Computers & the Human Brain Timeline

In 1839 weaver Michel-Marie Carquillat, working for the firm of Didier, Petit et Cie, in Lyon, France wove in fine silk a Portrait of Joseph-Marie Jacquard, The image, including caption and Carquillat’s name, taking credit for the weaving, measures 55 x 34 cm.; the full piece of silk including blank margins measures 85 x 66 cm.

This image, of which only about 10 examples are known, was woven on a Jacquard loom using 24,000 Jacquard cards, each of which had over 1000 hole positions. The process of mis en carte, or converting the image details to punched cards for the Jacquard mechanism, for this exceptionally large and detailed image, would have taken several workers many months, as the woven image convincingly portrays superfine elements such as a translucent curtain over glass window panes.

Once all the “programming” was completed, the process of weaving the image with its 24,000 punched cards would have taken more than eight hours, assuming that the weaver was working at the usual Jacquard loom speed of about forty-eight picks per minute, or about 2800 per hour. More than once this woven image was mistaken for an engraved image. The image was produced only to order, most likely in an exceptionally small number of examples. In 2012 the only recorded examples were those in the Metropolitan Museum of Art, the Science Museum, London, The Art Institute of Chicago, and the Computer History Museum, Mountain View, California. The image was the subject of the book by James Essinger entitled, Jacquard's Web. How a Hand Loom led to the Birth of the Information Age (2004).

To Charles Babbage the incredible sophistication of the information processing involved in the mis en carte — what we call programming— of this exceptionally elaborate and beautiful image confirmed the potential of using punched cards for the input, programming, output and storage of information in his design and conception of the first general-purpose programmable computer—the Analytical Engine. The highly aesthetic result also confirmed to Babbage that machines were capable of amazingly complex and subtle processes—processes which might eventually emulate the subtlety of the human mind.

“In June 1836 Babbage opted for punched cards to control the machine [the Analytical Engine]. The principle was openly borrowed from the Jacquard loom, which used a string of punched cards to automatically control the pattern of a weave. In the loom, rods were linked to wire hooks, each of which could lift one of the longitudinal threads strung between the frame. The rods were gathered in a rectangular bundle, and the cards were pressed one at a time against the rod ends. If a hole coincided with a rod, the rod passed through the card and no action was taken. If no hole was present then the card pressed back the rod to activate a hook which lifted the associated thread, allowing the shuttle which carried the cross-thread to pass underneath. The cards were strung together with wire, ribbon or tape hinges, and fan-folded into large stacks to form long sequences. The looms were often massive and the loom operator sat inside the frame, sequencing through the cards one at a time by means of a foot pedal or hand lever. The arrangement of holes on the cards determined the pattern of the weave.

“As well as patterned textiles for ordinary use, the technique was used to produce elaborate and complex images as exhibition pieces. One well-known piece was a shaded portrait of Jacquard seated at table with a small model of his loom. The portrait was woven in fine silk by a firm in Lyon using a Jacquard punched-card loom. . . . Babbage was much taken with the portrait, which is so fine that it is difficult to tell with the naked eye that it is woven rather than engraved. He hung his own copy of the prized portrait in his drawing room and used it to explain his use of the punched cards in his Engine. The delicate shading, crafted shadows and fine resolution of the Jacquard portrait challenged existing notions that machines were incapable of subtlety. Gradations of shading were surely a matter of artistic taste rather than the province of machinery, and the portrait blurred the clear lines between industrial production and the arts. Just as the completed section of the Difference Engine played its role in reconciling science and religion through Babbage’s theory of miracles, the portrait played its part in inviting acceptance for the products of industry in a culture in which aesthetics was regarded as the rightful domain of manual craft and art” (Swade, The Cogwheel Brain. Charles Babbage and the Quest to Build the First Computer [2000] 107-8).

In his book, The Process of Thought Adapted to Words and Language, English surgeon Alfred Smee suggested the possibility of information storage and retrieval by a mechanical logical machine operating analogously to the human mind.

This was an attempt to produce an artificial system of reasoning based upon neurological principles which were then primarily a matter of speculation. The problem was that Smee's hypothetical “electro-biological” machine, built out of mechanical parts, which he conceived in generality but had no way of engineering, or building even in part, might have occupied a space larger than London.

George Parker Bidder, an engineer and one of the most remarkable human computers of all time, published his paper on Mental Calculation. (See Reading 3.1)

In 1912 Spanish civil engineer and mathematician, and Director of the Laboratory of Applied Mechanics at the Ateneo Científico, Literario y Artístico de Madrid, Leonardo Torres y Quevedo built the first decision-making automaton — a chess-playing machine that pit the machine’s rook and king against the king of a human opponent.  Torres's machine, which he called El Ajedrecista (The Chessplayer) used electromagnets under the board to "play" the endgame rook and king against the lone king.

"Well, not precisely play. But the machine could, in a totally unassisted and automated fashion, deliver mate with King and Rook against King. This was possible regardless of the initial position of the pieces on the board. For the sake of simplicity, the algorithm used to calculate the positions didn't always deliver mate in the minimum amount of moves possible, but it did mate the opponent flawlessly every time. The machine, dubbed El Ajedrecista (Spanish for “the chessplayer”), was built in 1912 and made its public debut during the Paris World Fair of 1914, creating great excitement at the time. It used a mechanical arm to make its moves and electrical sensors to detect its opponent's replies." (http://www.chessbase.com/newsprint.asp?newsid=1799, accessed 10-31-2012).

The implications of Torres's machines were not lost on all observers. On November 6, 1915 Scientific American magazine in their Supplement 2079 pp. 296-298 published an illustrated article entitled "Torres and his Remarkable Automatic Devices. He Would Substitute Machinery for the Human Mind."

American neurophysiologist and cybernetician of the University of Illinois at Chicago Warren McCulloch and logician Walter Pitts published “A Logical Calculus of the ideas Imminent in Nervous Activity,” describing the McCulloch - Pitts neuron, the first mathematical model of a neural network.

Building on ideas in  Alan Turing’s “On Computable Numbers”, McCulloch and Pitts's paper provided a way to describe brain functions in abstract terms, and showed that simple elements connected in a neural network can have immense computational power. The paper received little attention until its ideas were applied by John von Neumann, Norbert Wiener, and others. (See Reading 7.4.)

Mathematician and physicist John von Neumann of Princeton  privately circulated copies of his First Draft on a Report on the EDVAC to twenty-four people connected with the EDVAC project. This document, written between February and June 1945, provided the first theoretical description of the basic details of a stored-program computer: what later became known as the Von Neumann architecture.

To avoid the government's security classification, and to avoid engineering problems that might detract from the logical considerations under discussion, Von Neumann avoided mentioning specific hardware. Influenced by Alan Turing and by Warren McCulloch and Walter Pitts, von Neumann patterned the machine to some degree after human thought processes. (See Reading 8.1.)

In June 2009 I was able to download a PDF of the text of von Neumann's report at this link: http://www.virtualtravelog.net/entries/2003-08-TheFirstDraft.pdf.

Vannevar Bush of MIT published an article entitled "As We May Think" in the Atlantic Monthly (Vol. 176, No. 1 [1945] 641-49) describing the Memex, an electromechanical microfilm machine which evolved from his "Rapid Selector" project, capable of making permanent associative links in information. This hypothetical machine foreshadowed aspects of the personal computer and hyperlinks on the Internet. 

Vannevar Bush published a condensed, illustrated version of "As We May Think" in Life magazine, 19, No. 11 (1945) 112-114, 116, 121, 123-24.

Life's editors added the following subtitle: "A Top U.S. Scientist Foresees a Possible Future World in Which Man-Made Machines Will Start to Think." They also replaced the Atlantic Monthly's numbered sections with headings, and added illustrations of the "cyclops camera,' the "supersecretary" and the "Memex" microfilm machine in the form of a desk. This was the first published illustration of what the Memex might look like.

In From Memex to Hypertext: Vannever Bush and the Mind's Machine (1991) James Nyce and Paul Kahn published a version of "As We May Think" that shows the differences between the two 1945 published versions of Bush's essay. Nyce and Kahn also developed a brief animated film showing how the Memex might have operated. You can download it at this link: http://sloan.stanford.edu/MouseSite/Secondary.html

At the initiative of Warren McCulloch, the Macy Conferences occurred in New York to set the foundations for a general science of the workings of the human mind.  They resulted in breakthroughs in systems theory, cybernetics, and what eventually became known as cognitive science.

In 1948 mathematician Norbert Wiener at MIT published Cybernetics or Control and Communication in the Animal and the Machine, a widely circulated and influential book that applied theories of information and communication to both biological systems and machines. Computer-related words with the “cyber” prefix, including "cyberspace," originate from Wiener’s book. Cybernetics was also the first conventionally published book to discuss electronic digital computing. Writing as a mathematician rather than an engineer, Wiener’s discussion was theoretical rather than specific. Strangely the first edition of the book was published in English in Paris at the press of Hermann et Cie. The first American edition was printed offset from the French sheets and issued by John Wiley in New York, also in 1948. I have never seen an edition printed or published in England. 

Independently of Claude Shannon, Wiener conceived of communications engineering as a brand of statistical physics and applied this viewpoint to the concept of information. Wiener's chapter on "Time series, information, and communication" contained the first publication of Wiener's formula describing the probability density of continuous information. This was remarkably close to Shannon's formula dealing with discrete time published in A Mathematical Theory of Communication (1948). Cybernetics also contained a chapter on "Computing machines and the nervous system." This was a theoretical discussion, influenced by McCulloch and Pitts, of differences and similarities between information processing in the electronic computer and the human brain. It contained a discussion of the difference between human memory and the different computer memories then available. Tacked on at the end of Cybernetics were speculations by Wiener about building a chess-playing computer, predating Shannon's first paper on the topic.

Cybernetics is a peculiar, rambling blend of popular and highly technical writing, ranging from history to philosophy, to mathematics, to information and communication theory, to computer science, and to biology. Reflecting the amazingly wide range of the author's interests, it represented an interdisciplinary approach to information systems both in biology and machines. It influenced a generation of scientists working in a wide range of disciplines. In it were the roots of various elements of computer science, which by the mid-1950s had broken off from cybernetics to form their own specialties. Among these separate disciplines were information theory, computer learning, and artificial intelligence.

It is probable that Wiley had Hermann et Cie supervise the typesetting because they specialized in books on mathematics.  Hermann printed the first edition by letterpress; the American edition was printed offset from the French sheets. Perhaps because the typesetting was done in France Wiener did not have the opportunity to read proofs carefully, as the first edition contained many typographical errors which were repeated in the American edition, and which remained uncorrected through the various printings of the American edition until a second edition was finally published by John Wiley and MIT Press in 1961. 

Though the book contained a lot of technical mathematics, and was not written for a popular audience, the first American edition went through at least 5 printings during 1948,  and several later printings, most of which were probably not read in their entirety by purchasers. Sales of Wiener's book were helped by reviews in wide circulation journals such as the review in TIME Magazine on December 27, 1948, entitled "In Man's Image." The reviewer used the word calculator to describe the machines; at this time the word computer was reserved for humans.

"Some modern calculators 'remember' by means of electrical impulses circulating for long periods around closed circuits. One kind of human memory is believed to depend on a similar system: groups of neurons connected in rings. The memory impulses go round & round and are called upon when needed. Some calculators use 'scanning' as in television. So does the brain. In place of the beam of electrons which scans a television tube, many physiologists believe, the brain has 'alpha waves': electrical surges, ten per second, which question the circulating memories.

"By copying the human brain, says Professor Wiener, man is learning how to build better calculating machines. And the more he learns about calculators, the better he understands the brain. The cyberneticists are like explorers pushing into a new country and finding that nature, by constructing the human brain, pioneered there before them.

"Psychotic Calculators. If calculators are like human brains, do they ever go insane? Indeed they do, says Professor Wiener. Certain forms of insanity in the brain are believed to be caused by circulating memories which have got out of hand. Memory impulses (of worry or fear) go round & round, refusing to be suppressed. They invade other neuron circuits and eventually occupy so much nerve tissue that the brain, absorbed in its worry, can think of nothing else.

"The more complicated calculating machines, says Professor Wiener, do this too. An electrical impulse, instead of going to its proper destination and quieting down dutifully, starts circulating lawlessly. It invades distant parts of the mechanism and sets the whole mass of electronic neurons moving in wild oscillations" (http://www.time.com/time/magazine/article/0,9171,886484-2,00.html, accessed 03-05-2009).

Presumably the commercial success of Cybernetics encouraged Wiley to publish Berkeley's Giant Brains, or Machines that Think in 1949.

♦ In October 2012 I offered for sale the copy of the first American printing of Cybernetics that Wiener inscribed to Jerry Wiesner, the head of the laboratory at MIT where Wiener conducted his research. This was the first inscribed copy of the first edition (either the French or American first) that I had ever seen on the market, though the occasional signed copy of the American edition did turn up. Having read our catalogue description of that item, my colleague Arthur Freeman emailed me this story pertinent to Wiener's habit of not inscribing books:

"Norbert, whom I grew up nearby (he visited our converted barn in Belmont, Mass., constantly to play frantic theoretical blackboard math with my father, an economist/statistician at MIT, which my mother, herself a bit better at pure math, would have to explain to him later), was a notorious cheapskate. His wife once persuaded him to invite some colleagues out for a beer at the Oxford Grill in Harvard Square, which he did, and after a fifteen-minute sipping session, he got up to go, and solemnly collected one dime each from each of his guests. So when *Cybernetics* appeared on the shelves of the Harvard Coop Bookstore, my father was surprised and flattered that Norbert wanted him to have an inscribed copy, and together they went to Coop, where Norbert duly picked one out, wrote in it, and carried it to the check-out counter--where he ceremoniously handed it over to my father to pay for. This was a great topic of family folklore. I wonder if Jerry Wiesner paid for his copy too?"

Alan Turing wrote a report for the National Physical Laboratory, Teddington, England, entitled Intelligent Machinery.

In the report Turing stated that a thinking machine should be given the blank mind of an infant instead of an adult mind filled with opinions and ideas. The report contained an early discussion of neural networks. Turing estimated that it would take a battery of programmers fifty years to bring this learning machine from childhood to adult mental maturity. The report was not published until 1968.

At the Hixon Symposium in Pasadena, California, John von Neumann spoke on The General and Logical Theory of Automata. Within this speech von Neumann compared the functions of genes to self-reproducing automata.  This was the first of a series of five works (some posthumous) in which von Neumann attempted to develop a precise mathematical theory allowing comparison of computers and the human brain.

“For instance, it is quite clear that the instruction I is roughly effecting the functions of a gene. It is also clear that the copying mechanism B performs the fundamental act of reproduction, the duplication of the genetic material, which is clearly the fundamental operation in the multiplication of living cells. It is also easy to see how arbitrary alterations of the system E, and in particular of I, can exhibit certain typical traits which appear in connection with mutation, which is lethality as a rule, but with a possibility of continuing reproduction with a modification of traits.” (pp. 30-31).

Molecular biologist Sydney Brenner read this brief discussion of the gene within the context of information in the proceedings of the Hixon Symposium, published in 1951. Later he wrote about in his autobiography:

“The brilliant part of this paper in the Hixon Symposium is his description of what it takes to make a self-reproducing machine. Von Neumann shows that you have to have a mechanism not only of copying the machine, but of copying the information that specifies the machine. So he divided the machine--the automaton as he called it--into three components; the functional part of the automaton, a decoding section which actually takes a tape, reads the instructions and builds the automaton; and a device that takes a copy of this tape and inserts it into the new automaton. . . . I think that because of the cultural differences between most biologists on the one hand, and physicists and mathematicians on the other, it had absolutely no impact at all. Of course I wasn’t smart enough to really see then that this is what DNA and the genetic code was all about. And it is one of the ironies of this entire field that were you to write a history of ideas in the whole of DNA, simply from the documented information as it exists in the literature--that is, a kind of Hegelian history of ideas--you would certainly say that Watson and Crick depended upon von Neumann, because von Neumann essentially tells you how it’s done. But of course no one knew anything about the other. It’s a great paradox to me that in fact this connection was not seen” (Brenner, My Life, 33-36).

Sir Geoffrey Jefferson, a neurological surgeon at Manchester, England, delivered a speech entitled The Mind of Mechanical Man in which he discussed the differences between computers and the human brain. (See Reading 11.1).

In 1950 English mathematician, logician, cryptanalyst, and computer scientist Alan Turing published Computing Machinery and Intelligence, in which he described the “Turing test" for determining whether a machine is “intelligent.”

The cover by Boris Artzybasheff on the January 23, 1950 issue of TIME Magazine depicted the Harvard Mark III partly electronic and partly electromechanical computer as a Naval officer in Artzybasheff's "bizarrely anthropomorphic" style. The caption under the image read, "Mark III. Can Man Build a Superman?" The cover story of the magazine was entitled "The Thinking Machine."

The Mark III, delivered to U.S. Naval Proving Ground at the US Navy base at Dahlgren, Virginia in March 1950, operated at 250 times the speed of the Harvard Mark I (1944). 

Among its interesting elements,  the Time article included an early use of the word computer for machines rather than people. The review of Wiener's Cybernetics published in TIME in December 1948, referred to the machines as calculators.

"What Is Thinking? Do computers think? Some experts say yes, some say no. Both sides are vehement; but all agree that the answer to the question depends on what you mean by thinking.

"The human brain, some computermen explain, thinks by judging present information in the light of past experience. That is roughly what the machines do. They consider figures fed into them (just as information is fed to the human brain by the senses), and measure the figures against information that is "remembered." The machine-radicals ask: 'Isn't this thinking?'

"Their opponents retort that computers are mere tools that do only what they are told. Professor [Howard] Aiken, a leader of the conservatives, admits that the machines show, in rudimentary form at least, all the attributes of human thinking except one: imagination. Aiken cannot define imagination, but he is sure that it exists and that no machine, however clever, is likely to have any."

"Nearly all the computermen are worried about the effect the machines will have on society. But most of them are not so pessimistic as [Norbert] Wiener. Professor Aiken thinks that computers will take over intellectual drudgery as power-driven tools took over spading and reaping. Already the telephone people are installing machines of the computer type that watch the operations of dial exchanges and tot up the bills of subscribers.

"Psychotic Robots. In the larger, "biological" sense, there is room for nervous speculation. Some philosophical worriers suggest that the computers, growing superhumanly intelligent in more & more ways, will develop wills, desires and unpleasant foibles' of their own, as did the famous robots in Capek's R.U.R.

"Professor Wiener says that some computers are already "human" enough to suffer from typical psychiatric troubles. Unruly memories, he says, sometimes spread through a machine as fears and fixations spread through a psychotic human brain. Such psychoses may be cured, says Wiener, by rest (shutting down the machine), by electric shock treatment (increasing the voltage in the tubes), or by lobotomy (disconnecting part of the machine).

"Some practical computermen scoff at such picturesque talk, but others recall odd behavior in their own machines. Robert Seeber of I.B.M. says that his big computer has a very human foible: it hates to wake up in the morning. The operators turn it on, the tubes light up and reach a proper temperature, but the machine is not really awake. A problem sent through its sleepy wits does not get far. Red lights flash, indicating that the machine has made an error. The patient operators try the problem again. This time the machine thinks a little more clearly. At last, after several tries, it is fully awake and willing to think straight.

"Neurotic Exchange. Bell Laboratories' Dr. [Claude] Shannon has a similar story. During World War II, he says, one of the Manhattan dial exchanges (very similar to computers) was overloaded with work. It began to behave queerly, acting with an irrationality that disturbed the company. Flocks of engineers, sent to treat the patient, could find nothing organically wrong. After the war was over, the work load decreased. The ailing exchange recovered and is now entirely normal. Its trouble had been 'functional': like other hard-driven war workers, it had suffered a nervous breakdown" (quotations from http://www.time.com/time/magazine/article/0,9171,858601-7,00.html, accessed 03-05-2009).

The Paris symposium,  Les Machines á calculer et la pensée humaine (Calculating Machines and Human Thought) took place at l'Institut Blaise Pascal.

Unlike the other early computer conferences, no demonstration of a stored-program electronic computer occurred.  Louis Couffignal demonstrated the prototype of his non-stored-program machine.

Hook & Norman, Origins of Cyberspace (2002) no. 526.

Bell Laboratories engineer John Meszar published "Switching Systems as Mechanical Brains," Bell Laboratories Record XXXI (1953) 63-69.

This paper, written in the earliest days of automatic switching systems, when few electronic computers existed, and, for the most part, human telephone operators served as "highly intelligent and versatile switching systems," raised the question of whether certain aspects of human thought are computable and others are not. Meszar argued for "the necessity of divorcing certain mental operations from the concept of thinking," in order to "pave the way for ready acceptance of the viewpoint that automatic systems can accomplish many of the functions of the human brain." 

"We are faced with a basic dilemma; we are forced either to admit the possibility of mechanized thinking, or to restrict increasingly our concept of thinking. However, as is apparent from this article, many of us do not find it hard to make the choice. The choice is to reject the possibility of mechanized thinking but to admit readily the necessity for an orderly declassification of many areas of mental effort from the high level of thinking. Machines will take over such areas, whether we like it or not.

"This declassification of wide areas of mental effort should not dismay any one of us. It is not an important gain for those who are sure that even as machines have displaced muscles, they will also take over the functions of the 'brain.' Neither is it a real loss for those who feel that there is something hallowed about all functions of the human mind. What we are giving up to the machines— some of us gladly, others reluctantly— are the uninteresting flat lands of routine mental chores, tasks that have to be performed according to rigorous rules. The areas we are holding unchallenged are the dominating heights of creative mental effort, which comprise the ability to speculate, to invent, to imagine, to philosophize, the dream better ways for tomorrow than exist today. These are the mental activities for which rigorous rules cannot be formulated— they constitute real thinking, whose mechanization most of us cannot conceive" (p. 69).

Because of failing health, John von Neumann did not finish his last book, The Computer and the Brain, in which he compared the functions of computers and the human brain.

English psychiatrist and cybernetician W[illiam] Ross Ashby wrote of intelligence amplification by machines in his book, Introduction to Cybernetics.

American linguist, philosopher, cognitive scientist, and activist Noam Chomsky published "Three Models for the Description of Language" in IRE Transactions on Information Theory IT-2, 113-24.

In the paper Chomsky introduced two key concepts, the first being “Chomsky’s hierarchy” of syntactic forms, which was widely applied in the construction of artificial computer languages.

“The Chomsky hierarchy places regular (or linear) languages as a subset of the context-free languages, which in turn are embedded within the set of context-sensitive languages also finally residing in the set of unrestricted or recursively enumerable languages. By defining syntax as the set of rules that define the spatial relationships between the symbols of a language, various levels of language can be also described as one-dimensional (regular or linear), two-dimensional (context-free), three-dimensional (context sensitive) and multi-dimensional (unrestricted) relationships. From these beginnings, Chomsky might well be described as the ‘father of formal languages’ ” (Lee, Computer Pioneers [1995] 164). 

The second concept Chomsky presented here was his transformational-generative grammar theory, which attempted to define rules that can generate the infinite number of grammatical (well-formed) sentences possible in a language, and seeks to identify rules (transformations) that govern relations between parts of a sentence, on the assumption that beneath such aspects as word order a fundamental deep structure exists. As Chomsky expressed it in his abstract of the present paper,

"We investigate several conceptions of linguistic structure to determine whether or not they can provide simple and “revealing” grammars that generate all of the sentences of English and only these. We find that no finite-state Markov process [a random process whose future probabilities are determined by its most recent values] that produces symbols with transition from state to state can serve as an English grammar. We formalize the notion of “phrase structure” and show that this gives us a method for describing language which is essentially more powerful. We study the properties of a set of grammatical transformations, showing that the grammar of English is materially simplified if phrase-structure is limited to a kernel of simple sentences from which all other sentences are constructed by repeated transformation, and that this view of linguistic structure gives a certain insight into the use and understanding of language" (p. 113).

Minsky, "A Selected Descriptor-Indexed Bibliography to the Literature on Artificial Intelligence" in Feigenbaum & Feldman eds., Computers and Thought (1963) 453-523, no. 484. Hook & Norman, Origins of Cyberspace (2002) no. 531.

Frank Rosenblatt invented the Perceptron, or Mark I, at Cornell University. Completed in 1960, this was the first computer that could learn new skills by trial and error, using a type of neural network that simulated human thought processes.

"Somewhat the same problem arises in communicating with a machine entity that would arise in communicating with a person of an entirely different language background than your own. A system of logical definition and translation would have to be available. In order that meanings should not be lost, such a system of translation would also need to be precise. We are all familiar with the unhappy results of language translations which are either lacking in precision or where suitable words of equivalent meaning cannot be found. Likewise, translating into a machine language cannot be anything but an exact operation. Machines even more than people must be addressed with clarity and unambiguity, for machines cannot improvise on their own or imagine that about which they have not been specifically informed, as a human might do within reasonable limits of error. . . .

"We must now ascertain how concepts are formulated within the framework of computer language. For analogy, let us first consider the manner in which instructions are usually given to a non-mechanical entity. When we instruct, for example, a human being, we are aided by the fact that the human is usually able to fill in gaps in our instructions through acumen acquired from his own past experiences. It is seldom necessary that instructions be either detailed or literal, although we may have lost sight of this fact.

"The computer in a correlate example is a mechanical 'being' which must be instructed at each and every step. But it can be given a very long list of instructions upon which it can be expected to subsequently act with great speed and accuracy and with untiring repetition. Machine traits are: low comprehension, high retention, extreme reliability, and tremendous speed. The use of superlatives here to describe these traits is not exaggerative. Since speed becomes in practice the equivalent of number, the machine might be, and has sometimes been, equated to legions — an army, if you will — of lowgrade morons whose conceptualization is entirely literal, who remember as long as is necessary or as you desire them to, whose loyalty and subservience is complete, who require no holidays, no spurious incentives, no morale programs, pensions, not even gratitude for past service, and who seemingly never tire of doing elementary repetitive tasks such as typing, accounting, bookkeeping, arithmetic, filling in forms, and the like. In about all these respects the machine may be seen to be the exact opposite of nature's loftiest creature, the intellligent human being, who becomes bored with the petty and repetitious, who is unreliable, who wanders from the task for the most trivial reasons, who gets out of humor, who forgets, who requires constant incentives and rewards, who improvises on his own even when to do so is impertinent to the objectives being undertaken, and who in summary (let's face it) is unsuitable to most forms of industry as the latter are ideally and practically conceived in our times. It becomes apparent in retrospect that the only excuse we might ever have had for employing him to do many of civilization's more literal and repetitious tasks was the absence of something more efficient with which to replace him!

"It is not the purpose of this volume to explore further the ramifications of the above statements of fact. . . ."(Nett & Hetzler, An Introduction to Electronic Data Processing [1959] 86-88).

Douglas Engelbart of the Stanford Research Institute, Menlo Park, California, completed his report, Augmenting Human Intellect: A Conceptual Framework, for the Director of Information Sciences, Air Force Office of Scientific Research. This report led J. C. R. Licklider of DARPA to fund SRI's Augmentation Research Center.

Self-styled "systems humanist" Ted Nelson Theodor Holm Nelson) published "Complex Information Processing: A File Structure for the Complex, the Changing, and the Indeterminate," ACM '65 Proceedings of the 1965 20th national conference, 84-100. 

In this paper Nelson coined the terms hypertext and hypermedia  to refer to features of a computerized information system.  He used the word "link" to refer the logical connections that came to be associated with the word "hyperlink."  

Nelson is also credited with inventing the word hyperlink, though its published origin is less specific:

"The term "hyperlink" was coined in 1965 (or possibly 1964) by Ted Nelson and his assistant Calvin Curtin at the start of Project Xanadu. Nelson had been inspired by "As We May Think", a popular essay by Vannevar Bush. In the essay, Bush described a microfilm-based machine (the Memex) in which one could link any two pages of information into a "trail" of related information, and then scroll back and forth among pages in a trail as if they were on a single microfilm reel. The closest contemporary analogy would be to build a list of bookmarks to topically related Web pages and then allow the user to scroll forward and backward through the list.

In a series of books and articles published from 1964 through 1980, Nelson transposed Bush's concept of automated cross-referencing into the computer context, made it applicable to specific text strings rather than whole pages, generalized it from a local desk-sized machine to a theoretical worldwide computer network, and advocated the creation of such a network. Meanwhile, working independently, a team led by Douglas Engelbart (with Jeff Rulifson as chief programmer) was the first to implement the hyperlink concept for scrolling within a single document (1966), and soon after for connecting between paragraphs within separate documents (1968)" (Wikipedia article on Hyperlink, accessed 08-29-2010). 

Wardrip-Fruin and Montfort, the NewMedia Reader (2003) 133-45.

British mathematician Irving John Good, originally named Isidore Jacob Gudak, published "Speculations Concerning the First Ultraintelligent Machine," Advances in Computers, vol. 6 (1965) 31ff.

This paper, published while Good held research positions at Trinity College, Oxford and at Atlas Computer Laboratory, originated the concept later known as "technological singularity," which anticipates the eventual existence of superhuman intelligence:

"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." 

Stanley Kubrick consulted Good regarding aspects of computing and artificial intelligence when filming 2001: A Space Odyssey (1968), one of whose principal characters was the paranoid HAL 9000 supercomputer.

Computer scientist Jacques J. Vidal of UCLA coined the term brain-computer interface (BCI) in his paper "Toward Direct Brain-Computer Communication," Annual Review of Biophysics and Bioengineering 2: 157–80. doi:10.1146/annurev.bb.02.060173.001105. PMID 4583653.

In 1985 psychologist and cognitive scientist George A. Miller and his team at Princeton began development of WordNet, a lexical database for the English language.

WordNet

"groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. The purpose is twofold: to produce a combination of dictionary and thesaurus that is more intuitively usable, and to support automatic text analysis and artificial intelligence applications" (Wikipedia article on WordNet).

You can browse Wordnet at http://wordnet.princeton.edu/.

WordNet has been used for a number of different purposes in information systems, including word sense disambiguation, information retrieval, automatic text classification, automatic text summarization, and even automatic crossword puzzle generation.

Psychologist, neuroscientist and cognitive scientist James A. Anderson of Brown University, Providence, RI, published "The BSB Model: A simple non-linear autoassociative network," M. Hassoun (Ed), Associative Neural Memories: Theory and Implementation (1993).  Anderson's neural networks were applied to models of human concept formation, decision making, speech perception, and models of vision.

Anderson, J. A., Spoehr, K. T. and Bennett, D.J.  "A study in numerical perversity: Teaching arithmetic to a neural network,"  D.S. Levine and M. Aparicio (Eds.) Neural Networks for Knowledge Representation and Inference, (1994).

Mathematician, computer scientist and science fiction writer Vernor Vinge called the creation of the first ultraintelligent machine the Singularity in Omni magazine. Vinge's follow-up paper entitled "What is the Singularity?" presented at the VISION-21 Symposium sponsored by NASA Lewis Research Center( now NASA John H. Glenn Research Center at Lewis Field) and the Ohio Aerospace Institute, March 30-31, 1993, and  slightly changed in the Winter 1993 issue of Whole Earth Review, contained the oft-quoted statement,

"Within thirty years, we will have the technological means to create superhuman intelligence. Shortly thereafter, the human era will be ended."

"Vinge refines his estimate of the time scales involved, adding, 'I'll be surprised if this event occurs before 2005 or after 2030.

"Vinge continues by predicting that superhuman intelligences, however created, will be able to enhance their own minds faster than the humans that created them. 'When greater-than-human intelligence drives progress," Vinge writes, "that progress will be much more rapid.' This feedback loop of self-improving intelligence, he predicts, will cause large amounts of technological progress within a short period of time" (Wikipedia article on Technological singularity, accessed 05-24-2009).

At the Second Man-Machine World Championship, Chinook, a computer checkers program developed around 1989 at the University of Alberta by a team led by Jonathan Schaeffer, won due to human frailty.

This was the first time that a computer program defeated a human champion in a game competition.  "In 1996 the Guinness Book of World Records recognized Chinook as the first program to win a human world championship" (http://webdocs.cs.ualberta.ca/~chinook/project/, accessed 01-24-2010).

Michael Lesk attempted to calculate "How Much Information is There in the World?" He included information on how much information a human brain may be able to retain.

Gary Kasparov, sometimes regarded as the greatest chess player of all time, resigned 19 moves into Game 6 against Deep Blue, an IBM RS/6000 SP supercomputer capable of calculating 200 million chess positions per second. This was the first time that a human world chess champion lost to a computer under tournament conditions.

The event was broadcast live from IBM's website via a Java viewer, and became the world's record "Net event" at the time.

"The AI crowd, too, was pleased with the result and the attention, but dismayed by the fact that Deep Blue was hardly what their predecessors had imagined decades earlier when they dreamed of creating a machine to defeat the world chess champion. Instead of a computer that thought and played chess like a human, with human creativity and intuition, they got one that played like a machine, systematically evaluating 200 million possible moves on the chess board per second and winning with brute number-crunching force. As Igor Aleksander, a British AI and neural networks pioneer, explained in his 2000 book, How to Build a Mind:  

" 'By the mid-1990s the number of people with some experience of using computers was many orders of magnitude greater than in the 1960s. In the Kasparov defeat they recognized that here was a great triumph for programmers, but not one that may compete with the human intelligence that helps us to lead our lives.'

"It was an impressive achievement, of course, and a human achievement by the members of the IBM team, but Deep Blue was only intelligent the way your programmable alarm clock is intelligent. Not that losing to a $10 million alarm clock made me feel any better" (Gary Kasparov, "The Chess Master and the Computer," The New York Review of Books, 57, February 11, 2010).

Cognitive scientist / entrepreneur Jeffrey Stibel, physicist, psychologist, neural scientist James A. Anderson, and others from the Department of Cognitive and Linguistic Sciences at Brown University created a word sense disambiguator using George A. Miller's WordNet lexical database.

Stibel and others applied this technology in Simpli, "an early search engine that offered disambiguation to search terms. A user could enter in a search term that was ambiguous (e.g., Java) and the search engine would return a list of alternatives (coffee, programming language, island in the South Seas)."

"The technology was rooted in brain science and built by academics to model the way in which the mind stored and utilized language."

"Simpli was sold in 2000 to NetZero. Another company that leveraged the Simpli WordNet technology was purchased by Google and they continue to use the technology for search and advertising under the brand Google AdSense.

"In 2001, there was a buyout of the company and it was merged with another company called Search123. Most of the original members joined the new company. The company was later sold in 2004 to ValueClick, which continues to use the technology and search engine to this day" (Wikipedia article on Simpli, accessed 05-10-2009).

Steven Spielberg directed the science fiction film Minority Report, loosely based on the short story, "The Minority Report" by Philip K. Dick.

"It is set primarily in Washington, D.C. and Northern Virginia in the year 2054, where "Precrime", a specialized police department, apprehends criminals based on foreknowledge provided by three psychics called 'precogs'. The cast includes Tom Cruise as Precrime officer John Anderton, Colin Farrell as Department of Justice agent Danny Witwer, Samantha Morton as the senior precog Agatha, and Max von Sydow as Anderton's superior Lamar Burgess. The film has a distinctive look, featuring desaturated colors that make it almost resemble a black-and-white film, yet the blacks and shadows have a high contrast, resembling film noir."

"Some of the technologies depicted in the film were later developed in the real world – for example, multi-touch interfaces are similar to the glove-controlled interface used by Anderton. Conversely, while arguing against the lack of physical contact in touch screen phones, PC Magazine's Sascha Segan argued in February 2009, 'This is one of the reasons why we don't yet have the famous Minority Report information interface. In that movie, Tom Cruise donned special gloves to interact with an awesome PC interface where you literally grab windows and toss them around the screen. But that interface is impractical without the proper feedback—without actually being able to feel where the edges of the windows are' " (Wikipedia article on Minority Report [film] accessed 05-25-2009).

The two-disc special edition of the film issued on DVD in 2002 contained excellent supplementary material on the special digital effects.

"Current thinking about long-term memory in the cortex is focused on changes in the strengths of connections between neurons. But ongoing structural plasticity in the adult brain, including synapse formation/elimination and remodelling of axons and dendrites, suggests that memory could also depend on learning-induced changes in the cortical ‘wiring diagram’. Given that the cortex is sparsely connected, wiring plasticity could provide a substantial boost in storage capacity, although at a cost of more elaborate biological machinery and slower learning."

"The human brain consists of 10 to the 11th power neurons connected by 10 to the 15 power synapses. This awesome network has a remarkable capacity to translate experiences into vast numbers of memories, some of which can last an entire lifetime. These long-term memories survive surgical anaesthesia and epileptic episodes, and thus must involve modifications of neural circuits, most likely at synapses" (Chklovskii, Mel & K. Svoboda, "Cortical Rewiring and Information Storage," Nature, Vol. 431, 782-88).

The author/editor of this database, Jeremy Norman, issued From Gutenberg to the Internet: A Sourcebook on the History of Information Technology.

This printed book was the first anthology of original publications, reflecting the origins of the various technologies that converged to form the Internet. Each reading is introduced by the editor.

Neuroscientists Olaf Sporns of Indiana University, Giulio Tononi of the University of Wisconsin, and Rolf Köttler of Heinrich Heine University, Düsseldorf, Germany, published "The Human Connectome: A Structural Description of the Human Brain," PLoS Computational Biology I (4). This paper and the PhD thesis of Patric Hagmann from the Université de Lausanne, From diffusion MRI to brain connectomics, coined the term connectome:

In their 2005 paper  Sporns et al. wrote:

"To understand the functioning of a network, one must know its elements and their interconnections. The purpose of this article is to discuss research strategies aimed at a comprehensive structural description of the network of elements and connections forming the human brain. We propose to call this dataset the human 'connectome,' and we argue that it is fundamentally important in cognitive neuroscience and neuropsychology. The connectome will significantly increase our understanding of how functional brain states emerge from their underlying structural substrate, and will provide new mechanistic insights into how brain function is affected if this structural substrate is disrupted."

In his 2005 Ph.D. thesis, From diffusion MRI to brain connectomics, Hagmann wrote:

"It is clear that, like the genome, which is much more than just a juxtaposition of genes, the set of all neuronal connections in the brain is much more than the sum of their individual components. The genome is an entity it-self, as it is from the subtle gene interaction that [life] emerges. In a similar manner, one could consider the brain connectome, set of all neuronal connections, as one single entity, thus emphasizing the fact that the huge brain neuronal communication capacity and computational power critically relies on this subtle and incredibly complex connectivity architecture" (Wikipedia article on Connectome, accessed 12-28-2010).

Jeff W. Lichtman and Joshua R. Sanes, both professors of Molecular & Cellular Biology in the Department of Neurobiology at Harvard Medical School, and colleagues, published "Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system," Nature 450 (7166): 56–62. doi:10.1038/nature06293. This described the visualization process they called "Brainbow."

"Detailed analysis of neuronal network architecture requires the development of new methods. Here we present strategies to visualize synaptic circuits by genetically labelling neurons with multiple, distinct colours. In Brainbow transgenes, Cre/lox recombination is used to create a stochastic choice of expression between three or more fluorescent proteins (XFPs). Integration of tandem Brainbow copies in transgenic mice yielded combinatorial XFP expression, and thus many colours, thereby providing a way to distinguish adjacent neurons and visualize other cellular interactions. As a demonstration, we reconstructed hundreds of neighbouring axons and multiple synaptic contacts in one small volume of a cerebellar lobe exhibiting approximately 90 colours. The expression in some lines also allowed us to map glial territories and follow glial cells and neurons over time in vivo. The ability of the Brainbow system to label uniquely many individual cells within a population may facilitate the analysis of neuronal circuitry on a large scale." (From the Nature abstract).

According to the article referenced below, the entire archived content of the Internet occupied three petabytes (3 x 1000 terabytes) in April 2009. 

It is thought that one human brain may store roughly one petabyte. Though there may be some similarity in storage capacity between the quantity of information on the Internet and information stored in the human brain, quantity is the main point of similarity, since the information is stored and processed in totally different ways by people and computers.

Sandra Aamodt and Sam Wang, "Guest Column: Computers vs. Brains," New York Times Blogs, 03-31-2009.

The Human Connectome Project, a five-year project sponsored by sixteen components of the National Institutes of Health (NIH) split between two consortia of research institutions, was launched as the first of three Grand Challenges of the National Institutes of Health's Blueprint for Neuroscience Research. 

The project was described as "an ambitious effort to map the neural pathways that underlie human brain function. The overarching purpose of the Project is to acquire and share data about the structural and functional connectivity of the human brain. It will greatly advance the capabilities for imaging and analyzing brain connections, resulting in improved sensitivity, resolution, and utility, thereby accelerating progress in the emerging field of human connectomics. Altogether, the Human Connectome Project will lead to major advances in our understanding of what makes us uniquely human and will set the stage for future studies of abnormal brain circuits in many neurological and psychiatric disorders" (http://www.humanconnectome.org/consortia/, accessed 12-28-2010).

At the CeBit exhibition in Hannover, Germany, Christoph Guger of Guger Technologies (g.tech) of Graz, Austria, offered intendiX, "the world's first personal Brain Computer Interface speller" for sale at the retail price of €9000.

"The world’s first patient-ready and commercially available brain computer interface just arrived at CeBIT 2010. The Intendix from Guger Technologies (g*tec) is a system that uses an EEG cap to measure brain activity in order to let you type with your thoughts. Meant to work with those with locked-in syndrome, or other disabilities, Intendix is simple enough to use after just 10 minutes of training. You simply focus on a grid of letters as they flash. When your desired letter lights up, brain activity spikes and Intendix types it. As users master the system, a few will be able to type as quickly as 1 letter a second. Besides typing, it can also trigger alarms, convert text to speech, print, copy, or email" (http://singularityhub.com/2010/03/07/intendix-the-brain-computer-interface-goes-commercial-video/, accessed 03-16-2010).

♦You can watch a video of intendiX in operation entitled Select words by thinking—world record on YouTube at this link: http://www.youtube.com/watch?v=NlUPFpZswJk, accessed 03-16-2010).

Social scientist Martin Hilbert of the University of Southern California (USC) and information scientist Priscilla Lopez published "The World's Technological Capacity to Store, Communicate, and Compute Information," Science, 332, 60-64.

Notably, the authors did not attempt to address the information processing done by human brains—possibly impossible to quantify at the present time, if ever. 

"We estimated the world’s technological capacity to store, communicate, and compute information, tracking 60 analog and digital technologies during the period from 1986 to 2007. In 2007, humankind was able to store 2.9 × 10 20 optimally compressed bytes, communicate almost 2 × 10 21 bytes, and carry out 6.4 × 10 18 instructions per second on general-purpose computers. General-purpose computing capacity grew at an annual rate of 58%. The world’s capacity for bidirectional telecommunication grew at 28% per year, closely followed by the increase in globally stored information (23%). Humankind’s capacity for unidirectional information diffusion through broadcasting channels has experienced comparatively modest annual growth (6%). Telecommunication has been dominated by digital technologies since 1990 (99.9% in digital format in 2007), and the majority of our technological memory has been in digital format since the early 2000s (94% digital in 2007)" (The authors' summary).

"To put our findings in perspective, the 6.4 × 10 18 instructions per second that humankind can carry out on its general-purpose computers in 2007 are in the same ballpark area as the maximum number of nerve impulses executed by one human brain per second (10 17 ). The 2.4 × 10 21 bits stored by humanity in all of its technological devices in 2007 is approaching an order of magnitude of the roughly 10 23 bits stored in the DNA of a human adult, but it is still minuscule as compared with the 10 90 bits stored in the observable universe. However, in contrast to natural information processing, the world’s technological information processing capacities are quickly growing at clearly exponential rates" (Conclusion of the paper).

"Looking at both digital memory and analog devices, the researchers calculate that humankind is able to store at least 295 exabytes of information. (Yes, that's a number with 20 zeroes in it.)

"Put another way, if a single star is a bit of information, that's a galaxy of information for every person in the world. That's 315 times the number of grains of sand in the world. But it's still less than one percent of the information that is stored in all the DNA molecules of a human being. 2002 could be considered the beginning of the digital age, the first year worldwide digital storage capacity overtook total analog capacity. As of 2007, almost 94 percent of our memory is in digital form.

"In 2007, humankind successfully sent 1.9 zettabytes of information through broadcast technology such as televisions and GPS. That's equivalent to every person in the world reading 174 newspapers every day. On two-way communications technology, such as cell phones, humankind shared 65 exabytes of information through telecommunications in 2007, the equivalent of every person in the world communicating the contents of six newspapers every day.

"In 2007, all the general-purpose computers in the world computed 6.4 x 10^18 instructions per second, in the same general order of magnitude as the number of nerve impulses executed by a single human brain. Doing these instructions by hand would take 2,200 times the period since the Big Bang.

"From 1986 to 2007, the period of time examined in the study, worldwide computing capacity grew 58 percent a year, ten times faster than the United States' GDP. Telecommunications grew 28 percent annually, and storage capacity grew 23 percent a year" (http://www.sciencedaily.com/releases/2011/02/110210141219.htm)

IBM's Watson question answering system supercomputer, developed at IBM's T J Watson Research Center, Yorktown Heights, New York, running DeepQA software, defeated the two best human Jeopardy! players, Ken Jennings and Brad Rutter. Watson's hardware consisted of 90 IBM Power 750 Express servers. Each server utilized a 3.5 GHz POWER7 eight-core processor, with four threads per core. The system operatesd with 16 terabytes of RAM.

The success of the machine underlines very significant advances in deep analytics and the ability of a machine to process unstructured data, and especially to intepret and speak natural language.

"Watson is an effort by I.B.M. researchers to advance a set of techniques used to process human language. It provides striking evidence that computing systems will no longer be limited to responding to simple commands. Machines will increasingly be able to pick apart jargon, nuance and even riddles. In attacking the problem of the ambiguity of human language, computer science is now closing in on what researchers refer to as the “Paris Hilton problem” — the ability, for example, to determine whether a query is being made by someone who is trying to reserve a hotel in France, or simply to pass time surfing the Internet.  

"If, as many predict, Watson defeats its human opponents on Wednesday, much will be made of the philosophical consequences of the machine’s achievement. Moreover, the I.B.M. demonstration also foretells profound sociological and economic changes.  

"Traditionally, economists have argued that while new forms of automation may displace jobs in the short run, over longer periods of time economic growth and job creation have continued to outpace any job-killing technologies. For example, over the past century and a half the shift from being a largely agrarian society to one in which less than 1 percent of the United States labor force is in agriculture is frequently cited as evidence of the economy’s ability to reinvent itself.  

"That, however, was before machines began to 'understand' human language. Rapid progress in natural language processing is beginning to lead to a new wave of automation that promises to transform areas of the economy that have until now been untouched by technological change.  

" 'As designers of tools and products and technologies we should think more about these issues,' said Pattie Maes, a computer scientist at the M.I.T. Media Lab. Not only do designers face ethical issues, she argues, but increasingly as skills that were once exclusively human are simulated by machines, their designers are faced with the challenge of rethinking what it means to be human.  

"I.B.M.’s executives have said they intend to commercialize Watson to provide a new class of question-answering systems in business, education and medicine. The repercussions of such technology are unknown, but it is possible, for example, to envision systems that replace not only human experts, but hundreds of thousands of well-paying jobs throughout the economy and around the globe. Virtually any job that now involves answering questions and conducting commercial transactions by telephone will soon be at risk. It is only necessary to consider how quickly A.T.M.’s displaced human bank tellers to have an idea of what could happen" (John Markoff,"A Fight to Win the Future: Computers vs. Humans," http://www.nytimes.com/2011/02/15/science/15essay.html?hp, accessed 02-17-2011).

♦ As a result of this technological triumph, IBM took the unusal step of building a colorful website concerning all aspects of Watson, including numerous embedded videos.

♦ A few of many articles on the match published during or immediately after it included:

John Markoff, "Computer Wins on 'Jeopardy!': Trivial, It's Not," http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html?hpw

Samara Lynn, "Dissecting IBM Watson's Jeopardy! Game," PC Magazine. http://www.pcmag.com/article2/0,2817,2380351,00.asp

John C. Dvorak, "Watson is Creaming the Humans. I Cry Foul," PC Magazine. http://www.pcmag.com/article2/0,2817,2380451,00.asp

Henry Lieberman published a three-part article in MIT Technology Review, "A Worthwhile Contest for Artificial Intelligence" http://www.technologyreview.com/blog/guest/26391/?nlid=4132

♦ An article which discussed the weaknesses of Watson versus a human in Jeopardy! was Greg Lindsay, "How I Beat IBM's Watson at Jeopardy! (3 Times)" http://www.fastcompany.com/1726969/how-i-beat-ibms-watson-at-jeopardy-3-times

♦ An opinion column emphasizing the limitations of Watson compared to the human brain was Stanley Fish, "What Did Watson the Computer Do?" http://opinionator.blogs.nytimes.com/2011/02/21/what-did-watson-the-computer-do/

♦ A critical response to Stanley Fish's column by Sean Dorrance Kelly and Hubert Dreyfus, author of What Computers Can't Do, was published in The New York Times at: http://opinionator.blogs.nytimes.com/2011/02/28/watson-still-cant-think/?nl=opinion&emc=tya1

Betsy Sparrow of Columbia University, Jenny Liu, and Daniel M. Wegner of Harvard University published "Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips," published online 14 July 2011, Science 5 August 2011: Vol. 333 no. 6043 pp. 776-778 DOI: 10.1126/science.1207745.

Abstract: 

"The advent of the Internet, with sophisticated algorithmic search engines, has made accessing information as easy as lifting a finger. No longer do we have to make costly efforts to find the things we want. We can “Google” the old classmate, find articles online, or look up the actor who was on the tip of our tongue. The results of four studies suggest that when faced with difficult questions, people are primed to think about computers and that when people expect to have future access to information, they have lower rates of recall of the information itself and enhanced recall instead for where to access it. The Internet has become a primary form of external or transactive memory, where information is stored collectively outside ourselves."

First two paragraphs (footnotes removed):

"In a development that would have seemed extraordinary just over a decade ago, many of us have constant access to information. If we need to find out the score of a ball game, learn how to perform a complicated statistical test, or simply remember the name of the actress in the classic movie we are viewing, we need only turn to our laptops, tablets, or smartphones and we can find the answers immediately. It has become so commonplace to look up the answer to any question the moment it occurs that it can feel like going through withdrawal when we can’t find out something immediately. We are seldom offline unless by choice, and it is hard to remember how we found information before the Internet became a ubiquitous presence in our lives. The Internet, with its search engines such as Google and databases such as IMDB and the information stored there, has become an external memory source that we can access at any time.

"Storing information externally is nothing particularly novel, even before the advent of computers. In any long-term relationship, a team work environment, or other ongoing group, people typically develop a group or transactive memory (1), a combination of memory stores held directly by individuals and the memory stores they can access because they know someone who knows that information. Like linked computers that can address each other’s memories, people in dyads or groups form transactive memory systems (2, 3). The present research explores whether having online access to search engines, databases, and the like, has become a primary transactive memory source in itself. We investigate whether the Internet has become an external memory system that is primed by the need to acquire information. If asked the question whether there are any countries with only one color in their flag, for example, do we think about flags or immediately think to go online to find out? Our research then tested whether, once information has been accessed, our internal encoding is increased for where the information is to be found rather than for the information itself."

An article by Alexander Bloom published in Harvard Magazine, November 2011 had this to say regarding the research:

"Wegner, the senior author of the study, believes the new findings show that the Internet has become part of a transactive memory source, a method by which our brains compartmentalize information. First hypothesized by Wegner in 1985, transactive memory exists in many forms, as when a husband relies on his wife to remember a relative’s birthday. '[It is] this whole network of memory where you don’t have to remember everything in the world yourself,' he says. 'You just have to remember who knows it.' Now computers and technology as well are becoming virtual extensions of our memory. The idea validates habits already forming in our daily lives. Cell phones have become the primary location for phone numbers. GPS devices in cars remove the need to memorize directions. Wegner points out that we never have to stretch our memories too far to remember the name of an obscure movie actor or the capital of Kyrgyzstan—we just type our questions into Google. 'We become part of the Internet in a way,' he says. 'We become part of the system and we end up trusting it.' "(http://harvardmagazine.com/2011/11/how-the-web-affects-memory, accessed 12-11-2011).

"IBM researchers unveiled a new generation of experimental computer chips designed to emulate the brain’s abilities for perception, action and cognition. The technology could yield many orders of magnitude less power consumption and space than used in today’s computers. 

"In a sharp departure from traditional concepts in designing and building computers, IBM’s first neurosynaptic computing chips recreate the phenomena between spiking neurons and synapses in biological systems, such as the brain, through advanced algorithms and silicon circuitry. Its first two prototype chips have already been fabricated and are currently undergoing testing.  

"Called cognitive computers, systems built with these chips won’t be programmed the same way traditional computers are today. Rather, cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.  

"To do this, IBM is combining principles from nanoscience, neuroscience and supercomputing as part of a multi-year cognitive computing initiative. The company and its university collaborators also announced they have been awarded approximately $21 million in new funding from the Defense Advanced Research Projects Agency (DARPA) for Phase 2 of the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project.

"The goal of SyNAPSE is to create a system that not only analyzes complex information from multiple sensory modalities at once, but also dynamically rewires itself as it interacts with its environment – all while rivaling the brain’s compact size and low power usage. The IBM team has already successfully completed Phases 0 and 1.  

" 'This is a major initiative to move beyond the von Neumann paradigm that has been ruling computer architecture for more than half a century,' said Dharmendra Modha, project leader for IBM Research. 'Future applications of computing will increasingly demand functionality that is not efficiently delivered by the traditional architecture. These chips are another significant step in the evolution of computers from calculators to learning systems, signaling the beginning of a new generation of computers and their applications in business, science and government.' " (http://www-03.ibm.com/press/us/en/pressrelease/35251.wss, accessed 08-21-2011).

On May 16, 2012 Leigh R. Hochberg, Daniel Bacher and team published "Reach and grasp by people with tetraplegia using a neurally controlled robotic arm," Nature 485 (17 May 2012) 372-75.  This was the first published demonstration that humans with severe brain injuries could effectively control a prosthetic arm, using tiny brain implants that transmitted neural signals to a computer.

"Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices Able-bodied monkeys have used a neural interface system to control a robotic arm, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals" (http://www.nature.com/nature/journal/v485/n7398/full/nature11076.html#/ref

"The researchers still have many hurdles to clear before this technology becomes practical in the real world, experts said. The equipment used in the study is bulky, and the movements made with the robot are still crude. And the silicon implants generally break down over time (though the woman in the study has had hers for more than five years, and it is still effective).  

"No one has yet demonstrated an effective wireless system, nor perfected one that could bypass the robotics altogether — transmitting brain signals directly to muscles — in a way that allows for complex movements. 

"In an editorial accompanying the study, Andrew Jackson of the Institute of Neuroscience at Newcastle University wrote that economics might be the largest obstacle: 'It remains to be seen whether a neural-interface system that will be of practical use to patients with diverse clinical needs can become a commercially viable proposition' ' (http://www.nytimes.com/2012/05/17/science/bodies-inert-they-moved-a-robot-with-their-minds.html?hpw, accessed 05-17-2012)

On November 19, 2012 physicists Massimiliano Di Ventra at the University of California, San Diego and Yuriy Pershin at the University of South Carolina, Columbia, outlined an emerging form of computation called memcomputing based on the discovery of nanoscale electronic components that simultaneously store and process information, much like the human brain.

At the heart of this new form of computing are nanodevices called the memristor, memcapacitor and meminductor, fundamental electronic components that store information while respectively operating as resistors, capacitors and inductors. These devices were predicted theoretically in the 1970s but first manufactured in 2008. Because these devices consume very little energy computers using them could approach the energy efficiency of natural systems such as the human brain for the first time.  

"In present day technology, storing and processing of information occur on physically distinct regions of space. Not only does this result in space limitations; it also translates into unwanted delays in retrieving and processing of relevant information. There is, however, a class of two-terminal passive circuit elements with memory, memristive, memcapacitive and meminductive systems – collectively called memelements – that perform both information processing and storing of the initial, intermediate and final computational data on the same physical platform. Importantly, the states of these memelements adjust to input signals and provide analog capabilities unavailable in standard circuit elements, resulting in adaptive circuitry, and providing analog massively-parallel computation. All these features are tantalizingly similar to those encountered in the biological realm, thus offering new opportunities for biologically-inspired computation. Of particular importance is the fact that these memelements emerge naturally in nanoscale systems, and are therefore a consequence and a natural by-product of the continued miniaturization of electronic devices. . . ." (Di Ventra & Pershin, "Memcomputing: a computing paradigm to store and process information on the same physical platform," http://arxiv.org/pdf/1211.4487v1.pdf, accessed 11-22-2012). 

On January 28, 2013 The European Commission announced funding for The Human Brain Project.

From the press release:

"The goal of the Human Brain Project is to pull together all our existing knowledge about the human brain and to reconstruct the brain, piece by piece, in supercomputer-based models and simulations. The models offer the prospect of a new understanding of the human brain and its diseases and of completely new computing and robotic technologies. On January 28, the European Commission supported this vision, announcing that it has selected the HBP as one of two projects to be funded through the new FET Flagship Program.

''Federating more than 80 European and international research institutions, the Human Brain Project is planned to last ten years (2013-2023). The cost is estimated at 1.19 billion euros. The project will also associate some important North American and Japanese partners. It will be coordinated at the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, by neuroscientist Henry Markram with co-directors Karlheinz Meier of Heidelberg University, Germany, and Richard Frackowiak of Centre Hospitalier Universitaire Vaudois (CHUV) and the University of Lausanne (UNIL).

The Swiss Contribution

"Switzerland plays a vital role in the Human Brain Project. Henry Markram and his team at EPFL will coordinate the project and will also be responsible for the development and operation of the project’s Brain Simulation Platform. Richard Frackowiak and his team will be in charge of the project’s medical informatics platform; the Swiss Supercomputing Centre in Lugano will provide essential supercomputing facilities. Many other Swiss groups are also contributing to the project. Through the ETH Board, the Swiss Federal Government has allocated 75 million CHF (approximately 60 million Euros) for the period 2013-2017, to support the efforts of both Henry Markram’s laboratory at EPFL and the Swiss Supercomputing Center in Lugano. The Canton of Vaud will give 35 million CHF (28 million Euros) to build a new facility called Neuropolis for in silico life science, and centered around the Human Brain Project. This building will also be supported by the Swiss Confederation, the Rolex Group and third-party sponsors.

"The selection of the Human Brain Project as a FET Flagship is the result of more than three years of preparation and a rigorous and severe evaluation by a large panel of independent, high profile scientists, chosen by the European Commission. In the coming months, the partners will negotiate a detailed agreement with the Community for the initial first two and a half year ramp-up phase (2013-mid 2016). The project will begin work in the closing months of 2013."

On April 11, 2013 ScientificAmerican.com, the online version of Scientific American magazine, published "The Reading Brain in the Digital Age: The Science of Paper versus Screens" by Ferris Jabr. From this I quote a portion:

"Before 1992 most studies concluded that people read slower, less accurately and less comprehensively on screens than on paper. Studies published since the early 1990s, however, have produced more inconsistent results: a slight majority has confirmed earlier conclusions, but almost as many have found few significant differences in reading speed or comprehension between paper and screens. And recent surveys suggest that although most people still prefer paper—especially when reading intensively—attitudes are changing as tablets and e-reading technology improve and reading digital books for facts and fun becomes more common. In the U.S., e-books currently make up between 15 and 20 percent of all trade book sales.

"Even so, evidence from laboratory experiments, polls and consumer reports indicates that modern screens and e-readers fail to adequately recreate certain tactile experiences of reading on paper that many people miss and, more importantly, prevent people from navigating long texts in an intuitive and satisfying way. In turn, such navigational difficulties may subtly inhibit reading comprehension. Compared with paper, screens may also drain more of our mental resources while we are reading and make it a little harder to remember what we read when we are done. A parallel line of research focuses on people's attitudes toward different kinds of media. Whether they realize it or not, many people approach computers and tablets with a state of mind less conducive to learning than the one they bring to paper.

" 'There is physicality in reading,' says developmental psychologist and cognitive scientist Maryanne Wolf of Tufts University, 'maybe even more than we want to think about as we lurch into digital reading—as we move forward perhaps with too little reflection. I would like to preserve the absolute best of older forms, but know when to use the new.' "