: International Congress on Environment Modelling and Software Modelling for Environment’s Sake, Fifth Biennial Meeting, Ottawa, Canada

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Gupta, M.K., Chandra, P. A comprehensive survey of data mining. Int. j. inf. tecnol. 12 , 1243–1257 (2020). https://doi.org/10.1007/s41870-020-00427-7

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Computer Systems: Technology Impact on Society Research Paper

Introduction, impacts of the new technology in our societies today.

Technology has had so many impacts in our societies. It was with the invention and the adoption of the new technology, which saw the rise of so many communities. This is because technology involves the use of advanced tools which are used in the production of goods and services and a good example to explain this is the use of computers. So through the paper, we will try to see some of the uses of the new technology in our societies today.

In a broader concept, technology can be taken to deal with the use of knowledge of tools and crafts hence its impacts on the ability to try and adapt and control our environment. Technology has involved the use of science and advanced tools in many economic activities we do today. It is out of this advanced technology which has seen so many communities benefiting so much since those communities that have used the new technology have benefited through effective work. It is due to the role of the new technology that we will try and see some of the impacts of the new technology in most of the societies today. So through the paper, we will try to see some of the uses of the new technology in our societies today. (Abbate, 2000).

One of the impacts of technology today can be seen with the introduction of computers in many schools. The new technology has been in a position to provide a good learning and effective learning to most of our students hence leading to improved performance by most of these students. This is because the educational technology which is used by many schools has enabled these students to even learn online hence making it possible for most of them to do other activities during their free times. The students can be in a position to even learn through the internet hence can add on their knowledge what they have been told in class. You tend to find that most of the students make use of these computers even at there homes hence you will find that in most of the times, these students will stick in these computers hence try and learn more. It is through the introduction of the computers in the classroom and also at home which has made the learning system to be quite effective for them since it has led to improved performance. These students will try and learn better in less time when they receive computer based instructions. They will also try and develop a positive attitude towards learning with the introduction of such computers in their classes since they would wish to learn more from them hence leading to improved performance by these students. This will eventually lead to educative society which has so many people who very much enlightened are hence leading to community development. This is very much important in any community since you will find that effective labor will be used in the allocation of the available resources hence leading to the growth of the country. (Adams, 2000).

The new technology has led to the growth of our economy today. You find that due to the global economy, many countries have applied the new technology in the production of their goods and services hence leading to the growth of these societies. An example to explain this phenomena can be seen in the case of internet whereby many companies today in our country has made use of the internet in advertising of their many products. This is one of the key competencies which so many people have applied in their business since the internet is much more common to everyone hence its through advertising through the internet you will find that many people will have access of these products hence leading to the realization of their competitive advantage. So in this case, you find that many business men in our societies use the internet to advertise their many products hence can be in a position to compete so well with the rest of their competitors. It is due to this fact they will be in a position to have a competitive advantage hence leading to the growth of our economy so much. (Agency for instructional technology. 2000).

The new technology is also applicable in the production of goods and services. You find that so many machines have been used in the production of goods and services hence leading to a more and quality output in the society. An example to explain this is the case of the combine harvesters and the use of tractors by many farmers in their farms. Unlike the tradional ways of farming whereby many farmers use the traditional means of farming say the case of animals to plough their farms, the new technology had been in a position to replace all these things hence leading to increased production in the country. You will find that many farmers in the society have adapted in the use of these machines in the farming and harvesting hence it means that quality products can be produced in these farms hence leading to improved living standards of these people. IT is with the introduction of the new technology you will find that many people can be in a position to make use of quality goods and services which are produced by these farmers hence leading to improved living standards. (Caro, 1998).

Biometric technology is also another technology which has been used in many societies and this is used in trying to prevent criminals in our country. Due to the increase of criminals in many societies which has led to insecurity, the new technology has been used in trying to identify these criminals. This technology may be taken to refer to those technologies which are used to measure and analyze persons physiological and behavioral characteristics and some of the behavioral characteristics include irises, voice patterns, fingerprints, hand measurements and facial patterns. These measurements are usually taken for identification and verification purposes. In the ancient times, identification was usually based on showing something which you already have and some of the things which were used for identification include passport and licence.If one did not have any of these things, other measures were used which include something you know like a pin or a password. It was due to the difficulties in these measurements which were used traditionally which led to the development of biometrics technology which is a new technology and could be used to identify individual behaviors and characteristics. This type of technology has benefited the society so much since it can be used to identify criminals in our societies. This is one of the leading contributors to poor performance of the economy since you will find that many resources were used in trying to fight these criminals. But with the use of the new technology, it has become so much easy to try and get these people hence criminal justice can then be followed. (Commoner, 2000).

Despite the many impacts which the new technology has in our societies, it has also some disadvantages which may include the impact on our environment. You will find that the new technology has led to the pollution of the environment and a good example is the case of the greenhouse gasses which are produced by these plants. This is because they use the new technology which has lead to global warming something affecting our climate so much. The new technology has also led to unemployment since you will find that ,many machines which are used in industries have replaced human labor hence so many people have been rendered jobless in most of our societies. (Drucker, 2005).

Technology is one of the key contributors to our economic growth. This is because due to globalization process, many societies have adopted the new technology hence leading to growth of the community so much. You find that the new technology has been applied by so many societies in their production farms hence leading to more output. It is due to this fact that more inventions and innovations are still going on in trying to venture on more technologies since it has more advantages in our societies than the disadvantages.

Abbate, J. (2000): Inventing the internet. Cambridge, Mass.

Adams, M. (2000): Machines as the measure of men: Science, ideologies and technology. Ithaca.

Adriano, C. (2000): Teachers competence, familiarity, affects and perceived skill. Regarding instructional technology. Princeton, N.J.

Basalla, G. (2003): The evolution of technology. New York. Free Press.

Agency for instructional technology. (2000): A survey of the use of technology with Students at risk of school failure. Bloomington, IN.

Caro, R. (1998): The managerial revolution in American business. Cambridge, Mass.

Cowan, R. (2001): The impact of the new technology in education. Journal of economics. Vol. 20(1).

Commoner, B. (2000): Technology and the natural environment. The architectural Forum. Vol. 8(1).

Drucker, P. (2005): technology and business. New York. Free Press.

Hafner, K. (1996): Power, pleasure and technology. Boston.

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Computers: essay on the importance of computer in the modern society.

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Read this comprehensive essay on the Importance of Computer in the Modern Society !

As the world progresses on in this never ending chase for a time and wealth, it is undeniable that science has made astounding developments.

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As the 21st century looms ahead, it is clear to see that it has advancements that humanity may never have dreamed of and one of these shining developments is the well-recognized computer. Having the Latin meaning of ‘computing’ or ‘reckoning’ the computer is an invention that was called the ‘MAN OF THE YEAR’ in a survey carried out by an international magazine.

The computer system is not a simple machine. It is like a very modern and highly complex calculator. It can do all the functions at a speedy rate and also helps us to search and progress in our homes and businesses. A computer can therefore be called a calculator with a twist for not only does it perform fast calculations, but it also has other special characteristics. The computer has thoroughly changed the way we witness things, with its special auto correcting tools, which work with all languages, all logic and all subjects.

There was a time when computers were only heard of as a luxury. However today they are an unavoidable part of success and development. No longer are they owned only through theft and by the filthy rich, in fact computers are and will in the coming days and months be used to accomplish the brilliant goals of success and unparalleled development. For example, in India, the accurate knowledge and use of computers will bring change in a big and astonishing way. It will lead to the demolition of illiteracy, and lead to optimism, efficiency, productivity and high quality.

Even now in our day to day lives, computers have been allotted an integral role to play. They can be seen being used not only at the office or at home, but in all kinds of sectors and businesses. They are used at airports, restaurants, railway stations, banks etc. slowly and gradually, as computers are penetrating through the modern society, people are getting more and more optimistic about the promises its invention made. They are also used in the government sectors, businesses and industry, and through witnessing the rapid progress of the computer; mankind slowly sees the lights it has brought along.

One of the best things about the computer is the fact that it can help us to save so much of manual power, cost, and time. By the use of a computer, tasks can be done automatically and that will lead to saving the countless hours that may otherwise have been spent on doing the job manually.

Computers also ensure more accuracy. Examples of such cases include ticket booking, payment of bills, insurance and shopping. Interestingly, automatic operations of vehicles, like trains also help to ensure further safety and reliability of the journey. Computers can be used to observe and predict traffic patterns which would be a grand benefit to all and would save the hassle of getting stuck for hours in the roadblocks and traffics.

Computers can also drastically change the way agricultural tasks and businesses are carried out all over the world. With regard to agriculture, computers are being used to find out the best possible kinds of soil, plants and to check which match of these would result in the perfect crops. Use of computers thus in this sector along with the use of better agricultural practices and products in several countries, like India, could help the agricultural industry reach soaring heights, directly assuring the welfare of the economy.

It is also wonderful to see that the invention of this unbelievable machine has brought a ray of hope in the darkness of the sick citizens’ world. Computers are very capable of bringing along a medical revolution. Where in health sectors computers are being used for research regarding blood groups, medical histories, etc. and helping to improve medicine in a big way. The knowledge that computers are providing in this field may lead to better use and purchase of medicinal drugs and ensure better health. This also leads to a better diagnosing pattern and makes health care faster and more efficiently.

Although computers are bringing the evolution of technology and changing the way lives are lived, it cannot be denied that there are areas where the impacts of the computer system are not fully recognized yet. For instance if we take the education sector, the literacy rates have not been improved by computers the way other sectors have seemed to have gotten better over night.

The fact remains that 64% of our population remains to date illiterate, and it will be a revolutionary act if computers were made the full use of and worked with to spread educational awareness, in all areas, especially the underprivileged sector. They can be used to plan out lessons, and lessons can be taught on the computers too, the benefit of the prospect lying in the fact that computers excel at lots of different things altogether, which means they can be used to teach not only limited subjects but be used to spread education with reference to all kinds, including text, numbers and graphics.

Perhaps one may think the horrendous thought that computers may take the teacher’s place in the classroom, but we must look at the prospect with the brighter side. No longer will the teacher remain a person who only fits data into a pupil’s mind; and once again become that one supreme authority who inculcates both philosophical and spiritual education amongst his or her students, rising in esteem and role play.

The advantage of computers can also be seen in the fact that they might just be able to improve administration through the world. By providing daily accurate information to the administration departments, computers may change the way decisions are taken across the globe.Keeping all the above mentioned things in mind, we must accept that if used the right way, computers are a gift of science to mankind.

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How to Write a Survey Paper: Brief Overview

Every student wishes there was a shortcut to learning about a subject. Writing a survey paper can be an effective tool for synthesizing and consolidating information on a particular topic to gain mastery over it.

There are several techniques and best practices for writing a successful survey paper. Our team is ready to guide you through the writing process and teach you how to write a paper that will benefit your academic and professional career.

What is a Survey Paper

A survey paper is a type of academic writing that aims to give readers a comprehensive understanding of the current state of research on a particular topic. By synthesizing and analyzing already existing research, a survey paper provides good shortcuts highlighting meaningful achievements and recent advances in the field and shows the gaps where further research might be needed.

The survey paper format includes an introduction that defines the scope of the research domain, followed by a thorough literature review section that summarizes and critiques existing research while showcasing areas for further research. A good survey paper must also provide an overview of commonly used methodologies, approaches, key terms, and recent trends in the field and a clear summary that synthesizes the main findings presented.

Our essay writing service team not only provides the best survey paper example but can also write a custom academic paper based on your specific requirements and needs.

How to Write a Survey Paper: Important Steps

If you have your head in your hands, wondering how to write a survey paper, you must be new here. Luckily, our team of experts got you! Below you will find the steps that will guide you to the best approach to writing a successful survey paper. No more worries about how to research a topic . Let's dive in!

Obviously, the first step is to choose a topic that is both interesting to you and relevant to a large audience. If you are struggling with topic selection, go for only the ones that have the most literature to compose a comprehensive research paper.

Once you have selected your topic, define the scope of your survey paper and the specific research questions that will guide your literature review. This will help you establish boundaries and ensure that your paper is focused and well-structured.

Next, start collecting existing research on your topic through various academic databases and literature reviews. Make sure you are up to date with recent discoveries and advances. Before selecting any work for the survey, make sure the database is credible. Determine what sources are considered trustworthy and reputable within the specific domain.

Continue survey paper writing by selecting the most relevant and significant research pieces to include in your literature overview. Make sure to methodically analyze each source and critically evaluate its relevance, rigor, validity, and contribution to the field.

At this point, you have already undertaken half of the job. Maybe even more since collecting and analyzing the literature is often the most challenging part of writing a survey paper. Now it's time to organize and structure your paper. Follow the well-established outline, give a thorough review, and compose compelling body paragraphs. Don't forget to include detailed methodology and highlight key findings and revolutionary ideas.

Finish off your writing with a powerful conclusion that not only summarizes the key arguments but also indicates future research directions.

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Survey Paper Outline

The following is a general outline of a survey paper.

• Introduction - with background information on the topic and research questions
• Literature Overview - including relevant research studies and their analysis
• Methodologies and Approaches - detailing the methods used to collect and analyze data in the literature overview
• Findings and Trends - summarizing the key findings and trends from the literature review
• Challenges and Gaps - highlighting the limitations of studies reviewed
• Future Research Direction - exploring future research opportunities and recommendations
• Conclusion - a summary of the research conducted and its significance, along with suggestions for further work in this area.
• References - a list of all the sources cited in the paper, including academic articles and reports.

You can always customize this outline to fit your paper's specific requirements, but none of the components can be eliminated. Our custom essay writer

Further, we can explore survey paper example formats to get a better understanding of what a well-written survey paper looks like. Our custom essay writer can assist in crafting a plagiarism-free essay tailored to meet your unique needs.

Survey Paper Format

Having a basic understanding of an outline for a survey paper is just the beginning. To excel in survey paper writing, it's important to become proficient in academic essay formatting techniques. Have the following as a rule of thumb: make sure each section relates to the others and that the flow of your paper is logical and readable.

Title - You need to come up with a clear and concise title that reflects the main objective of your research question.

Survey paper example title: 'The analysis of recommender systems in E-commerce.'

Abstract - Here, you should state the purpose of your research and summarize key findings in a brief paragraph. The abstract is a shortcut to the paper, so make sure it's informative.

Introduction - This section is a crucial element of an academic essay and should be intriguing and provide background information on the topic, feeding the readers' curiosity.

Literature with benefits and limitations - This section dives into the existing literature on the research question, including relevant studies and their analyses. When reviewing the literature, it is important to highlight both benefits and limitations of existing studies to identify gaps for future research.

Result analysis - In this section, you should present and analyze the results of your survey paper. Make sure to include statistical data, graphs, and charts to support your conclusions.

Conclusion - Just like in any other thesis writing, here you need to sum up the key findings of your survey paper. How it helped advance the research topic, what limitations need to be addressed, and important implications for future research.

Future Research Direction - You can either give this a separate section or include it in a conclusion, but you can never overlook the importance of a future research direction. Distinctly point out areas of limitations and suggest possible avenues for future research.

References - Finally, be sure to include a list of all the sources/references you've used in your research. Without a list of references, your work will lose all its credibility and can no longer be beneficial to other researchers.

Writing a Good Survey Paper: Helpful Tips

After mastering the basics of how to write a good survey paper, there are a few tips to keep in mind. They will help you advance your writing and ensure your survey paper stands out among others.

Select Only Relevant Literature

When conducting research, one can easily get carried away and start hoarding all available literature, which may not necessarily be relevant to your research question. Make sure to stay within the scope of your topic. Clearly articulate your research question, and then select only literature that directly addresses the research question. A few initial readings might not reveal the relevance, so you need a systematic review and filter of the literature that is directly related to the research question.

Use Various Sources and Be Up-to-Date

Our team suggests only using up-to-date material that was published within the last 5 years. Additional sources may be used if they contribute significantly to the research question, but it is important to prioritize current literature.

Use more than 10 research papers. Though narrowing your pool of references to only relevant literature is important, it's also crucial that you have a sufficient number of sources.

Rely on Reputable Sources

Writing a survey paper is a challenge. Don't forget that it is quality over quantity. Be sure to choose reputable sources that have been peer-reviewed and are recognized within your field of research. Having a large number of various research papers does not mean that your survey paper is of high quality.

Construct a Concise Research Question

Having a short and to-the-point research question not only helps the audience understand the direction of your paper but also helps you stay focused on a clear goal. With a clear research question, you will have an easier time selecting the relevant literature, avoiding unnecessary information, and maintaining the structure of your paper.

Use an Appropriate Format

The scholarly world appreciates when researchers follow a standard format when presenting their survey papers. Therefore, it is important to use a suitable and consistent format that adheres to the guidelines provided by your academic institution or field.

Our paper survey template offers a clear structure that can aid in organizing your thoughts and sources, as well as ensuring that you cover all the necessary components of a survey paper.

Don't forget to use appropriate heading, font, spacing, margins, and referencing style. If there is a strict word limit, be sure to adhere to it and use concise wording.

Use Logical Sequence

A survey paper is different from a regular research paper. Every element of the essay needs to relate to the research question and tie into the overall objective of the paper.

Writing research papers takes a lot of effort and attention to detail. You will have to revise, edit and proofread your work several times. If you are struggling with any aspect of the writing process, just say, ' Write my research paper for me ,' and our team of tireless writers will be happy to assist you.

Starting Point: Survey Paper Example Topics

Learning how to write a survey paper is important, but it is only one aspect of the process.

Now you need a powerful research question. To help get you started, we have compiled a list of survey paper example topics that may inspire you.

• Survey of Evolution and Challenges of Electronic Search Engines
• A Comprehensive Survey Paper on Machine Learning Algorithms
• Survey of Leaf Image Analysis for Plant Species Recognition
• Advances in Natural Language Processing for Sentiment Analysis
• Emerging Trends in Cybersecurity Threat Detection
• A Comprehensive Survey of Techniques in Big Data Analytics in Healthcare
• A Survey of Advances in Digital Art and Virtual Reality
• A Systematic Review of the Impact of Social Media Marketing Strategies on Consumer Behavior
• A Survey of AI Systems in Artistic Expression
• Exploring New Research Methods and Ethical Considerations in Anthropology
• Exploring Data-driven Approaches for Performance Analysis and Decision Making in Sports
• A Survey of Benefits of Optimizing Performance through Diet and Supplementation
• A Critical Review of Existing Research on The Impact of Climate Change on Biodiversity Conservation Strategies
• Investigating the Future of Blockchain Technology for Secure Data Sharing
• A Critical Review of the Literature on Mental Health and Innovation in the Workplace

Final Thoughts

Next time you are asked to write a survey paper, remember it is not just following an iterative process of gathering and summarizing existing research; it requires a deep understanding of the subject matter as well as critical analysis skills. Creative thinking and innovative approaches also play a key role in producing high-quality survey papers.

Our expert writers can help you navigate the complex process of writing a survey paper, from topic selection to data analysis and interpretation.

Finding It Difficult to Write a Survey Paper?

Our essay writing service offers plagiarism-free papers tailored to your specific needs.

Are you looking for advice on how to create an engaging and informative survey paper? This frequently asked questions (FAQ) section offers valuable responses to common inquiries that researchers frequently come across when writing a survey paper. Let's delve into it!

What is Survey Paper in Ph.D.?

What is the difference between survey paper and literature review paper, related articles.