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.
[Illustration:
_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:
[Illustration:
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
_n_th 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_