10: The Academic Computer
It was inevitable that the computer invade, or perhaps “infiltrate” is the better word, our education system. Mark I and ENIAC were university-born and -bred, and early research work was done by many institutions using computers. A logical development was to teach formal courses in using the computer. While application of the machine in mathematical and scientific work came first, its application to business and to the training of executives for such use of the computer was soon recognized. As an example, one of two computers installed by U.C.L.A. in 1957 was for use exclusively in training engineering executives as well as undergraduates in engineering economy.
Early courses were aimed at those already in industry, in an attempt to catch them up with the technology of computer-oriented systems in business and science. As special courses, many of these carried a high tuition fee. Next came the teaching of professors and deans of engineering institutions in techniques of computer education for undergraduates. Today the computer is being taught to many students in many schools. New York University has a $3 million computer at its Courant Institute of Mathematical Sciences, being used by students in basic and applied research on projects ranging from the design of bridges to the analysis of voting patterns in Congress.
M.I.T. recently added a digital computer to teach its students the operation of electronic data-processing equipment. Another computer is used in more sophisticated work including speech analysis, study of bioelectrical signals, and the simulation of automata as in the “Hand” project. At the computing center of the University of Michigan a second generation of computers is being installed. Students in some one hundred different courses use these computers, programming them with a language developed at the University and called MAD, for Michigan Algorithm Decoder. These are typical examples of perhaps two hundred schools using computers.
That knowledge of computer techniques is essential for the engineering graduate is evident in the fact that of a recent class of such students at Purdue, 1,600 used the computer during the term. Less known is the integration of computer courses in secondary education. The Royal McBee Corporation teaches a special course on the computer to youngsters at Staples High in Westport, Connecticut. At the end of the first four-week session it was found that the students, fifteen to seventeen years old, had learned faster than adults. At New York’s St. Vincent Ferrer Catholic High School, 400 girls participated in a similar project conducted by Royal McBee. Other high schools are following suit, and computers are expected to appear in significant numbers in high schools before the end of 1962. Textbooks on computers, written for high-school students, are available. As an example of the ability of young people in this field, David Malin of Walter Johnson High School in Rockville, Maryland, read his own paper on the use of computers to simulate human thought processes to science experts attending the 1961 Eastern Joint Computer Conference held in Washington, D.C.
The use of the computer in the classroom encompasses not only colleges and high schools, but extends even to prisons. Twenty inmates of a Pennsylvania state institution attended a pilot program teaching computer techniques with a UNIVAC machine.
Datamation
Seventeen-year-old David Malin who presented a paper on computers at the Eastern Joint Computer Conference in 1961.
The United States is not alone in placing importance on the computer in schools. Our Department of Commerce has published details of Russian work in this direction, noting that it began in 1955 and places high priority on the training of specialists in computer research, machine translation, automation, and so on. The Department of Commerce feels that these courses, taught at the graduate, undergraduate, and even high-school level, are of high quality.
Teaching Machines
Thus far we have talked of the computer only as a tool to be studied and not as an aid to learning in itself. In just a few years, however, the “teaching machine” has become familiar in the press and controversial from a number of standpoints, including those of being a “dehumanizer” of the process of teaching and a threat to the apple business!
Actually, the computer has functioned for some time outside the classroom as a teaching machine. Early applications of analog computers as flight simulators were true “teaching machines” although perhaps the act was not as obvious as classroom use of a computer to teach the three R’s. Even today, there are those who insist that such use of the computer by the military or industry offers more potential than an academic teaching machine. Assembly workers have been taught by programmed audiovisual machines such as Hughes Aircraft’s Videosonic trainer, and the government has taught many technicians by computer techniques. A shrewd observer, however, noting that the computer is called stupid, bluntly points out that any untaught student is in the same category, and that perhaps it takes one to teach one.
A strong motivation for looking to the machine as a public teaching tool is the desperation occasioned by the growing shortage of teachers. If the teaching machine could take over even some of the more simple chores of the classroom, early advocates said, it would be worth the effort.
Formal study of machine methods of teaching have a history of forty years or more. In the 20’s, Sydney Pressey designed and built automatic teaching—or more precisely, testing—machines at Ohio State University. These were simply multiple-choice questions so mechanized as to be answered by the push of a button rather than with a pencil mark. A right answer advanced the machine to the next question, while an error required the student to try again. Pressey wisely realized the value in his machines; the student could proceed at his own pace, and his learning was also stimulated by immediate recognition of achievement. To further enforce this learning, some of the teaching machines dispensed candy for a correct answer. Using this criterion, it would seem that brighter students could be recognized by their weight.
Unfortunately, Pressey’s teaching machines did not make a very big splash in the academic world, because of a combination of factors. The machines themselves had limitations in that they did not present material to be learned but were more of the nature of a posteriori testing devices. Too, educators were loath to adopt the mechanized teachers for a variety of reasons, including skepticism, inertia, economics, and others. However, machine scoring of multiple-choice tests marked with special current-conducting pencils became commonplace.
Another researcher, B. F. Skinner, commenced work on a different kind of teaching machine thirty years ago at Harvard. Basically his method consists of giving the subject small bits—not computer “bits,” but the coincidence is interesting—of learning at a time, and reinforcing these bits strongly and immediately. Skinner insists that actual “recall” of information is more important than multiple-choice “recognition,” and he asks for an answer rather than a choice. Called “operant reinforcement,” the technique has been used not only on man, but on apes, monkeys, rats, dogs, and surprisingly, pigeons.
During World War II, Dr. Skinner conducted “Project Pigeon” for the military. In this unusual training program, the feathered students were taught to peck at certain targets in return for which they received food as a reward. This combination of apt pupils and advanced teaching methods produced pigeons who could play ping-pong. This was in the early days of missile guidance, and the pigeons next went into training as a homing system for these new weapons! To make guidance more reliable, not one but three pigeons were to be carried in the nose of the device. Lenses in the missile projected an image before each pigeon, who dutifully pecked at his “target.” If the target was in the center of the cross hairs, the missile would continue on its course; if off to one side, the pecking would actuate corrective maneuvers. As Project “Orcon,” for Organic Control, this work was carried on for some time after the end of the war. Fortunately for the birds, however, more sophisticated, inorganic guidance systems were developed.
The implications of the pigeon studies in time led to a new teaching method for human beings. Shortly after Skinner released a paper on his work in operant reinforcement with the pigeons, many workers in the teaching field began to move in this direction. For several years Skinner and James Holland have been using machines of this type to teach some sections of a course in human behavior to students at Radcliffe and Harvard. Rheem Califone manufactures the DIDAK machine to Skinner’s specifications.
To the reasons advanced by those who see teacher shortages looming, Skinner adds the argument that a machine can often teach better. Too much time, he feels, has been spent on details that are not basic to the problem. Better salaries for teachers, more teachers, and more schools do not in themselves improve the actual teaching. Operant reinforcement, Skinner contends, does get at the root of the problem and, in addition to relieving the teacher of a heavy burden, the teaching machine achieves better results in some phases of teaching. It also solves another problem that plagues the educator today. It is well known that not all of us can learn at the same rate. Since it is economically and culturally impossible except in rare cases to teach children in groups of equal ability, a compromise speed must be established. This is fine for the “average” child, of whom there may actually be none in the classroom; it penalizes the fast student, and the slow student perhaps even more. The teaching machine, its proponents feel, takes care of this difficulty and lets each proceed at his own rate. Since speed in itself is no sure indicator of intelligence, the slow child, left to learn as he can, may reach heights not before dreamed possible for him.
Many educators agree that automated teaching is past due. James D. Finn, Professor of Education at the University of Southern California, deplores the lack of modern technology in teaching. “Technology during the period from 1900 to 1950 only washed lightly on the shores of instruction,” he says. “The cake of custom proved to be too tough and the mass production state, at least 100 years behind industry, was not entered except here and there on little isolated islands.”
Educational Science Division,
U.S. Industries, Inc.
AutoTutor teaching machine has programs for teaching many subjects.
These little isolated islands are now getting bigger and closer together. The Air Force has for some time trained technicians at Keesler Field with U.S. Industries AutoTutor machines, and also uses them at the Wright Air Development Center. The Post Office Department has purchased fifty-five U.S. Industries’ Digiflex trainers. Following this lead, public education is beginning to use teaching machines. San Francisco has an electronic computer version that not only teaches, tests, and coaches, but even sounds an alarm if the student tries to “goof off” on any of the problems. The designers of the machine selected a sure-fire intellectual acronym, PLATO, for Programmed Logic for Automatic Teaching Operations. The System Development Corporation, the operations firm that designed the SAGE computer, calls its computer-controlled classroom teacher simply CLASS. This machine uses a Bendix G-15 computer to teach twenty youngsters at a time.
To show the awareness of the publishers of texts and other educational material, firms like Book of Knowledge, Encyclopedia Britannica Films, and TMI Grolier are in the “teaching machine” business, and the McGraw-Hill Book Company and Thompson Ramo Wooldridge, Inc., have teamed to produce computerized teaching machines and the programs for them. Other publishers using “programming” techniques in their books include Harcourt-Brace with its 2600 series (for 2,600 programmed steps the student must negotiate), Prentice-Hall, and D. C. Heath. Entirely new firms like Learning, Incorporated, are now producing “programs” on many subjects for teaching machines.
Subjects available in teaching machine form include algebra, mathematics, trigonometry, slide rule fundamentals, electronics, calculus, analytical geometry, plane geometry, probability theory, electricity, Russian, German, Spanish, Hebrew, spelling, music fundamentals, management science, and even Goren’s bridge for beginners.
While many of these teaching machines are simply textbooks programmed for faster learning, the conversion of such material into computer-handled presentation is merely one of economics. For example, a Doubleday TutorText book costs only a few dollars; an automatic AutoTutor Mark II costs $1,250 because of its complex searching facility that requires several thousand branching responses. However, the AutoTutor is faster and more effective and will operate twenty-four hours a day if necessary. With sufficient demand the machine may be the cheaper in the long run.
The System Development Corporation feels that its general concept of automated group education will be feasible in the near future despite the high cost of advanced electronic digital computers. It cites pilot studies being conducted by the State of California on data-processing for a number of schools through a central facility. Using this same approach, a single central computer could serve several schools with auxiliary lower-priced equipment. Even a moderately large computer used in this way could teach a thousand or more students simultaneously and individually, the Corporation feels. After school hours, the computer can handle administrative tasks.
System Development Corp.
The CLASS facility incorporates an administrative area, hallway, combined observation and counseling area, and a large classroom area divided by a folding wall.
In the CLASS system developed by the System Development Corporation, the “branching” concept is used. In a typical lesson program, if the student immediately answers that America was discovered by Christopher Columbus, he will be told he is correct and will then be branched to the next item. If he answers Leif Ericson, the computer takes time out to enlighten the pupil on that score. Next, it reinforces the correct date in the student’s mind before asking another question. Although it would seem that a lucky student could progress through the programmed lesson on guesswork alone, the inexorable laws of probability rule this out. He cannot complete the lesson until he has soaked up all the information it is intended to impart. He can do this without an error, in a very short time, or he can learn by the trial-and-error process, whichever is better suited to his speed and mental ability.
Making up the program for the teaching machine is a difficult task and requires the services of technical expert, psychologist, and programmer. An English-like language is used in preparing a CLASS program for the computer. Put on magnetic tape, the program goes into the memory of the computer and is called out by proper responses from the student as he progresses through the lesson.
System Development Corp.
Students in CLASS are learning French in a group mode of automated instruction.
Complex as the programming is, entries from the student’s control are processed into the computer in about one-tenth of a second, and an answer is flashed back in about the same amount of time. Remember that the CLASS computer is handling twenty students at a time, and that in addition to teaching it is keeping a complete record of how the student fared at each step of the lesson.
It is obvious that the binary or yes-no logic of the computer ties in with the concept put forth by Skinner and others of presenting small bits of information at a time. We can use the game of 20 Questions as a good analogy. Even getting only simple yes-no answers, skilled players can elicit an amazing amount of information in often far less than the permitted number of questions. Thus even complex subjects can be broken down into simple questions answerable by discrete choices from the student.
The automated group education system of the System Development Corporation is made up of the following components: a digital computer to control and select the material presented and to analyze responses, a magnetic tape storage unit, a typewriter for printing out data analysis, a slide projector and screen for presenting educational materials, and individual desks with keyboards for the students’ responses.
We have pointed out that even though it is possible to break down educational material into multiple-choice or yes-no answers to which are assigned intrinsic values, the ideal system permits answers on a linear scale. In other words, instead of picking what he considers the most nearly correct, a student writes his own answer. Some experts feel that the advances being made in optical scanning, or “reading” techniques for computers, will result in linear programming of the teaching machines within the next ten years. Such a development will do much to alleviate the complaint that the machine exerts a rigid mechanizing effect on the teaching process.
While fear of displacement motivates some teachers to distrust the machine, an honest belief that the human touch is necessary in the schoolroom is also a large factor against acceptance. Yet these same wary teachers generally use flash cards, flip charts, and other mechanical aids with no qualms. The electronic computer is a logical extension of audiovisual techniques, and in time the teacher will come to accept it for what it is.
The human teacher will continue to be an indispensable element in education, but he must recognize that as our technology becomes more complex he will need more and more help. In 1960 there were about 44 million students in our classrooms, and about 135,000 too few teachers. By 1965 it is estimated there will be 48 million students and 250,000 teachers fewer than we need. Parallel with this development is the rapidly growing need for college graduates. One large industrial firm which employs 150,000 hires only 300 college graduates a year at present, but will need 7,000 when it automates its plants. The pressure of need thus is forcing our educational system to make use of the most efficient means of educating our students.
Beyond simply taking its place with other aids, however, the computer will make great changes in our basic concepts of teaching, according to Dr. Skinner. He asks the question “Are the students who learn in spite of a confusing presentation of a subject better for the experience, or were they better students at the outset?” He advances this argument to say that perhaps “easy” learning is actually the best; that we would do well to analyze the behavior called thinking and then produce it according to these specifications. The traditional teacher finds the prospect alarming and questions the soundness of minimizing failure and maximizing success.
There is not yet definite agreement by other psychologists with Skinner’s contention that recall rather than recognition is the desired method. Neither is it sure that the negative reinforcement of a number of incorrect choices may result in remembering wrong answers. And of course the division between rote learning and creativity is an important consideration. The answers may well lie in the computer, which when properly programmed is about the most logical device we have available to us. Thus the machine may determine the best teaching methods and then use them to teach us. Regardless of these as yet unanswered questions, however, the future of the teaching machine seems to be assured. One authority has predicted that it will be a $100 million market by 1965.
An intriguing use of computer techniques in teaching is being investigated by Corrigan Communications, which scores students answering questions on telecourses. This work is being done with a course in medicine, and with the rapid growth of educational television the implications of combining it and teaching machine techniques are of great importance.
Classroom teaching is not the only educational application for the teaching machine. A computer-controlled library is an interesting thought, with the patron requesting information from a central computer and having it presented instantaneously on a viewing screen in front of him. Such a system could conceivably have access to a national library hookup, constantly updated with new material. Such a service would also be available for use during school study hall, or by the teacher during class.
Visitors to the World’s Fair in Seattle previewed the computerized information center of the future. Called Library 21, it is considered a prototype of the next century’s core libraries which will be linked to smaller branches by communications networks. Many computers were displayed, tied in with teaching machines, language laboratories, and information from the Great Books, tailored to the individual questioner’s sex, personality, and mental level. Also shown was a photo process that reduces a 400-page book to the size of a postage stamp for storage.
With this kind of progress, we can in the foreseeable future request and receive up-to-date information of any kind of human knowledge anywhere—in language we can understand. Another computer application sure to come is that of handling correspondence courses. The teaching of extension courses in the home, through television and some sort of response link, has been mentioned, and it is not impossible that the school as a physical plant may one day no longer be necessary.
International Business Machines Corp.
This system supplies legal information in minutes, with insertion of punched-card query (top). Using inquiry words, computer prints citations of statutes (middle); then, on request, full text (below).
Since the computer itself does not “teach,” but merely acts as a go-between for the man who prepared the lesson or program and the student who learns, it would seem that some of our teachers may become programmers. The System Development Corporation has broken the teaching machine program into three phases: experimenting with the effects of many variables on teaching machine effectiveness, developing a simplified teaching machine, and finally, analyzing the educational system to find where and how the machine fits. Research is still in the first phase, that of experiment. But it is known that some programs produced so far show better results than conventional teaching methods, and also that teaching machines can teach any subject involving factual information. Thus it is evident they will be useful in schools and also in industry and military training programs.
Language
If man is to use the computer to teach himself, he must be able to converse with it. In the early days of computers it was said with a good deal of justification that the machine was not only stupid but decidedly insular as well. In other words, man spoke to it in its own language or not at all. A host of different languages, or “compilers” as they are often called, were constructed and their originators beat the drums for them. With tongues like ALGY, ALGOL, COBOL, FACT, FLOWMATIC, FORTRAN, INTERCOM, IT, JOVIAL, LOGLAN, MAD, PICE, and PROLAN, to name a few, the computer has become a tower of Babel, and a programmer’s talents must include linguistics.
One language called ALGOL, for Algorithmic Oriented Language, had pretty smooth sailing, since it consists of algebraic and arithmetic notation. Out of the welter of business languages a compromise Common Business Oriented Language, or COBOL, evolved. What COBOL does for programming computer problems is best shown by comparing it with instructions once given the machine. The sample below is typical of early machine language:
SUBTRACT QUANTITY-SOLD FROM BALANCE-ON-HAND. IF BALANCE-ON-HAND IS NOT LESS THAN REORDER-LEVEL THEN GO TO BALANCE-OK ELSE COMPUTE QUANTITY-TO-BUY = TOTAL-SALES-3-MOS/3.
Recommended by a task force for the Department of Defense, industry, and other branches of the government, COBOL nevertheless has had a tough fight for acceptance, and there is still argument and confusion on the language scene. New tongues continue to proliferate, some given birth by ALGOL and COBOL themselves. Examples of this generation are GECOM, BALGOL, and TABSOL. One worthy attempt at a sort of machine Esperanto is called a pun-inviting UNCOL, for Universal Computer-Oriented Language and seems to be a try for the computer’s vote. One harried machine-language user has suggested formation of an “ALGOLICS Anonymous” group for others of his ilk, while another partisan accuses his colleagues in Arizona of creating a new language while “maddened by the scent of saguaro blossoms.”
It was recently stated that perhaps by the time a decision is ultimately reached as to which will be the general language, there will be no need of it because by then the computer will have learned to read and write, and perhaps to listen and to speak as well. Recent developments bear out the contention.
Although it has used intermediate techniques, the computer has proved it can do a lot with our language in some of the tasks it has been given. Among these is the preparation of a Bible concordance, listing principal words, frequency of appearance, and where they are found. The computer tackled the same job on the poems of Matthew Arnold. For this chore, Professor Stephen Maxfield Parrish of Cornell worked with three colleagues and two technicians to program an IBM 704 data-processing system. In addition to compiling the list of more than 10,000 words used most often by Arnold, the computer arranged them alphabetically and also compiled an appendix listing the number of times each word appeared. To complete the job, the computer itself printed the 965-page volume. The Dead Sea Scrolls and the works of St. Thomas Aquinas have also been turned over to the computer for preparation of analytical indexes and concordances.
At Columbia University, graduate student James McDonough gave an IBM 650 the job of sleuthing the author of The Iliad and The Odyssey. Since the computer can detect metric-pattern differences otherwise practically undiscoverable, McDonough felt that the machine could prove if Homer had written both poems, or if he had help on either. Thus far he is sure the entire Iliad is the work of one man, after computer analysis of its 112,000 words. The project is part of his doctoral thesis. A recent article in a technical journal used a title suggested by an RCA 501, and suspicion is strong that the machines themselves are guilty of burning midnight kilowatts to produce the acronyms that abound in the industry. The computer is even beginning to prove its worth as an abstracter.
Other literary jobs the computer has done include the production of a book of fares for the International Air Transport Association. The computer compiled and then printed out this 420-page book which gives shortest operating distances between 1,600 cities of the world. Now newspapers are beginning to use computers to do the work of typesetting. These excursions into the written language of human beings, plus its experience as a poet and in translation from language to language, have undoubtedly brought the computer a long way from its former provincialism.
As pointed out, computer work with human language generally is not accomplished without intermediate steps. For example, in one of the concordances mentioned, although the computer required only an hour to breeze through the work, a programmer had spent weeks putting it in the proper shape. What is needed is a converter which will do the work directly, and this is exactly what firms like Digitronics supply to the industry. This computer-age Berlitz school has produced converters for Merrill Lynch, Pierce, Fenner & Smith for use in billing its stock-market customers, Wear-Ever as an order-taking machine, Reader’s Digest for mailing-list work, and Schering Corporation for rat-reaction studies in drug research, to mention a few.
The importance of such converters is obvious. Prior to their use it was necessary to type English manually into the correct code, a costly and time-consuming business. Converters are not cheap, of course, but they operate so rapidly that they pay for themselves in short order. Merrill Lynch’s machine cost $120,000, but paid back two-thirds of that amount in savings the first year. There is another important implication in converter operation. It can get computer language out of English—or Japanese, or even Swahili if the need arises. A more recent Digitronics’ converter handles information in English or Japanese.
If the computer has its language problems, man has them also, to the nth degree. There are about 3,000 tongues in use today; mercifully, scientific reports are published in only about 35 of these. Even so, at least half the treatises published in the world cannot be read by half the world’s scientists. Unfortunately, UNESCO estimates that while 50 per cent of Russian scientists read English, less than 1 per cent of United States scientists return the compliment! The ramifications of these facts we will take up a little later on; for now it will be sufficient to consider the language barrier not only to science but also to culture and the international exchange of good will that can lead to and preserve peace. Esperanto, Io, and other tongues have been tried as common languages. One recent comer to the scientific scene is called Interlingua and seems to have considerable merit. It is used in international medical congresses, with text totaling 300,000 words in the proceedings of one of these. But a truly universal language is, like prosperity, always just around the corner. Even the scientific community, recognizing the many benefits that would accrue, can no more adopt Interlingua or another than it can settle on the metric system of measurement. Our integration problems are not those of race, color, and creed only.
Before Sputnik our interest in foreign technical literature was not as keen as it has been since. One immediate result of the satellite launching by the Russians was amendment of U.S. Public Law 480 to permit money from the sale of American farm equipment abroad to be used for translation of foreign technical literature. We are vitally concerned with Russia, but have also arranged for thousands of pages of scientific literature from Poland, Yugoslavia, and Israel. Communist China is beginning to produce scientific reports too, and Japanese capability in such fields as electronics is evident in the fact that the revolutionary “tunnel diode” was invented by Esaki in Japan.
It is understandable that we should be concerned with the output of Russian literature, and much attention has been given to the Russian-English translator developed by IBM for the Air Force. It is estimated that the Russians publish a billion words a year, and that about one-third of this output is technical in nature. Conventional translating techniques, in addition to being tedious for the translators, are hopelessly slow, retrieving only about 80 million words a year. Thus we are falling behind twelve years each year! Outside of a moratorium on writing, the only solution is faster translation.
The Air Force translator was a phenomenal achievement. Based on a photoscopic memory—a glass disc 10 inches in diameter capable of storing 55,000 words of Russian-English dictionary in binary code—the system used a “one-to-one” method of translation. The result initially was a translation at the rate of about 40 words per minute of Russian into an often terribly scrambled and confusing English. The speed was limited not by the memory or the computer itself but by the input, which had to be prepared on tape by a typist. Subsequently a scanning system capable of 2,400 words a minute upped the speed considerably.
Impressive as the translator was, its impact was dulled after a short time when it was found that a second “translation” was required of the resulting pidgin English, particularly when the content was highly technical. As a result, work is being done on more sophisticated translation techniques. Making use of predictive analysis, and “lexical buffers” which store all the words in a sentence for syntactical analysis before final printout, scientists have improved the translation a great deal. In effect, the computer studies the structure of the sentence, determining whether modifiers belong with subject or object, and checking for the most probable grammatical form of each word as indicated by other words in the sentence.
The advanced nature of this method of translation requires the help of linguistics experts. Among these is Dr. Sydney Lamb of the University of California at Berkeley who is developing a computer program for analysis of the structure of any language. One early result of this study was the realization that not enough is actually known of language structure and that we must backtrack and build a foundation before proceeding with computer translation techniques. Dr. Lamb’s procedure is to feed English text into the computer and let it search for situations in which a certain word tends to be preceded or followed by other words or groups of words. The machine then tries to produce the grammatical structure, not necessarily correctly. The researcher must help the machine by giving it millions of words to analyze contextually.
What the computer is doing in hours is reproducing the evolution of language and grammar that not only took place over thousands of years, but is subject to emotion, faulty logic, and other inaccuracies as well. Also working on the translation problem are the National Bureau of Standards, the Army’s Office of Research and Development, and others. The Army expects to have a computer analysis in 1962 that will handle 95 per cent of the sentences likely to be encountered in translating Russian into English, and to examine foreign technical literature at least as far as the abstract stage.
Difficult as the task seems, workers in the field are optimistic and feel that it will be feasible to translate all languages, even the Oriental, which seem to present the greatest syntactical barriers. An indication of success is the announcement by Machine Translations Inc. of a new technique making possible contextual translation at the rate of 60,000 words an hour, a rate challenging the ability of even someone coached in speed-reading! The remaining problem, that of doing the actual reading and evaluation after translation, has been brought up. This considerable task too may be solved by the computer. The machines have already displayed a limited ability to perform the task of abstracting, thus eliminating at the outset much material not relevant to the task at hand. Another bonus the computer may give us is the ideal international and technical language for composing reports and papers in the first place. A logical question that comes up in the discussion of printed language translation is that of another kind of translation, from verbal input to print, or vice versa. And finally from verbal Russian to verbal English. The speed limitation here, of course, is human ability to accept a verbal input or to deliver an output. Within this framework, however, the computer is ready to demonstrate its great capability.
A recent article in Scientific American asks in its first sentence if a computer can think. The answer to this old chestnut, the authors say, is certainly yes. They then proceed to show that having passed this test the computer must now learn to perceive, if it is to be considered a truly intelligent machine. A computer that can read for itself, rather than requiring human help, would seem to be perceptive and thus qualify as intelligent.
Even early computers such as adding machines printed out their answers. All the designers have to do is reverse this process so that printed human language is also the machine’s input. One of the first successful implementations of a printed input was the use of magnetic ink characters in the Magnetic Ink Character Recognition (MICR) system developed by General Electric. This technique called for the printing of information on checks with special magnetic inks. Processed through high-speed “readers,” the ink characters cause electrical currents the computer can interpret and translate into binary digits.
Close on the heels of the magnetic ink readers came those that use the principle of optical scanning, analogous to the method man uses in reading. This breakthrough came in 1961, and was effected by several different firms, such as Farrington Electronics, National Cash Register, Philco, and others, including firms in Canada and England. We read a page of printed or written material with such ease that we do not realize the complex way our brains perform this miracle, and the optical scanner that “reads” for the computer requires a fantastically advanced technology.
As the material to be read comes into the field of the scanner, it is illuminated so that its image is distinct enough for the optical system to pick up and project onto a disc spinning at 10,000 revolutions per minute. In the disc are tiny slits which pass a certain amount of the reflected light onto a fixed plate containing more slits. Light which succeeds in getting through this second series of slits activates a photoelectric cell which converts the light into proportionate electrical impulses. Because the scanned material is moving linearly and the rotating disc is moving transversely to this motion, the character is scanned in two directions for recognition. Operating with great precision and speed, the scanner reads at the rate of 240 characters a second.
National Cash Register claims a potential reading rate for its scanner of 11,000 characters per second, a value not reached in practice only because of the difficulty of mechanically handling documents at this speed. Used in post-office mail sorting, billing, and other similar reading operations, optical scanners generally show a perfect score for accuracy. Badly printed characters are rejected, to be deciphered by a human supervisor.
It is the optical scanner that increased the speed of the Russian-English translating computer from 40 to 2,400 words per minute. In post-office work, the Farrington scanner sorts mail at better than 9,000 pieces an hour, rejecting all handwritten addresses. Since most mail—85 per cent, the Post Office Department estimates—is typed or printed, the electronic sorter relieves human sorters of most of their task. Mail is automatically routed to proper bins or chutes as fast as it is read.
The electronic readers have not been without their problems. A drug firm in England had so much difficulty with one that it returned it to the manufacturer. We have mentioned the one that was confused by Christmas seals it took for foreign postage stamps. And as yet it is difficult for most machines to read anything but printed material.
An attempt to develop a machine with a more general reading ability, one which recognizes not only material in which exact criteria are met, but even rough approximations, uses the gestalt or all-at-once pattern principle. Using a dilating circular scanning method, the “line drawing pattern recognizer” may make it possible to read characters of varying sizes, handwritten material, and material not necessarily oriented in a certain direction. A developmental model recognizes geometric figures regardless of size or rotation and can count the number of objects in its scope. Such experimental work incidentally yields much information on just how the eye and brain perform the deceptively simply tasks of recognition. Once 1970 had been thought a target date for machine recognition of handwritten material, but researchers at Bell Telephone Laboratories have already announced such a device that reads cursive human writing with an accuracy of 90 per cent.
The computer, a backward child, learned to write long before it could read and does so at rates incomprehensible to those of us who type at the blinding speed of 50 to 60 words a minute. A character-generator called VIDIAC comes close to keeping up with the brain of a high-speed digital computer and has a potential speed of 250,000 characters, or about 50,000 words, per second. It does this, incidentally, by means of good old binary, 1-0 technique. To add to its virtuosity, it has a repertoire of some 300 characters. Researchers elsewhere are working on the problems to be met in a machine for reading and printing out 1,000,000 characters per second!
None of us can talk or listen at much over 250 words a minute, even though we may convince ourselves we read several thousand words in that period of time. A simple test of ability to hear is to play a record or tape at double speed or faster. Our brains just won’t take it. For high-speed applications, then, verbalized input or output for computers is interesting in theory only. However, there are occasions when it would be nice to talk to the computer and have it talk back.
In the early, difficult days of computer development, say when Babbage was working on his analytical engine, the designer probably often spoke to his machine. He would have been stunned to hear a response, of course, but today such a thing is becoming commonplace. IBM has a computer called “Shoebox,” a term both descriptive of size and refreshing in that is not formed of initial capitals from an ad writer’s blurb. You can speak figures to Shoebox, tell it what you want done with them, and it gets busy. This is admittedly a baby computer, and it has a vocabulary of just 16 words. But it takes only 31 transistors to achieve that vocabulary, and jumping the number of transistors to a mere 2,000 would increase its word count to 1,000, which is the number required for Basic English.
The Russians are working in the field of speech recognition too, as are the Japanese. The latter are developing an ambitious machine which will not only accept voice instructions, but also answer in kind. To make a true speech synthetizer, the Japanese think they will need a computer about 5,000 times as fast as any present-day type, so for a while it would seem that we will struggle along with “canned” words appropriately selected from tape memory.
We have mentioned the use of such a tape voice in the computerized ground-controlled-approach landing system for aircraft, and the airline reservation system called Unicall in which a central computer answers a dialed request for space in less than three seconds—not with flashing lights or a printed message but in a loud clear voice. It must pain the computer to answer at the snail-like human speed of 150 words a minute, so it salves its conscience by handling 2,100 inputs without getting flustered.
The writer’s dream, a typewriter that has a microphone instead of keys and clacks away merrily while you talk into it, is a dream no longer. Scientists at Japan’s Kyoto University have developed a computer that does just this. An early experimental model could handle a hundred Japanese monosyllables, but once the breakthrough was made, the Japanese quickly pushed the design to the point where the “Sonotype” can handle any language. At the same time, Bell Telephone Laboratories works on the problem from the other end and has come up with a system for a typewriter that talks. Not far behind these exotic uses of digital computer techniques are such things as automatic translation of telephone or other conversations.
Information Retrieval
It has been estimated that some 445 trillion words are spoken in each 16-hour day by the world’s inhabitants, making ours a noisy planet indeed. To bear out the “noisy” connotation, someone else has reckoned that only about 1 per cent of the sounds we make are real information. The rest are extraneous, incidentally telling us the sex of the speaker, whether or not he has a cold, the state of his upper plate, and so on. It is perhaps a blessing that most of these trillions of words vanish almost as soon as they are spoken. The printed word, however, isn’t so transient; it not only hangs around, but also piles up as well. The pile is ever deeper, technical writings alone being enough to fill seven 24-volume encyclopedias each day, according to one source. As with our speech, perhaps only 1 per cent of this outpouring of print is of real importance, but this does not necessarily make what some have called the Information Explosion any less difficult to cope with.
The letters IR once stood for infra-red; but in the last year or so they have been appropriated by the words “information retrieval,” one of the biggest bugaboos on the scientific horizon. It amounts to saving ourselves from drowning in the fallout from typewriters all over the earth. There are those cool heads who decry the pushing of the panic button, professing to see no exponential increase in literature, but a steady 8 per cent or so each year. The button-pushers see it differently, and they can document a pretty strong case. The technical community is suffering an embarrassment of riches in the publications field.
While a doubling in the output of technical literature has taken the last twelve years or so, the next such increase is expected in half that time. Perhaps the strongest indication that IR is a big problem is the obvious fact that nobody really knows just how much has been, is being, or will be written. For instance, one authority claims technical material is being amassed at the rate of 2,000 pages a minute, which would result in far more than the seven sets of encyclopedias mentioned earlier. No one seems to know for sure how many technical journals there are in the world; it can be “pinpointed” somewhere between 50,000 and 100,000. Selecting one set of figures at random, we learn that in 1960 alone 1,300,000 different technical articles were published in 60,000 journals. Of course there were also 60,000 books on technical subjects, plus many thousands of technical reports that did not make the formal journals, but still might contain the vital bit of information without which a breakthrough will be put off, or a war lost. Our research expenses in the United States ran about $13 billion in 1960, and the guess is they will more than double by 1970. An important part of research should be done in the library, of course, lest our scientist spend his life re-inventing the wheel, as the saying goes.
To back up this saying are specific examples. For instance, a scientific project costing $250,000 was completed a few days before an engineer came across practically the identical work in a report in the library. This was a Russian report incidentally, titled “The Application of Boolean Matrix Algebra to the Analysis and Synthesis of Relay Contact Networks.” In another, happier case, information retrieval saved Esso Research & Engineering Co. a month of work and many thousands of dollars when an alert—or lucky—literature searcher came across a Swedish scientist’s monograph detailing Esso’s proposed exploration. Another literature search obviated tests of more than a hundred chemical compounds. Unfortunately not all researchers do or can search the literature in all cases. There is even a tongue-in-cheek law which governs this phenomenon—“Mooer’s” Law states, “An information system will tend not to be used whenever it is more painful for a customer to have information than for him not to have it.”
As a result, it has been said that if a research project costs less than $100,000 it is cheaper to go ahead with it than to conduct a rigorous search of the literature. Tongue in cheek or not, this state of affairs points up the need for a usable information retrieval system. Fortune magazine reports that 10 per cent of research and development expense could be saved by such a system, and 10 per cent in 1960, remember, would have amounted to $1.3 billion. Thus the prediction that IR will be a $100 million business in 1965 does not seem out of line.
The Center for Documentation at Western Reserve University spends about $6-1/2 simply in acquiring and storing a single article in its files. In 1958 it could search only thirty abstracts of these articles in an hour and realized that more speed was vital if the Center was to be of value. As a result, a GE 225 computer IR system was substituted. Now researchers go through the entire store of literature—about 50,000 documents in 1960—in thirty-five minutes, answering up to fifty questions for “customers.”
International Business Machines Corp.
The document file of this WALNUT information retrieval system contains the equivalent of 3,000 books. A punched-card inquiry system locates the desired filmstrip for viewing or photographic reproduction.
International Business Machines Corp.
This image converter of the WALNUT system optically reduces and transfers microfilm to filmstrips for storage. Each strip contains 99 document images. As a document image is transferred from microfilm to filmstrip, the image converter simultaneously assigns image file addresses and punches these addresses into punched cards controlling the conversion process.
The key to information retrieval lies in efficient abstracting. It has been customary to let people do this task in the past because there was no other way of getting it done. Unfortunately, man does not do a completely objective job of either preparing or using the abstract, and the result is a two-ended guessing game that wastes time and loses facts in the process. A machine abstracting system, devised by H. Peter Luhn of IBM, picks the words that appear most often and uses them as keys to reduce articles to usable, concise abstracts. A satisfactory solution seems near and will be a big step toward a completely computerized IR system.
For several years there has been a running battle between the computer IR enthusiast and the die-hard “librarian” type who claims that information retrieval is not amenable to anything but the human touch. It is true that adapting the computer to the task of information retrieval did not prove as simple as was hoped. But detractors are in much the same fix as the man with a shovel trying to build a dike against an angry rising sea, who scoffs at the scoop-shovel operator having trouble starting his engine. The wise thing to do is drop the shovel and help the machine. There will be a marriage of both types of retrieval, but Verner Clapp, president of the Washington, D.C., Council on Library Resources, stated at an IR symposium that computers offer the best chance of keeping up with the flood of information.
One sophisticated approach to IR uses symbolic logic, the forte of the digital computer. In a typical reductio ad logic, the following request for information:
An article in English concerning aircraft or spacecraft, written neither before 1937 or after 1957; should deal with laboratory tests leading to conclusions on an adhesive used to bond metal to rubber or plastic; the adhesive must not become brittle with age, must not absorb plasticizer from the rubber adherent, and must have a peel-strength of 20 lbs/in; it must have at least one of these properties—no appreciable solution in fuel and no absorption of solvent.
becomes the logical statement:
KKaVbcPdeCfg, and KAhiKKKNjNklSmn.
Armed with this symbolic abbreviation, the computer can dig quickly into its memory file and come up with the sought-for article or articles.
It has been suggested that the abstracting technique be applied at the opposite end of the cycle with a vengeance amounting to birth control of new articles. A Lockheed Electronics engineer proposes a technical library that not only accepts new material, but also rejects any that is not new. Here, of course, we may be skirting danger of the type risked by human birth control exponents—that of unwittingly depriving the world of a president, or a powerful scientific finding. Perhaps the screening, the function of “garbage disposal,” as one blunt worker puts it, should be left as an after-the-fact measure.
Despite early setbacks, the computer is making progress in the job of information retrieval. Figures of a 300 per cent improvement in efficiency in this new application are cited over the last several years. Operation HAYSTAQ, a Patent Office project in the chemical patent section accounting for one-fifth of all patents, showed a 50 per cent improvement in search speed and 100 per cent in accuracy as a result of using automated methods. Desk-size computer systems with solid-state circuits are being offered for information retrieval.
The number of scientific information centers in this country, starting with one in 1830, reached 59 in 1940 and now stands at 144. Significantly, of 2,000 scientists and engineers working at these centers, 381 are computer people.
Some representative information retrieval applications making good use of computer techniques are the selection of the seven astronauts for the Mercury Project from thousands of jet pilots, Procter & Gamble’s Technical Information Service, demonstration of an electronic law library to the American Bar Association, and Food Machinery and Chemical Corporation’s Central Research Laboratory. The National Science Foundation, the National Bureau of Standards, and the U.S. Patent Office are among the government agencies in addition to the military services that are interested in electronic information retrieval.
Summary
The impact of the computer on education, language and communication, and the handling of information is obviously already strongly felt. These inroads will be increased, and progress hastened in the years ahead of us. Perhaps of the greatest importance is the assigning to the machine functions closer to the roots of all these things. Rather than simply read or translate language, for example, the computer seems destined to improve on it. The same applies to the process of teaching and to the storage and retrieval of data. The electronic computer has shown that it is not a passive piece of equipment, but active and dynamic in nature. It will soon be as much a part of the classroom and library as books; one day it may take the place of books themselves.
Lichty, © Field Enterprises, Inc.
“How come they spend over a million on our new school, Miss Finch, and then forget to put in computer machines?”
“’Tis one and the same Nature that rolls on her course, and whoever has sufficiently considered the present state of things might certainly conclude as to both the future and the past.”
—Montaigne