Myth of Being Digital
Examining the Artificial Promise of Encoded Knowledge
A thesis exploring the age-old pursuit of knowledge in the electronic digital era – by Carson.
Chapter Bullets Explained
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Throughout the thesis you will notice four dots ○●○● at the start of each chapter. This is a digital binary encoding of the chapter number. If you wish you can play with the encoding yourself using the tool below.
How Fast is Your Machine?
Ada Lovelace, became the worlds first programmer when she wrote instructions for the Analytical Engine, created by Charles Babbage. Lovelace must have had a world of patience since the machine took three minutes to ‘multiply two twenty-digit numbers’.
Below is a simple program created for the purposes of this thesis that lets you compare your digital machine to the Analytical Engine.
Note: This code avoids FPU double or long data-type operations because 64-bit systems cannot hold full twenty-digit values, let alone fourty-digit answers. Instead, the software arbitrary BigInt data-type is used to achieve the same perfect precision as the Analytical Machine. This test also compares your machine to the PDP-1 and OLPC.
What is Digital Data?
At the lowest level digital information is stored in a 0 and 1 binary format. In this encoded symbolic state there is no indication of media: Is this audio, image, text?
Message, and meaning, are effectively destroyed in the process of digitization. In its stored state digital represents everything and yet nothing at all. And so, a machine does not read or recall information, because no message exists. The machine must fabricate a message every time data is accessed.
The inquisitive reader can us the tool below to explore their own media. You can upload anything you want.
Drag & drop file
or click to browse files
Maximum file size: 100MB
The Ethics of Observation and the Violence of Data
θ
Revolution is without Resolution
Qubits are like classical bits in that they are both conceptual units of information. Just as a bit can be physically represented by any system with two distinct states—like a transistor’s switch, a finger being up or down, or the position of a bead on an abacus—a qubit can be represented by any physical system that possesses the quantum property of superposition, such as an electron’s spin or a photon’s polarization.
What’s interesting is how this connects to the nature of physical properties we perceive, such as rotation, which are continuous—or analogue—in the sense that they have no resolution. This quantum property is traditionally visualized using the Bloch sphere I have provided below. The state of a qubit, like our example photon, can point to an infinite number of possible locations across the surface of this sphere. The surface is a manifold, meaning perfectly smooth with no discrete steps between one location and another. Much like the hand-drawn line discussed in the thesis.
When humans measure, quantum phenomena, or the continuum, collapses yielding a binary result of 0 or 1. The seemingly simple act of measurement, simply observing the phenomena, is a violent event in the quantum world. It forces the qubit to give up its superposition and “snap” to a definite classical binary answer.
This thesis uses quantum principles to argue that ‘data’ does not simply exist; data is created in the act of gathering. This emphasizes that data not only filters according to bias but that measurement itself is a destructive act, destroying information, and with it possibilities, carried in the analogue. The same applies to measuring a cultures embodied beliefs, collecting data on a culture demands those affected effectively violate their rituals which cannot be measured- and so observation too is a profound act of violence.
It is important that the reader keep in mind that perception of that act matters. For example, a doctor measuring a patient’s vital signs also collapses a continuum (blood pressure, heart rate) into discrete numbers, but the ethical context (healing vs. extraction) matters.
Bit Rot
How transistors work, and when they don’t
Left to the vagaries of environmental conditions, without maintenance or power, transistors eventually lose the charge necessary to maintain a 0 bit value (by default, without charge a transistor is 1). This non-volatile memory, called NAND (“Not-AND”) flash memory, is found in most storage devices today. Memory registers store this data by trapping electrical charge in a series of transistors (a 64-bit system would have memory registers that hold 64-bits).
Cells gradually leak charge, similar to a battery. This accelerates when heat is introduced, and when the wear & tear of ‘Program/Erase cycles’ increase. If too much charge is lost, the bits become ambiguous, difficult to distinguish, and the data is then consider corrupt. This is what is referred to as bit rot. When enough charge has leaked the transistor can no longer perform its main function, to hold a value (0 state in the case of Single Level Cell transistors). When this happens data is considered corrupt. To prevent this, a drive’s controller constantly works in the background, performing “housekeeping”, to recharge each cell long before they can corrupt. This housekeeping can only occur when power and resources are available, normally when a computer is turned on. Ultimately, if left turned off for long enough (10+- years for TLC drives), drives will lose their data.
You can use the tool below to charge and erase the transistor.
Transistors are made up of the following core components:
- Control Gate – The system applies either a positive voltage or negative voltage to add or remove charge to the cell in the floating gate.
- Floating Gate – inside this gate is a cell which holds charge much like a battery would.
- Substrate – this acts as a base to and from which electrons flow.
- Tunnel and Gate oxide – these act as an insulation layer that prevent the electron charges from flowing freely between the Floating gate and substrate when the Control Gate is NOT active.
- Source and Drain – these sit on either side of the substrate and are what allow the electrons to flow in and out of.
Eliza
A conversational machine from 1966
In 1966 Joseph Weizenbaum created a program; Written in MAD-SLIP for the IBM 7094 while he was part of the MIT ProjectMAC group. One of the few AI researchers in history to acknowledge computers can only be made to ‘Appear Intelligent’, that at best an intelligent machine is a ‘powerful illusion’.
The following Eliza simulations based on one of the original program modules created to mimic the role of a Rogerian psychotherapist. It became known as ‘DOCTOR’.
Eliza – mock Rogerian psychotherapist. Original program by Joseph Weizenbaum in MAD-SLIP for “Project MAC” at MIT: Weizenbaum, Joseph “ELIZA – A Computer Program For the Study of Natural Language Communication Between Man and Machine” in: Communications of the ACM; Volume 9 , Issue 1 (January 1966): p 36-45.
This JavaScript port was originally developed by Norbert Landsteiner 2005; http://www.masserk.at
Quotes
&
Conversations
The following serves as a lookup table of quotes that the reader of this thesis can delve further into:
Chapter 1
1.
“In projecting language as a rule-governed manipulation of symbols, we all too easily dismiss the concerns of human meaning that make up the humanities and indeed of any socially grounded understanding of human language and action. In projecting language back as the model for thought, we lose sight of the tacit embodied understanding that undergirds our intelligence. Through a broader understanding, we can recapture our view of these lost dimensions and in the process better understand both ourselves and our machines.” (Winograd 1991: 220)
2.
“In an oral culture, knowledge, once acquired, had to be constantly repeated or it would be lost: fixed, formulaic thought patterns were essential for wisdom and effective administration… But, by Plato’s day (427?–347 BC) a change had set in: the Greeks had at long last effectively interiorized writing— something which took several centuries after the development of the Greek alphabet around 720–700 BC” (Havelock 1963, p. 49, citing Rhys Carpenter)…
“The new way to store knowledge was not in mnemonic formulas but in the written text. This freed the mind for more original, more abstract thought … For Plato expresses serious reservations in the Phaedrus and his Seventh Letter about writing, as a mechanical, inhuman way of processing knowledge, unresponsive to questions and destructive of memory, although, as we now know, the philosophical thinking Plato fought for depended entirely on writing.” (Ong and Hartley 2012: 24–25, emphasis added)
3.
“Parry theorized that the Iliad and Odyssey were the collective creations of many generations of bards working not individually but within a poetic tradition. This tradition, as Parry described it, developed its own diction, a specialized poetic language consisting of substitutable “formulas” that enabled a poet to make his verses extemporaneously without having to depend on rote memorization (3). In much the same way as a mathematician manipulates algebraic formulas or a computer programmer tailors various algorithms to suit specific tasks, the ancient Greek poet was seen as employing the generalized idiomatic language to compose an individual poem (or version of a poem) on the model given him by tradition. Parry’s theory accounted in a startling new way for textual problems that had remained unsolved since the time of the Alexandrians.” (Foley 1986: 3, emphasis added)
“The formula in the Homeric poems may be defined as a group of words which is regularly employed under the same metrical conditions to express a given essential idea.” (Parry 1971: 272, emphasis added)
4.
“We acquire a multitude of beliefs, attitudes, preferences, knowledge, skills, customs, and norms from other members of our species culturally, through social learning processes such as imitation, teaching, and language. This culturally acquired information affects our behaviour in quite fundamental ways.” (Mesoudi 2011: 1, emphasis added)
5.
“The Telephone game has been used by psychologists for decades to study cultural transmission, in the form of what is known as the transmission chain method. In the psychologists’ version, some carefully prepared stimulus material, such as a written story, is presented to the first participant in the chain. The stimulus is then removed and the participant writes out the original from memory. The resulting written re-call of the first participant is then given to the second participant in the chain to read; it is again withdrawn and the second participant writes this out from memory. Their output is given to the third participant, and so on down the chain. The experimenter then analyses the content of each step (or “cultural generation”) in the chain in order to measure systematic distortions and biases in cultural evolution.” (Mesoudi 2011: 140, emphasis added)
6.
“information is frequently lost and distorted in systematic ways. Random elements (such as “purple monkey dish-washer”) are not added to existing faithfully transmitted messages; instead, messages are transformed according to preexisting expectations and prejudices.’… ‘The findings of transmission chain studies, then, suggest that content biases and guided variation may play potentially important roles in human cultural evolution.” (Mesoudi 2011: 141, emphasis added)
7.
“Content biases (also referred to as ‘direct’ biases) lead individuals towards preferentially copying or learning a behaviour or trait based on its inherent characteristics, rather than the characteristics of the model or the context in which the transmission takes place.” (Stubbersfield 2022: 41)
“Cultural evolution theory proposes that information transmitted through social learning is not trans- mitted indiscriminately but is instead biased by heuristics and mechanisms which increase the likelihood that individuals will copy particular cultural traits based on their inherent properties (content biases) and copy the cultural traits of particular models, or under particular circumstances (context biases). Recent research suggests that content biases are as important, or more important, than context biases in the selection and faithful transmission of cultural traits.” (Stubbersfield 2022: 41)
Chapter 2
8.
“It is our intellectual technologies that have the greatest and most lasting power over what and how we think. They are our most intimate tools, the ones we use for self-expression, for shaping personal and public identity, and for cultivating relations with others. What Nietzsche sensed as he typed his words onto the paper clamped in his writing ball—that the tools we use to write, read, and otherwise manipulate information work on our minds even as our minds work with them—is a central theme of intellectual and cultural history.” (Carr 2011: 35, emphasis added)
9.
“Nietzsche, as proud of the publication of his mechanization as any philosopher, changed from arguments to aphorisms, from thoughts to puns, from rhetoric to telegram style. That is precisely what is meant by the sentence that our writing tools are also working on our thoughts. Malling Hansen’s writing ball, with its operating difficulties, made Nietzsche into a laconic.” (Kittler 2006: 203, emphasis added)
10.
“Nothing seems more possible to me than that people some day will come to the definite opinion that there is no copy in the … nervous system which corresponds to a particular thought, or a particular idea, or memory. —Ludwig Wittgenstein ‘Last writings on the philosophy of psychology’” (Wittgenstein et al. 1996, emphasis added) (Culbertson 2023)
11.
Normally one says inscriptions are transportable or transmissible, but perhaps a more appropriate term to describe their circulation is transmigration. Just as the soul, conceived as a disembodied entity, is to move from one corporeal body to another in transmigration, so the abstract form of the inscription is counted as moving from one incorporation to another, despite differences between material instantiations .… This presumption of embodiment appears to be giving way with the spread of new communication technologies; faxes, for example, are increasingly accepted as legally binding documents. Even here, however, there continues to be some whiff of embodiment, for a fax occupies a different legal position than email, which cannot carry a signature linked with embodiment… Inscription, then, is crucially important to the transformation of embodied reality into abstract forms. (Hayles 1999b: 18)
12.
Moving from analogue resemblance to coding arrangements opens possibilities for leveraging unthinkable with analogue resemblance, which by virtue of being a resemblance must preserve proportional similarity. The difference can be illustrated with a typewriter and computer word processing program. To make a letter darker on a typewriter, proportionately more ink and/or pressure must be applied for each letter, whereas to make a screen of letters bold, a single keystroke will suffice. Coding arrangements have powerful transformative properties precisely because they have been freed from the morphological resemblances of analogue technologies. (Hayles 1999b: 19)
The power of codes should not, however, obscure the fact that the bold letters on screen also have a material basis; at the point where the embodied materiality of electronic polarities is transformed into binary code, analogue resemblance necessarily reenters the picture. (Hayles 1999b: 19)
13.
We must not believe those, who today with philosophical bearing and a tone of superiority prophesy the downfall of culture and accept the ignorabimus. For us there is no ignorabimus, and in my opinion even none whatever in natural science. In place of the foolish ignorabimus let stand our slogan:
‘Wir müssen wissen — wir werden wissen’
(’We must know — we will know’)
(Hilbert 1930) (English translation of David Hilbert’s 1930 Radio Address)
14.
It is no accident that in our age linguists are coming to regard human language very much as computer specialists regard their codes. We began by emphasizing the differences between natural language and computer codes, but to many the similarities now seem more significant. Modern linguistics is by no means an off-spring of the computer; rather, both linguistics and computer language are children of their day, working synergetically to change the culture that gave birth to them. The work began with the structural linguists of the forties and fifties, who analyzed English hierarchically (from words to phrases to clauses) and by mechanical procedures they hoped would eliminate altogether the question of meaning. (Bolter 1984: 147, emphasis added)
15.
Noam Chomsky’s book Syntactic Structures appeared in 1957 (the same year as the release of FORTRAN), with its proposal for ‘transformational-generative’ grammar. Chomsky’s approach and others like it have been vastly influential in the English-speaking world…The trick is to identify rules that allow the production of legitimate sentences and to use these rules to describe as much of recognized English as possible. These rules are often written in abstract symbols and sometimes closely resemble symbolic logic. (Bolter 1984: 147) [see note 4 on FORTRAN]
15b.
This disparity arises from a fundamental difference between the coding strategy employed in language and the coding strategy employed in the nervous system. Language employs a set of discrete names, decidedly finite in number , and it falls back on lame metaphor when the subtlety of the sensory situation outruns the standard names, which regularly it does. By contrast , the nervous system employs a combinatorial system of representation , one that permits a fine -grained analysis of each of the sensory subtleties it encounters . This allows us to discriminate and recognize far more than we can typically express in words. (Churchland 1995: 21)
16.
Much has been written about what computers cannot do. From Descartes and Leibniz in the seventeenth century, to my colleagues Dreyfus and Searle and Penrose in the closing decades of the twentieth, computation has repeatedly been judged inadequate to account for the full range of human cognition. Not all of this writing has been wasted, since there are indeed types and classes and styles of computers that Can’t. But this book is not about them. This book is about the Computer that Could. Let us turn finally to examine how it Can. (Churchland 1995: 19)
We are now in a position to explain how our vivid sensory experience arises in the sensory cortex of our brains: how the smell of baking bread, the sound of an oboe, the taste of a peach, and the color of a sunrise are all embodied in a vast chorus of neural activity. We now have the resources to explain how the motor cortex, the cerebellum, and the spinal cord conduct an orchestra of muscles to perform the cheetah’s dash, the falcon’s strike, or the ballerina’s dying swan. More centrally, we can now understand how the infant brain slowly develops a framework of concepts with which to comprehend the world. And we can see how the matured brain deploys that framework almost instantaneously: to recognize similarities, to grasp analogies, and to anticipate both the immediate and the distant future. (Churchland 1995: 3)
17.
Wiener’s work with servomechanisms to aim antiaircraft guns and to do much else besides had convinced him that forms of life could be understood entirely in mechanical terms; they could not be understood as Cartesian clockwork, which was too crude and rigid, but rather as electromechanical or even electronic devices …. He wanted machines to imitate the man who acts in the world as well as the man who reasons, to explain muscle action in terms of feedback loops as well as chess in terms of a digital program. He relied on hardware devices for his metaphor of man and demanded a close correspondence between man and the machine made to imitate him. Vacuum tubes were meant to be a physical substitute for neurons, servomechanisms for nerves acting upon muscles… (Bolter 1984: 193)
18.
…Those following Wiener’s approach spoke of creating artificial brain cells and neural networks and allowing the machine to learn as a baby was presumed to do…In general, Wiener’s preferences gave way to others in the 1950s, as computer hardware and especially programming languages became more sophisticated… Specialists more or less gave up the idea of building a machine whose components would mirror the elements of the human brain; they no longer demanded a literal correspondence between man and machine. (Bolter 1984: 193, emphasis added)
The cybernetics researchers, whose self-contained experiments were often animal-like and mobile, began their investigation of nervous systems by attempting to duplicate the sensorimotor abilities of animals. The artificial intelligence community ignored this approach in their early work and instead set their sights directly on the intellectual acme of human thought, in experiments running on large, stationary mainframe computers dedicated to mechanizing pure reasoning…While cybernetics scratched the underside of real intelligence, artificial intelligence scratched the topside. The interior bulk of the problem remains inviolate. (Moravec 1988: 16, emphasis added)
The human body with its five senses is fundamental to the human capacity for intelligent thought… misses the point. The artificial intelligence specialist is not interested in imitating the whole man. The very reason he regards intelligence (rational “problem solving”) as fundamental is that such intelligence corresponds to the new and compelling qualities of electronic technology. Today, as before, technology determines what part of the man will be imitated. (Bolter 1984: 213, emphasis added)
19.
As an instrument for organizing large quantities of information, or performing extremely complex symbolic operations beyond human capabilities within a normal lifespan, the computer is an invaluable adjunct to the brain, though not a substitute for it. Since the computer is limited to handling only so much experience as can be abstracted in symbolic or numerical form, it is incapable of dealing directly, as organisms must, with the steady influx of concrete, unprogrammable experience. With respect to such experience, the computer is necessarily always out of date. (Mumford 1970: 203, emphasis added)
The computer’s lack of other human dimensions is of course no handicap to it as a labor-saving device, whether in astronomy or bookkeeping: but such creativity as the computer may simulate is always in the first place a contribution of the minds that formulate the program… Those who are so fascinated by the computer’s lifelike feats—it plays chess! it writes ‘poetry’!—that they would turn it into the voice of omniscience, betray how little understanding they have of either themselves, their mechanical-electronic agents, or the potentialities of life. A city of even three hundred thousand people, ten per cent of whom have access to regional or national libraries with as few as a million volumes, would actually have a total capacity for storing, transforming, integrating, and not least applying both symbolic information and concrete experience that no computer will ever rival. (Mumford 1970: 203)
20.
Honesty should lead us to concede that we understand little more about creativity than Harte did in the sixteenth century. Nor do we even know whether the essential questions should fall within the scope of human understanding or whether they are what David Hume took to be nature’s ultimate secrets consigned to that obscurity in which they ever did and ever will remain… we are, after all, organic creatures. We’re not angels, so that all of our capacities should be expected to have limits, which in fact are implicit in the fact that they have scope. So our biological endowment permits us to grow arms and legs, but for exactly the same reason it prevents us from growing wings, and the logic carries over to the cognitive domain. (Chomsky 2016)[00:15:25:16 – 00:15:57:08]
Philosopher logician, Charles Sanders Peirce, he argued persuasively, I think that our reasoning in science and ordinary life is guided by what he called abductive principles, which crucially limit the range of the hypotheses that we are capable of entertaining. Otherwise, he argues… that learning and discovery would be impossible. These very same principles render other hypotheses either inaccessible or so remote in some accessibility hierarchy that they cannot be entertained. In fact, there is very little reason to believe that we have even the intellectual resources to pose the correct questions, let alone to find the answers. (Chomsky 2016)[00:15:57 – 00:17:17]
In fact, it’s not even clear that we know how to formulate the questions properly. (Chomsky 2016) [00:40:39:04 – 00:41:09:16]
21.
Whereas the anthropologist’s schema will show fields, houses, and calendars arranged according to such dualities as hot and cold, male and female, for the Kabyle this knowledge exists not as abstractions but as patterns of daily life learned by practicing actions until they become habitual. Abstraction thus not only affects how one describes learning but also changes the account of what is learned. (Hayles 1999a: 202)
Every group entrusts to bodily automatisms those principles most basic to it and most indispensable to its conservation. In societies which lack any other recording and objectifying instrument, inherited knowledge can survive only in its embodied state. Among other consequences, it follows that it is never detached from the body which bears it and which – as Plato noted – can deliver it only at the price of a sort of gymnastics intended to evoke it: mimesis. The body is thus continuously mingled with all the knowledge it reproduces, which can never have the objectivity and distance stemming from objectification in writing. (Bourdieu and Bourdieu 2010: 218)
22.
While I wanted thus to think that everything was false, it necessarily had to be the case that I, who was thinking this, was something. And noticing that this truth—I think, therefore I am—was so firm and so assured that all the most extravagant suppositions of the skeptics were incapable of shaking it, I judged that I could accept it without scruple as the first principle of the philosophy I was seeking. (Descartes et al. 1998: 18) [Discourse on Method was originally published in 1637]
[There are variations on this which translate to – I am thinking, therefore I exist – see note 5]
The transformation begun in theory by Copernicus, Kepler, and Galileo was carried further by Rene Descartes, for he coupled the new world picture to the two new phenomena that gave it immense authority: the behavior of clockwork automatons and the claims of monarchical absolutism. He proved to his own satisfaction that all the manifestations of life could be explained on a purely mechanical basis, and that except in the case of man, organism and mechanism were interchangeable terms. Descartes’ ‘Discourse on Method’ stands as a landmark in the history of Western thought: through its elegant style and its fusion of mathematical and mechanical modes of reasoning, it left a permanent imprint on later scientific formulations. (Mumford 1970: 79)
23.
‘I think, therefore I am.’ This equation of thought with being removed it from all qualifying limitations: thinking itself tended to become unconditional and absolute: in fact, the sole imperative demand of existence. In order to reach this point Descartes forgot that before he uttered these words, ‘I think … ’, he needed the cooperation of countless fellow-beings, extending back to his own knowledge as far as the thousands of years that Biblical history recorded. Beyond that, we know now, he needed the aid of an even remoter past that mankind too long remained ignorant of: the millions of years required to transform his dumb animal ancestors into conscious human beings. (Mumford 1970: 79)
In rejecting the cumulative contributions of history, Descartes lost sight, then, of both the significance of nature and the nature of significance, and failed to understand their interdependence, since the mind that explores nature is itself a part of nature and exhibits otherwise hidden or inaccessible characteristics. Without this larger time-span to sustain it, life would shrink and shrivel into nothingness; and the ego would lack the very words needed to deny the mind’s existence or to curse its own impotence. It is in such a state, incidentally, that many of our contemporaries actually find themselves today, since they accept the momentary reports of their senses as final revelations—however hideous— of truth. (Mumford 1970: 82)
Chapter 3
24.
The computer is not as much an invention as it is a discovery. Computers neither began, nor will they end, with the technological medium of electronic circuitry etched onto silicon. It is important to realize that what constitutes a ‘computer’ changes through the years, coevolving with the cultural and technological practices of the period. Significantly, it also means that there is continuity in the concept of computation that transcends the cultural milieu and the technological medium in which those computations take place. The computer, as a box of circuit boards and chips, may be more apparent than the concepts underlying its creation, but it is the processes that we have discovered and captured in its operation that are, in the long run, more important than physical form. (Gessler 2002)
25.
In an analog machine each number is represented by a suitable physical quantity, whose values, measured in some pre-assigned unit, is equal to the number in question. This quantity may be the angle by which a certain disk has rotated, or the strength of a certain current, or the amount of a certain (relative) voltage, etc. To enable the machine to compute, i.e. to operate on these numbers according to a predetermined plan, it is necessary to provide organs (or components) that can perform on these representative quantities the basic operations of mathematics. These basic operations are usually understood to be the “four species of arithmetic”:
- Addition (the operation x + y) ,
- Subtraction ( x – y) ,
- Multiplication (xy) ,
- Division (x/y ).
(Von Neumann and Kurzweil 1958: 3) [my bullet re-formatting] [see thesis notes for a breakdown of these operations, differential analyzer/gears, and integrators]
26.
Unlike analogue subjectivity, where morphological resemblance imposes constraints on how much the relevant units can be broken up, the digital subject allows for and indeed demands more drastic fragmentation. This difference can easily be seen by comparing the analogue aspects of print media to the fragmentation of digital technologies. Each letter of the alphabet must be treated as a distinct unit for writing to be legible, and the corresponding phoneme also acts as an intact unit. In contrast are digital sampling techniques, where sound waves may be sampled some 40,000 times a second, digitally manipulated, and then recombined to produce the perception of smooth analogue speech (‘WM,’ p. 20). (Hayles 1999: 15)
27.
To achieve a model that does justice to the reality of a range of levels, we shall need to be faithful to the reality of the world the brain is in, and to the components of all the relevant levels. This undoubtedly means we shall need to edge closer to constructing an artificial device, rather than making do with a Simulation. (Churchland and Sejnowski 2016: 416)
28.
Let us for a moment consider the machine. Marx (along with Charles Babbage, whose work he drew upon) was one of the first to bring out the importance of the machine – a mechanism differing from a simple tool, as from a set of tools brought together in a workshop where both workers and tools are subject to a division of labour. A machine draws energy from a natural source (at first water, then steam, and later still electricity) and uses it to perform a sequence of productive tasks. The worker, instead of manipulating a tool, now serves a machine. The result is a radical but contradictory transformation of the productive process: whereas labour is ever more divided and segmented, the machine is organized into an ensemble that is ever vaster, ever more cohesive, ever more unified, and ever more productive. (Lefebvre 1991: 344)
29.
I have been led to conceive the most important elements of another Engine upon a new principle (the details of which are reduced accurately to paper), the power of which over the most complicated analytical operations appears nearly unlimited; but no portion of which is yet commenced. I have called this engine, in contradistinction to the other, the Analytical Engine. (Babbage 1864: 104, emphasis added)
30.
I have invented and brought to maturity a system of signs for the explanation of machinery, which I have called Mechanical Notation, by means of which the drawings, the times of action, and the trains for the transmission of force, are expressed in a language at once simple and concise. Without the aid of this language I could not have invented the Analytical Engine; nor do I believe that any machinery of equal complexity can ever be contrived without the assistance of that or of some other equivalent language. (Babbage 1864: 104).
31.
It is known as a fact that the Jacquard loom is capable of weaving any design which the imagination of man may conceive. It is also the constant practice for skilled artists to be employed by manufacturers in designing patterns. These patterns are then sent to a peculiar artist, who, by means of a certain machine, punches holes in a set of pasteboard cards in such a manner that when those cards are placed in a Jacquard loom, it will then weave upon its produce the exact pattern designed by the artist. (Babbage 1864: 117)
32.
In a decimal digital machine each number is represented in the same way as in conventional writing or printing [my emphasis], i.e. as a sequence of decimal digits. Each decimal digit, in turn, is represented by a system of “markers.”
A marker which can appear in ten different forms suffices by itself to represent a decimal digit. A marker which can appear in two different forms only will have to be used so that each decimal digit corresponds to a whole group. (A group of three two-valued markers allows 8 combinations; this is inadequate. A group of four such markers allows 16 combinations; this is more than adequate. Hence, groups of at least four markers must be used per decimal digit. (Von Neumann and Kurzweil 1958: 6, emphasis added)
33.
To give an idea of this rapidity, we need only mention that Mr. Babbage believes he can, by his engine, form the product of two numbers, each containing twenty figures, in three minutes. (Notes of Ada Lovelace in Menebrea, L. F. Scientific Memoirs p. 688)
34.
The bounds of arithmetic were however outstepped the moment the idea of applying the cards had occurred; and the Analytical Engine does not occupy common ground with mere “calculating machines.” It holds a position wholly its own; and the considerations it suggests are most interesting in their nature. In enabling mechanism to combine together general symbols, in successions of unlimited variety and extent, a uniting link is established between the operations of matter and the abstract mental processes of the most abstract branch of mathematical science. A new, a vast, and a powerful language is developed for the future use of analysis, in which to wield its truths so that these may become of more speedy and accurate practical application for the purposes of mankind than the means hitherto in our possession have rendered possible. Thus not only the mental and the material, but the theoretical and the practical in the mathematical world, are brought into more intimate and effective connection with each other. (Toole 2010: 68, emphasis added) (Lovelace 1843: 696–697)(Lovelace, From From Note A, pp. 696-697)
35.
Those who view mathematical science not merely as a vast body of abstract and immutable truths, whose intrinsic beauty, symmetry and logical completeness, when regarded in their connexion together as a whole, entitle them to a prominent place in the interest of all profound and logical minds, but as possessing a yet deeper interest for the human race, when it is remembered that this science constitutes the language through which alone we can adequately express the great facts of the natural world, and those unceasing changes of mutual relationship which, visibly or invisibly, consciously or unconsciously to our immediate physical perceptions, are interminably going on in the agencies of the creation we live amidst: those who thus think on mathematical truth as the instrument through which the weak mind of man can most effectually read his Creator’s works, will regard with especial interest all that can tend to facilitate the translation of its principles into explicit practical forms. (Toole 2010: 66, emphasis added) Lovelace, From Note A, p. 696)
36.
It is desirable to guard against the possibility of exaggerated ideas that might arise as to the powers of the Analytical Engine. In considering any new subject, there is frequently a tendency, first, to overrate what we find to be already interesting or remarkable; and, secondly, by a sort of natural reaction, to undervalue the true state of the case, when we do discover that our notions have surpassed those that were really tenable.
The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths. Its province is to assist us in making available what we are already acquainted with. This it is calculated to effect primarily and chiefly of course, through its executive faculties; but it is likely to exert an indirect and reciprocal influence on science itself in another manner. For, in so distributing and combining the truths and the formulae of analysis, that they may become most easily and rapidly amenable to the mechanical combinations of the engine, the relations and the nature of many subjects in that science are necessarily thrown into new lights, and more profoundly investigated. This is a decidedly indirect, and a somewhat speculative, consequence of such an invention. It is however pretty evident, on general principles, that in devising for mathematical truths a new form in which to record and throw themselves out for actual use, views are likely to be induced, which should again react on the more theoretical phase of the subject. There are in all extensions of human power, or additions to human knowledge, various collateral influences, besides the main and primary object attained. [Ada Lovelace italic emphasis – TRANSLATOR’S NOTES TO M. MENABREA’S MEMOIR] (Lovelace 1843: 722; Babbage and Bromley 1984: 44; Toole 2010: 72) (Lovelace, From From Note G, pp. 689)
37.
Let us return for a moment to Lady Lovelace’s objection, which stated that the machine can only do what we tell it to do. One could say that a man can ‘inject’ an idea into the machine, and that it will respond to a certain extent and then drop into quiescence, like a piano string struck by a hammer. Another simile would be an atomic pile of less than critical size: an injected idea is to correspond to a neutron entering the pile from without. Each such neutron will cause a certain disturbance which eventually dies away. If, however, the size of the pile is sufficiently increased, the disturbance caused by such an incoming neutron will very likely go on and on increasing until the whole pile is destroyed. Is there a corresponding phenomenon for minds, and is there one for machines? There does seem to be one for the human mind. The majority of them seem to be ‘sub-critical’, i.e. to correspond in this analogy to piles of sub-critical size. An idea presented to such a mind will on average give rise to less than one idea in reply. A smallish proportion are super-critical. An idea presented to such a mind may give rise to a whole ‘theory’ consisting of secondary, tertiary and more remote ideas. Animals’ minds seem to be very definitely subcritical. Adhering to this analogy we ask, ‘Can a machine be made to be super-critical?’ (Turing 1950: 454)
Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain. Presumably the child-brain is something like a note-book as one buys it from the stationers. Rather little mechanism, and lots of blank sheets. (Mechanism and writing are from our point of view almost synonymous.) Our hope is that there is so little mechanism in the childbrain that something like it can be easily programmed. The amount of work in the education we can assume, as a first approximation, to be much the same as for the human child. We have thus divided our problem into two parts. The childprogramme and the education process. These two remain very closely connected. We cannot expect to find a good child-machine at the first attempt. One must experiment with teaching one such machine and see how well it learns (Turing 1950: 456).
The original question, ‘Can machines think!’ I believe to be 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. I believe further that no useful purpose is served by concealing these beliefs. The popular view that scientists proceed inexorably from well-established fact to well-established fact, never being influenced by any unproved conjecture, is quite mistaken. Provided it is made clear which are proved facts and which are conjectures, no harm can result. (Turing 1950: 442)
We may hope that machines will eventually compete with men in all purely intellectual fields. But which are the best ones to start with? Even this is a difficult decision. Many people think that a very abstract activity, like the playing of chess, would be best. It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English. This process could follow the normal teaching of a child. Things would be pointed out and named, etc. Again I do not know what the right answer is, but I think both approaches should be tried. We can only see a short distance ahead, but we can see plenty there that needs to be done. (Turing 1950: 460)
38.
This test has become known as the Turing Test. Philosophers may doubt whether merely behavioral similarity could ever give adequate ground for the attribution of intelligence, but as a goal for those actually trying to construct thinking machines, and as a criterion for critics to use in evaluating their work, Turing’s test was just what was needed. (Dreyfus 1972: xxi)
39.
Modern linguistics shares the delusion – the accurate term, I believe – that the modern “behavioral sciences” have in some essential respect achieved a transition from “speculation” to “science” and that earlier work can be safely consigned to the antiquarians. Obviously any rational person will favor rigorous analysis and careful experiment; but to a considerable degree, I feel, the “behavioral sciences” are merely mimicking the surface features of the natural sciences; much of their scientific character has been achieved by a restriction of subject matter and a concentration on rather peripheral issues. Such narrowing of focus can be justified if it leads to achievements of real intellectual significance, but in this case, I think it would be very difficult to show that the narrowing of scope has led to deep and significant results. Furthermore, there has been a natural but unfortunate tendency to “extrapolate,” from the thimbleful of knowledge that has been attained in careful experimental work and rigorous data-processing, to issues of much wider significance and of great social concern. This is a serious matter. The experts have the responsibility of making clear the actual limits of their understanding and of the results they have so far achieved, and a careful analysis of these limits will demonstrate, I believe, that in virtually every domain of the social and behavioral sciences the results achieved to date will not support such “extrapolation.” (Chomsky 2006: xvii, emphasis added)
40.
Imagination is the Discovering Faculty, pre-eminently. It is that which penetrates into the unseen worlds around us, the worlds of Science. It is that which feels & discovers what is, the real which we see not, which exists not for our senses. Those who have learned to walk on the threshold of the unknown worlds, by means of what are commonly termed par excellence the exact sciences, may then with the fair white wings of Imagination hope to soar further into the unexplored amidst which we live. Mathematical Science shows what is. It is the language of unseen relations between things. But to use & apply that language we must be able fully to appreciate, to feel, to seize, the unseen, the unconscious. Imagination too shows what is, the is that [sic] is beyond the senses. Hence she is or should be especially cultivated by the truly Scientific, – those who wish to enter into the worlds around us! (Toole 2010: 40) (Ada Lovelace)
41.
Altogether, the ancient scribal culture was inclined to regard a written page as a palpable texture, a pattern of words that reproduced the patterns of the larger world beyond the page. Hence Plato’s comparison of writing to the art of drawing. Elsewhere Plato even used the metaphor of weaving (another manual art) to explain how a name reproduces reality. When Socrates suggests the definition ‘A name, then, is a sort of didactic instrument that separates out reality as a shuttle separates fabric on a loom?’ his interlocutor agrees without hesitation. (Fowler et al. 1926: 23; Bolter 1984: 138, quoting Plato’s Cratylus, 387c–388c, emphasis added).
42.
After abandoning his malfunctioning machine, Nietzsche elevated the typewriter… suggesting… humanity has shifted away from its inborn faculties (such as knowledge, speech, and virtuous action) in favor of a memory machine. (Kittler 2006: 210, emphasis added) [Translators note, xxix]
writing . . . is no longer a natural extension of humans who bring forth their voice, soul, individuality through their handwriting. On the contrary, … humans change their position-they turn from the agency of writing to become an inscription surface. (Kittler 2006: 210, emphasis added)
Chapter 4
43.
The digital-computer field defined computers as machines that manipulated numbers. The great thing was, its adherents said, that everything could be encoded into numbers, even instructions. In contrast, scientists in AI saw computers as machines that manipulated symbols. The great thing was, they said, that everything could be encoded into symbols, even numbers. (Newell 1983: 196)
44.
Computation performed by computer programs or machines is referred to, in general, as “information processing.” In reality, the information is not in the processing. It becomes information only when interpreted by an observer that recognizes its meaning and significance, be it a human interpreter or a mechanical actuator. A computer program can effectively control a robot or other man-made machines, thus giving rise to the illusion that information resides in the program. However, the process is based on a human-designed solution even if it involves a complex trial-and-error learning process, including “deep learning” in artificial intelligence programs. In short, information is not inherent in the computation (in machines or brains) but becomes such when it is interpreted (Buzsáki 2021: 28, original emphasis).
45.
Let us consider now what happens when we determine the energy in one of the components. The result of such a determination must be either the whole photon or nothing at all. Thus the photon must change suddenly from being partly in one beam and partly in the other to being entirely in one of the beams. This sudden change may be counted as due to the disturbance of the photon which the observation necessarily makes. It is impossible to predict in which of the two beams the photon will be found. Only the probability of either result can be calculated from the previous distribution of the photon over the two beams. (Dirac 1930: 5, emphasis added)
46.
As you consider the role of measurements in quantum circuits, it is important to keep in mind that in its role as an interface between the quantum and classical worlds, measurement is generally considered to be an irreversible operation, destroying quantum information and replacing it with classical information. In certain carefully designed cases, however, this need not be true, as is vividly illustrated by teleportation and quantum error-correction (Chapter 10). What teleportation and quantum error-correction have in common is that in neither instance does the measurement result reveal any information about the identity of the quantum state being measured. Indeed, we will see in Chapter 10 that this is a more general feature of measurement – in order for a measurement to be reversible, it must reveal no information about the quantum state being measured! (Nielsen and Chuang 2010: 187, Measurement)
47.
In the course of the development up to now, electromechanical relays, vacuum tubes, crystal diodes, ferromagnetic cores, and transistors have been successively used—some of them in combination with others, some of them preferably in the memory organs of the machine (cf. below), and others preferably outside the memory (in the “active” organs) giving rise to as many different species of digital machines. (Von Neumann and Kurzweil 1958: 7)
48.
That is, each such organ must be able to “store” a number—removing the one it may have stored previously—accepting it from some other organ to which it is at the time connected, and to “repeat” it upon “questioning”: to emit it to some other organ to which it is at that (other) time connected. Such an organ is called a “memory register,” the totality of these organs is called a “memory,” and the number of registers in a memory is the “capacity” of that memory. (1958: 14)
The basic component of this system is the nerve cell, the neuron, and the normal function of a neuron is to generate and to propagate a nerve impulse. This impulse is a rather complex process, which has a variety of aspects—electrical, chemical, and mechanical. It seems, nevertheless, to be a reasonably uniquely defined process, i.e. nearly the same under all conditions; it represents an essentially reproducible, unitary response to a rather wide variety of stimuli. (Von Neumann and Kurzweil 1958: 40)
49.
The nervous pulses can clearly be viewed as (two-valued) markers, in the sense discussed previously: the absence of a pulse then represents one value (say, the binary digit 0), and the presence of one represents the other (say, the binary digit 1). (Von Neumann and Kurzweil 1958: 43)
50.
However, there is some plausibility in assuming that things can, in reality, be even more complicated than this. It may well be that certain nerve pulse combinations will stimulate a given neuron not simply by virtue of their number but also by virtue of the spatial relations of the synapses to which they arrive. (Von Neumann and Kurzweil 1958: 54)
51.
While the computer concept itself dates back to Charles Babbage and Ada Lovelace (Isaacson, 2014), it was not until the invention of the transistor and then the spread of affordable personal devices coupled with ubiquitous Internet connectivity that digital technology drastically altered human society as we experience it today. (Kwet 2019)
52.
People underweight outcomes that are merely probable in comparison with outcomes that are obtained with certainty. This tendency, called the certainty effect, contributes to risk aversion in choices involving sure gains and to risk seeking in choices involving sure losses… Decision weights are generally lower than the corresponding probabilities, except in the range of low probabilities. Overweighting of low probabilities may contribute to the attractiveness of both insurance and gambling (Kahneman and Tversky 1979: 263, emphasis added).
53.
All information, all knowledge, must be coded in some binary representation in order to be acted upon by the computer. It must be broken into a series of discrete values. It has already been noted that even in mathematics this is a limitation, that infinitely many numbers (irrational, algebraic, transcendental) cannot be represented exactly in a digital computer because they cannot be reduced to a finite series of decimal or binary places. Approximation leads to error, the central problem of computer mathematics. On the other hand, symbolic logic has no difficulty adapting itself to discrete representation, for it is characteristic of logic to seek to reduce the continuous to the discrete, the ambiguities and uncertainties of everyday thinking to the binary scheme of true and false. What is remarkable is that the idea of a two-valued truth function should be so well suited to representation in electronic circuits. In any case, there is no room in the computer for the continuous. (Bolter 1984: 75, emphasis added)
54.
The observation I wish to make is this: processes which go through the nervous system may, as I pointed out before, change their character from digital to analog, and back to digital, etc., repeatedly… Nerve pulses, i.e. the digital part of the mechanism, may control a particular stage of such a process, e.g. the contraction of a specific muscle or the secretion of a specific chemical. This phenomenon is one belonging to the analog class, but it may be the origin of a train of nerve pulses which are due to its being sensed by suitable inner receptors. When such nerve pulses are being generated, we are back in the digital line of progression again. As mentioned above, such changes from a digital process to an analog one, and back again to a digital one, may alternate several times. Thus the nerve-pulse part of the system, which is digital, and the one involving chemical changes or mechanical dislocations due to muscular contractions, which is of the analog type, may, by alternating with each other, give any particular process a mixed character. (Von Neumann and Kurzweil 1958: 68, emphasis added)
Chapter 5
55.
The manifesto of the new electronic order of things was a paper (“On Computable Numbers”) published by the mathematician and logician A. M. Turing in 1936. Turing set out the nature and theoretical limitations of logic machines before a single fully programmable computer had been built. What Turing provided was a symbolic description, revealing only the logical structure and saying nothing about the realization of that structure (in relays, vacuum tubes, or transistors). A Turing machine, as his description came to be called, exists only on paper as a set of specifications, but no computer built in the intervening half century has surpassed these specifications; all have at most the computing power of Turing machines. (Turing 1937; Bolter 1984: 12)
56.
In the first stage of this ‘liberation,’ as McLuhan sees it, instantaneous planetary communication will bring about a release from all previous cultures and past modes of regimentation: machines themselves will vanish, to be replaced by electronic equivalents or substitutes. In McLuhan’s trancelike vaticinations, he actually appears to believe that this has already happened, and that even the wheel is about to disappear, while mankind as a whole will return to the pre-primitive level, sharing mindless sensations and pre-linguistic communion. In the electronic phantasmagoria that he conjures up, not alone will old-fashioned machines be permanently outmoded but nature itself will be replaced: the sole vestige of the multifarious world of concrete forms and ordered experience will be the sounds and ‘tactile’ images on the constantly present television screen or such abstract derivative information as can be transferred to the computer. Psychiatry reveals the true nature of this promised state. What is it but the electronic equivalent of the dissociation and subjective inflation that takes place under lysergic acid and similar drugs? In so far as McLuhan’s conception corresponds to any existential reality, it is that of an electronically induced mass psychosis. Not surprisingly, perhaps, now that the facilities for instantaneous communication have planetary outlets, symptoms of this psychosis are already detectable in every part of the planet. In McLuhan’s case, the disease poses as the diagnosis. (Mumford 1970: 294, emphasis added)
57.
“Work,” however, does not exist in a nonliterate world. The primitive hunter or fisherman did no work, any more than does the poet, painter, or thinker of today. Where the whole man is involved there is no work. Work begins with the division of labor and the specialization of functions and tasks in sedentary, agricultural communities. In the computer age we are once more totally involved in our roles. In the electric age the “job of work” yields to dedication and commitment, as in the tribe. (McLuhan 1964: 129, emphasis added)
58.
Minds are simply what brains do … There is not the slightest reason to doubt that brains are anything other than machines with enormous numbers of parts that work in perfect accord with physical laws. (Minsky and Lee 1988: 288)
59.
Turing stated his conviction that computers were capable of imitating human intelligence perfectly and that indeed they would do so by the year 2000. This paper too has served as a manifesto for a group of computer specialists dedicated to realizing Turing’s claim by creating what they call “artificial intelligence,” a computer that thinks. Put aside for the moment the question of whether the computer can ever rival human intelligence. The important point is that Turing, a brilliant logician and a sober contributor to the advance of electronic technology, believed it would and that many have followed him in that belief. (Bolter 1984: 12, emphasis added)
60.
Hallucination —
- The mental condition of being deceived or mistaken , or of entertaining unfounded notions… with an idea of belief to which nothing real corresponds.
- The apparent perception (usually by sight or hearing) of an external object when no such object is actually present (Distinguished from illusion in the strict sense, as not necessarily involving a false belief).
61.
Scott Pelley: Are you getting a lot of hallucinations?
- Sundar Pichai: Yes, you know, which is expected. No one in the, in the field has yet solved the hallucination problems. All models do have this as an issue….
Scott Pelley: You don’t fully understand how it works. And yet, you’ve turned it loose on society?
- Sundar Pichai: Yeah. Let me put it this way. I don’t think we fully understand how a human mind works either.
62.
I believe further that no useful purpose is served by concealing these beliefs… Provided it is made clear which are proved facts and which are conjectures, no harm can result. Conjectures are of great importance since they suggest useful lines of research. (Turing 1950: 442, emphasis added)
63.
The committed formalist, however, has one more move. He can exploit the ambiguity of the notion of “laws of behavior,” and take behavior to mean not meaningful human actions, but simply the physical movements of the human organism. Then, since human bodies are part of the physical world and, as we have seen, objects in the physical world have been shown to obey laws which can be expressed in a formalism manipulable on a digital computer, the formalist can still claim that there must be laws of human behaviour of the sort required by his formalism…
To be more specific, if the nervous system obeys the laws of physics and chemistry, which we have every reason to suppose it does, then even if it is not a digital computer, and even if there is no input-output function directly describing the behavior of the human being, we still ought to be able to
reproduce the behavior of the nervous system with some physical device which might, for example, take the form of a new sort of “analogue computer” using ion solutions whose electrical properties change with various local saturations…
…Thus, [the formalist assumes] given enough memory and time, any computer—even such a special sort of analogue computer—could be simulated on a digital machine. In general, by accepting [the formalist accepting] the fundamental assumptions that the nervous system is part of the physical world and that all physical processes can be described in a mathematical formalism which can in turn be manipulated by a digital computer, one can arrive at the strong claim that the behavior which results from human “information processing” whether directly formalizable or not, can always be indirectly reproduced on a digital machine. (Dreyfus 1972: 106, emphasis added)
64.
This claim may well account for the formalist’s smugness, but what in fact is justified by the fundamental truth that every form of “information processing” (even those which in practice can only be carried out on an “analogue computer”) must in principle be simulable on a digital computer? We have seen it does not prove the mentalist claim that, even when a human being is unaware of using discrete operations in processing information, he must nonetheless be unconsciously following a set of
instructions. Does it justify the epistemological assumption that all non-arbitrary behavior can be formalized?
One must delimit what can count as information processing in a computer. A digital computer solving the equations describing an analogue information-processing device and thus simulating its function is not thereby simulating its “information processing.” It is not processing the information which is processed by the simulated analogue, but entirely different information concerning the physical or chemical properties of the analogue. Thus the strong claim that every form of information can be processed by a digital computer is misleading. One can only show that for any given type of information a digital computer can in principle be programmed to simulate a device which can process that information. (Dreyfus 1972: 107, emphasis added)
65.
When a man reasoneth, he does nothing else but conceive a sum total, from addition of parcels; or conceive a remainder …. These operations are not incident to numbers only, but to all manner of things that can be added together, and taken one out of another . . . the logicians teach the same in consequences of words; adding together two names to make an affirmation, and two affirmations to make a syllogism; and many syllogisms to make a demonstration. (Hobbes 1651: 18)
If we had it… we should be able to reason in metaphysics and morals in much the same way as in geometry and analysis ” (G. vii. 21 ) … “If controversies were to arise, there would be no more need of disputation between two philosophers than between two accountants. For it would suffice to take their pencils in their hands, to sit down to their slates, and to say to each other (with a friend as witness, if they liked) : Let us calculate ” (G. vii. 200) . (Michels and Science 1907: 176) (Russell 2012: 306, emphasis added)
66.
To say [what the computer scientists suggest] that “all of the world’s knowledge” could be explicitly articulated in any symbolic form (computational or not), we must assume the possibility of reducing all forms of tacit knowledge (skills, intuition, etc.) to explicit facts and rules. Heidegger and other phenomenologists have challenged this, and many of the strongest criticisms of artificial intelligence are based on the phenomenological analysis of human understanding as a “readiness-to-hand” of action in the world, rather than as the manipulation of “present-to-hand” representations. (Winograd 1991: 210, emphasis added)
67.
“Technological possibilities are irresistible to man. If man can go to the moon, he will. If he can control the climate, he will.” von Neumann…. There is a simple way of establishing the downright absurdity—or more accurately the menacing irrationality—of accepting such technological compulsiveness; and that is to carry von Neumann’s dictum to its logical conclusion: If man has the power to exterminate all life on earth, he will. Since we know that the governments of the United States and Soviet Russia have already created nuclear, chemical, and bacterial agents in the massive quantities needed to wipe out the human race, what prospects are there of human survival, if this practice of submitting to extravagant and dehumanized technological imperatives is ‘irresistibly’ carried to its final stage?…
Our contemporaries are already so conditioned to accept technological ‘progress’ as absolute and irresistible, however painful, ugly, mentally cramping, or physiologically damaging its results, that they accept the latest technical offering, whether a supersonic plane or a ‘learning cell,’ with smiling consent, particularly if the equipment is accompanied by a ‘scientific’ explanation and seems technologically an ‘advanced’ type. (Mumford 1970: 186) [Relevant to Latour’s arguments about non-humans that provide evidence science/moderns are above nature]
68.
Wiener, too, makes kubernetically-marked forecasts. In the short term, he believes ‘we are proceeding on our course on the basis of charts on the idea of progress which do not mark the threatening shoals’ (Wiener, 1950: 214). When he surveys our world, engineered toward capitalist ideas of progress, he foresees the long term, and it does not look good: our destiny is to be ‘ship-wrecked passengers on a doomed planet’ (Wiener, 1954: 40). He has little confidence that the power of our future choices can overcome that of our past ones. We are all in the same boat. (Kennerly 2023: 97)
69.
What awaits is not oblivion but rather a future which, from our present vantage point, is best described by the words “postbiological” or even “supernatural.” It is a world in which the human race has been swept away by the tide of cultural change, usurped by its own artificial progeny. The ultimate consequences are unknown, though many intermediate steps are not only predictable but have already been taken…within the next century they will mature into entities as complex as ourselves, and eventually into something transcending everything we know—in whom we can take pride when they refer to themselves as our descendants.
Unleashed from the plodding pace of biological evolution, the children of our minds will be free to grow to confront immense and fundamental challenges in the larger universe. We humans will benefit for a time from their labors, but sooner or later, like natural children, they will seek their own fortunes while we, their aged parents, silently fade away. (Moravec 1988: 1, emphasis added) See Thesis Note 6: An excerpt where Moravecs describes the moment of human transcendence in detail.
I much prefer the attitude of Hans Moravec of Carnegie-Mellon University, who suggests that we think of those future intelligent machines as our own “mind-children.” (Minsky 1994: 4, emphasis added)
70.
First they use what they call “grammar” to change them into simple tree-like structures. Then they use certain terms called ‘pronouns’ to make a few cross-links in those trees. Naturally, this leaves no room for nuances. So they have to decode whatever they hear in terms of things they already know. This can work very well for familiar things but makes it devilishly hard for them to learn anything really new. (Minsky 1992, emphasis added)
71.
Marvin Minsky precisely expressed this dream when, in a recent lecture, he suggested it will soon be possible to extract human memories from the brain and import them, intact and unchanged, to computer disks. The clear implication is that if we can become the information we have constructed, we can achieve effective immortality. (Hayles 1999: 13, emphasis added)
72.
Might technology increase affective bandwidth? Virtual environments and computer-mediated communication offer possibilities that we do not ordinarily have in person-to-person communication. Potentially, communication through virtual environments could provide new channels for affect perhaps, as one idea, via sensors that detect physiological information and relay its significant information. In this way, computer-mediated communication might potentially have higher affective bandwidth than traditional “in person” communication… (Picard 2000: 57)
73.
Yet only recently a well accredited scientist stated in so many words that “man is born a machine and becomes a person”. On what planet does this take place? Certainly not on earth, where machines are never born but fabricated: what is more, a baby, from the moment of its conception, exhibits many traits not found in any observed or conceivable machine. If a machine should become a person that would be an infinitely greater miracle than any recorded in the Bible or the Koran. (Mumford 1970: 97, emphasis added)
When intelligent machines are constructed, we should not be surprised to find them as confused and as stubborn as men in their convictions about mind-matter, consciousness, free will, and the like. For all such questions are pointed at explaining the complicated interactions between parts of the self-model. A man’s or a machine’s strength of conviction about such things tells us nothing about the man or about the machine except what it tells us about his model of himself. The gross divisions of our models probably have much-heuristic value to us. Indeed we identify (in children) some stages in delineating the distinctions between these models as associated with the growth of intelligence. The distinctions could be abandoned only at great cost in everyday practice. That is why, even if one accepts the conclusions of this essay, he is unlikely to note any serious effect on his way of thinking about most things. (Minsky 1968: 430)
74.
With nuclear energy, electric communication, and the computer, all the necessary components of a modernized megamachine at last became available: ‘Heaven’ had at last been brought near. Theoretically, at the present moment, and actually soon in the future, God—that is, the Computer—will be able to find, to locate, and to address instantly, by voice and image, via the priesthood, any individual on the planet: exercising control over every detail of the subject’s daily life by commanding a dossier which would include his parentage and birth; his complete educational record; an account of his illnesses and his mental breakdowns, if treated; his marriage; his sperm bank account; his income, loans, security payments; his taxes and pensions; and finally the disposition of such further organs as may be surgically extracted from him just prior to the moment of his official death. (Mumford 1970: 274)
75.
The goal of artificial intelligence is to demonstrate that man is all surface, that there is nothing dark or mysterious in the human condition, nothing that cannot be lit by the even light of operational analysis. Like any program, an artificial intelligence program is a set of instructions to manipulate symbolic data: every symbol and every instruction is as clearly defined and accessible as the next. There are no shades or degrees, and nothing can remain undefined. A dislike of mystery is ingrained in every programmer by hard experience; for every one has spent untold hours “debugging” his programs, tracking down subtle errors that have crept into his commands as he wrote or copied them… It is no surprise, then, that Minsky claims: “It may be so with man, as with machine, that, when we understand finally the structure and program, the feeling of mystery (and self-approbation) will weaken” (“Steps toward Artificial Intelligence,” 27). To put it another way, the symbolic logic by which the machine functions demands total unidimensional understanding. (Bolter 1984: 221)
76.
[Expert systems] . . . can actually help to codify and improve expert human knowledge, taking what was fragmentary, inconsistent and error-infested and turning it into knowledge that is more precise, reliable and comprehensive. This new process, with its enormous potential for the future, we call “knowledge refining.” (Michie and Johnston 1984: 129, emphasis added)
77.
We have the opportunity at this moment to do a new version of Diderot’s Encyclopedia, a gathering up of all knowledge—not just the academic kind, but the informal, experiential, heuristic kind—to be fused, amplified, and distributed, all at orders of magnitude difference in cost, speed, volume, and usefulness over what we have now. (Feigenbaum and McCorduck 1987: 229, emphasis added)
78.
Any task the computer performs is a matter of the logical manipulation of symbols; each new problem solved is a conquest for the logical calculus of thought and gives encouragement to those who still follow Leibniz’s program. “If I were to choose a patron saint for cybernetics [the study of living organisms as logical machines] out of the history of science,” wrote Norbert Wiener in 1948, “I should have to choose Leibniz. . . . The calculus ratiocinator [calculus of reasoning] of Leibniz contains the germs of the machina ratiocinatrix, the reasoning machine” (Cybernetics, 20). When a computer specialist speaks of his machine “thinking,” “reasoning,” “manifesting intelligence,” or “solving problems,” he means that it is operating according to the rules of its embodied symbolic logic. (Bolter 1984: 73)
Chapter 6
79.
“As soon as we have succeeded in finding the proper method,” Comenius elsewhere explains, “it will be no harder to teach schoolboys in any number desired, than with the help of the printing press to cover a thousand sheets daily with the neatest writing.” Close upon this follows another revealing sentence: “It will be as pleasant to see education carried out on my plan as to look at an automatic machine, and the process will be as free from failure as these mechanical contrivances when skillfully made.” Precisely: and what Comenius formulated in the seventeenth century, Gradgrind and M’Choakumchild would carry out clumsily and brutally in the nineteenth century, to be followed by the more facile pigeon-conditioners and programmers of the present age, equally captivated by their own automatisms. (Mumford 1970: 103)
80.
For Comenius, as for his fellow-encyclopedist J. H. Alsted, and later for John Locke, the mind of man was a blank sheet of paper. The task of education was to leave on this sheet the desired uniform imprint: again the image of the printing press. Like the inventor and the physical scientist, the new educator sought to achieve perfect mechanical order—but eliminated the spontaneities of life and all the intangible and unprogrammable functions that go with life. (Mumford 1970: 103, emphasis added)
81.
The deliberate process we call reasoning is, I believe, the thinnest veneer of human thought, effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge.
The intellectual tasks, such as chess playing, chemical structure analysis, and calculus are relatively easy to perform with a computer. Much harder are the kinds of activities that even a one-year-old human or a rat could do. This is called Moravec’s paradox, which I think should better be called Moravec’s irony. The things that people find difficult are relatively easy to do with a computer (checkers playing, reasoning, logic), but the things that people find easy, automatic, or even unconscious have been a challenge for computers (Roitblat 2020: 71).
82.
It seems tempting to think, at least many seem so tempted, that when we use the words ‘learn’, ‘learning’, learnt’ and ‘learned’, there is some mental state or process to which those words refer, and that what those words refer to is always the same thing. Those thoughts have led to claims about machines being able to learn, and the claim that connectionist ‘cognitive architecture’ explains how human beings learn. (Culbertson 2023: 44)
83.
If embodiment is not essentialist, it is also not algorithmic. This conclusion has important implications for debates over what difference embodiment makes to thinking and learning. In What Computers Can’t Do, Hubert Dreyfus argues that many human behaviors cannot be formalized in a heuristic program for a digital computer because these behaviors are embodied. For Dreyfus, embodiment means that humans have available to them a mode of leaming, and hence of intellection, different from that deriving from cogitation alone.
The advantage of this kind of learning is that everything does not need to be specified in advance. Moreover, the learning can be structured into complex relations without the necessity of a formal recognition that the relations exist. Drawing from Maurice Merleau-Ponty, Karl Polanyi, Jean Piaget, and other phenomenologists, Dreyfus delineates three functions that are characteristic of embodied learning and are not present in computer programs: an “inner horizon” that consists of a partly determined, partly open context of anticipation; the global character of the anticipation, which relates it to other pertinent contexts in fluid, shifting patterns of connection; and the transferability of such anticipation from one sense modality to another. (Hayles 1999: 201)
84.
The body contributes three functions not present, and not as yet conceived in digital computer programs: (1) the inner horizon, that is, the partially indeterminate, predelineated anticipation of partially indeterminate data (this does not mean the anticipation of some completely determinate alternatives, or the anticipation of completely unspecified alternatives, which would be the only possible digital implementation); (2) the global character of this anticipation which determines the meaning of the details it assimilates and is determined by them; (3) the transferability of this anticipation from one sense modality and one organ of action to another. All these are included in the general human ability to acquire bodily skills. Thanks to this fundamental ability an embodied agent can dwell in the world in such a way as to avoid the infinite task of formalizing everything.
This embodied sort of ‘information processing’, in which the meaning of the whole is prior to the elements, would seem to be at work in the sort of complex pattern recognition such as speech recognition with which we began our discussion. Indeed, sensory motor skills underlie perception whose basic figure/ground structure seems to underlie all ‘higher’ rational functions; even logic and mathematics have an horizontal character. In all these cases individual features get their significance in terms of an underdetermined anticipation of the whole.
If these global forms of pattern recognition are not open to the digital computer, which, lacking a body, cannot respond as a whole, but must build up its recognition starting with determinate details, then Oettinger is justified in concluding his speech recognition paper on a pessimistic note: “If indeed we have an ability to use a global context without recourse to formalization… then our optimistic discrete enumerative approach is doomed.” (Dreyfus 1972: 255)
85.
‘…develop and promote a national policy for scientific research and scientific education, should support basic research in nonprofit organizations, should develop scientific talent in American youth by means of scholarships and fellowships, and should by contract and otherwise support long-range research on military matters.’ (Bush and Holt 1945: 28)
86.
Some modern calculators “remember” by means of electrical impulses circulating for long periods around closed circuits…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. (Anon 1948, emphasis added)
87.
The Congress hereby finds and declares that the security of the Nation requires the fullest development of the mental resources and technical skills of its young men and women… The present emergency demands that additional and more adequate educational opportunities be made available. The defense of this Nation depends upon the mastery of modern techniques developed from complex scientific principles. It depends as well upon the discovery and development of new principles, new techniques, and new knowledge. (National Defense Education Act -1958: 1581, emphasis added)
88.
I was last in the United States, in 1970, I was very much impressed by the new ideas in the education of children developed by Marvin Minsky and Seymour Papert of M.I.T. Minsky and Papert threw overboard the cliche that children learn subconsciously by imitation. They proved that men learn best when they form flow charts of action in their heads, when subroutines are separated out and informational connections traced. Using the problem of juggling with two balls, and appealing to my abilities as a programmer, Professor Papert taught me in ten minutes what I myself wouldn’t be able to learn in several hours, thus converting me to his faith forever. (Ershov 1972: 505)
89.
There must be an ‘industrial revolution’ in education, in which educational science and the ingenuity of educational technology combine to modernize the grossly inefficient and clumsy procedures of conventional education….
There will be many labor-saving schemes and devices, and even machines – not at all for the mechanizing of education, but for the freeing of teacher and pupil from educational drudgery and incompetence (Pressey et al. 1944: 640).
90.
If, by a miracle of mechanical ingenuity, a book could be so arranged that only to him who had done what was directed on page one would page two become visible, and so on, much that now requires personal instruction could be managed by print (Thorndike 1912: 165).
In this article it was emphasized that labor-saving devices in education should be entirely possible, and should instead of mechanizing education leave the teacher free of much burdensome routine so that she could do more real teaching. (Pressey 1927a: 550)
91.
Experiments on pigeons may not throw much light on the ‘nature’ of man, but they are extraordinarily helpful in enabling us to analyse man’s environment more effectively. What is common to pigeon and man is a world in which certain contingencies of reinforcement prevail. The schedule of reinforcement which makes a pigeon a pathological gambler is to be found at race track and roulette table, where it has a comparable effect (Skinner 1965: 439).
92.
On a morning in October 1957, Americans were awakened by the beeping of a satellite. It was a Russian satellite, Sputnik. Why was it not American? Was something wrong with American education? Evidently so, and money was quickly voted to improve American schools, Now we are being awakened by the beepings of Japanese cars, Japanese radios, phonographs, and television sets, and Japanese wristwatch alarms, and again questions are being asked about American education, especially in science and mathematics (Skinner 1984: 947).
93.
The Automatic Teacher was a technology of normalization, but it was at the same time a product of liberality. The Automatic Teacher provided for self-instruction and self-regulated, therapeutic treatment, It was designed to provide the right kind and amount of treatment for the individual, scholastic deficiencies; thus, it was individualizing. Pressey articulated this liberal rationale during the 1920’s and 1930’s, and again in the 1950’s and 1960’s. Although intended as an act of freedom, the self-instruction provided by an Automatic Teacher also habituated learners to the authoritative norms underwriting self-regulation and self-governance. (Petrina 2004: 330, emphasis added)
94.
There must be an “industrial revolution” in education, in which educational science and the ingenuity of educational technology combine to modernize the grossly inefficient and clumsy procedures of conventional education….
There will be many labor-saving schemes and devices, and even machines – not at all for the mechanizing of education, but for the freeing of teacher and pupil from educational drudgery and incompetence. Teachers and pupils will cooperate in fascinating efforts to develop further conveniences. The future school will, in consequence, be as much more efficient than the schools of the past as modern industry is more productive than the handicraft of 200 years ago. Will all this make education an industrialized monster, neglectful of human values? That it can never do; the purposes of education would prevent it—and a major service of such devices would be to guide educational effort with ever-increasing clearness and adequacy toward the accomplishment of those purposes. (Pressey et al. 1944: 640)
95.
I liked the Roanoke experiment because it confirmed something I had said a few years earlier to the effect that with teaching machines and programmed instruction, one could teach what is now taught in American schools in half the time with half the effort. (Skinner 1984: 948, emphasis added)
96.
The basic processes and relations which give verbal behavior its special characteristics are now fairly well understood. Much of the experimental work responsible for this advance has been carried out on other species, but the results have proved to be surprisingly free of species restrictions. Recent work has shown that the methods can be extended to human behavior without serious modification. (Skinner 1957: 36, emphasis added)
Chapter 7
97.
But it turned out that the parents were even more against it than the school… One parent said that the servants might have diseases, so they couldn’t be expected to sit at the same benches that the kids sat in. We argued, saying, these were the people who were caring for their kids, but we couldn’t budge them. I remember being puzzled and shocked that people could have such feelings as those. (Weber 1997: A2, quoting Papert)
98.
I was taught to see racism only in individual acts of meanness, not in invisible systems conferring dominance on my group… I see a pattern running through the matrix of white privilege, a pattern of assumptions which were passed on to me as a white person. There was one main piece of cultural turf; it was my own turf, and I was among those who could control the turf. My skin color was an asset for any move I was educated to want to make. I could think of myself as belonging in major ways, and of making social systems work for me. I could freely disparage, fear, neglect, or be oblivious to anything outside of the dominant cultural forms. (McIntosh 1989, emphasis added)
99.
Now a very important person in my South African life is someone—that is Seymour Papert. Seymour was someone about my age, contemporary of mine, and Seymour was a brilliant, is still a brilliant mathematician and in fact, much later in the course, he was also very interested in politics and philosophy and it was much later, while I was a medical student, that I got to know him very well and in fact, he taught me mathematics and I taught him physiology. Thank God it wasn’t the other way around. And he is someone who I think got me interested in the whole idea of mathematical theories and ultimately in computers and things like this because he went off to MIT where he worked with Marvin Minsky on the whole field of Artificial Intelligence and so on. So he was a very good friend and a very important one and also, because of the fringe left-wing politics that we were associated together. (Brenner 1994, emphasis added)
100.
As a young radical, I was drawn to the atmosphere of change and its possibilities… For the first time, I envisaged not going back home to South Africa. It was an agonizing time for me, yet going back seemed futile. I certainly would have been imprisoned. —Papert (Weber 1997: A2)
101.
The weight of my argument is against any universalistic theory, especially one that is already weakened by the prediction that individuals in “primitive societies would not develop beyond the stage of concrete operations” (Piaget 1966:309; Dasen 1972). How can we accept a universal theory that makes an exception of a large segment of mankind, unless we assume either a psycho-genetic difference in abilities or a non-psycho-genetic difference in capacities that we are failing to specify? (Goody 1987: 255)
Clearly, there is not nearly enough evidence on which to draw firm conclusions. However, it seems that Piaget’s “prediction” (1966, p. 13; 1968, pp.97-9) that the reasoning of many individuals in so-called “primitive” societies would not develop beyond the stage of concrete operations, may one day be verified. (Dasen 1972: 26)
102.
Early in his career Piaget (1928/1995) argued for the existence of important cultural variations in thought processes. Citing the work of Lucien Levy-Bruhl (1910) and Emile Durkheim (1912), he distinguished between two kinds of societies, which Levy-Bruhl characterized as “primitive” and “civilized” and which today might be called “traditional” and “modern.” He claimed that there is a distinct “mentality” corresponding to each type of social organization, “the mentality called primitive to the conformist or segmentary societies, the rational mentality to our differentiated societies” (1928/1995, p. 191). However, he disagreed with Levy-Bruhl’s claim that there should be no developmental ordering of the two kinds of mentality; in Piaget’s view at the time, primitive mentality is a precursor of civilized mentality much as child thought is a developmental precursor of adult thought…Many of Piaget’s early books contain references to the childlike thought of primitive adults. (Cole 1996: 86, emphasis added)
103.
Constructivism offers a window into what children are interested in, and able to achieve, at different stages of their development. The theory describes how children’s ways of doing and thinking evolve over time, and under which circumstance children are more likely to let go of—or hold onto—their currently held views. (Ackermann 2001: 1, emphasis added)
104.
I am not trying to contrast New York with Chad. I am interested in the difference between precomputer cultures (whether in American cities or African tribes) and the “computer cultures” that may develop everywhere in the next decades. (Papert 1980: 20)
106.
Everyone concerned with how children think has an immense general debt to Jean Piaget. I have a special debt as well. If Piaget had not intervened in my life I would now be a “real mathematician” instead of being whatever it is that I have become. Piaget invested a lot of energy and a lot of faith in me. I hope that he will recognize what I have contributed to the world of children as being in the spirit of his life enterprise. (Papert 1980: 215)
107.
He points out how over the course of history the visual has increasingly taken precedence over elements of thought and action deriving from the other senses (the faculty of hearing and the act of listening, for instance, or the hand and the voluntary acts of ‘grasping’, ‘holding’, and so on). So far has this trend gone that the senses of smell, taste, and touch have been almost completely annexed and absorbed by sight. (Lefebvre 1991: 139)
108.
The detecting of occlusions caused by moving objects and by movements of the observer himself is something that every animal and every child must become capable of, either by learning or by development of the nervous system or by innate endowment. Detecting the permanence of the objective environment behind barriers or outside the momentary field of view or behind one’s back is entailed in the fact of intelligent behavior. In the human child, as Piaget (1954) has shown, it develops slowly. But this development need not be conceived as an intellectual construction of reality from data that do not contain the reality; it can be conceived as a process of learning to extract the information from light that does convey reality. (Gibson 1983: 205)
109.
…it seems to be true that the child cannot be expected to perceive certain facts about the world until he is ready to perceive them… His ability to select and abstract information about the world grows as he does. (Gibson 1983: 269)
110.
I have learned to see things differently through my Piaget-trained eyes. At the core of Piaget’s theory of development is the process he calls assimilation: when new ideas are taken in by a child they are first reconstituted to fit the child’s mental structures. Only later, through the interaction of many such elements, do the structures themselves change in a phase he calls accommodation. I am quite amazed at how educators who try to follow Piaget’s ideas when thinking about children fail to understand that change in School, or any other complex system, must come about in the same way. (Papert 2005: 366, emphasis added)
111.
I left Geneva enormously inspired by Piaget’s image of the child, particularly by his idea that children learn so much without being taught. But I was also enormously frustrated by how little he could tell us about how to create conditions for more knowledge to be acquired by children through this marvelous process of “Piagetian learning.” I saw the popular idea of designing a “Piagetian Curriculum” as “standing Piaget on his head” (Papert 1980: 215)
112.
My goals are education, not just understanding. So, in my own thinking I have placed a greater emphasis on two dimensions implicit but not elaborated in Piaget’s own work: an interest in intellectual structures that could develop as opposed to those that actually at present do develop in the child, and the design of learning environments that are resonant with them. (Papert 1980: 161)
113.
For McCulloch as for Piaget, the study of people and the study of what they learn and think are inseparable. Perhaps paradoxically for some, research on the nature of that inseparable relationship has been advanced by the study of machines and the knowledge they can embody. And it is to this research methodology, that of artificial intelligence, that we now turn.
In artificial intelligence, researchers use computational models to gain insight into human psychology as well as reflect on human psychology as a source of ideas about how to make mechanisms emulate human intelligence. This enterprise strikes many as illogical: Even when the performance looks identical, is there any reason to think that underlying processes are the same? Others find it illicit: The line between man and machine is seen as immutable by both theology and mythology. There is a fear that we will dehumanize what is essentially human by inappropriate analogies between our “judgments” and those computer “calculations.” I take these objections very seriously, but feel that they are based on a view of artificial intelligence that is more reductionist than anything I myself am interested in. (Papert 1980: 164)
114.
One of the strengths of artificial intelligence (‘AI’) as a way of thinking about thinking is that it forces one to model the movement of the mind, the way in which a particular mental phenomenon comes about. It is not enough to say that a phenomenon can happen: the programmer must specify a way in which it can happen. (Boden 1978: 389)
115.
I admired Skinner for having such clear theories and systematic ones. And… and I was good at electronics, so I hung around that laboratory and we became friends and he got me to help wire some cages. So, I really had this experience of helping to set up these experiments, although I had concluded that they were too simple minded and you would never understand how people would learn things like language based on… on that sort of thing and… but the… the main thing was that he… he was happy to discuss these things and he introduced me to the other two young assistant professors who were major figures in the rest of my life: George Miller, who was perhaps the most important scientist starting modern cognitive computational psychology, and Joseph Licklider, who was… had worked on theories of how hearing worked, but was beginning to envision the computer of the future and eventually who went to Washington and organized the Advanced Research Project Agency to fund basic research on artificial intelligence and psychology… it was Miller and Licklider and this next generation who were interested in making theories. What could be in the brain that would learn that way? (Minsky 2011, video interview with Christopher Sykes)
116.
For extremists like B.F. Skinner the mind did not even exist. One could study the act of remembering, but to investigate memory itself transgressed scientific discipline. Ironically, when in the 1940s and 1950s engineers started building machines that played checkers, proved mathematical theorems, and also contained a device they called a “memory,” the engineers discussed the “minds” of their machines in as much detail as they wanted to. Nevertheless, Minsky liked Skinner very much, and spent some time helping the psychologist design equipment for his experiments. As I shall show, Skinner’s ideas about reinforcement learning also later inspired Minsky to build a neural net machine. (Crevier 1993: 33)
Chapter 8
117.
It’s a challenge to explain how to see the magic in something with no interesting qualities at all. This challenge is at the core of math education and is one that both Marvin and Seymour Papert stepped up to. In the introduction to Mindstorms, Seymour talks about a similar kind of magic in his relationship with gears. (Minsky and Solomon 2019: 77, text by Brian Silverman)
118.
Piaget’s work gave me a new framework for looking at the gears of my childhood. The gear can be used to illustrate many powerful “advanced” mathematical ideas, such as groups or relative motion. But it does more than this. As well as connecting with the formal knowledge of mathematics, it also connects with the “body knowledge,” the sensorimotor schemata of a child. You can be the gear, you can understand how it turns by projecting yourself into its place and turning with it. It is this double relationship—both abstract and sensory—that gives the gear the power to carry powerful mathematics into the mind. In a terminology I shall develop in later chapters, the gear acts here as a transitional object. (Papert 1980: viii, emphasis added)
119.
The word constructionism is coined from two words. There is a psychological theory that I first learned to appreciate from Piaget, but which one also finds in Vygotsky and in other theorists. This theory says that knowledge is not transmitted like information in a pipeline. In fact, there is something called the theory of information that in many ways gives us exactly the wrong picture of education. Knowledge is not transmitted, it is constructed. Each individual must reconstruct knowledge. Of course, one does not necessarily do this alone. Everyone needs the help of other people and the support of a material environment, of a culture and society. But still, knowledge must be constructed — and that’s what Piaget meant by the term constructivism.
Constructionism adds a second side to Piaget’s idea of constructivism. Constructivism is the idea that knowledge is something you build in your head. Constructionism reminds us that the best way to do that is to build something tangible — something outside your head — that is also personally meaningful. (Papert 1988: 3, original emphasis)
120.
Papert is interested in how learners engage in a conversation with [their own or other people’s] artifacts, and how these conversations boost self-directed learning, and ultimately facilitate the construction of new knowledge. (Ackermann 2001: 1)
Papert’s approach helps us understand how ideas get formed and transformed when expressed through different media, when actualized in particular contexts, when worked out by individual minds. The emphasis shifts from universals to individual learners’ conversation with their own favorite representations, artifacts, or objects-to-think with. (Ackermann 2001: 4, emphasis added)
121.
Much of what the child learns we don’t even notice. Piaget was able, with some ingenious experiments, to demonstrate this fact. If you place six eggs and six egg cups regularly on a table, and ask a four-year-old child, “Are there more eggs or more egg cups?” the child will recognize that there is the same number of each. But if you spread out the eggs and clump together the cups, the child will say there are more eggs. Yet when the child is a year or two older, he or she will say that they are the same. It won’t matter how you cluster or spread out the eggs or egg cups; the child will still recognize that they are the same, that the eggs have a one-to-one correspondence with the egg cups. (Papert 1984: 36)
122.
Everyone has the right to a basic education, including adult basic education; and to further education, which the state, through reasonable measures, must make progressively available and accessible—South African Schools Act 84 of 1996 (South Africa and South Africa 1996: 12- Section 29(1)(a,b))
123.
The worker’s activity, reduced to a mere abstraction of activity, is determined and regulated on all sides by the movement of the machinery, and not the opposite. The science which compels the inanimate limbs of the machinery, by their construction, to act purposefully, as an automaton, does not exist in the worker’s consciousness, but rather acts upon him through the machine as an alien power, as the power of the machine itself. [….] The transformation of the means of labour into machinery, and of living labour into a mere living accessory of this machinery, as the means of its action, also posits the absorption of the labour process in its material character as a mere moment of the realization process of capital. The increase of the productive force of labour and the greatest possible negation of necessary labour is the necessary tendency of capital, as we have seen. [From a section known as the ‘Fragment on Machines’] (Marx 1973: 693)
124.
My thesis could be summarized as: What the gears cannot do the computer might. The computer is the Proteus of machines. Its essence is its universality, its power to simulate. Because it can take on a thousand forms and can serve a thousand functions, it can appeal to a thousand tastes. (Papert 1980: viii)
125.
In my vision the computer acts as a transitional object to mediate relationships that are ultimately between person and person. There are mathophobes with a fine sense of moving their bodies, and there are mathophiles who have forgotten the sensory motor roots of their mathematical knowledge. The Turtle establishes a bridge. It serves as a common medium in which can be recast the shared elements of body geometry and formal geometry. (Papert 1980: 183, emphasis added)
126.
Amoeba proteus and the other large amoebae have been used, to date, almost exclusively for “pure” or “basic” research. These amoebae are now beginning to be understood well enough so that their use for solving some of the more complex phenomena of cell biology is likely over the next ten years. (Jeon 1973: xiii).
127.
LISP I is a programming system for the IBM 704 for computing with symbolic expressions. It has been used for symbolic calculations in differential and integral calculus, electric circuit theory, mathematical logic, and artificial intelligence. (McCarthy et al. 1960)
128.
In May 1962, at the annual MIT Open House, the hackers fed the paper tape with twenty-seven pages worth of PDP-1 assembly language code into the machine, set up an extra display screen actually a giant oscilloscope and ran Spacewar all day to a public that drifted in and could not believe what they saw. The sight of it, a science-fiction game written by students and controlled by a computer, was so much on the verge of fantasy that no one dared predict that an entire genre of entertainment would eventually be spawned from it. (Levy 2010: 55)
129.
Marvin was the real thing; the PDP-1 hackers would often sit in on his course, Intro to AI 6.544, because not only was Minsky a good theoretician, but he knew his stuff. By the early 1960s, Minsky was beginning to organize what would come to be the world’s first laboratory in artificial intelligence; and he knew that to do what he wanted, he would need programming geniuses as his foot soldiers—so he encouraged hackerism in any way he could. (Levy 2010: 48)
130.
…hackers, the people to whom “computers were the most interesting thing in the world.” The kind of people who, for a lark, would hack up something even wilder than Spacewar and then, instead of playing it all night (as sometimes was happening in the Kluge Room), would hack some more. Instead of space simulations, the hackers who did the scut work at Project MAC would be tackling larger systems—robotic arms, vision projects, mathematical conundrums, and labyrinthine time-sharing systems that boggled the imagination. Fortunately, the classes that entered MIT in the early sixties were to provide some of the most devoted and brilliant hackers who ever sat at a console. (Levy 2010: 59)
131.
The Department of Defense, especially through its Advanced Research Projects Agency (ARPA), had been supporting computers since the war, mindful of their eventual applications toward military use. So by the early sixties, MIT had obtained a long-range grant for its time-sharing project, which would be named Project MAC (the initials stood for two things: Multiple Access Computing, and Machine Aided Cognition). Uncle Sam would cough up three million dollars a year. Dennis would be in charge. Marvin Minsky would also be a large presence, particularly in using the one-third share of the money that would go not for time-sharing development, but for the still ephemeral field of artificial intelligence. Minsky was delighted, since the million dollars was ten times his previous budget for AI, and he realized that a good part of the remaining two thirds would see its way into AI activities as well. It was a chance to set up an ideal facility, where people could plan for the realization of the hacker dream with sophisticated machines, shielded from the bureaucratic lunacy of the outside world. Meanwhile, the hacker dream would be lived day-by-day by devoted students of the machine. (Levy 2010: 58)
132.
I went to MIT partly because I wondered what this new thing called a computer could be in the hands of a child… My first day at MIT, I saw Minsky’s computer sitting there. It was probably a million-dollar machine, massive in size but puny compared to the power of a laptop today. I started playing with it, trying to figure out a math problem I’d been working on, and within a couple of days I had an answer. I felt so empowered that I imagined giving that same sense to kids.
—Seymour Papert, Bangor Daily News (Weber 1997, emphasis added)
133.
The phrase “technology and education” usually means inventing new gadgets to teach the same old stuff in a thinly disguised version of the same old way. Moreover, if the gadgets are computers, the same old teaching becomes incredibly more expensive and biased towards its dullest parts, namely the kind of rote learning in which measurable results can be obtained by treating the children like pigeons in a Skinner box. … Stated more simply, I believe with Dewey, Montessori, and Piaget that children learn by doing and by thinking about what they do. And so the fundamental ingredients of educational innovation must be better things to do and better ways to think about oneself doing these things. … I claim that computation is by far the richest known source of these ingredients. We can give children unprecedented power to invent and carry out exciting projects by providing them with access to computers, with a suitably clear and intelligible programming language and with peripheral devices capable of producing on-line real-time action. (Papert 1972: 253, emphasis added)
134.
In many schools today, the phrase “computer-aided instruction” means making the computer teach the child. One might say the computer is being used to program the child. In my vision, the child programs the computer and, in doing so, both acquires a sense of mastery over a piece of the most modern and powerful technology and establishes an intimate contact with some of the deepest ideas from science, from mathematics, and from the art of intellectual model building. (Papert 1980: 5, emphasis added)
IN MOST contemporary educational situations where children come into contact with computers the computer is used to put children through their paces, to provide exercises of an appropriate level of difficulty, to provide feedback, and to dispense information. The computer programming the child. In the LOGO environment the relationship is reversed: The child, even at preschool ages, is in control: The child programs the computer. And in teaching the computer how to think, children embark on an exploration about how they themselves think.” (Papert 1980: 19, emphasis added)
135.
Example of GHOST: aka Chain Spelling; Donkey; Ghost; Monkey; Wraiths.
The first player thinks of a word of three or more letters, and calls out its first letter. The second player adds another letter which continues but does not complete a word, and so on, until one player is forced to finish a word.
Any player who adds a letter can be challenged by the next player to say what the word will be. Any player who loses such a challenge, or completes a word, becomes ‘one third of a ghost’. When this player loses again, he or she becomes ‘two-thirds of a ghost’ and the third time becomes a ‘whole ghost’ and has to drop out of the game.
Example: Example Anna thinks of the word jet and says ‘J’. Chas thinks of jester and says ‘E’. Kate thinks of jettison but cannot say ‘T’ because that would make a whole word. So she thinks instead of jelly and says ‘L’. Tony has to say another ‘L’, because he cannot think of any word except for jell or jelly. He thus completes a word and he becomes ‘one-third of a ghost’. (Augarde 1995: 91)
136.
Initially the interest was not only mathematics. I named it “Logo” from the Greek “λόγος” which means a word, a thought, the idea, but word is very prominent. And the notion was that computers were not just for doing science or math technical kinds of things; they could be used for language, for music, for all kinds of things, that computers would be interesting to people in various ways. We were interested not only in mathematics but other areas, too. (Agalianos 1997: 132, Interview with Feurzeig)
137.
We had the hope that this would be transformational … we were interested not only in mathematics … And the hope was that this was going to really revolutionise education. It was a very different view about what computers and programming and kids were all about from what people were doing with other technologies like CAI or with BASIC … The hope was that Logo would really get kids to think in a more fundamental way about thinking in all kinds of contexts, to become strategic thinkers, to become more involved in designing and building knowledge. (Wally Feurzeig Interview) (Agalianos et al. 2001: 480)
138.
Working with the turtle mobilises the child’s expertise and pleasure in motion. It draws on the child’s well-established knowledge of “body geometry” as a starting point for the development of bridges into formal geometry. (Papert 1980: 58)
139.
There are mathophobes with a fine sense of moving their bodies, and there are mathophiles who have forgotten the sensory motor roots of their mathematical knowledge. The Turtle establishes a bridge. It serves as a common medium in which can be recast the shared elements of body geometry and formal geometry. (Papert 1980: 183. emphasis added)
140.
The mirror is, after all, a utopia, since it is a placeless place. In the mirror, I see myself there where I am not, in an unreal, virtual space that opens up behind the surface; I am over there, there where I am not, a sort of shadow that gives my own visibility to myself, that enables me to see myself there where I am absent: such is the utopia of the mirror. But it is also a heterotopia in so far as the mirror does exist in reality, where it exerts a sort of counteraction on the position that I occupy. (Foucault 1984)
141.
One of his [Papert’s] favorites is that we are to thinking as the Victorians were to sex. He aims this barb at formal education’s insistence on structured, faultless expression of reasoning, in direct contradiction to the actuality that normal thought is in fact muddled, and that clear explanations come after one has reached a conclusion. This insistence leads many children to perceive their own thought processes as inadequate, to feel ashamed, and to give up on learning. (Crevier 1993: 86)
Chapter 9
141b.
Around 1958, I published my first paper, in the commercial magazine Datamation. I had written a program that could play a game called “five in a row.” It’s like ticktacktoe, except you need rows of five exes or noughts to win. It’s also played on an unbounded board: ordinary coordinate paper will do. The program used a ridiculously simple strategy with no look ahead, but it could beat anyone who played at the same naive level. Since most people had never played the game before, that included just about everybody. Significantly, the paper was entitled: “How to Make a Computer Appear Intelligent“. (Crevier 1993: 133, quoting Weizenbaum)
142.
Around 1958, I published my first paper, in the commercial magazine Datamation. I had written a program that could play a game called “five in a row.” It’s like ticktacktoe, except you need rows of five exes or noughts to win. It’s also played on an unbounded board: ordinary coordinate paper will do. The program used a ridiculously simple strategy with no look ahead, but it could beat anyone who played at the same naive level. Since most people had never played the game before, that included just about everybody. Significantly, the paper was entitled: “How to Make a Computer Appear Intelligent“. (Crevier 1993: 133, quoting Weizenbaum)
143.
I went to considerable trouble in the paper to explain that there wasn’t much behind the scenes, that the machine wasn’t thinking. I explained the strategy well enough that anybody could write that program, which is the same thing I did with ELIZA. (Crevier 1993: 134, quoting Weizenbaum, emphasis added)
144.
One of these tricks was actually a very sensible idea: Weizenbaum decided that the domain knowledge would reside in a program module separate from the one handling the conversations. He reasoned that if different kinds of knowledge were described in different knowledge modules (or “scripts,” as he called them), the program could then chat about a variety of topics. Feel like talking about haute couture rather than baseball today? Just load the haute-couture software module! (Crevier 1993: 134)
145.
The work was done in the period 1964–1966, and was reported in the computer-science literature in January 1966 and August 1967. To summarize it briefly, I composed a computer program with which one could “converse” in English. The human conversationalist partner would type his portion of the conversation on a typewriter connected to a computer, and the computer, under control of my program, would analyze the message that had so been transmitted to it, compose a response to it in English, and cause the response to be typed on the computer’s typewriter. (Weizenbaum 1976: 2)
The Rogerian psychotherapist is relatively easy to imitate because much of his technique consists of drawing his patient out by reflecting the patient’s statements back to him. The following conversation between a young lady and Eliza playing doctor illustrates both the Rogerian technique of encouraging a patient to keep talking and the operation of the computer program ELIZA. The first to “speak” is the young lady. The computer’s responses are printed entirely in capitals. (Weizenbaum 1976: 3)
146.
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. (Weizenbaum 1976: 144, emphasis added)
The point is precisely that computers do interpret texts given to them, in other words, that texts determine computers’ behaviour. Theories written in the form of computer programs are ordinary theories as seen from one point of view. (Weizenbaum 1976: 144, emphasis added)
A physicist may, for example, communicate his theory of the pendulum either as a set of mathematical equations or as a computer program. In either case he will have to identify the terms of his theory-his “variables,” in technical jargon-with whatever they are to correspond to in reality. (He may say l is the length of the pendulum’s string, p its period of oscillation, g the acceleration due to gravity, and so on.) But the computer program has the advantage not only that it may be understood by anyone suitably trained in its language, just as a mathematical formulation can be readily understood by a physicist, but that it may also be run on a computer. (Weizenbaum 1976: 144)
147.
Further work must be done before the program will be ready for clinical use. If the method proves beneficial, then it would provide a therapeutic tool which can be made widely available to mental hospitals and psychiatric centers suffering a shortage of therapists. Because of the time-sharing capabilities of modern and future computers, several hundred patients an hour could be handled by a computer system designed for this purpose. The human therapist, involved in the design and operation of this system, would not be replaced, but would become a much more efficient man since his efforts would no longer be limited to the one-to-one patient-therapist ratio as now exists. (Colby et al. 1966: 152)
148.
This astonishing—one is very tempted to say perceptive—response from the computer is, of course, preprogrammed. But then, so are the responses of human psychotherapists. No such computer program is adequate for psychiatric use today, but the same can be remarked about some human psychotherapists. In a period when more and more people in our society seem to be in need of psychiatric counseling, and when time sharing of computers is widespread, I can imagine the development of a network of computer psychotherapeutic terminals, something like arrays of large telephone booths, in which, for a few dollars a session, we would be able to talk with an attentive, tested, and largely nondirective psychotherapist. Insuring [sic] the confidentiality of the psychiatric dialogue is probably the most important step to be worked out. (Sagan 1975: 10)
149.
The main obstacle to their development seems to be a human problem: the quiet feeling that comes stealthily and unbidden to claim that there is something unpleasant or “inhuman” about machines performing certain tasks as well as, or better than, humans; the feeling that generates a sense of loathing for creatures made of silicon and germanium rather than proteins and nucleic acids. (Sagan 1975: 20)
150.
To avoid the nightmare and realize the dream requires a wholesale restructuring of the planet’s political institutions—a restructuring that is clearly required quite apart from the implications of intelligent machines. If we survive, I think our future will depend to a significant degree on a partnership between human and machine intelligence. (Sagan 1975: 20)
151.
There are cardiac pacemakers in existence that sense the beat of the human heart. Only at the slightest hint of fibrillation does the pacemaker stimulate the heart. This is a mild but useful sort of machine intelligence. I cannot imagine the wearer of this device resenting its intelligence. (Sagan 1975: 20)
152.
Western man’s entire milieu is now pervaded by complex technological extensions of his every functional capacity. Being the enormously adaptive animal he is, man has been able to accept as authentically natural (that is, as given by nature) such technological bases for his relationship to himself, for his identity. Perhaps this helps to explain why he does not question the appropriateness of investing his most private feelings in a computer. But then, such an explanation would also suggest that the computing machine represents merely an extreme extrapolation of a much more general technological usurpation of man’s capacity to act as an autonomous agent in giving meaning to his world. It is therefore important to inquire into the wider senses in which man has come to yield his own autonomy to a world viewed as a machine. (Weizenbaum 1976: 9)
153.
The fact that individuals bind themselves with strong emotional ties to machines ought not in itself to be surprising. The instruments man uses become, after all, extensions of his body. Most importantly, man must, in order to operate his instruments skillfully, internalize aspects of them in the form of kinesthetic and perceptual habits. In that sense at least, his instruments become literally part of him and modify him, and thus alter the basis of his affective relationship to himself. (Weizenbaum 1976: 9, emphasis added)
154.
Even among the skeptics who insisted it was a trick, there was disagreement about how the automaton worked, leading to a series of claims and counterclaims. Did it rely on mechanical trickery, magnetism, or sleight of hand? Was there a dwarf, or a small child, or a legless man hidden inside it? Was it controlled by a remote operator in another room or concealed under the fIoor? None of the many explanations put forward over the years succeeded in fully fathoming Kempelen’s secret and served only to undermine each other. (Standage 2002: xiii)
155.
The ability to switch from one series of numbers to another suggested that the Difference Engine was more than just a mindless automaton; Babbage contended that this behavior constituted machine intelligence. The machine appeared to have memory and foresight and could change its behavior in a way that appeared random, but which was in fact governed by logical rules. Lady Byron, who witnessed such a demonstration with her daughter, Ada Lovelace, wrote to a friend that they had been “to see the thinking machine (for so it seems)” Unlike the new machines of the industrial revolution, which replaced human physical activity, this fragment of the Difference Engine, like the Turk, raised the possibility that machines might eventually be capable of replacing mental activity too.’ (Standage 2002: 145)
156.
Perhaps no exhibition of the kind has ever elicited so general attention as the Chess-Player of Maelzel. Wherever seen it has been an object of intense curiosity, to all persons who think. Yet the question of its modus operandi is still undetermined. Nothing has been written on this topic which can be considered as decisive—and accordingly we find every where men of mechanical genius, of great general acuteness, and discriminative understanding, who make no scruple in pronouncing the Automaton a pure machine, unconnected with human agency in its movements, and consequently, beyond all comparison, the most astonishing of the inventions of mankind. (Poe 1836: 318)
157.
This prompted an article in the National Intelligencer in Washington, D.C.—one of the most respected newspapers in the country—which claimed that Maelzel himself was behind the story. “The tale of a discovery was but a clever device of the proprietor lO keep alive the interest of the community in his exhibition,” the newspaper declared, mocking the Gazette for falling for such an obvious publicity stunt. As a result, no other newspapers picked up the story, and it was swiftly forgotten. (Standage 2002: 166).
It was Schlumberger who was seen getting out of the Turk’s cabinet by the two young boys in Baltimore, whose story had of course been true all along, and Poe rightly pointed out that when Schlumberger was unwell, exhibitions of the Turk were suspended. (Standage 2002: 181)
158.
It is ironic that the Turing test relies on concealment and deception: on machines trying to act like people, and people acting like machines. Kempelen would surely have been surprised to discover that, in a sense, little has changed since he unveiled his mechanical Turk. (Standage 2002: 246)
159.
“There are two classes of critics of AI. In one class are people like Dreyfus and Searle: they misunderstand, and should be ignored. And then there’s Weizenbaum. I owe him a profound apology. I’ve long misunderstood what he’s been saying, and I now see that his criticisms are correct and should be taken seriously.” After a while I could hardly see because of the flashes! I would like to believe in this great reconciliation scene, but I think Marvin just did what comes naturally to him: he said the most sensational thing he could think of. (Crevier 1993: 143, emphasis added)
160.
To me “intelligence” seems to denote little more than the complex of performances which we happen to respect, but do not understand. So it is, usually, with the question of “depth” in mathematics. Once the proof of a theorem is really understood its content seems to become trivial. (Still, there may remain a sense of wonder about how the proof was discovered.) (Minsky 1961: 27)
161.
In the end, we will find ways to replace every part of the body and brain – and thus repair all the defects and flaws that make our lives so brief … In the past we have tended to see ourselves as a final product of evolution – but our evolution has not ceased. Indeed, we are now evolving more rapidly – although not in the slow Darwinian way … We now can design systems based on new kinds of “unnatural selection” that can exploit explicit plans and goals … It took a century for evolutionists to train themselves to avoid such ideas – biologists call them ‘teleological’ and ‘Lamarckian’ – but now we may have to change those rules. (Minsky 1994)
162.
Minsky’s editorial for Science Journal, which reads like the scenario for 2001: At first machines had simple claws. Soon they will have fantastically graceful articulations. Computers’ eyes once could sense only a hole in a card. Now they recognize shapes on simple backgrounds. Soon they will rival man’s analysis of his environment. Computer programs once merely added columns of figures. Now they play games well, understand simple conversations, weigh many factors in decisions. What next? Today, machines solve problems mainly according to the principles we build into them. Before long, we may learn how to set them to work upon the very special problem of improving their own capacity to solve problems. Once a certain threshold is passed, this could lead to a spiral of acceleration and it may be hard to perfect a reliable ‘governor’ to restrain it. (Minsky, 1968, as cited in Dreyfus 1992: 80)
163.
If micro-worlds were sub-worlds one would not have to extend and combine them to reach the everyday world, because the everyday world would have to be included already. Since, however, micro-worlds are not worlds, there is no way they can be combined and extended to the world of everyday life. As a result of failing to ask what a world is, five more years of stagnation in AI was mistaken for progress. Papert and Minsky’s 1973 grant proposal is perhaps the last time the artificially isolated character of the micro-world is defended as a scientific virtue-at least at M.I.T:
Artificial Intelligence, as a new technology, is in an intermediate stage of development. In the first stages of a new field, things have to be simplified so that one can isolate and study the elementary phenomena. In most successful applications, we use a strategy we call “working within a Micro-World”. (Minsky and Papert 1973: 95)
164.
It is not clear how complete such a definition would have to be for a heuristic program to be able to play good or even mediocre chess. The poor performance of chess programs may indicate that thus far evaluations have been too static and crude (Dreyfus 1965: 73)
165.
I remember discussions we had on what to do about Dreyfus. A general conclusion was reached more or less explicitly by the AI community: the best thing to do was to give Dreyfus the silent treatment. Just not to talk about him, not to try to defend against him, not to laugh at him, nothing. Basically, as far as the AI community was concerned, Dreyfus became a nonperson. We shudder when we hear that this was done in communist dictatorships, yet we did the same thing. (Crevier 1993: 123, emphasis added)
166.
Indeed, at M.I.T. the rejection was so total that students and professors working on the robot project dared not be seen having lunch with me without risking getting into trouble with their superiors. When Joseph Weizenbaum, the only professor who had any doubts, wanted to discuss his concerns with me, we had to meet at his home in the suburbs. I spent the next semester at the Harvard Computer Laboratory, which, not being committed to artificial intelligence, offered to make me a research associate in computer science so I could continue my investigation. (Dreyfus and Dreyfus 2009: 9)
167.
I organized the famous chess match. That was beautiful. He was—well, it wasn’t all pathetic and sad because he was quite convincing. He was going to beat it very easily. And that also said something about him, something almost naive. We didn’t know. About halfway through we all thought Dreyfus was going to win. (McCorduck 2019: 231)
Excerpt from The New Yorker:
In the face of these and parallel stymies, workers in cognitive simulation refuse to be discouraged. Mr. Dreyfus compares their pertinacity to that of the alchemists, who, after initial triumphs in distilling quicksilver from what seemed to be dirt, labored fruitlessly for several hundred years to transmute lead into gold, feeling themselves to be ever on the verge of a break-through. By defining “progress” as “displacement toward the ultimate goal,” today’s alchemists, the cognitive-simulation workers, obscure the prospects for artificial intelligence. According to this definition, Dreyfus points out, the first man to climb a tree could claim progress toward flight to the moon. He suggests that the workers’ unrelenting optimism is founded on the unfortunate Cartesian assumption that human information-processing must be analyzable into “simple determinate operations,” or discrete steps, much like those achieved by a digital computer. If, in reality, “thinking” entails more mysterious kinds of mental activity as well, there are a great many offices that machines won’t ever be moving into. Some weeks ago, in a pleasant turnabout, a collection of scientists at a Symposium on Computer Usage agreed that machines will forever be confused by complexity. “Computers can add and subtract one million numbers in one second,” Dr. Warren S. McCulloch, of M.I.T., explained. “No man can do this, but man can slip into any of fourteen different modes of action-from sleeping to fighting-in about three-tenths of a second. He has about one trillion computing neurons that bring together two million separate biological components all at once.” (Anon 1966: 28)
168.
How had Papert come to be pulled into the Dreyfus dispute? For he ended up writing a refutation of Dreyfus’s paper that in fact was requested by Paul Armer, but RAND’S attorneys felt nervous about publishing it because it contained what they thought might be libelous material. It was eventually brought out as a Project MAC report, with no lawsuits ensuing. About this paper Papert says now:
It was really a serious distraction which I shouldn’t have done. I was thinking of writing a paper or a little monograph—I’m still thinking of doing it—on the difficulties of accepting the idea of AI. This means you consider what it is about the idea of machines being intelligent that makes it hard for people to understand or accept that idea. Incidentally, I think an important part of the training of students in AI is for them to work through this kind of difficulty, accept that they do have conflicts and it does clash with many aspects of our culture, so that even if you’re convinced intellectually that it’s possible, you really have to work through all these resistances. Otherwise you’re always going to be hung up about some aspect. Anyway, that was a plan and I was making a draft of it when Dreyfus came along. So I got this probably bad idea that a good way to do that would be to take a real subject—let’s take this guy who seems to be having these real difficulties, and deal with him as an interesting subject for study. It’s a nice, seductive idea, but it wasn’t in the end really so, largely because Dreyfus doesn’t really come to grips with the issues: he stays too near the surface.
(McCorduck 2019: 229) [McCorduck quoting Papert about The Artificial Intelligence of Hubert L. Dreyfus]
169.
This horror of criticism is very real. When I was invited to review two recent publications of Minsky, Papert, and Winston for Creative Computing, the book review editor at the same time invited Seymour Papert to review my book, so as to balance the presentation. According to the book review editor, Papert replied by threatening that if Creative Computing published my critique there would be disapproval from the AI community and reprisals from M.I.T. Press. Moreover, he promised that if Creative Computing rejected my review, he would submit an article which the editor had been trying in vain to get from him, as well as furnish additional articles by his students. (Dreyfus 1992: 307)
170.
So our biological endowment permits us to grow arms and legs, but for exactly the same reason it prevents us from growing wings. And the logic carries over to the cognitive domain. It’s been discussed significantly for one, but one figure of the great 19th century philosopher logician, the Charles Sanders Perce. He argued persuasively, I think, that our reasoning in science and ordinary life is guided by what he called deductive principles, which crucially limit the range of the hypotheses that we are capable of entertaining. (Chomsky 2016) [Video Transcribed by author 00:15:57:10 – 00:16:42:28].
171.
Otherwise, he argues, I think—again—convincingly, learning and discovery would be impossible. But these very same principles render other hypotheses either inaccessible or so remote in some accessibility hierarchy that they can’t be entertained. In fact, there’s little reason to believe that we have even the intellectual resources to pose the correct nostrums, let alone to find the answers and this being the case, as I think it is, all sciences can be regarded as a domain of convergence between our innate intellectual capacities and the truths about the world, a convergence that might well be partial apart from some biologic miracle. (Chomsky 2016, emphasis added) [Video Transcribed by author 00:16:43:01 – 00:17:38:21]
172.
Doing theoretical physics also requires background skills that may not be formalizable, but the domain itself can be described by abstract laws that make no reference to these background skills. AI researchers mistakenly conclude that commonsense physics too must be expressible as a set of abstract principles. But it just may be that the problem of finding a theory of commonsense physics is insoluble because the domain has no theoretical structure. By playing with all sorts of liquids and solids every day for several years, a child may simply learn to discriminate prototypical cases of solids, liquids, and so on and learn typical skilled responses to their typical behavior in typical circumstances. The same might well be the case for the social world. If background understanding is indeed a skill and if skills are based on whole patterns and not on rules, we would expect symbolic representations to fail to capture our commonsense understanding. (Dreyfus and Dreyfus 1988: 33, emphasis added)
173.
There is no stagnation. The crudely empirical criterion of observing performance of machines suffices to demonstrate steady progress. But even if Dreyfus had bothered to find out how well modern programs actually perform, he would have missed a far deeper point, which I shall introduce through an analogy with another branch of engineering. The innovators in aviation at the beginning of the century worked by building whole airplanes and flying them. The problems of supersonic airliners and atomic aircraft are being solved now by people who could no more construct an airplane than fly themselves. Artificial intelligence follows this pattern like any other area of science or technology. The sign of its maturity is the emergence of specific technical problems. But the amateur observer sees this maturation as “stagnation.” (Papert 1968: 11)
174.
The question then arises, and it answers itself, do we wish to encourage people to lead their lives on the basis of patent fraud, charlatanism, and unreality? And, more importantly, do we really believe that it helps people living in our already overly machine-like world to prefer the therapy administered by machines to that given by other people? (Weizenbaum 1976: 269)
175.
If the teacher, if anyone, is to be an example of a whole person to others, he must first strive to be a whole person. Without the courage to confront one’s inner as well as one’s outer worlds, such wholeness is impossible to achieve. Instrumental reason alone cannot lead to it. And there precisely is a crucial difference between man and machine: Man, in order to become whole, must be forever an explorer of both his inner and his outer realities. His life is full of risks, but risks he has the courage to accept, because, like the explorer, he learns to trust his own capacities to endure, to overcome.
What could it mean to speak of risk, courage, trust, endurance, and overcoming when one speaks of machines? (Weizenbaum 1976: 280)
176.
It is perhaps paradoxical that just, when in the deepest sense man has ceased to believe in—let alone to trust—his own autonomy, he has begun to rely on autonomous machines, that is, on machines that operate for long periods of time entirely on the basis of their own internal realities. (Weizenbaum 1976: 9, emphasis added)
What is it about the computer that has brought the view of man as a machine to a new level of plausibility? Clearly there have been other machines that imitated man in various ways, e.g., steam shovels. But not until the invention of the digital computer have there been machines that could perform intellectual functions of even modest scope; i.e., machines that could in any sense be said to be intelligent. Now “artificial intelligence” (AI) is a subdiscipline of computer science. This new field will have to be discussed. Ultimately a line dividing human and machine intelligence must be drawn. If there is no such line, then advocates of computerized psychotherapy may be merely heralds of an age in which man has finally been recognized as nothing but a clock-work. Then the consequences of such a reality would need urgently to be divined and contemplated. (Weizenbaum 1976: 8, emphasis added)
Chapter 10
177.
It is an approach to social life and history frequently to be found in newspaper articles and television documentaries. It is a model of history and social change explained as a result of the talents and “inspirational” activities of great individuals. This is the Logo story as a narrative of an individual life and an institution. (Agalianos 1997: 123)
178.
In the post-Sputnik years of technological racing against the Russians. Within this context of enormous financial investment in technological development as well as in science and maths education, Logo—unlike most other programming languages—was specifically designed for education. The initial research about what was to become Logo goes back to 1966 and it was carried out by a research team at the Educational Technologies Department of BBN, a Research & Development company in Cambridge, Massachusetts. The initial research group was pretty small including people who later faded out. Wallace Feurzeig was the group leader. Daniel Bobrow, Robert Lawler, Cynthia Solomon and Seymour Papert were among those involved. (Agalianos 1997: 125)
179.
‘Children define what’s special about people by contrasting them with their nearest neighbors, which have always been the animals. People are special because they know how to think. Now children who work with computers see the computer as their nearest neighbour, so they see that people are special because they feel. This may become much more central to the way people think about themselves. We may be moving towards a re-evaluation of what makes us human’. (Friedrich 1983: 24, quoting Turkle)
180.
We, the Architecture Machine Group at MIT, are embarking on the construction of a machine that can work with missing information. To do this an architecture machine must understand our metaphors, must solicit information on its own, must acquire experiences, must talk to a wide variety of people, must improve over time, and must be intelligent. It must recognize context, particularly changes in goals and meanings brought about by changes in context. (Brand 1987: 150, emphasis added)
181.
Excerpt from the Software exhibition catalog
Seek is a sensing / effecting device controlled by a small general purpose computer. In contrast to an input/output peripheral, Seek is a mechanism that senses the physical environment, effects that environment, and in turn attempts to handle local unexpected events within the environment. Seek deals with toy blocks which it can stack, align and sort. At the same time, these blocks form the built environment for a small colony of gerbils which live within Seek’s three-dimensional world.
Unbeknownst to Seek, the little animals are bumping into blocks, disrupting constructions, and toppling towers. The result is a substantial mismatch between the three-dimensional reality and the computed remembrances which reside in the memory of Seek’s computer. Seek’s role is to deal with these inconsistencies. In the process, Seek exhibits inklings of a responsive behavior inasmuch as the actions of the gerbils are not predictable and the reactions of Seek purposefully correct or amplify gerbil-provoked dislocations.
Seek consists of a 5×8 foot superstructure supporting a carriage which has three dimensions of freedom. Its extremity is composed of an electromagnet, several micro-switches, and pressure-sensing devices. This elementary prosthesis is guided by the blind and handless computer to pick up or deposit its payload of a single two-inch cube. The nucleus of the system is an Interdata Model 3 Computer with 65536 single (yes/no) bits of memory which are shared by instructions and data. (Burnham et al. 1970: 23)
182.
Seek has been developed and constructed by M.I.T. students who form part of the Architecture Machine Group, a Ford Foundation sponsored research effort within the M.I.T. Urban Systems Laboratory. The participants have ranged from freshmen working in an Undergraduate Research Opportunities Program, to post-graduates designing elements as part of their research assistantships. (Burnham et al. 1970: 23, emphasis added)
183.
The M.I.T. Minsky/Papert eye. In this case the eye is an image dissector, a random-access device that does not scan back and forth but rather goes to discrete positions under computer control. This was the eye used for the Platt/Drazen vision experiment under the supervision of Seymour Papert. (Negroponte 1970: 107, emphasis added)
At present, these works are being applied to architectural problems as an exercise preliminary to the construction of an architecture machine. Anthony Platt and Mark Drazen are applying the Minsky-Papert eye to the problem of looking at physical models. The interim goal of this exercise is to observe, recognize, and determine the “intents” of several models built from plastic blocks… In contrast to describing criteria and asking the machine to generate physical form, this exercise focuses on generating criteria from physical form. (Negroponte 1970: 105)
184.
Long range goals of work directed by Marvin L. Minsky, Professor of Electrical Engineering, and Seymour A. Papert, Visiting Professor of Applied Mathematics, envisage machines with finer and more varied visual abilities and more manual dexterity than are required for such semi-routine tasks. Work is progressing on binocular vision, color vision, the ability to perceive textures, touch sensors, improved mechanical hands and other areas whose development is necessary for accomplishing significant real-world tasks. Outlining goals such as these, especially the ability to program machines to acquire and use a substantial fund of knowledge about the real world, reveals the extent of scientific and engineering progress toward “artificial intelligence.”
185.
The art world has certainly had its share of demos, and perhaps deaths. In 1970 the Jewish Museum in New York staged an exhibition that is today widely hailed as one of the pioneering moments in the history of digital media. Famous for both its failures and its successes, Software. Information Technology: Its New Meaning for Art was among the first efforts to introduce art as information processing to the American public. At the heart of this pioneering exhibition was the idea that we exist in a new age reconfigured by communication technologies and the new sciences of communication and control titled cybernetics. (Thylstrup et al. 2021: 194, original emphasis in italics, emphasis added in boldface)
186.
SEEK, part of the SOFTWARE exhibit at the Jewish Museum, NewYork, September 16–November 8, 1970. Its purpose was to show how a machine handled a mismatch between its model of the world and the real world—in this case five hundred two-inch metal- plated cubes. The mismatch was created by a colony of gerbils whose activity constantly disturbed the strictly rectilinear arrangement called for by the machine’s model.
Gerbils were selected for their curiosity. The plastic box straightened blocks corner straightened blocks when SEEK discovered them to be crooked. A block slightly askew would be realigned. One substantially dislocated would be placed (straight, of course) in the new position, on the assumption that the gerbils wanted it there. The outcome was a constantly changing architecture that reflected the way the little animals used the place. (Negroponte 1975: 47)
187.
Space, cities, and environments were not stable things but rather ecologies that emerged through the interactions between individuals and agents—in this case, from the interactions between the gerbils and the machine. This little demo also exhibited the problems and dangers of an algorithmically managed and computationally networked world where change becomes catastrophic when it cannot be programmed and controlled. If things go wrong once every- thing is connected, they go really wrong. (Thylstrup et al. 2021: 139)
188.
Compiling in ’51, nobody believed that, I had a running compiler and nobody would touch it, because, they carefully told me, computers could only do arithmetic, they could not write programs. It was a selling job to get people to try it. I think with any new idea, because people are allergic to change, you have to get out and sell the idea. (Cushman Jr. 1986: B6)
189.
As a part of the Hessdorfer experiment, a teletypewriting device was brought into the South End, Boston’s ghetto area. Three inhabitants of the neighborhood were asked to converse with this machine about their local environment. Though the conversation was hampered by the necessity of typing English sentences, the chat was smooth enough to reveal two important results. First, the three residents had no qualms or suspicions about talking with a machine in English, about personal desires; they did not type uncalled-for remarks; instead, they immediately entered a discourse about slum land- lords, highways, schools, and the like. Second, the three user-inhabitants said things to this machine they would probably not have said to another human, particularly a white planner or politician: to them the machine was not black, was not white, and surely had no prejudices. (The reader should know, as the three users did not, that this experiment was conducted over telephone lines with teletypes, with a human at the other end, not a machine. The same experiment will be rerun shortly, this time with a machine at the other end of the telephone line.) (Negroponte 1970: 55)
190.
What gives this demo force is that it is the performance of a “wish image” of the future. If from a historical distance it might appear to be nothing more than playacting, at the time Negroponte argued that demos are truth—experiments that prove which forms of research and technology need to be invoked next; that should exist and must be built. Negroponte envisioned a world driven through crowdsourcing and the technical resolution of political conflicts. (Thylstrup et al. 2021: 138)
191.
I advise all students to read some philosophy and with great sympathy, not to understand what the philosopher said, but to feel compassionate and say, “Think of those poor people years ago who tried so hard to cook without ingredients, who tried to build a house without wood and nails, who tried to build a car without steel, rubber or gasoline.” So, look at philosophy with sympathy, but don’t look for knowledge. There is none. (Minsky 1996, emphasis added) [Transcribed, excerpted and edited by Nicholas Gessler]
192.
While, in a typical math class, a child’s reaction to a wrong answer is to try to forget it as fast as possible, in a Logo environment the process of “debugging” is a normal part of the process of understanding a program. The programmer is encouraged to study the bug rather than forget the error (Papert 1980: 61).
Most of what has been done up to now under the name of “educational technology” or “computers in education” is still at the stage of the linear mix of old instructional methods with new technologies (Papert, 1980:36).
193.
I’m trying to help Nicholas filter the activities of the Media Lab so that in each case there’s
a chance for some new theory to grow and lead to really new things, instead of just helping people along a little bit in their craft. Even shallow cognitive ideas can lead to deep engineering. Computers are not a new idea now, and there are millions of people out there who are very smart and are doing all the easy things. So if there is a place for the Lab, it’s going to have to be better than a toy company, and that’s hard to do, because the toy companies are so good. (Brand 1987: 106, emphasis added)
194.
The Centre Mondial was established early in 1982 with a lot of publicity, an initial $100m budget and a social-political aim: to be a research place for the development of computer systems that would take a larger view of social development, to look into how Informatics could be part of larger social change. It was thought that the place where this could be seen most clearly was in Third World countries. Among the Centre’s proposals was a project to install a personal computer in each of 500 villages (most of them in the Third World).
Seventy-five researchers were recruited from all over the world, many of them at enormous scale salaries. Papert wrote the key paper formulating the centre’s programme. Brian Harvey worked there and many other members of the Logo community like Hal Abelson and Andy diSessa. Papert and Nicholas Negreponte from MIT took leave of absence from their academic posts to become the co-directors of the Centre. (Agalianos 1997: 292)
The WCC was to be an international research centre independent of all commercial, political and national interests. Naturally, it failed. Nothing is that independent, especially an organisation backed by a socialist government [what does he really mean here?] and staffed by highly individualistic industry visionaries from around the world. Besides, altruism has a credibility problem in an industry that thrives on intense commercial competition. By the end of the Centre’s first year, Papert had quit, so had American experts Nicholas Negreponte and Bob Lawler. It had become a battlefield, scarred by clashes of management style, personality, and political conviction. (Agalianos 1997: 295) (Paul Tate, Datamation).
195.
Why would a kid in the developing world need a laptop of all things, when they might not have food, they probably in some cases don’t live beyond the age of five, they don’t have drinking water, and the parents earn a dollar a day or less. Good grief, why should they have a laptop? Take the word ‘laptop’ and substitute the word ‘education’, and nobody would say that. This is probably the only hope, and I don’t want to place too much on OLPC, but if I really have to look at sort of how to eliminate poverty, create peace, and work on the environment, I can’t think of a better way to do it. (Negroponte 2007, emphasis added) [transcribed by self]
196.
Once people start looking at this, they say, “Ah, this is a laptop project.” Well, no, it’s not a laptop project. It’s an education project. And the fun part — and I’m quite focused on it – I tell people I used to be a light bulb, but now I’m a laser – I’m just going to get that thing built, and it turns out it’s not so hard. (Negroponte 2006a)
It’s an education project not a laptop project. For people it’s like the hazard of being a beautiful blonde—people pay attention to the wrong thing. It’s almost an attractive nuisance. We were driven by the elimination of poverty. With building more schools, it would take forever and ever. What we’re trying to do in the meantime is get more children to do more on their own. (Negroponte 2006b, emphasis added)
197.
And everybody says—I say—it’s an education project. Are we providing the software? The answer is: The system certainly has software, but no, we’re not providing the education content. That is really done in the countries. But we are certainly constructionists. And we certainly believe in learning by doing and everything from Logo, which was started in 1968, to more modern things, like Scratch, if you’ve ever even heard of it, are very, very much part of it. And that’s the rollout. (Negroponte 2006a, emphasis added) [15:43]
198.
OLPC’s website claims this project as a precursor: “In a French government-sponsored pilot project, Papert and Negroponte distribute Apple II microcomputers to school children in a suburb of Dakar, Senegal [in 1982]. The experience confirms one of Papert’s central assumptions: children in remote, rural, and poor regions of the world take to computers as easily and naturally as children anywhere.” (Ames 2019: 35)
199.
The computer presence will enable us to so modify the learning environment outside the classrooms that much if not all the knowledge schools presently try to teach with such pain and expense and such limited success will be learned painlessly, successfully, and without organised instruction. This obviously implies that schools as we know them today will have no place in the future (Papert, 1980:9).
200.
The computer presence will enable us to so modify the learning environment outside the classrooms that
Today, when 20 percent of the world consumes 80 percent of its resources, when a quarter of us have an acceptable standard of living and three-quarters don’t, how can this divide possibly come together? While the politicians struggle with the baggage of history, a new generation is emerging from the digital landscape free of many of the old prejudices. These kids are released from the limitation of geographic proximity as the sole basis of friendship, collaboration, play, and neighborhood. Digital technology can be a natural force drawing people into greater world harmony.
The harmonizing effect of being digital is already apparent as previously partitioned disciplines and enterprises find themselves collaborating, not competing. A previously missing common language emerges, allowing people to understand across boundaries. Kids at school today experience the opportunity to look at the same thing from many perspectives. A computer program, for example, can be seen simultaneously as a set of computer instructions or as concrete poetry formed by the indentations in the text of the program. What kids learn very quickly is that to know a program is to know it from many perspectives, not just one. (Negroponte 1995: 230)
much if not all the knowledge schools presently try to teach with such pain and expense and such limited success will be learned painlessly, successfully, and without organised instruction. This obviously implies that schools as we know them today will have no place in the future (Papert, 1980:9).
201.
By changing the conversation from ‘how do we fix the system?’ to ‘how do we meet the unique needs of all individual learners?’ we begin a critical transformation. Insofar as literacy is the key to eliminating poverty, creating world peace and saving the environment, a paradigm shift is urgently needed. (Anon 2013, emphasis added)
202.
All efforts are avoided by successfully replacing reality by images. A magician does nothing, or almost nothing, but makes everyone believe that he is doing everything, and all the more so since he puts to work collective forces and ideas to help the individual imagination in its belief. The art of the magician involves suggesting means, enlarging on the virtues of objects, anticipating effects, and by these methods fully satisfying the desires and expectations which have been fostered by entire generations in common. Magic gives form and shape to those poorly co-ordinated or impotent gestures by which the needs of the individual are expressed, and because it does this through ritual, it renders them effective. (Mauss 1972: 141, emphasis added)
Conclusion Chapter
203.
So at the Media Lab, we don’t just do hardware. We do all kinds of things. We do biology, we do hardware, and Nicholas Negroponte famously said, “Demo or die,” as opposed to “Publish or perish,” which was the traditional academic way of thinking. And he often said, the demo only has to work once, because the primary mode of us impacting the world was through large companies being inspired by us and creating products like the Kindle or Lego Mindstorms. But today, with the ability to deploy things into the real world at such low cost, I’m changing the motto now, and this is the official public statement. I’m officially saying, “Deploy or die.” You have to get the stuff into the real world for it to really count, and sometimes it will be large companies, and Nicholas can talk about satellites. [Applause] Thank you. (Ito 2014, emphasis added)
204.
I’ll tell you what my prediction is, and my prediction, and this is a prediction, because it’ll be 30 years. I won’t be here. But one of the things about learning how to read, we have been doing a lot of consuming of information going through our eyes, and so that may be a very inefficient channel. So my prediction is that we are going to ingest information. You’re going to swallow a pill and know English. You’re going to swallow a pill and know Shakespeare. And the way to do it is through the bloodstream. So once it’s in your bloodstream, it basically goes through it and gets into the brain, and when it knows that it’s in the brain in the different pieces, it deposits it in the right places. So it’s ingesting. (Negroponte 2014, emphasis added)
205.
We had academic conversations about end-to-end cryptography that the government couldn’t crack, and the importance of scientific research into the personal and societal impact of robot sex. We discussed releasing genetically modified organisms with gene drive technology into the wild and extreme geological engineering, for example, throwing diamond dust into the stratosphere to reflect the sun’s rays to cool the earth. (Itō and Howe 2016: 39)
206.
The Disobedience Award—a $250,000, no-strings-attached prize—recognized individuals and groups who engage in responsible, ethical disobedience aimed at challenging norms, rules, or laws that sustain society’s injustices.
This award honored work that is focused on positive impact and is consistent with a set of key principles, including:
- nonviolence
- creativity
- courage
- personal responsibility
The award was open to people and groups working in any discipline anywhere in the world, including, but not limited to, scientific research, civil rights, freedom of speech, human rights, and the freedom to innovate. By rewarding thoughtful, nonviolent acts of disobedience, we hoped to raise the public profile of these activities and ultimately inspire new agents of change. (Media Lab 2017)
207.
In a lot of large institutions there’s really two ways you make progress, you make progress when people follow the rules and work their way through the processes, and then sometimes you make very radical progress by someone who essentially says, ‘Look, these processes don’t work anymore, and I need to have a radical shift in what I’m doing.’ (Best 2017) [Interview with Ethan Zuckerman] [emphasis added]
208.
Psychiatry reveals the true nature of this promised state. What is it but the electronic equivalent of the dissociation and subjective inflation that takes place under lysergic acid and similar drugs? In so far as McLuhan’s conception corresponds to any existential reality, it is that of an electronically induced mass psychosis. Not surprisingly, perhaps, now that the facilities for instantaneous communication have planetary outlets, symptoms of this psychosis are already detectable in every part of the planet. In McLuhan’s case, the disease poses as the diagnosis. (Mumford 1970: 294)
208b.
[As] McLuhan sees it, instantaneous planetary communication will bring about a release from all previous cultures and past modes of regimentation: machines themselves will vanish, to be replaced by electronic equivalents or substitutes. In McLuhan’s trancelike vaticinations, he actually appears to believe that this has already happened, and that even the wheel is about to disappear, while mankind as a whole will return to the pre-primitive level, sharing mindless sensations and pre-linguistic communion. (Mumford 1970: 293, emphasis added)
209.
Theuth: Here is something that, once learned, will make the Egyptians wiser and will improve their memory; I have discovered a potion for memory and for wisdom.
Thamus: One man can give birth to the elements of an art, but only another can judge how they can benefit or harm those who will use them. And now, since you are the father of writing, your affection for it has made you describe its effects as the opposite of what they really are.
In fact, it will introduce forgetfulness into the soul of those who learn it: they will not practise using their memory because they will put their trust in writing, which is external and depends on signs that belong to others, instead of trying to remember from the inside, completely on their own. (Nehamas and Woodruff 1995: 79)
210.
When intelligent machines are constructed, we should not be surprised to find them as confused and as stubborn as men in their convictions about mind-matter, consciousness, free will, and the like. For all such questions are pointed at explaining the complicated interactions between parts of the self-model. A man’s or a machine’s strength of conviction about such things tells us nothing about the man or about the machine except what it tells us about his model of himself. (Minsky 1965: 4; Bernstein 1981, emphasis added)
211.
You know, Phaedrus, writing shares a strange feature with painting. The offsprings of painting stand there as if they are alive, but if anyone asks them anything, they remain most solemnly silent. The same is true of written words. You’d think they were speaking as if they had some understanding, but if you question anything that has been said because you want to learn more, it continues to signify just that very same thing forever. (Nehamas and Woodruff 1995: 80) [Quoting Socrates 275D]
212.
The fact is, from the moment that we are placed within the framework of Oedipus—from the moment that we are measured in terms of Oedipus—the cards are stacked against us, and the only real relation-ship, that of production, has been done away with. The great discovery of psychoanalysis was that of the production of desire, of the productions of the unconscious. But once Oedipus entered the picture, this discovery was soon buried beneath a new brand of idealism: a classical theater was substituted for the unconscious as a factory; representation was substituted for the units of production of the unconscious; and an unconscious that was capable of nothing but expressing itself—in myth, tragedy, dreams—was substituted for the productive unconscious. (Deleuze and Guattari 1983: 24) [emphasises added]
213.
We must speak of “castration” in the same way we speak of oedipalization, whose crowning moment it is: castration designates the operation by which psychoanalysis castrates the unconscious, injects castration into the unconscious. Castration as a practical operation on the unconscious is achieved when the thousand breaks-flows of desiring-machines-all positive, all productive-are projected into the same mythical space, the unary stroke of the signifier. (Deleuze and Guattari 1983: 60)
214.
We may be machines, but these experienced aspects of our biology (the consequences of there being meat in our meat machines) compel a search for transcendence. People look for it in religion, history, art, the relationships through which we hope to live on. Emergent AI does not offer us minds that are born of mothers, grow up in families, or know the fear of death. When AI offered a rational and rule-driven machine, it led to a romantic reaction. Current romanticizations of the machine may lead to a rationalistic one: a too-easy acceptance of the idea that what is essential about human beings can be captured in what makes us kin to mechanism. (Turkle 1991: 249)
215.
If cryptography, i.e., cryptanalysis, is the search for the design of the Enigma machine and the protocols of its use, the hardware and software of an electromechanical computer, then it is also the search for the design of the natural world and the protocols that govern it, the hardware and the software of the computer on which the world as we experience it is run. (Gessler 2013: 525)
216.
Then, since human bodies are part of the physical world and, as we have seen, objects in the physical world have been shown to obey laws which can be expressed in a formalism manipulable on a digital computer, the formalist can still claim that there must be laws of human behaviour of the sort required by his formalism. (Dreyfus 1972: 106) [emphasis added]
217.
Desiring-machines are binary machines, obeying a binary law or set of rules governing associations: one machine is always coupled with another. The productive synthesis, the production of production, is inherently connective in nature: “and . . . ” “and then . . . ” This is because there is always a flow-producing machine, and another machine connected to it that interrupts or draws off part of this flow (the breast-the mouth). And because the first machine is in turn connected to another whose flow it interrupts or partially drains off, the binary series is linear in every direction. Desire constantly couples continuous flows and partial objects that are by nature fragmentary and fragmented. Desire causes the current to flow, itself flows in turn, and breaks the flows. (Deleuze and Guattari 1983: 6)
218.
Lie down, then, on the soft couch which the analyst provides, and try to think up something different. The analyst has endless time and patience; every minute you detain him means money in his pocket. He is like God, in a sense—the God of your own creation. Whether you whine, howl, beg, weep, implore, cajole, pray or curse—he listens. He is just a big ear minus a sympathetic nervous system. He is impervious to everything but truth. If you think it pays to fool him then fool him. Who will be the loser? If you think he can help you, and not yourself, then stick to him until you rot. (Miller 1965: 429)
219.
He has nothing to lose. But if you realize that he is not a god but a human being like yourself, with worries, defects, ambitions, frailties, that he is not the repository of an all-encompassing wisdom but a wanderer, like yourself, along the path, perhaps you will cease pouring it out like a sewer, however melodious it may sound to your ears, and rise up on your own two legs and sing with your own God-given voice. To confess, to whine, to complain, to commiserate, always demands a toll. To sing it doesn’t cost you a penny. (Miller 1965: 429)
220.
Obviously computers cannot invent new symbols or conceive new ideas not already outlined in the very setting up of their programs. Within its strict limits, a computer can perform logical operations intelligently, and even, given a program that includes random factors, can simulate ‘creation,’ but under no circumstances can it dream of a different mode of organization than its own. Faced with the problem of translation from one language to another—a function once hopefully assigned to the computer—its choices become absurd and its meanings scrambled, as in a case of brain damage.
Man, on the contrary, is constitutionally an open system, reacting to another open system, that of nature. Only an infinitesimal part of either system can be interpreted by man, or come under his control, and only an even minuter portion accordingly falls within the province of the computer. At any moment new and unexpected factors of subjective origin may upset or falsify the computer’s most confident predictions—which latter has happened more than once in election forecasts. Such order as man has achieved through his laws and customs, his ideologies and moral codes, has proved precious—however infirm—precisely because it helps to keep both organic systems open without permitting man’s capability for integration to be totally destroyed by exorbitant quantifications or irrelevant novelties. (Mumford 1970: 191)
221.
With Leibniz, the connection between the traditional idea of knowledge and the Minsky-like view that the world must be analyzable into discrete elements becomes explicit. According to Leibniz, in understanding we analyze concepts into more simple elements. In order to avoid regress of simpler and simpler elements, then, there must be ultimate simples in terms of which all complex concepts can be understood. Moreover, if concepts are to apply to the world, there must be logical simples to which these elements apply. Leibniz envisaged “a kind of alphabet of human thoughts”. (Dreyfus 1972: 123) [emphasis added]
222.
Free-will or volition is one such notion— people are incapable of explaining how it differs from stochastic caprice, but feel strongly that it does. I conjecture that this idea has its genesis in a strong primitive defense mechanism. Briefly, in childhood we learn to recognize various forms of aggression and compulsion, and to dislike them, whether we submit or resist. Older, when told that our behavior is controlled by such-and-such a set of laws, we insert this fact in our model (inappropriately) along with other recognizers of compulsion. We resist compulsion no matter from whom. Although resistance is logically futile the resentment persists and is rationalized by defective explanations, since the alternative is emotionally unacceptable. (Minsky 1965: 4)
223.
[Minsky’s] argument, based on the fact that reliable computers do only that which they are instructed to do, has a basic flaw; it does not follow that the programmer therefore has full knowledge (and therefore full responsibility and credit for) what will ensue. For certainly the programmer may set up an evolutionary system whose limitations are to him unclear and possibly incomprehensible. (Weizenbaum 1978: 17).
224.
‘We Westerners are absolutely different from others!’ – such is the moderns’ victory cry, or protracted lament. The Great Divide between Us – Occidentals – and Them – everyone else, from the China seas to the Yucatan, from the Inuit to the Tasmanian aborigines – has not ceased to obsess us. Whatever they do, Westerners bring history along with them in the hulls of their caravels and their gunboats, in the cylinders of their telescopes and the pistons of their immunizing syringes. They bear this white man’s burden sometimes as an exalting challenge, sometimes as a tragedy, but always as a destiny. They do not claim merely that they differ from others as the Sioux differ from the Algonquins, or the Baoules from the Lapps, but that they differ radically, absolutely, to the extent that Westerners can be lined up on one side and all the cultures on the other, since the latter all have in common the fact that they are precisely cultures among others. In Westerners’ eyes the West, and the West alone, is not a culture, not merely a culture. (Latour, 1993, p. 97) [emphasis added]
Notes
Note 1: An example of how the digital creates precision
This does not yet explain exactly how digital systems achieve error-free transmission. All digital messages employ physical phenomena for transmission, ultimately depending on analogue signals such as light waves, radio waves, and electricity. Although light has a continuous spectrum with infinite variations and resolution, technology manipulates the light source to remove noise and digitally encode a message using binary states of on/off. A basic illustration of this is using a flashlight to transmit data using Morse code, where the analogue light source acts as a binary medium. The observer, an individual receiving this message, can ignore the continuous spectrum of light and focus only on the two discrete states: on and off (bright or dark). This binary approach makes digital encoding robust, as it reduces the number of potential states where errors might occur during transmission. While the full spectrum of light is infinite in theory, a base-two system of on/off (1 and 0) allows everything in-between to be ignored.
This highlights a key aspect of digital encoding and base systems discussed earlier: while the underlying medium (in this case, the light) is binary (on/off), the message encoded within it employs a different system. The rules of Morse code, which translate patterns of light into letters and words, act as a kind of ‘software’ running on top of this binary hardware. Morse code effectively functions as a quinary-base digital system, utilising five distinct elements: dots, dashes, short spaces (between dots and dashes within a character), letter spaces (between characters), and word spaces (between words). The binary states of the light (on/off), combined with the duration of the on/off states, create these five distinct elements. This demonstrates that although a message may be encoded using base-5 elements, the medium itself can be base-2 binary. Think of it as two layers of encoding.
- On State: dit (period.)
- On State: dah (dash–)
- Off State: short break between letters
- Off State: medium break between words
- Off State: long break between sentences
Note 2: Break Down of Audio and Image
Both the 31.25 second, 24-bit/192kHz mono audio file and the 24-bit 6 Megapixel (2000×3000 pixels) image file contain approximately 18 million bytes (or 144 million bits) of information.
Bit Depth: Both files share a 24-bit depth, meaning each sample (in audio) or pixel (in image) can represent 2^24 (16,777,216) different values. In audio, this translates to potential amplitude levels, while in images, it represents possible colors.
Approximate File Size: Both files are around 18 million bytes (18MB) in their uncompressed form, showcasing how the same amount of raw data can be used for vastly different types of media.
Audio File: 24-bit 192kKHz, 31.25 seconds
(Uncompressed bitrate for single channel: 4.61Mbps = 576KB/s)
- Bit Depth: 24 bits per sample
- Sample Rate: 192,000 samples per second (192 kHz)
- Duration: 31 seconds
- Channels: 1 (mono)
- 16,777,216 possible values per sample (224).
Calculation
- (24-bits/8-bit) x 5,952,000 audio samples (192,000 samples per second x ~ 31.25 seconds)
- Total Samples: 192,000 samples/second * 31.25 seconds = 6,000,000 samples
- Total Bits: 6,000,000 samples * 24 bits/sample = 144,000,000 bits
- Total Bytes: 144,000,000 bits / 8 bits/byte = 18,000,000 bytes
Image File: 24-bit (6 Megapixels)
(Uncompressed)
- Bit Depth: 24 bits per pixel
- Resolution: 3000 pixels * 2000 pixels = 6,000,000 pixels (6 Megapixels)
- 16,777,216 possible colours per pixel (224)
Calculation
- (24-bits/8-bits RGB colour) x 6,000,000 pixels (2000 x 3000 resolution)
- Total Bits: 6,000,000 pixels * 24 bits/pixel = 144,000,000 bits
- Total Bytes: 144,000,000 bits / 8 bits/byte = 18,000,000 bytes
Note 3: Atomic Clock
An atomic physicist, Zacharias was a Columbia University graduate, a scientist at MIT Radiation Laboratory, and helped build MITs physics department. He focused primarily on modern particle physics. During the 2nd World War, while working on the Los Alamos atomic bomb project, he helped make radar a reality for the Navy. He invented the atomic clock in 1956 while director of the Laboratory of Nuclear Science at MIT (Gleick 1986). In 2022, the atomic clock remains the most accurate method of measuring time and can keep time for 100 million years with an error of only 1 second.