29 December 2005

🧩IT: Automation (Just the Quotes)

"Systems engineering embraces every scientific and technical concept known, including economics, management, operations, maintenance, etc. It is the job of integrating an entire problem or problem to arrive at one overall answer, and the breaking down of this answer into defined units which are selected to function compatibly to achieve the specified objectives. [...] Instrument and control engineering is but one aspect of systems engineering - a vitally important and highly publicized aspect, because the ability to create automatic controls within overall systems has made it possible to achieve objectives never before attainable, While automatic controls are vital to systems which are to be controlled, every aspect of a system is essential. Systems engineering is unbiased, it demands only what is logically required. Control engineers have been the leaders in pulling together a systems approach in the various technologies." (Instrumentation Technology, 1957)

"As the decision-making function becomes more highly automated, corporate decision making will perhaps provide fewer outlets for creative drives than it now does." (Herbert A Simon," Management and Corporations 1985", 1960)

"Objectives recorded on the System Specification work sheet, even though preliminary in nature, should be specific. It is never sufficient to state an objective in terms of simply improving an existing system or of implementing a computerized system. The idea that a system or an 'automated' system is a better system has been a popular concept too long. An improved system, per se, is of no benefit to a business client; implementing a better system in order to increase profits or reduce costs is of great benefit." (Robert D Carlsen & James A Lewis, "The Systems Analysis Workbook: A complete guide to project implementation and control", 1973)

"Autonomation [automation with a human touch] changes the meaning of management as well. An operator is not needed while the machine is working normally. Only when the machine stops because of an abnormal situation does it get human attention. As a result, one worker can attend several machines, making it possible to reduce the number of operators and increase production efficiency. [...] Implementing autonomation is up to the managers and supervisors of each production area. The key is to give human intelligence to the machine and, at the same time, to adapt the simple movement of the human operator to the autonomous machines." (Taiichi Ohno, "Toyota Production System: Beyond Large-Scale Production", 1978)

"Autonomation [..] performs a dual role. It eliminates overproduction, an important waste in manufacturing, and prevents the production of defective products. To accomplish this, standard work procedures, corresponding to each player's ability, must be adhered to at all times." (Taiichi Ohno, "Toyota Production System: Beyond Large-Scale Production", 1978)

"When you automate an industry you modernize it; when you automate a life you primitivize it." (Eric Hoffer, "Between the Devil and the Dragon", 1982)

"Automation is certainly one way to improve the leverage of all types of work. Having machines to help them, human beings can create more output." (Andrew S. Grove, "High Output Management", 1983)

"Information engineering has been defined with the reference to automated techniques as follows: An interlocking set of automated techniques in which enterprise models, data models and process models are built up in a comprehensive knowledge-base and are used to create and maintain data-processing systems." (James Martin, "Information Engineering, 1989)

"At the heart of reengineering is the notion of discontinuous thinking - of recognizing and breaking away from the outdated rules and fundamental assumptions that underlie operations. Unless we change these rules, we are merely rearranging the deck chairs on the Titanic. We cannot achieve breakthroughs in performance by cutting fat or automating existing processes. Rather, we must challenge old assumptions and shed the old rules that made the business underperform in the first place." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990)

"In short, a reengineering effort strives for dramatic levels of improvement. It must break away from conventional wisdom and the constraints of organizational boundaries and should be broad and cross-functional in scope. It should use information technology not to automate an existing process but to enable a new one." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990)

"When a database is computerized, it represents the automation of the knowledge component of a business, which is manifest through the business's quality operation, planning, and management With a successful database, the managers of a business can research the past, organize the present, and plan for the future."  (Michael M Gorman, "Database Management Systems: Understanding and Applying Database Technology", 1991)

"Replacing workers on their present jobs with machines is not the major function of automation. Its greater promise is its ability to do new things, to create new products, new services and new jobs, and to meet the increasing requirements of a growing population." (David Sarnoff)

"The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency." (Bill Gates)

"[...] the more efficient the automated system is, the more essential the human contribution that is needed to run the automation system. Humans are less involved in heavily automated systems, but their involvement becomes more critical." (Josh Kaufman)

"The more reliable the plant, the less opportunity there will be for the operator to practice direct intervention, and the more difficult will be the demands of the remaining tasks requiring operator intervention."(Josh Kaufman)

28 December 2005

🧩IT: Machines (Just the Quotes)

"The successful construction of all machinery depends on the perfection of the tools employed; and whoever is a master in the arts of tool-making possesses the key to the construction of all machines. [...] The contrivance and construction of tools must therefore ever stand at the head of the industrial arts." (Charles Babbage, "The Exposition of 1851: Views Of The Industry, The Science, and the Government Of England", 1851)

"As soon as an Analytical Engine exists, it will necessarily guide the future course of the science. Whenever any result is sought by its aid, the question will then arise - by what course of calculation can these results be arrived at by the machine in the shortest time?" (Charles Babbage, "Passages from the Life of a Philosopher", 1864) 

"Let us look for a moment at the general significance of the fact that calculating machines actually exist, which relieve mathematicians of the purely mechanical part of numerical computations, and which accomplish the work more quickly and with a greater degree of accuracy; for the machine is not subject to the slips of the human calculator. The existence of such a machine proves that computation is not concerned with the significance of numbers, but that it is concerned essentially only with the formal laws of operation; for it is only these that the machine can obey - having been thus constructed - an intuitive perception of the significance of numbers being out of the question." (Felix Klein, "Elementarmathematik vom holieren Standpunkte aus", 1908)

"A computer would deserve to be called intelligent if it could deceive a human into believing that it was human." (Alan Turing, "Computing Machinery and Intelligence", Mind Vol. 59, 1950)

"If one wants to make a machine mimic the behaviour of the human computer in some complex operation one has to ask him how it is done, and then translate the answer into the form of an instruction table. Constructing instruction tables is usually described as 'programming'." (Alan Turing, "Computing Machinery and Intelligence", Mind Vol. 59, 1950)

"The idea behind digital computers may be explained by saying that these machines are intended to carry out any operations which could be done by a human computer." (Alan Turing, "Computing Machinery and Intelligence", Mind Vol. 59, 1950)

"The original question, 'Can machines think?:, I believe too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted." (Alan M Turing, 1950) 

"The view that machines cannot give rise to surprises is due, I believe, to a fallacy to which philosophers and mathematicians are particularly subject. This is the assumption that as soon as a fact is presented to a mind all consequences of that fact spring into the mind simultaneously with it. It is a very useful assumption under many circumstances, but one too easily forgets that it is false. A natural consequence of doing so is that one then assumes that there is no virtue in the mere working out of consequences from data and general principles." (Alan Turing, "Computing Machinery and Intelligence", Mind Vol. 59, 1950)

"Cybernetics is not merely another branch of science. It is an intellectual revolution that rivals in importance the earlier Industrial Revolution. Is it possible that just as a machine can take over the routine functions of human muscle, another can take over the routine uses of human mind? Cybernetics answers, yes." (Isaac Asimov, [preface to Pierre de Latil’s Thinking by Machine] 1957)

"A machine is not a genie, it does not work by magic, it does not possess a will, and […] nothing comes out which has not been put in, barring of course, an infrequent case of malfunctioning. [..] The “intentions” which the machine seems to manifest are the intentions of the human programmer, as specified in advance, or they are subsidiary intentions derived from these, following rules specified by the programmer. […] The machine will not and cannot do any of these things until it has been instructed as to how to proceed. […] To believe otherwise is either to believe in magic or to believe that the existence of man’s will is an illusion and that man’s actions are as mechanical as the machine’s." (Arthur L Samuel, Science, 1960) 

"Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an "intelligence explosion", and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make." (Irving J Good, "Speculations Concerning the First Ultraintelligent Machine", Advances in Computers Vol. 6, 1965)

"In short, the scientific account of affairs involved the formalization of interactions between men, machines, materials and money, all spread over [a] complex system." (Stafford Beer, "Decision and Control", 1966)

"These machines have no common sense; they have not yet learned to 'think', and they do exactly as they are told, no more and no less. This fact is the hardest concept to grasp when one first tries to use a computer." (Donald Knuth, “The Art of Computer Programming”, 1968)

"The real question is not whether machines think but whether men do." (Burrhus F Skinner, "Contingencies of Reinforcement", 1969)

"An autopoietic machine is a machine organized (defined as a unity) as a network of processes of production (transformation and destruction) of components which: (i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and (ii) constitute it (the machine) as a concrete unity in space in which they (the components) exist by specifying the topological domain of its realization as such a network." (Humberto Maturana, “Autopoiesis and cognition: The realization of the living”, 1980)

"The relations that define a system as a unity, and determine the dynamics of interaction and transformations which it may undergo as such a unity constitute the organization of the machine." (Humberto Maturana, “Autopoiesis and cognition: The realization of the living”, 1980)

"Automation is certainly one way to improve the leverage of all types of work. Having machines to help them, human beings can create more output." (Andrew S Grove, "High Output Management", 1983)

"How machines are related to each other often determines the organizational structure [of the people]." (John A Reinecke & William F Schoell, , Introduction to Business, 1983)

"Cybernetics is simultaneously the most important science of the age and the least recognized and understood. It is neither robotics nor freezing dead people. It is not limited to computer applications and it has as much to say about human interactions as it does about machine intelligence. Today’s cybernetics is at the root of major revolutions in biology, artificial intelligence, neural modeling, psychology, education, and mathematics. At last there is a unifying framework that suspends long-held differences between science and art, and between external reality and internal belief." (Paul Pangaro, "New Order From Old: The Rise of Second-Order Cybernetics and Its Implications for Machine Intelligence", 1988)

"[management by objectives] has become one more way to make organizations behave like machines." (Julien Phillips, "Success", 1988)

"The real definition of a supercomputer is a machine that is just one generation behind the problems it is asked to solve." (Neil Lincoln, Time, 1988)

"If machines knew as much about each other as we know about each other (even in our privacy), the ecology of machines would be indomitable." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995) 

"Computer programs are the most intricate, delicately balanced and finely interwoven of all the products of human industry to date. They are machines with far more moving parts than any engine: the parts don't wear out, but they interact and rub up against one another in ways the programmers themselves cannot predict." (James Gleick, “What Just Happened: A chronicle from the information frontier”, 2002)

"But intelligence is not just a matter of acting or behaving intelligently. Behavior is a manifestation of intelligence, but not the central characteristic or primary definition of being intelligent. A moment's reflection proves this: You can be intelligent just lying in the dark, thinking and understanding. Ignoring what goes on in your head and focusing instead on behavior has been a large impediment to understanding intelligence and building intelligent machines." (Jeff Hawkins, "On Intelligence", 2004)

"When a machine manages to be simultaneously meaningful and surprising in the same rich way, it too compels a mentalistic interpretation. Of course, somewhere behind the scenes, there are programmers who, in principle, have a mechanical interpretation. But even for them, that interpretation loses its grip as the working program fills its memory with details too voluminous for them to grasp."  (Ray Kurzweil, "The Singularity is Near", 2005)

"Computers bootstrap their own offspring, grow so wise and incomprehensible that their communiqués assume the hallmarks of dementia: unfocused and irrelevant to the barely-intelligent creatures left behind. And when your surpassing creations find the answers you asked for, you can't understand their analysis and you can't verify their answers. You have to take their word on faith." (Peter Watts, "Blindsight", 2006)

"Every system that we build will surprise us with new kinds of flaws until those machines become clever enough to conceal their faults from us." (Marvin Minsky, "The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind", 2006)

"Programmers need to be fluent in the language of the machine, whether real or virtual, and in the abstractions that can be related to that language via development tools. It is important to learn many different abstractions, otherwise some ideas become incredibly hard to express. Good programmers need to be able to stand outside their daily routine, to be aware of other languages that are expressive for other purposes. The time always comes when this pays off." (Klaus Marquardt, [in Kevlin Henney’s "97 Things Every Programmer Should Know", 2010])

"The attribution of intelligence to machines, crowds of fragments, or other nerd deities obscures more than it illuminates. When people are told that a computer is intelligent, they become prone to changing themselves in order to make the computer appear to work better, instead of demanding that the computer be changed to become more useful." (Jaron Lanier, "You Are Not a Gadget", 2010)

"The architecture - the mind - is knitting together. It’s sentience. Vague sentience. All these years of formulating machines that know something, while the secret is to create machines that don’t know something." (Scott Hutchins,  "A Working Theory of Love", 2012)

"Artificial intelligence is a concept that obscures accountability. Our problem is not machines acting like humans - it's humans acting like machines." (John Twelve Hawks, "Spark", 2014)

"A computer is a general-purpose machine. It takes its instructions from memory, and one can change the computation it performs by putting different instructions in the memory. Instructions and data are indistinguishable except by context; one person’s instructions are another person’s data." (Brian W Kernighan, "Understanding the Digital World", 2017)

"The good news is that a computer is a general-purpose machine, capable of performing any computation. Although it only has a few kinds of instructions to work with, it can do them very fast, and it can largely control its own operation. The bad news is that it doesn’t do anything itself unless someone tells it what to do, in excruciating detail. A computer is the ultimate sorcerer’s apprentice, able to follow instructions tirelessly and without error, but requiring painstaking accuracy in the specification of what to do." (Brian W Kernighan, "Understanding the Digital World", 2017)

"Machines can do many things, but they cannot create meaning. They cannot answer these questions for us. Machines cannot tell us what we value, what choices we should make. The world we are creating is one that will have intelligent machines in it, but it is not for them. It is a world for us." (Paul Scharre, "Army of None: Autonomous Weapons and the Future of War", 2018)

"If we don't truly know what something is programmed to do, chances are it is programming us. Once that happens, we may as well be machines ourselves." (Douglas Rushkoff, "Team Human", 2019)

"Our machines are helpers, not decision makers. Their insights are not the final word in the discussion, merely the work of our most nimble observers who can ramp up time spent on analysis by factors that our counterparts even a generation ago would have a hard time believing." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"A learning machine is any device whose actions are influenced by past experiences."  (Nils Nilsson)

"Every machine has artificial intelligence. And the more advanced a machine gets, the more advanced artificial intelligence gets as well. But, a machine cannot feel what it is doing. It only follows instructions - our instructions - instructions of the humans. So, artificial intelligence will not destroy the world. Our irresponsibility will destroy the world." (Abhijit Naskar)

"Replacing workers on their present jobs with machines is not the major function of automation. Its greater promise is its ability to do new things, to create new products, new services and new jobs, and to meet the increasing requirements of a growing population." (David Sarnoff)

25 December 2005

🧩IT: Computer Science (Just the Quotes)

"[Computers] are developing so rapidly that even computer scientists cannot keep up with them. It must be bewildering to most mathematicians and engineers. [...] In spite of the diversity of the applications, the methods of attacking the difficult problems with computers show a great unity, and the name of Computer Sciences is being attached to the discipline as it emerges. It must be understood, however, that this is still a young field whose structure is still nebulous. The student will find a great many more problems than answers." (George Forsythe, "Engineering students must learn both computing and mathematics", 1961)

"I consider computer science to be the art and science of exploiting automatic digital computers, and of creating the technology necessary to understand their use. It deals with such related problems as the design of better machines using known components:, the design and implementation of adequate software systems for communication between man and machine, and the design and analysis of methods of representing information by abstract symbols and of processes for manipulating these symbols." (George E Forsythe, "Stanford University's Program in Computer Science", 1965)

"Computer science is a restless infant and its progress depends as much on shifts in point of view as on the orderly development of our current concepts." (Alan Perlis, "The Synthesis of Algorithmic Systems", 1966)

"Computer science is at once abstract and pragmatic. The focus on actual computers introduces the pragmatic component: our central questions are economic ones like the relations among speed, accuracy, and cost of a proposed computation, and the hardware and software organization required. The (often) better understood questions of existence and theoretical computability - however fundamental - remain in the background. On the other hand, the medium of computer science - information - is an abstract one. The meaning of symbols and numbers may change from application to application, either in mathematics or in computer science. Like mathematics, one goal of computer science is to create a basic structure in terms of inherently defined concepts that is independent of any particular application." (George E Forsythe, "What to do till the computer scientist comes", 1968)

"Without real experience in using the computer to get useful results the computer science major is apt to know all about the marvelous tool except how to use it. Such a person is a mere technician, skilled in manipulating the tool but with little sense of how and when to use it for its basic purposes." (Richard Hamming, "One Man's View of Computer Science", 1969)

"Numerical analysis has begun to look a little square in the computer science setting, and numerical analysts are beginning to show signs of losing faith in themselves. Their sense of isolation is accentuated by the present trend towards abstraction in mathematics departments which makes for an uneasy relationship." (James H Wilkinson, "Some Comments from a Numerical Analyst", 1971)

"Software engineering is the part of computer science which is too difficult for the computer scientist." (Friedrich Bauer, "Software Engineering." Information Processing: Proceedings of the IFIP Congress, 1971)

"Computer science is an empirical discipline. [...] Each new machine that is built is an experiment. Actually constructing the machine poses a question to nature; and we listen for the answer by observing the machine in operation and analyzing it by all analytical and measurement means available. Each new program that is built is an experiment. It poses a question to nature, and its behavior offers clues to an answer." (Allen Newell & Herbert A Simon, "Computer Science as Empirical Inquiry: Symbols and Search", 1975)

"In this quest for simplification, mathematics stands to computer science as diamond mining to coal mining. The former is a search for gems. [...]The latter is permanently involved with bulldozing large masses of ore - extremely useful bulk material." (Jacob T Schwartz, "Discrete Thoughts: Essays on Mathematics, Science, and Philosophy, Computer Science", 1986)

"The potential of computer science, if fully explored and developed, will take us to a higher  plane of knowledge about the world. Computer science will assist us in gaining a greater  understanding of intellectual processes. It will enhance our knowledge of the learning  process, the thinking process, and the reasoning process. Computer science will provide  models and conceptual tools for the cognitive sciences. Just as the physical sciences have  dominated humanity's intellectual endeavors during this century as researchers explored  the nature of matter and the beginning of the universe, today we are beginning the exploration of the intellectual universe of ideas, knowledge structures, and language." (John E Hopcroft, [ACM Turing Award Lecture] 1987)

"Computer science only indicates the retrospective omnipotence of our technologies. In other words, an infinite capacity to process data (but only data - i.e. the already given) and in no sense a new vision. With that science, we are entering an era of exhaustivity, which is also an era of exhaustion." (Jean Baudrillard, "Cool memories", 1990)

"There is an art, craft, and science to programming that extends far beyond the program. The act of programming marries the discrete world of computers with the fluid world of human affairs. Programmers mediate between the negotiated and uncertain truths of business and the crisp, uncompromising domain of bits and bytes and higher constructed types." (Kevlin Henney, "97 Things Every Programmer Should Know", 2010)

"Computer science is concerned with the theories and methods that underlie computers and software systems, whereas software engineering is concerned with the practical problems of producing software. Some knowledge of computer science is essential for software engineers in the same way that some knowledge of physics is essential for electrical engineers. Computer science theory, however, is often most applicable to relatively small programs. Elegant theories of computer science cannot always be applied to large, complex problems that require a software solution." (Ian Sommerville, "Software Engineering" 9th Ed., 2011)

"Perhaps an underlying cause [of doubt as to the future of information science] is in some cases [...] the apprehension that information science may become submerged in the larger field of computer science." (Brian C Vickery)

23 December 2005

🧩IT: Computing (Just the Quotes)

"Let it be remarked [...] that an important difference between the way in which we use the brain and the machine is that the machine is intended for many successive runs, either with no reference to each other, or with a minimal, limited reference, and that it can be cleared between such runs; while the brain, in the course of nature, never even approximately clears out its past records. Thus the brain, under normal circumstances, is not the complete analogue of the computing machine but rather the analogue of a single run on such a machine." (Norbert Wiener, "Cybernetics: Or Control and Communication in the Animal and the Machine", 1948)

"There are two types of systems engineering - basis and applied. [...] Systems engineering is, obviously, the engineering of a system. It usually, but not always, includes dynamic analysis, mathematical models, simulation, linear programming, data logging, computing, optimating, etc., etc. It connotes an optimum method, realized by modern engineering techniques. Basic systems engineering includes not only the control system but also all equipment within the system, including all host equipment for the control system. Applications engineering is - and always has been - all the engineering required to apply the hardware of a hardware manufacturer to the needs of the customer. Such applications engineering may include, and always has included where needed, dynamic analysis, mathematical models, simulation, linear programming, data logging, computing, and any technique needed to meet the end purpose - the fitting of an existing line of production hardware to a customer's needs. This is applied systems engineering." (Instruments and Control Systems Vol. 31, 1958)

"The mathematical and computing techniques for making programmed decisions replace man but they do not generally simulate him." (Herbert A Simon, "Management and Corporations 1985", 1960)

"There is the very real danger that a number of problems which could profitably be subjected to analysis, and so treated by simpler and more revealing techniques. will instead be routinely shunted to the computing machines [...] The role of computing machines as a mathematical tool is not that of a panacea for all computational ills." (Richard E Bellman & Paul Brock, "On the Concepts of a Problem and Problem-Solving", American Mathematical Monthly 67, 1960)

"The purpose of computing is insight, not numbers." (Richard W Hamming, "Numerical Methods for Scientists and Engineers", 1962)

"Another thing I must point out is that you cannot prove a vague theory wrong. If the guess that you make is poorly expressed and rather vague, and the method that you use for figuring out the consequences is a little vague - you are not sure, and you say, 'I think everything's right because it's all due to so and so, and such and such do this and that more or less, and I can sort of explain how this works' […] then you see that this theory is good, because it cannot be proved wrong! Also if the process of computing the consequences is indefinite, then with a little skill any experimental results can be made to look like the expected consequences." (Richard P Feynman, "The Character of Physical Law", 1965)

"Computational reducibility may well be the exception rather than the rule: Most physical questions may be answerable only through irreducible amounts of computation. Those that concern idealized limits of infinite time, volume, or numerical precision can require arbitrarily long computations, and so be formally undecidable." (Stephen Wolfram, Undecidability and intractability in theoretical physics", Physical Review Letters 54 (8), 1985)

"We distinguish diagrammatic from sentential paper-and-pencil representations of information by developing alternative models of information-processing systems that are informationally equivalent and that can be characterized as sentential or diagrammatic. Sentential representations are sequential, like the propositions in a text. Diagrammatic representations are indexed by location in a plane. Diagrammatic representations also typically display information that is only implicit in sentential representations and that therefore has to be computed, sometimes at great cost, to make it explicit for use. We then contrast the computational efficiency of these representations for solving several. illustrative problems in mathematics and physics." (Herbert A Simon, "Why a diagram is (sometimes) worth ten thousand words", 1987)

"Neural computing is the study of cellular networks that have a natural property for storing experimental knowledge. Such systems bear a resemblance to the brain in the sense that knowledge is acquired through training rather than programming and is retained due to changes in node functions. The knowledge takes the form of stable states or cycles of states in the operation of the net. A central property of such nets is to recall these states or cycles in response to the presentation of cues." (Igor Aleksander & Helen Morton, "Neural computing architectures: the design of brain-like machines", 1989)

"Beauty is more important in computing than anywhere else in technology because software is so complicated. Beauty is the ultimate defense against complexity." (David Gelernter, "Machine Beauty: Elegance And The Heart Of Technolog", 1998)

"As systems became more varied and more complex, we find that no single methodology suffices to deal with them. This is particularly true of what may be called information intelligent systems - systems which form the core of modern technology. To conceive, design, analyze and use such systems we frequently have to employ the totality of tools that are available. Among such tools are the techniques centered on fuzzy logic, neurocomputing, evolutionary computing, probabilistic computing and related methodologies. It is this conclusion that formed the genesis of the concept of soft computing." (Lotfi A Zadeh, "The Birth and Evolution of Fuzzy Logic: A personal perspective", 1999)

"In science, it is a long-standing tradition to deal with perceptions by converting them into measurements. But what is becoming increasingly evident is that, to a much greater extent than is generally recognized, conversion of perceptions into measurements is infeasible, unrealistic or counter-productive. With the vast computational power at our command, what is becoming feasible is a counter-traditional move from measurements to perceptions. […] To be able to compute with perceptions it is necessary to have a means of representing their meaning in a way that lends itself to computation." (Lotfi A Zadeh, "The Birth and Evolution of Fuzzy Logic: A personal perspective", 1999)

"Why was progress in computing technology so fast compared with the lack of progress in space travel? The reason is very simple: computing technology is only now approaching scientific limits such as quantum uncertainty and the speed of light, while space technology has already run into its limits that derive from the basic principles of physics and chemistry." (Mordechai Ben-Ari, "Just a Theory: Exploring the Nature of Science", 2005)

"Granular computing is a general computation theory for using granules such as subsets, classes, objects, clusters, and elements of a universe to build an efficient computational model for complex applications with huge amounts of data, information, and knowledge. Granulation of an object a leads to a collection of granules, with a granule being a clump of points (objects) drawn together by indiscernibility, similarity, proximity, or functionality. In human reasoning and concept formulation, the granules and the values of their attributes are fuzzy rather than crisp. In this perspective, fuzzy information granulation may be viewed as a mode of generalization, which can be applied to any concept, method, or theory." (Salvatore Greco et al, "Granular Computing and Data Mining for Ordered Data: The Dominance-Based Rough Set Approach", 2009)

22 December 2005

🧩IT: Privacy (Just the Quotes)

"The personal life of every individual is based on secrecy, and perhaps it is partly for that reason that civilized man is so nervously anxious that personal privacy should be respected." (Anton Chekhov, "A Doctor's Visit", 1898)

"Solitude and privacy have become more essential to the individual; but modern enterprise and invention have, through invasions upon his privacy, subjected him to mental pain and distress." (Samuel D Warren, "The Development of the Right of Privacy in New York", 1954)

"The fantastic advances in the field of electronic communication constitute a greater danger to the privacy of the individual." (Earl Warren, "Concurring in the judgment, Lopez v. United States 373 U.S. 427", 1963)

"Complete and accurate surveillance as a means of control is probably a practical impossibility. What is much more likely is a loss of privacy and constant inconvenience as the wrong people gain access to information, as one wastes time convincing the inquisitors that one is in fact innocent, or as one struggles to untangle the errors of the errant machine." (Victor C Ferkiss, "Technological Man: The Myth and the Reality", 1969)

"Privacy invasion is now one of biggest knowledge industries." (Marshall McLuhan, "Culture Is Our Business", 1970)

"Privacy - like eating and breathing - is one of life's basic requirements." (Katherine Neville, "A Calculated Risk: A Novel", 1992)

"If machines knew as much about each other as we know about each other (even in our privacy), the ecology of machines would be indomitable." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995) 

"Privacy is a type of conversation. Firms should view privacy not as some inconvenient obsession of customers that must be snuck around but more as a way to cultivate a genuine relationship." (Kevin Kelly, "New Rules for the New Economy: 10 radical strategies for a connected world", 1998)

"The first form of semantic data on the Web was metadata information about information. (There happens to be a company called Metadata, but I use the term here as a generic noun, as it has been used for many years.) Metadata consist of a set of properties of a document. By definition, metadata are data, as well as data about data. They describe catalogue information about who wrote Web pages and what they are about; information about how Web pages fit together and relate to each other as versions; translations, and reformattings; and social information such as distribution rights and privacy codes." (Tim Berners-Lee, "Weaving the Web", 1999)

"Privacy is not a static construct. It is not an inherent property of any particular information or setting. It is a process by which people seek to have control over a social situation by managing impressions, information flows, and context." (Danah Boyd, "It's Complicated: The Social Lives of Networked Teens", 2014)

"Arguing that you don't care about the right to privacy because you have nothing to hide is no different than saying you don't care about free speech because you have nothing to say." (Edward Snowden, 2015)

"Data governance policies must not enforce constraints on data - Data governance intends to control the level of democracy within the data lake. Its sole purpose of existence is to maintain the quality level through audits, compliance, and timely checks. Data flow, either by its size or quality, must not be constrained through governance norms. [...] Effective data governance elevates confidence in data lake quality and stability, which is a critical factor to data lake success story. Data compliance, data sharing, risk and privacy evaluation, access management, and data security are all factors that impact regulation." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Metadata is the key to effective data governance. Metadata in this context is the data that defines the structure and attributes of data. This could mean data types, data privacy attributes, scale, and precision. In general, quality of data is directly proportional to the amount and depth of metadata provided. Without metadata, consumers will have to depend on other sources and mechanisms." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data privacy, data confidentiality, and data protection are sometimes incorrectly diluted with security. For example, data privacy is related to, but not the same as, data security. Data security is concerned with assuring the confidentiality, integrity, and availability of data. Data privacy focuses on how and to what extent businesses may collect and process information about individuals." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

20 December 2005

🧩IT: Computers (Just the Quotes)

"Let it be remarked [...] that an important difference between the way in which we use the brain and the machine is that the machine is intended for many successive runs, either with no reference to each other, or with a minimal, limited reference, and that it can be cleared between such runs; while the brain, in the course of nature, never even approximately clears out its past records. Thus the brain, under normal circumstances, is not the complete analogue of the computing machine but rather the analogue of a single run on such a machine." (Norbert Wiener, "Cybernetics: Or Control and Communication in the Animal and the Machine", 1948)

"A computer would deserve to be called intelligent if it could deceive a human into believing that it was human." (Alan Turing, "Computing Machinery and Intelligence" , Mind Vol. 59, 1950)

"It is interesting to consider what a thinking machine will be like. It seems clear that as soon as the machines become able to solve intellectual problems of the highest difficulty which can be solved by humans they will be able to solve most of the problems enormously faster than a human." (John F Nash, "Parallel Control", 1954)

"We could define the intelligence of a machine in terms of the time needed to do a typical problem and the time needed for the programmer to instruct the machine to do it." (John F Nash, "Parallel Control", 1954)

"A computer is a person or machine that is able to take in information (problems and data), perform reasonable operations on the iformation, and put out answers. A computer is identified by the fact that it (or he) handles information reasonably." (Edmund C Berkeley & Lawrence Wainwright, Computers: Their Operation and Applications", 1956)

"An information retrieval system is therefore defined here as any device which aids access to documents specified by subject, and the operations associated with it. The documents can be books, journals, reports, atlases, or other records of thought, or any parts of such records - articles, chapters, sections, tables, diagrams, or even particular words. The retrieval devices can range from a bare list of contents to a large digital computer and its accessories. The operations can range from simple visual scanning to the most detailed programming." (Brian C Vickery, "The Structure of Information Retrieval Systems", 1959)

"Computers do not decrease the need for mathematical analysis, but rather greatly increase this need. They actually extend the use of analysis into the fields of computers and computation, the former area being almost unknown until recently, the latter never having been as intensively investigated as its importance warrants. Finally, it is up to the user of computational equipment to define his needs in terms of his problems, In any case, computers can never eliminate the need for problem-solving through human ingenuity and intelligence." (Richard E Bellman & Paul Brock, "On the Concepts of a Problem and Problem-Solving", American Mathematical Monthly 67, 1960)

"There is the very real danger that a number of problems which could profitably be subjected to analysis, and so treated by simpler and more revealing techniques. will instead be routinely shunted to the computing machines [...] The role of computing machines as a mathematical tool is not that of a panacea for all computational ills." (Richard E Bellman & Paul Brock, "On the Concepts of a Problem and Problem-Solving", American Mathematical Monthly 67, 1960)

"The newest computer can merely compound, at speed, the oldest problem in the relations between human beings, and in the end the communicator will be confronted with the old problem, of what to say and how to say it." (Edward R. Murrow, "Family of Man", [award speech] 1964)

"These machines have no common sense; they have not yet learned to 'think', and they do exactly as they are told, no more and no less. This fact is the hardest concept to grasp when one first tries to use a computer." (Donald Knuth, "The Art of Computer Programming, Volume 1: Fundamental Algorithms", 1968)

 "Because the subject matter of cybernetics is the propositional or informational aspect of the events and objects in the natural world, this science is forced to procedures rather different from those of the other sciences. The differentiation, for example, between map and territory, which the semanticists insist that scientists shall respect in their writings must, in cybernetics, be watched for in the very phenomena about which the scientist writes. Expectably, communicating organisms and badly programmed computers will mistake map for territory; and the language of the scientist must be able to cope with such anomalies." (Gregory Bateson, "Steps to an Ecology of Mind", 1972)

"Computers can do better than ever what needn't be done at all. Making sense is still a human monopoly." (Marshall McLuhan, "Take Today: The Executive as Dropout", 1972)

"Everything we think we know about the world is a model. Every word and every language is a model. All maps and statistics, books and databases, equations and computer programs are models. So are the ways I picture the world in my head - my mental models. None of these is or ever will be the real world. […] Our models usually have a strong congruence with the world. That is why we are such a successful species in the biosphere. Especially complex and sophisticated are the mental models we develop from direct, intimate experience of nature, people, and organizations immediately around us." (Donella Meadows, "Limits to Growth", 1972)

"It follows from this that man's most urgent and pre-emptive need is maximally to utilize cybernetic science and computer technology within a general systems framework, to build a meta-systemic reality which is now only dimly envisaged. Intelligent and purposeful application of rapidly developing telecommunications and teleprocessing technology should make possible a degree of worldwide value consensus heretofore unrealizable." (Richard F Ericson, "Visions of Cybernetic Organizations", 1972)

"The mind is defined as the sum total of all the programs and the metaprograms of a given human computer, whether or not they are immediately elicitable, detectable, and visibly operational to the self or to others." (John C Lilly "Programming and Metaprogramming in the Human Biocomputer" 2nd Ed., 1972)

"The pseudo approach to uncertainty modeling refers to the use of an uncertainty model instead of using a deterministic model which is actually (or at least theoretically) available. The uncertainty model may be desired because it results in a simpler analysis, because it is too difficult (expensive) to gather all the data necessary for an exact model, or because the exact model is too complex to be included in the computer." (Fred C Scweppe, "Uncertain dynamic systems", 1973)

"Computers make possible an entirely new relationship between theories and models. I have already said that theories are texts. Texts are written in a language. Computer languages are languages too, and theories may be written in them. Indeed, for the present purpose we need not restrict our attention to machine languages or even to the kinds of 'higher-level' languages we have discussed. We may include all languages, specifically also natural languages, that computers may be able to interpret. The point is precisely that computers do interpret texts given to them, in other words, that texts determine computers' behavior. Theories written in the form of computer programs are ordinary theories as seen from one point of view." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"Man is not a machine, [...] although man most certainly processes information, he does not necessarily process it in the way computers do. Computers and men are not species of the same genus. [...] No other organism, and certainly no computer, can be made to confront genuine human problems in human terms. [...] However much intelligence computers may attain, now or in the future, theirs must always be an intelligence alien to genuine human problems and concerns." (Joesph Weizenbaum, Computer Power and Human Reason: From Judgment to Calculation, 1976)

"The connection between a model and a theory is that a model satisfies a theory; that is, a model obeys those laws of behavior that a corresponding theory explicitly states or which may be derived from it. [...] Computers make possible an entirely new relationship between theories and models. [...] A theory written in the form of a computer program is [...] both a theory and, when placed on a computer and run, a model to which the theory applies." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"It is essential to realize that a computer is not a mere 'number cruncher', or supercalculating arithmetic machine, although this is how computers are commonly regarded by people having no familiarity with artificial intelligence. Computers do not crunch numbers; they manipulate symbols. [...] Digital computers originally developed with mathematical problems in mind, are in fact general purpose symbol manipulating machines." (Margaret A Boden, "Minds and mechanisms", 1981)

"The basic idea of cognitive science is that intelligent beings are semantic engines - in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found." (John Haugeland, "Semantic Engines: An introduction to mind design", 1981)

"Computers and robots replace humans in the exercise of mental functions in the same way as mechanical power replaced them in the performance of physical tasks. As time goes on, more and more complex mental functions will be performed by machines. Any worker who now performs his task by following specific instructions can, in principle, be replaced by a machine. This means that the role of humans as the most important factor of production is bound to diminish - in the same way that the role of horses in agricultural production was first diminished and then eliminated by the introduction of tractors."  (Wassily Leontief, National perspective: The definition of problem and opportunity, 1983)

"If arithmetical skill is the measure of intelligence, then computers have been more intelligent than all human beings all along. If the ability to play chess is the measure, then there are computers now in existence that are more intelligent than any but a very few human beings. However, if insight, intuition, creativity, the ability to view a problem as a whole and guess the answer by the “feel” of the situation, is a measure of intelligence, computers are very unintelligent indeed. Nor can we see right now how this deficiency in computers can be easily remedied, since human beings cannot program a computer to be intuitive or creative for the very good reason that we do not know what we ourselves do when we exercise these qualities." (Isaac Asimov, "Machines That Think", 1983)

"The digital-computer field defined computers as machines that manipulated numbers. The great thing was, adherents said, that everything could be encoded into numbers, even instructions. In contrast, scientists in AI [artificial intelligence] saw computers as machines that manipulated symbols. The great thing was, they said, that everything could be encoded into symbols, even numbers." (Allen Newell, "Intellectual Issues in the History of Artificial Intelligence", 1983)

"Computation offers a new means of describing and investigating scientific and mathematical systems. Simulation by computer may be the only way to predict how certain complicated systems evolve." (Stephen Wolfram, "Computer Software in Science and Mathematics", 1984)

"Let us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do." (Donald E Knuth, "Literate Programming", 1984)

"Scientific laws give algorithms, or procedures, for determining how systems behave. The computer program is a medium in which the algorithms can be expressed and applied. Physical objects and mathematical structures can be represented as numbers and symbols in a computer, and a program can be written to manipulate them according to the algorithms. When the computer program is executed, it causes the numbers and symbols to be modified in the way specified by the scientific laws. It thereby allows the consequences of the laws to be deduced." (Stephen Wolfram, "Computer Software in Science and Mathematics", 1984)

"The trouble with an analog computer is that one begins to construct mathematical models which can be treated using an analog computer. In many cases this is not realistic." (Richard E Bellman, "Eye of the Hurricane: An Autobiography", 1984)

"Under pressure from the computer, the question of mind in relation to machine is becoming a central cultural preoccupation." (Sherry Turkle, "The Second Self: Computers and the Human Spirit", 1984)

"A computer is an interpreted automatic formal system - that is to say, a symbol-manipulating machine." (John Haugeland, "Artificial intelligence: The very idea", 1985)

"Computers are the first thing to come along since books that will sit there and interact with you endlessly, without judgment." (Steve Jobs, Playboy, 1985)

"Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped onto the other (the computer)." (George Johnson, Machinery of the Mind: Inside the New Science of Artificial Intelligence, 1986)

"Just like a computer, we must remember things in the order in which entropy increases. This makes the second law of thermodynamics almost trivial. Disorder increases with time because we measure time in the direction in which disorder increases."  (Stephen Hawking, "A Brief History of Time", 1988)

"Cybernetics is simultaneously the most important science of the age and the least recognized and understood. It is neither robotics nor freezing dead people. It is not limited to computer applications and it has as much to say about human interactions as it does about machine intelligence. Today’s cybernetics is at the root of major revolutions in biology, artificial intelligence, neural modeling, psychology, education, and mathematics. At last there is a unifying framework that suspends long-held differences between science and art, and between external reality and internal belief." (Paul Pangaro, "New Order From Old: The Rise of Second-Order Cybernetics and Its Implications for Machine Intelligence", 1988)

"A popular myth says that the invention of the computer diminishes our sense of ourselves, because it shows that rational thought is not special to human beings, but can be carried on by a mere machine. It is a short stop from there to the conclusion that intelligence is mechanical, which many people find to be an affront to all that is most precious and singular about their humanness." (Jeremy Campbell, "The improbable machine", 1989)

"Fuzziness, then, is a concomitant of complexity. This implies that as the complexity of a task, or of a system for performing that task, exceeds a certain threshold, the system must necessarily become fuzzy in nature. Thus, with the rapid increase in the complexity of the information processing tasks which the computers are called upon to perform, we are reaching a point where computers will have to be designed for processing of information in fuzzy form. In fact, it is the capability to manipulate fuzzy concepts that distinguishes human intelligence from the machine intelligence of current generation computers. Without such capability we cannot build machines that can summarize written text, translate well from one natural language to another, or perform many other tasks that humans can do with ease because of their ability to manipulate fuzzy concepts." (Lotfi A Zadeh, "The Birth and Evolution of Fuzzy Logic", 1989)

 "Looking at ourselves from the computer viewpoint, we cannot avoid seeing that natural language is our most important 'programming language'. This means that a vast portion of our knowledge and activity is, for us, best communicated and understood in our natural language. [...] One could say that natural language was our first great original artifact and, since, as we increasingly realize, languages are machines, so natural language, with our brains to run it, was our primal invention of the universal computer. One could say this except for the sneaking suspicion that language isn’t something we invented but something we became, not something we constructed but something in which we created, and recreated, ourselves. (Justin Leiber, "Invitation to cognitive science", 1991)

"The cybernetics phase of cognitive science produced an amazing array of concrete results, in addition to its long-term (often underground) influence: the use of mathematical logic to understand the operation of the nervous system; the invention of information processing machines (as digital computers), thus laying the basis for artificial intelligence; the establishment of the metadiscipline of system theory, which has had an imprint in many branches of science, such as engineering (systems analysis, control theory), biology (regulatory physiology, ecology), social sciences (family therapy, structural anthropology, management, urban studies), and economics (game theory); information theory as a statistical theory of signal and communication channels; the first examples of self-organizing systems. This list is impressive: we tend to consider many of these notions and tools an integrative part of our life […]" (Francisco Varela, "The Embodied Mind", 1991)

"What a computer is to me is the most remarkable tool that we have ever come up with. It’s the equivalent of a bicycle for our minds." (Steve Jobs, "Memory and Imagination: New Pathways to the Library of Congress", 1991)

"A computer terminal is not some clunky old television with a typewriter in front of it. It is an interface where the mind and body can connect with the universe and move bits of it about." (Douglas N Adams, "Mostly Harmless", 1992)

"Finite Nature is a hypothesis that ultimately every quantity of physics, including space and time, will turn out to be discrete and finite; that the amount of information in any small volume of space-time will be finite and equal to one of a small number of possibilities. [...] We take the position that Finite Nature implies that the basic substrate of physics operates in a manner similar to the workings of certain specialized computers called cellular automata." (Edward Fredkin, "A New Cosmogony", PhysComp ’92: Proceedings of the Workshop on Physics and Computation, 1993)

"The insight at the root of artificial intelligence was that these 'bits' (manipulated by computers) could just as well stand as symbols for concepts that the machine would combine by the strict rules of logic or the looser associations of psychology." (Daniel Crevier, "AI: The tumultuous history of the search for artificial intelligence", 1993)

"At first glance the theory of numbers is deprived of any geometricity. But this is actually not the case. At the contemporary stage of development of computers it has become possible to explain to a wide range of readers that visual geometry helps not only to illustrate some abstract situations from the number theory, but sometimes also to solve new problems." (Anatolij Fomenko, "Visual Geometry and Topology", 1994)

"On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood - which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Theodore Roszak, "The Cult of Information", 1994)

"Self-organization refers to the spontaneous formation of patterns and pattern change in open, nonequilibrium systems. […] Self-organization provides a paradigm for behavior and cognition, as well as the structure and function of the nervous system. In contrast to a computer, which requires particular programs to produce particular results, the tendency for self-organization is intrinsic to natural systems under certain conditions." (J A Scott Kelso, "Dynamic Patterns : The Self-organization of Brain and Behavior", 1995)

"Representation is the process of transforming existing problem knowledge to some of the known knowledge-engineering schemes in order to process it by applying knowledge-engineering methods. The result of the representation process is the problem knowledge base in a computer format." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Shearing away detail is the very essence of model building. Whatever else we require, a model must be simpler than the thing modeled. In certain kinds of fiction, a model that is identical with the thing modeled provides an interesting device, but it never happens in reality. Even with virtual reality, which may come close to this literary identity one day, the underlying model obeys laws which have a compact description in the computer - a description that generates the details of the artificial world." (John H Holland, "Emergence" , Philosophica 59, 1997)

"Modelling techniques on powerful computers allow us to simulate the behaviour of complex systems without having to understand them.  We can do with technology what we cannot do with science.  […] The rise of powerful technology is not an unconditional blessing.  We have  to deal with what we do not understand, and that demands new  ways of thinking." (Paul Cilliers,"Complexity and Postmodernism: Understanding Complex Systems", 1998)

"For most problems found in mathematics textbooks, mathematical reasoning is quite useful. But how often do people find textbook problems in real life? At work or in daily life, factors other than strict reasoning are often more important. Sometimes intuition and instinct provide better guides; sometimes computer simulations are more convenient or more reliable; sometimes rules of thumb or back-of-the-envelope estimates are all that is needed." (Lynn A Steen,"Twenty Questions about Mathematical Reasoning", 1999)

"Once a computer achieves human intelligence it will necessarily roar past it." (Ray Kurzweil, "The Age of Spiritual Machines: When Computers Exceed Human Intelligence", 1999)

"The classic example of chaos at work is in the weather. If you could measure the positions and motions of all the atoms in the air at once, you could predict the weather perfectly. But computer simulations show that tiny differences in starting conditions build up over about a week to give wildly different forecasts. So weather predicting will never be any good for forecasts more than a few days ahead, no matter how big (in terms of memory) and fast computers get to be in the future. The only computer that can simulate the weather is the weather; and the only computer that can simulate the Universe is the Universe." (John Gribbin, "The Little Book of Science", 1999)

"Conventional wisdom, fooled by our misleading 'physical intuition', is that the real world is continuous, and that discrete models are necessary evils for approximating the 'real' world, due to the innate discreteness of the digital computer." (Doron Zeilberger, "'Real' Analysis is a Degenerate Case of Discrete Analysis", 2001)

"The randomness of the card-shuffle is of course caused by our lack of knowledge of the precise procedure used to shuffle the cards. But that is outside the chosen system, so in our practical sense it is not admissible. If we were to change the system to include information about the shuffling rule – for example, that it is given by some particular computer code for pseudo-random numbers, starting with a given ‘seed value’ – then the system would look deterministic. Two computers of the same make running the same ‘random shuffle’ program would actually produce the identical sequence of top cards."(Ian Stewart, "Does God Play Dice: The New Mathematics of Chaos", 2002)

"There are endless examples of elaborate structures and apparently complex processes being generated through simple repetitive rules, all of which can be easily simulated on a computer. It is therefore tempting to believe that, because many complex patterns can be generated out of a simple algorithmic rule, all complexity is created in this way." (F David Peat, "From Certainty to Uncertainty", 2002)

"We build models to increase productivity, under the justified assumption that it's cheaper to manipulate the model than the real thing. Models then enable cheaper exploration and reasoning about some universe of discourse. One important application of models is to understand a real, abstract, or hypothetical problem domain that a computer system will reflect. This is done by abstraction, classification, and generalization of subject-matter entities into an appropriate set of classes and their behavior." (Stephen J Mellor, "Executable UML: A Foundation for Model-Driven Architecture", 2002) 

"Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algorithms are more scalable than statisticians ever thought possible. Formal statistical theory is more pervasive than computer scientists had realized." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Computers bootstrap their own offspring, grow so wise and incomprehensible that their communiqués assume the hallmarks of dementia: unfocused and irrelevant to the barely-intelligent creatures left behind. And when your surpassing creations find the answers you asked for, you can't understand their analysis and you can't verify their answers. You have to take their word on faith." (Peter Watts, "Blindsight", 2006)

"In specific cases, we think by applying mental rules, which are similar to rules in computer programs. In most of the cases, however, we reason by constructing, inspecting, and manipulating mental models. These models and the processes that manipulate them are the basis of our competence to reason. In general, it is believed that humans have the competence to perform such inferences error-free. Errors do occur, however, because reasoning performance is limited by capacities of the cognitive system, misunderstanding of the premises, ambiguity of problems, and motivational factors. Moreover, background knowledge can significantly influence our reasoning performance. This influence can either be facilitation or an impedance of the reasoning process." (Carsten Held et al, "Mental Models and the Mind", 2006)

"A neural network is a particular kind of computer program, originally developed to try to mimic the way the human brain works. It is essentially a computer simulation of a complex circuit through which electric current flows." (Keith J Devlin & Gary Lorden, "The Numbers behind NUMB3RS: Solving crime with mathematics", 2007)

"The burgeoning field of computer science has shifted our view of the physical world from that of a collection of interacting material particles to one of a seething network of information. In this way of looking at nature, the laws of physics are a form of software, or algorithm, while the material world - the hardware - plays the role of a gigantic computer." (Paul C W Davies, "Laying Down the Laws", New Scientist, 2007)

"We tend to form mental models that are simpler than reality; so if we create represented models that are simpler than the actual implementation model, we help the user achieve a better understanding. […] Understanding how software actually works always helps someone to use it, but this understanding usually comes at a significant cost. One of the most significant ways in which computers can assist human beings is by putting a simple face on complex processes and situations. As a result, user interfaces that are consistent with users’ mental models are vastly superior to those that are merely reflections of the implementation model." (Alan Cooper et al,  "About Face 3: The Essentials of Interaction Design", 2007)

"An algorithm refers to a successive and finite procedure by which it is possible to solve a certain problem. Algorithms are the operational base for most computer programs. They consist of a series of instructions that, thanks to programmers’ prior knowledge about the essential characteristics of a problem that must be solved, allow a step-by-step path to the solution." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"We evolved to be good at learning and using rules of thumb, not at searching for ultimate causes and making fine distinctions. Still less did we evolve to spin out long chains of calculation that connect fundamental laws to observable consequences. Computers are much better at it!" (Frank Wilczek,"The Lightness of Being – Mass, Ether and the Unification of Forces", 2008) 

"Chess, as a game of zero sum and total information is, theoretically, a game that can be solved. The problem is the immensity of the search tree: the total number of positions surpasses the number of atoms in our galaxy. When there are few pieces on the board, the search space is greatly reduced, and the problem becomes trivial for computers’ calculation capacity." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Generally, these programs fall within the techniques of reinforcement learning and the majority use an algorithm of temporal difference learning. In essence, this computer learning paradigm approximates the future state of the system as a function of the present state. To reach that future state, it uses a neural network that changes the weight of its parameters as it learns." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"From a historical viewpoint, computationalism is a sophisticated version of behaviorism, for it only interpolates the computer program between stimulus and response, and does not regard novel programs as brain creations. [...] The root of computationalism is of course the actual similarity between brains and computers, and correspondingly between natural and artificial intelligence. The two are indeed similar because the artifacts in question have been designed to perform analogs of certain brain functions. And the computationalist program is an example of the strategy of treating similars as identicals." (Mario Bunge, "Matter and Mind: A Philosophical Inquiry", 2010)

"[...] we also distinguish knowledge from information, because some pieces of information, such as questions, orders, and absurdities do not constitute knowledge. And also because computers process information but, since they lack minds, they cannot be said to know anything." (Mario Bunge, "Matter and Mind: A Philosophical Inquiry", 2010)

"System dynamics models have little impact unless they change the way people perceive a situation. A model must help to organize information in a more understandable way. A model should link the past to the present by showing how present conditions arose, and extend the present into persuasive alternative futures under a variety of scenarios determined by policy alternatives. In other words, a system dynamics model, if it is to be effective, must communicate with and modify the prior mental models. Only people's beliefs - that is, their mental models - will determine action. Computer models must relate to and improve mental models if the computer models are to fill an effective role." (Jay W Forrester, "Modeling for What Purpose?", The Systems Thinker Vol. 24 (2), 2013)

"A computer makes calculations quickly and correctly, but doesn’t ask if the calculations are meaningful or sensible. A computer just does what it is told." (Gary Smith, "Standard Deviations", 2014)

"Now think about the prospect of competition from computers instead of competition from human workers. On the supply side, computers are far more different from people than any two people are different from each other: men and machines are good at fundamentally different things. People have intentionality - we form plans and make decisions in complicated situations. We’re less good at making sense of enormous amounts of data. Computers are exactly the opposite: they excel at efficient data processing, but they struggle to make basic judgments that would be simple for any human." (Peter Thiel & Blake Masters, "Zero to One: Notes on Startups, or How to Build the Future", 2014)

"With fast computers and plentiful data, finding statistical significance is trivial. If you look hard enough, it can even be found in tables of random numbers." (Gary Smith, "Standard Deviations", 2014)

"Working an integral or performing a linear regression is something a computer can do quite effectively. Understanding whether the result makes sense - or deciding whether the method is the right one to use in the first place - requires a guiding human hand. When we teach mathematics we are supposed to be explaining how to be that guide. A math course that fails to do so is essentially training the student to be a very slow, buggy version of Microsoft Excel." (Jordan Ellenberg, "How Not to Be Wrong: The Power of Mathematical Thinking", 2014)

"The term data, unlike the related terms facts and evidence, does not connote truth. Data is descriptive, but data can be erroneous. We tend to distinguish data from information. Data is a primitive or atomic state (as in ‘raw data’). It becomes information only when it is presented in context, in a way that informs. This progression from data to information is not the only direction in which the relationship flows, however; information can also be broken down into pieces, stripped of context, and stored as data. This is the case with most of the data that’s stored in computer systems. Data that’s collected and stored directly by machines, such as sensors, becomes information only when it’s reconnected to its context."  (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015) 

"[…] the usefulness of mathematics is by no means limited to finite objects or to those that can be represented with a computer. Mathematical concepts depending on the idea of infinity, like real numbers and differential calculus, are useful models for certain aspects of physical reality." (Alfred S Posamentier & Bernd Thaller, "Numbers: Their tales, types, and treasures", 2015) 

"The human mind isn’t a computer; it cannot progress in an orderly fashion down a list of candidate moves and rank them by a score down to the hundredth of a pawn the way a chess machine does. Even the most disciplined human mind wanders in the heat of competition. This is both a weakness and a strength of human cognition. Sometimes these undisciplined wanderings only weaken your analysis. Other times they lead to inspiration, to beautiful or paradoxical moves that were not on your initial list of candidates." (Garry Kasparov, "Deep Thinking", 2017)

"There are other problems with Big Data. In any large data set, there are bound to be inconsistencies, misclassifications, missing data - in other words, errors, blunders, and possibly lies. These problems with individual items occur in any data set, but they are often hidden in a large mass of numbers even when these numbers are generated out of computer interactions." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

20 November 2005

Database Management: Trivia (Part I: Who's gonna rule the world?)

Database Management
Database Management Series

This morning I started to read an article from Information Week about the future of database administrators, as it seems they will rule the world, at least the world of data. It's not a joke, their role will become more and more important over the next years, why this? 

 1. Databases become bigger and more complex      
The size and complexity of databases is increasing from year to year, same their importance in making faster decision based on current or historical data. The bigger the size and complexity, bigger also the need for ad-hoc or one mouse click reports, and who’s the person who has the knowledge and resources to do this?! 

2. Data mining      
Historical data doesn't resume only to simple reporting, but data mining is becoming more popular, having as purpose the discovery of trends in the business, facilitating somehow predictions for business' future.     

Let’s not forget that the big players on the database market started to offer data mining solutions, incorporated or not in their database platforms, the data mining features are a start in providing new views over the data.      

Big companies will opt probably for data analysts, but for medium-sized and small companies, the role of a data analyst could be easily taken by a database administrator. 

3. The knowledge that matters      
Even if databases are backend for complex UI, in time, if not immediately, the database administrator becomes aware of underlaying overall structure of the database and, why not, leaded by curiosity or business' requirements he becomes aware of the content of the data. Even if is a paradox, the database administrator has sometimes more data knowledge than a manager and the skills to do with data that magic that is important for managers. 

4. Data Cleansing      
More and more companies are becoming aware of the outliers from their data, so data cleansing is becoming a priority, and again the database administrator has an important role in this process. 

5. Database Security      
Database platforms are growing, unfortunately the same thing happens with their security holes and the number of attacks. A skilled database administrator must have the knowledge to overcome these problems and solve the issues as soon as possible. 

6. Training      
Even if can be considered a consequence of the previously enumerated facts, it deserves to be considered separately. Companies are starting to realize how important it to have a skilled database administrator, so probably they will provide the requested training or help the DBA to achieve this. 

 7. On site/offshore compromise      
If in the past years, has been popular the concept of off shoring people, including database administrators; probably soon companies will become aware that reducing direct maintenance costs doesn’t mean a reduction of overall costs, which may contain also indirect costs. The delayed response time generated by communication issues, availability or resources plays an important role in the increase indirect costs, most of the big companies will be forced then to opt for a compromise between on site and offshore.      

In this case the direct benefit for a database administrator is not so obvious, except for the financial benefit there is also an image advantage, even if is disregarded, the image and the fact a person is not anymore isolated is important.

Reviewing the post almost 20 years later with the knowledge of today, I can just smile. Besides data analysts, the DBAs were at that time the closest to the data. Probably, many DBAs walked the new paths created by the various opportunities. Trying to uncover the unknown through the known is a foolish attempt, but it’s the best we can do to prepare us for what comes next.

Created: Nov-2005, Last Reviewed: Mar-2024

19 November 2005

💎SQL Reloaded: Cursors and Lists

I found cursors really useful when the set-based logic provided by a query make it difficult to solve special types of problems (e.g. concatenate in a list the values returned by a select, multiple updates/insert/deletions based on the success or failure of previous logic). Cursors are relatively easy to write though lot of code is redundant from one solution to another. Therefore, I prefer to have the simplest code and then modify it according to new requirements.   

Here's a simple example of a function that returns a list of emails:

--creating the sample table
CREATE TABLE EmailAddresses(ID int, Email varchar(50))

--insert test records
INSERT EmailAddresses
VALUES (1, 'John.Travolta@star.com')
INSERT EmailAddresses
VALUES (2, 'Robert.DeNiro@star.com')
INSERT EmailAddresses
VALUES (3, 'MegRyan@star.com')
INSERT EmailAddresses
VALUES (4, 'Helen.Hunt@star.com')
INSERT EmailAddresses
VALUES (5, 'Jodie.Foster@star.com')

-- creating the function 
CREATE FUNCTION dbo.GetEmails()
RETURNS varchar(1000)
/*
Purpose: returns a list of Emails
Parameters:
Notes:
Sample: SELECT dbo.GetEmails() AS ListEmails
*/
AS
BEGIN
   DECLARE @Email varchar(50)
   DECLARE @Emails varchar(1000)

   SET @Emails = ''

   -- Create Emails Cursor
   DECLARE Emails CURSOR FOR
   SELECT Email
   FROM EmailAddresses
   ORDER BY Email

   OPEN Emails -- Open Emails Cursor

   --fetch first set of records from Emails Cursor
   FETCH NEXT FROM Emails
   INTO @Email

   WHILE @@FETCH_STATUS = 0 --if the fatch was successful
   BEGIN
      SET @Emails = @Emails + @Email + ','

      --fetch next set of records from Emails Cursor
      FETCH NEXT FROM Emails
      INTO @Email
   END

   CLOSE Emails -- close Emails cursor
   DEALLOCATE Emails --deallocate Emails cursor

   --remove the extra comma
   IF Len(@Email)>0
      SET @Emails = Left(@Emails, Len(@Emails)-1)

   RETURN @Emails
END
 
--testing the function
SELECT dbo.GetEmails()

Notes:
The code was tested on SQL Server 2000 till 2017.
The logic from dbo.GetList function can use as source any other table as long the length of the target column is less than 50 characters as the @Result was defined as varchar(50).

Exploring the above idea, what if the content of the query is not known until runtime and all is known is that only one column of data is returned? This could be achieved with the help of a table data type:

--creating the source table
CREATE TABLE Countries(ID int, Country varchar(50))

--insert test records
INSERT Countries
VALUES (1, 'US')
INSERT Countries
VALUES (2, 'UK')
INSERT Countries
VALUES (3, 'Germany')
INSERT Countries
VALUES (4, 'Spain')
INSERT Countries
VALUES (5, 'France')

 
-- creating the function 
CREATE FUNCTION dbo.GetList(
@Query varchar(250))
RETURNS varchar(1000)
/*
Purpose: returns a list of List
Parameters:
Notes:
Sample: SELECT dbo.GetList('SELECT Country FROM Countries ORDER BY Country') AS List
*/

AS
BEGIN
   DECLARE @Result varchar(50)
   DECLARE @List varchar(1000)
   DECLARE @Temp TABLE (Val varchar(10))

   SET @List = ''

   --insert in a table data type the results returned by query
   INSERT @Temp
   EXEC (@Query)

   -- Create List Cursor
   DECLARE List CURSOR FOR
   SELECT Val
   FROM @Temp

   OPEN List -- Open List Cursor

   --fetch first set of records from List Cursor
   FETCH NEXT FROM List
   INTO @Result

   WHILE @@FETCH_STATUS = 0 --if the fatch was successful
   BEGIN
      SET @List = @List + @Result + ','

      --fetch next set of records from List Cursor
      FETCH NEXT FROM List
      INTO @Result
   END

   CLOSE List -- close List cursor
   DEALLOCATE List --deallocate List cursor

   --remove the extra comma
   IF Len(@Result)>0
      SET @List = Left(@List, Len(@List)-1)

   RETURN @List
END

--testing the function 
SELECT dbo.GetList('SELECT Country FROM Countries ORDER BY Country') AS List

Result: List --------------------------
France,Germany,Spain,UK,US (1 row(s) affected)   

Instead of using a query one can use a stored procedure as well. Here's the stored procedure and the example that calls it:

--creating the stored procedure
CREATE PROCEDURE dbo.pListCountries
AS
SELECT Country
FROM Countries
ORDER BY Country
 
--testing the function
SELECT dbo.GetList('dbo.pListCountries') AS List

Result: List
--------------------------
France,Germany,Spain,UK,US (1 row(s) affected)  

Notes:
1) As it seems the above code stopped working between the next editions of SQL Server.
2) Trying to create the dbo.GetList function in a SQL database in Microsoft Fabric leads to the following error message:
"Msg 443, Level 16, State 14, Line 21, Invalid use of a side-effecting operator 'INSERT EXEC' within a function."
Happy coding!
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IT Professional with more than 24 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.