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  1. 1. Information Technology and Information Science for Tomorrow By Tom Adi and Ken Ewell Management Information Technologies, Inc. A Seminar and Demonstration Presented to The Swedish Council for Job Security in Santa Clara, California, USA October 6, 1987 Unpublished Work, Copyright 1987 MIT, Inc. All Rights Reserved.No part of this document may be reproduced in any form without the written permission of MIT, Inc.
  2. 2. 1. IntroductionWe have been selected to present this seminar because we have successfullydeveloped what is being recognized as a new information technology. This newtechnology, which we call READWARE(TM) in consideration of the termsHARDWARE and SOFTWARE, signals a significant change in informationtechnology. It may have a measurable impact on industry and society in the verynear future.READWARE(TM) is a new approach to Information Science, whereupon thefaculty for the comprehension of natural language text is added to a computingmachine. One software implementation which you will see later is based on ourdiscovery of Letter Semantics. Letter Semantics is a theory of natural languagemeaning. It deals with understanding natural language texts, human knowledge,and intelligence, with and without the aid of computers.Some of the laws and principles of Letter Semantics have been reduced to elegantmathematical algorithms and implemented in READWARE(TM) software whichcan be run on machines as small as personal and desktop computers. In this form,the technology offers assistance to intelligent people in understanding texts andanalyzing knowledge extracted from those texts, thus, improving both knowledgeand intelligence of its users without ever attempting to replace their humanactivity as masters over machines.Later, we will demonstrate the READWARE(TM) Research Assistant which isone of the first software applications of our new technology. It can associate theconcepts represented in a string of natural words in Swedish, for instance, to theconcepts contained in a string of words in another language, like Arabic, English,French, German, Russian, or Spanish. This association, an unrealized dream ofArtificial Intelligence (AI) technology, is based entirely on Letter Semantics, i.e.,the analysis of words without the aid of dictionaries and merely on the basis ofwhat letters of alphabets and their combinations mean.Allow us to digress for a moment, as we would like to stress our independencefrom the commercial AI industry which aspires to mechanize intelligence andreplace intelligent humans. We neither use their methods nor share their views.Our technology provides a cognitive interface between the human and themachine. In this way, the ability of the machine is extended one step beyondsymbolic recognition, to concept apprehension and comprehension. We did notfail to understand human intelligence like the leading authorities of ArtificialIntelligence and Computational Linguistics admit to.Terry Winograd, a Stanford professor and a leading authority of AI, has criticizedthe very promise of AI in a recent publication. In it he states: "There is a tacit
  3. 3. acceptance of the point we have made in this book--that the techniques of currentAI are not adequate for an understanding of human thought and language."Winograd "strips" existing AI products and concepts of their "intelligence," oneby one, and in detail, including his own famous system SHRDLU. This includesproducts of Computational Linguistics used in natural language front-ends fordatabases, as well.Regarding "robots" he makes an interesting point: ". . . it is important to separateout the real potential for such devices from the implications that come fromcalling them applications of artificial intelligence, and even from the use of theword robot."Winograd suggests with his co-author Flores "a new foundation for design" (theunder title of their book), which is rather a philosophical speculation about whatto do after the failure of AI.Our company, Management Information Technologies, Inc., has done more thanspeculate, we have achieved a new foundation for design as is evidenced in theREADWARE(TM) implementation, which culminates a decade of empiricalresearch and several years of development and implementation effort.2. Why AI FailedWe believe that there are two reasons for the failure of AI in addressing languageand cognition. The first is a false linguistic view of natural language as anevolution of sounds with no relation to meaning. The famous linguist Saussure isone of countless linguists who firmly believe that words, which are seen assounds, relate arbitrarily to their meanings.This was a double error. Language is not even a matter of sounds. At thebeginning of this decade, "revolting" linguists searched in vain for symbolisms(meanings) of sounds. We have found out that meanings are in letters rather thansounds. For example, "seize" and "cease" sound the same, but are different inmeaning. Language change over the centuries makes sounds highly ambiguous.The linguistic community had to stop all research in that direction.Linguists still claim, from time to time, that they have a theory of meaning, anatural language semantics that they operate with. Recently, MITs famouslinguist, Chomsky, concluded that existing theories of meaning are not semanticsat all, but rather speculations over mental representations. In his view, semanticsmust explain the direct relations between language and the world. Linguistscannot find semantics because of the linguistic dogma that words relate arbitrarilyto their meanings.
  4. 4. In contrast to this, we started our research with a belief in the meaning strength ofwords in human languages. Further, we based our research on a very speciallanguage, often revered by linguists all over the world, a language where soundscorrespond unambiguously to letters and no language change has taken for overfourteen centuries: Arabic. The results were verified for many other unrelatedlanguages. It took less than one month to add Swedish.The second reason of AI’s fall is its stubborn attempt to derive the science ofcognition from an information science based on physics, believing that humansare just complex machines. It turns out that AI is not alone in the failure toadvance, but is rather part of a collapsing system of sciences. Physics is assumedto be the mother of all sciences such as chemistry, biology, and even psychologyand information science, as depicted in Figure 1.: Fig. 1. Old Hierarchy of Sciences ?? Physics (measurements, study of matter)      Mathematics--+--Information Chemistry Other | Science | Sciences . . .     +----->-------------------------------------- . . .This rather materialistic view of the world has produced confusion in science andtechnology in the long run. Knowledge is assumed to come from matter byundefined means, and the human role in knowledge acquisition is obscured byassuming this hierarchy.Mathematics seems to come from nowhere but it dictates itself everywhere.Mathematics has grown into monstrous complexity dragging behind a physics thatis not only complex, but one full of contradictions and unresolved dis-harmonies.Nowadays, the new buzzword "broken symmetry" is used as an excuse forinsisting contradictions in natural sciences.The scientific quarrel began with Einstein rejecting the quantum theoretical viewthat "God plays dice with the universe." It continues today after many failures toreach a unified theory of forces in nature. Loopholes in science opened fordifferent theologies that offer "alternatives" such as "Creationism" instead of"Evolutionism." Atheists utilize speculations about the physical vacuum tosuggest that "everything comes from the Big Nothing." Last, but not least, thefallacy of sound symbolism discussed above also stems from this materialisticview of sciences.
  5. 5. We do not believe that religious or anti-religious groups can overtake scientificresearch or dictate knowledge; their inherent dogmatic approaches will not allowit. But what happened to the elegance and harmony of science; where is scientificobjectivity? Where is that fascinating crystalline stuff from which the good oldphysics was made? Is confusion the price of progress?3. Restructuring Sciences: The Intellectual Dimension of READWARE(TM)Our discovery of Letter Semantics led us to revise the above mentionedmaterialistic world view and hierarchy of knowledge. We also found a moremeaningful place for mathematics in the hierarchy of sciences, as depicted inFigure 2. Fig. 2. New Hierarchy of Sciences Language (alphabets, names of things and events, texts)      Math Physics Chemistry Information Other | | | Science Sciences . . .      +----->--------------------------------------. . .Letter Semantics (illustrated in Figure 3) means that our knowledge comes fromthe names of things. Every letter in the name of a thing is an abstract hint aboutthat thing; the sequence of the letters hints to the structure of that thing. The nameof a thing is a collection of hints, a puzzle that motivates us to touch and studythat thing. Knowledge Acquisition is the process of following those hints andmaking links to natural facts; and Intelligence is the ability to do so. Finally,human language is natural, i.e., the names of things are supplied by nature and notinvented by humans. Language change may falsify the natural hints andintroduce ambiguity; on the other hand, language change can result from humanambiguity and then amplify and perpetuate that ambiguity.
  6. 6. Fig 3. Letter Semantics & Knowledge Acquisition by Intelligent Humans <-----Nature <-------Nature-------->      Thing- ->Name Thing - ->Name Natural Laws | | | |  | | Abstract hints- - - -> Residual | | | Concept clusters - - -> hints | | | | | | | | | | Closed | Recognition | | | | links | more | | | | | certainty | |   |   ? Facts----> Innate, inherited Acquired knowledge primitive knowledgeAll sciences originate and grow from human language in that the names of thingsmotivate and guide research. We can use pure language to formulate scientificresults. We can also derive from the names of things, abstract concepts (inherentin letters and their combinations) which crystallize in the form of mathematics.We can use mathematics to advance any science because mathematics comes fromlanguage, and language deals with everything. This explains the mystery thatmathematics produces far more results than the stuff you squeeze into it. You aresurprised by mathematical results in the same way you jump up when you get anew idea from the old words in your mind.This discovery has opened up a whole new world of possible perceptions andperspectives. Before finding chemical elements we had alchemy. Before theinsights of astronomy people believed only in astrology. We are already beyondthe astrology of artificial intelligence, and we are certain that as this discoverybecomes known and generally accepted, it will cure long standing problems andlead to many new discoveries. The difference now is that we have a fundamentaldiscovery in cognitive science (the science of knowledge) that will advance allsciences and lead to their unification.We have found out what knowledge acquisition means and what intelligence is.The question now is: which aspects of knowledge acquisition can be mechanizedand how intelligent can a computer be?
  7. 7. 4. Future Impacts on Intellectual Jobs and ActivitiesReturning to Figure 3, in the transfer from left to right, in the process ofknowledge acquisition, we notice that some abstract hints solidify into links tonatural facts, and some dont. As far as we can see, there will always be residualhints that are not resolved. This means that there can be no complete knowledge,neither by humans nor by machines. But the natural feedback, the recognition ofnatural laws in the facts attained by solidifying natural hints, increases ourcertainty about facts.We decide whether and how two different hints are related. The most primitiverule states that the hints from one letter of an alphabet are always related. Wedeveloped powerful and elegant rules regarding this issue that we call our Theoryof Associations. Our READWARE(TM) Research Assistant applies some rulesof this theory to make associations between two expressions in one or twodifferent languages. This type of application is called automated associativeresearch.Another possibility is to analyze existing solid links expressed in free text: tosummarize, generalize, test hypotheses, and otherwise extract information. Theoutput might surprise us, but there is no hint resolution, there is no coreintelligence in that. This is the subject of ongoing research and development atMIT, Inc., under the title of automated knowledge extraction.A future research project, now being conceptualized at MIT, Inc., deals withstudying hint resolution to determine laws of core intelligence, so that hints can beresolved by guessing and may be tested against the natural laws we already know.We call this automated knowledge acquisition and it is in fact our mostambitious research goal and may lead to very powerful computer tools. Forexample, with this technology it would be possible to suggest new inventions,natural laws, or theories, to be checked out by human experts; or to performunmanned research on far planets. This type of machine assistance would deserveto be called artificial intelligence, but we will not use that term any more.For example, imagine a manager who can supply an idea to a machine that willread millions of pages of text in minutes and automatically reply with opinionsand arguments supported by extracts from those texts. This is something thathumans are now unable to do. But our existing technology can already achieve amajor part of such a science fiction type analysis. In a short time, this completeand swift analytical processing will be commercially available. The manager inour example will still have to judge and decide, but the machine has already savedhim immense amounts of reading and comprehension.If your job is only to collect literature for your boss and to do some preliminaryanalysis, you might be replaced by a program. But you can alternatively increase
  8. 8. your capability by mastering the usage of such technology and you may thenreplace your boss. You will need less memorizing and a "smaller brain," so to say;because its the quality of intelligence that counts, not the quantity of facts thatyou can memorize.Expanding on this prospective, we expect people who are now regarded as lessintelligent because they do not master some stereotype of knowledge, to have newmotivation for learning. The user of this new technology is able to control theefficiency of, say, knowledge extraction, by using his very personal coreintelligence that his boss does not possess. Use of this technology enhanceshuman intelligence in ways that schools of today are unaware of.Information in free text, whether scientific, literary or technological, will beanalyzable by people all over the world, many of whom are regarded as lessintelligent nowadays. Education will change because an intermediate schoolstudent will be able to analyze college literature (with the aid of a computer) onthe basis of the powerful and yet simple universal concepts he or she learned inelementary school, which are directly derivable from his or her language andspelling lessons. School time and years should be shorter because if weunderstand what intelligence is, we can teach it much faster. Sciences that arecomplex and so anti-human can now become simple, elegant, and moreacceptable to the human soul than some of the badly structured and monstrousscientific complexities of today.The hostility between machines and nature, between robots and people, shouldslowly vanish, because computers will increasingly be used to understand natureand deal with it. Fitted with our technology, the computer can put a couple of yourideas together in a natural way instead of forcing you to "think" like a "robot" anddegenerate into mechanical logic like some future visions suggest. It will enhancehuman intelligence instead of burning it out. Whatever your IQ might be, it willhelp you promote yourself while working with it as a tool. Complex machines willbecome abominable and much less useful in an era of clarity of intelligence.Bureaucratic machines, whether human or mechanical, will be able to receive andprocess human information, as well as the fixed relational data and statistics usedexclusively today. Such restricted data presents very narrowed views on a subject.The capability to process human information in natural (written) discourse willexpand our perspectives and heighten our horizons.Courts will work much faster because legal analysis will profit from thistechnology, but neither judges nor lawyers will be less in number. The judgmentprocess can be mechanized, but it requires genuine human hint resolution(personal core intelligence) to validate and check out every case.Psychology and other social sciences will definitely profit from understanding the
  9. 9. relation between what people say and what they conceive, which is a directapplication of Letter Semantics. In this way, those children who now drop out ofsociety, those who become incarcerated, or become a burden or a danger tosociety, can be reached, and saved, through better intellectual understanding.Further, we know from past experience that language change over the centuriesmodifies our world views and that different languages each have their advantagesand disadvantages. The approaches to language processing to date, includinglanguage translation, are simply less productive ways of communicating. Webelieve READWARE(TM) is a technology for a Global Community.Throughout history and today, cross-cultural and cross-lingual sharing of ideasand experience is seen to be most fruitful. America owes much of her scientificand technological power to the diversity of lingual backgrounds, to an extent thatwe are just beginning to understand. Any technology that is able to aid us shareideas in such a fashion should be of great benefit to society in general. Thispremise became the basis of design for the READWARE(TM) ResearchAssistant.READWARE(TM) technology offers the means of putting together textualinformation in different languages to be analyzed interactively.READWARE(TM) technology utilizes letter-semantic clusters of concepts,information that is automatically derived from natural texts without the aid ofdictionaries. This universal information can be used in an interactive environmentin many different ways; in ways which we have yet to learn about.The term READWARE(TM), aside from its kinship to the terms HARDWAREand SOFTWARE, expresses two facts: first, that knowledge is acquired byREADing and second, that something is there, a -WARE, a computer or amethodology that helps you understand what you read.The computerized applications of READWARE(TM) are very efficientinteractive tools. We are also preparing READWARE(TM) COURSES forenhancing the intelligence and analytical capability of those who have to do a lotof thinking on their jobs. The first course to be offered will be calledREADWARE(TM) PHYSICS and will introduce physicists (and those who needto think on the basis of physics) to a special branch of Letter Semanticsconcentrating on concepts of physics.Currently, we are ready to prepare a CUSTOMIZED READWARE(TM)COURSE for any group of people interested in enhancing their cognitive-analytical capabilities in a special area of research. People teaching English to thedeaf have already shown an interest. You know that the deaf associate a singleobject, not a concept, with a certain word. If the deaf were taught LetterSemantics, they would be able to make concepts out of words and thus benefit by
  10. 10. new knowledge derived from their chosen language.READWARE(TM) helps its user to extract and relate acquired knowledge fromintelligent writings. We as humans cannot hope to attain perfect knowledge, yetwe can hope to achieve a valid definition of the process of intelligence in order toacquire knowledge. We believe that we have achieved this result. We determinedthat the best way to demonstrate machine-aided intelligence was to develop asystem that would reproduce the ability of the comprehending human reader.Finally, to distinguish our Technology from the Falling AI again, we point out thatAI has limited its endeavor to mechanizing humans and enforcing dry logic ontotheir thinking. We, on the contrary, derived an understanding of human thinkingfrom natural language, the humans very special gift. We found that progress canbe made by associations with nature, by understanding its words and learningfrom them, by refreshing our ways from language, rather than paralyzing ourenergy into the rules of logic.We have not created artificial intelligence, but have rather imitated parts ofnatural intelligence that we discovered by empirical experiment and analysis.5. How does READWARE(TM) really work, and what do we mean by Read?Any machine readable information, i.e., any text in its original form in English,Spanish, German, French, Russian, Arabic, or Swedish, can be accessed byREADWARE(TM).READWARE(TM) reads your text word by word and letter by letter andgenerates a file (one third its size) that contains the abstract ideas of your text incompiled clusters of concepts.You can then enter a free expression (in any of the seven languages) that containsan idea, a question or any combination of words that expresses what you arelooking for. READWARE(TM) will highlight very quickly the spots of the textthat are meaningfully associated with your input. What you get is found by"machine thinking" and will often surprise you and add to your knowledge. Itmust be pointed out that this is Idea Search, in contrast to Keyword Search. Wordsare not marked and indexed; no manual per-processing is required of printablefiles. Information is not retrieved by keyword, rather by associated ideas, or moreprecisely, primitive concepts. A word like pessimism used in an input expressionwill retrieve concepts like gloom, doubt, depression, grief, sadness, sorry, trouble,etc. . . . if they appear in the text, but the word pessimism need not appear.Pessimism would have to appear in the text to be retrieved by Keyword Search,whereas none of the other words would be retrieved.This is one difference between the applications of Keyword Search and those of
  11. 11. Literary Research for which READWARE(TM) has been designed. It is adecisive difference, but it is not the only one; READWARE(TM) is really"something else."READWARE(TM) uses four new and powerful analysis modules:• nineteen (19) Morphological Analyzers (for seven languages under different options) which detect and analyze prefixes, infixes and suffixes without using any dictionaries• seven Etymological Adapters that take care of such issues as Language Change and bad Transcription• seven Letter-Semantic Conceptualizers that construct the compiled clusters of concepts from the letter combinations in word stems• the Cross-Lingual Associator that performs a lot of thinking under the control of your input. It is so powerful that an idea in Arabic will relate properly to an English idea, both expressed in free languageThe READWARE(TM) RESEARCH ASSISTANT has been designed for use asan intellectual aid to any human performing: Situation Analysis, LiteraryResearch, and Associative Information Retrieval. Compelling proof to our theoryis that the software can be applied to any information regardless of vocabulary, inany combination of seven eastern and western languages. The programunderstands expressions in full text context and in concordance with the situationbeing described. Texts from magazines, books, business reports, business lettersand memos, legal contracts, corporate chargers, operating procedures, litigationproceedings, regulations, and military specifications have been tested, along withancient texts, religious text, theological doctrine, philosophical thought, andrecent scientific documents regarding discoveries in the fields of quantummechanics, particle physics, and superstring theory.The READWARE(TM) RESEARCH ASSISTANT includes multilingualinquiry editors and simple to use tools that help you to save standard inquiries toapply over and over again.Session results can be saved for reporting and manual analysis. A screen basedhorizontal and vertical cut and paste mechanism is provided for marking rows orcolumns of associated text for subsequent pasting to new (ascii) files, to savedinquiries (QBE, query by example), or for printing.A single inquiry or a series of queries about information contained in 1 to 1000files in the same or different languages and (DOS) disks and directories can beloaded and processed simultaneously. Inquiries loaded from disk have their results(session results) and their environments automatically stored; otherwise, sessionresults can be saved at any time by halting the search and pressing a function key,or on completion of the session by pressing the same function key.
  12. 12. A Zoom feature allows the User to zoom into the text files, mark parts of it toprint or save, or mark an idea and make it the basis of a further inquiry by pastingand editing.The READWARE(TM) RESEARCH ASSISTANT is completely screenoriented with all program operations and User options are fully explained andaccessible from the menu strips on each screen. The program makes use of thesoftware standards and conventions recommended by Microsoft Corporation forMS-DOS based IBM-PC type machines and their function keys. The Userinterface is straight-forward in both function and design and, in our opinion, is theepitome of simplistic operation. The Objective of the design was to reduce theamount of computer (if you know what we mean) between the human, and theknowledge represented in his information base.We believe that Letter Semantics is powerful enough to classify universallanguage structure. The READWARE(TM) RESEARCH ASSISTANT is only afirst step in this direction. Through our concept validation tools and theREADWARE(TM) RESEARCH ASSISTANT, we can demonstrate howelegantly we cope with the richness of human expression. The experts wouldagree that any theory that is powerful enough to cope with the richness of humanexpression should be powerful enough to deal with other aspects of cognition andcognitive processes as well.The basic difference between the computer systems of today and those oftomorrow will be the difference between the highly structured systems that dealwith data in terms of logic and rational, and unstructured systems that deal withdata in terms of concepts, associations, and dynamic relationships like most of usnon-mechanical humans. The AI people cannot deny that their research concernsrobotics. Thinking humans should not be deceived into believing that what isgood for robots is good for humans.When we understand and accept the concept that perfect knowledge is beyond ourapprehension. When we focus solely upon consistency and certainty. Perhapsthen, only the barrier of progress, and fear of change, will stand between humanshungry for new knowledge, and intelligent machines helping humans to evolveintellectually, by free choice of association rather than by per-programmedmechanics.Now that you know a little about the underlying technology and the designfeatures of READWARE(TM), we can proceed with a demonstration of theREADWARE(TM) RESEARCH ASSISTANT.REFERENCES:
  13. 13. Adi, T. (1985). Letter semantics. Unpublished manuscript.Adi, T., & Ewell, O.K. (1986a). Natural Language is not Artificial. Introductory paper. Washington, D.C. Management Information Technologies, Incorporated.Adi, T., & Ewell, O.K. (1986b). READWARE(TM). Internal report. San Jose, CA: Management Information Technologies, Inc.Adi, T., & Ewell, O.K. (1987). Artificial Intelligence versus Machine Comprehension. INTEC87, Washington, D.C.Chomsky, N. (1985). Knowledge of language: Its nature, origin, and use (pp. 15- 50). In Anshen, R.N. (ed.). Convergence. New York: Praeger.Saussure, Ferdinand de (1966). Course in general linguistics, Charles Bally et al. (eds.). Translated by W. Baskin. New York: McGraw-Hill.Winograd, T. & Flores. F. (1987). Understanding computers and cognition: A new foundation for design, (pp. 54-106). Norwood, NJ: Ablex Publishing Corporation.