CRITICAL PROGRAMMING: Toward A Philosophy Of Computing

Chapter 1 Introduction{11}

1.1 from automated genocide to the dumbest generation{11}

1.2 a collective intelligence problem, societies of control, the quintessential postmodern object, foss hopes, default philosophies of computing{11}

1.3 not to use old tools for new problems, scholarship requires a cybersage, digital humanities projects, critical programming studies, plan of the dissertation{11}

schedule

Chapter 2 Situation post-postmodern network dividual cyborg{11}

2.1 modernism and postmodernism, regressive subjectivity, Heideggers America, inventing the posthuman{11}

2.2 cybernetics, embodiment, techno-capitalist networks, dividual cyborg, cybersage{11}

Chapter 3 Theoretical framework and methodology{11}

3.1 critical theory, textuality studies, media studies, philosophy of technology{11}

3.2 social construction of technology, ensoniment, histories of computing networking and software, psycho-social studies of computer programmers{11}

3.3 software studies, game studies, code space, critical code studies{11}

3.4 platform studies, diachrony in synchrony, technogenesis and synaptogenesis, cyborg revisited{11}

Chapter 4 Philosophical programmers{11}

4.1 system engineers pioneers of babelization, distribued network visionaries, the new ontologists{11}

4.2 application developers beyond hard mastery and bricolage, auto-ethnographers of coding places{11}

Chapter 5 Critical programming studies{11}

5.1 working code places{11}

5.2 programming philosophers{11}

5.3 symposia, ensoniment{11}

5.4 tapoc, flossification{11}

5.5 pmrek, machine embodiment{11}

Chapter 6 Conclusion{11}

6.1 recommendations{11}

6.2 future directions{11}

Works Cited


1.1 from automated genocide to the dumbest generation

TOC 1.1 from automated genocide to the dumbest generation+

1.2 a collective intelligence problem, societies of control, the quintessential postmodern object, foss hopes, default philosophies of computing

TOC 1.2 a collective intelligence problem, societies of control, the quintessential postmodern object, foss hopes, default philosophies of computing+

1.3 not to use old tools for new problems, scholarship requires a cybersage, digital humanities projects, critical programming studies, plan of the dissertation

schedule

2.1 modernism and postmodernism, regressive subjectivity, Heideggers America, inventing the posthuman

TOC 2.1 modernism and postmodernism, regressive subjectivity, Heideggers America, inventing the posthuman+

2.2 cybernetics, embodiment, techno-capitalist networks, dividual cyborg, cybersage

3.1 critical theory, textuality studies, media studies, philosophy of technology

TOC 3.1 critical theory, textuality studies, media studies, philosophy of technology+

3.2 social construction of technology, ensoniment, histories of computing networking and software, psycho-social studies of computer programmers

TOC 3.2 social construction of technology, ensoniment, histories of computing networking and software, psycho-social studies of computer programmers+

3.3 software studies, game studies, code space, critical code studies

TOC 3.3 software studies, game studies, code space, critical code studies+

3.4 platform studies, diachrony in synchrony, technogenesis and synaptogenesis, cyborg revisited

4.1 system engineers pioneers of babelization, distribued network visionaries, the new ontologists

-4.1.1+++ {11}

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Limit scope of this first report to building the machine; next report will be about programming, a statement about how we will think as machines, quite a feat for 1940s humans. (ii) An attempt is made to give in this, the first half of this report, a general picture of the type of instrument now under consideration and in the second half a study of how actual mathematically problems can be coded, i.e., prepared in the language the machine can understand.

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Acknowledgment of materiality as initial design decisions concretize structural constraints and affordances, like Derrida describing struggle by Plato to wrest mythemes from historical context to transform into philosophemes. (4) 3.7 We proceed now to a more detailed discussion of the machine.

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Decoding defined as many-to-one function table crossing dual implementations as circuitry and software running code. (29) The type of circuit we propose to use for this purpose is know as a decoding or many-one function table.
(30) 6.3 each order must contain eighteen binary digits, the first twelve identifying a memory location and the remaining six specifying an operation. It can now be explained why orders are stored in the memory in pairs. Since the same memory organ is to be used in this computer for both orders and numbers, it is efficient to make the length of each about equivalent.

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Control counter is heart of state transition machine, along with its clock source. (31) 6.4 until it receives an order to do otherwise, the control will take its orders from the Selectrons in sequence. Hence the order location may be remembered in a twelve stage binary counter (one capable of counting 212) to which one unit is added whenever a pair of orders is executed. This counter is called the Control Counter, CC.

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Interactivity between machine and human limited to typewriter input for ad hoc data input, and a single machine instruction deployed to halt computer and notify completion by flashing a light or ringing a bell. (41) 6.8.4 . . . It is frequently very convenient to introduce data into a computation without producing a new wire. Hence it is planned to build one simple typewriter as an integral part of the computer.
(41) 6.8.5 There is one further order that the Control needs to execute. There should be some means by which the computer can signal to the operator when a computation has been concluded, or when the computation has reached a previously determined point. Hence an order is needed which will tell the computer to stop and to flash a light or ring a bell.

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Who are Leonardos of our recent era, the technology billionaires, or anonymously dispersed in collectives? (1) Whether from the Medici family or from his numerous other courtly patrons, Leonardoƒs career-building commissions were not as a painter, anatomist, or visionary inventor, as he is typically remembered today, but as a military engineer and architect.

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Seminal thinkers of computer science worked in recent past, compelling different historical methods. (ix) In most sciences, the seminal thinkers lived in the remote past. To uncover what they did and why they did it, we must scavenge in the historical record, picking among scraps of information, trying to separate facts from mythology.
(ix) Computer science is different. The mathematicians who first studied computation in its current form Alan Turing, Emil Post, and Alonzo Church did their work in the 1930s and 1940s.

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A bizarre parallel for ephemeral validity but quite obvious dealing with revisions of working code. (63) The explanation of the paradox is that the rules which get changed in the learning process are of a rather less pretentious kind, claiming only an ephemeral validity. The reader may draw a parallel with the Constitution of the United States.

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Game centric and logocentric, indeed Anglocentric popular Zizekean fantasies ground popular beliefs and attitudes about potential of computer technology, yet Turing appeals to need for sense organs: what kind of sense organs? (64) 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.

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Hierarchy of memories. (444) Von Neumann said that at that moment there was no technique for building a memory with both an adequate capacity and a sufficiently good access time. What is done is to construct a hierarchy of memories.

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Value of digital procedures in reducing computational noise level. (398-399) Thus the real importance of the digital procedure lies in its ability to reduce the computational noise level to an extent which is completely unobtainable by any other (analog) procedure. In addition, further reduction of the noise level is increasingly difficult in an analogy mechanism, and increasingly easy in a digital one. . . . This is clearly an entirely different milieu, from the point of view of the reduction of "random noise," from that of physical processes. It is here-and not in its practically ineffective absolute reliability-that the importance of the digital procedure lies.

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Extreme ratios of sizes between control elements of neurons and vacuum tubes nullified with future advances like integrated circuits. (404) The origin of this discrepancy lies in the fundamental control organ or, rather, control arrangement of the vacuum tube as compared to that of the neuron.

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Finitude important for practical automata versus formal theory: how many steps in actual chains of reasoning? (406-407) Throughout all modern logic, the only thing that is important is whether a result can be achieved in a finite number of elementary steps or not. . . . Any finite sequence of correct steps is, as a matter of principle, as good as any other. . . . In dealing with automata, this statement must be significantly modified. In the case of an automaton the thing which matters is not only whether it can reach a certain result in a finite number of steps at all but also how many such steps are needed. There are two reasons. First, automata are constructed in order to reach certain results in certain pre-assigned durations, or at least in pre-assigned orders of magnitude of duration. Second, the componentry employed has on every individual operation a small but nevertheless non-zero probability of failing. In a sufficiently long chain of operations the cumulative effect of these individual probabilities of failure may (if unchecked) reach the order of magnitude of unity-at which point it produces, in effect, complete unreliability.
(407) Thus the logic of automata will differ from the present system of formal logic in two relevant aspects.
(407) 1. The actual length of "chains of reasoning," that is, of the chains of operations, will have to be considered.

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The last thought is about what if I add Turing to chapter two to avoid clash as pioneer of babelization that he also used idiosyncratic programming languages.

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Last page is table of 21 machine operations.

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Having transcended formulating everything in numerical terms, such that larger address bit widths afford qualitatively different fantasies of machine operations including internetworked, distributed, object oriented processing of modern cyberspace, while still encoding everything in just that, reveals the fact that we have not left the basic ontology of computing posited by Burks, Goldstein and von Neumann. (1) 1.3 Conceptually we have discussed above two different forms of memory: Storage of numbers and storage of orders.

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Bulk of this document covers details of arithmetic computation because it is encoding that human-machine thought operation; these original circuits still present but at larger address bit widths. (1)

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Economic origin of stored program concept that, ironically, was solved positionally, by agency of discrete, deterministic program counter state changes built into the control. (1) 1.4 If the memory for orders is merely a storage organ there must exist an organ which can automatically execute the orders stored in the memory. We shall call this organ the Control.

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Control organ differentiated from storage organ rather than building the two features into the same device. (1)

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Arithmetic organ differentiated from background control organ: the architecture being proposed here not only exhibits multipurposive program and data storage but also division of functional units characteristic of industrial machinery; elementary operations are wired into the machine, and there is already acknowledgment of compromises between speed, complexity, cheapness. (1) 1.5 Inasmuch as the device is to be a computing machine there must be an arithmetic organ in it which can perform certain of the elementary arithmetic operations.
(1) The operations that the machine will view as elementary are clearly those which are wired into the machine. . . . In general, the inner economy of the arithmetic unit is determined by a compromise between the desire for speed of operation a non-elementary operation will generally take a long time to perform since it is constituted of a series of orders given by the Control and the desire for simplicity, or cheapness, of the machine.

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Use of communication to describe human machine relationship: that the final paragraph suggests ringing a bell or flashing a light to signal to the humans that the computation is complete, or has halted (Turing), of course reflects limited capabilities of the time but also institutes a human centric locus of the interface; other theorists and science fiction writers till take up the other possibility, that humans adapt to the machines, to the extent of synaptogenesis and into the technological nonconscious of the latest Hayles. (1) 1.6 Lastly there must exist devices, the input and output organ, whereby the human operator and the machine can communicate with each other.

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Thus, this early document we scan to ground our understanding of terms like stored program associated with the human author (rather than the machine hardware) von Neumann originally served to document knowledge that still resided primarily in human brains and printed materials, as Kittler makes so clear in There Is No Software, by the early 1970s shepherding the thoughts expressed by the many pages describing binary arithmetic operations resided in electronic circuits and their representations, blueprints for making new circuits and for CAD language games. (1) 1.2 . . . In a special-purpose machine these instructions are an integral part of the device and constitute a part of its design structure. For an all-purpose machine it must be possible to instruct the device to carry out any computation that can be formulated in numerical terms. Hence there must be some organ capable of storing these program orders. There must, moreover, be a unit which can understand these instructions and order their execution.

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Note difference between special purpose and general purpose computer intertwined with semiotics, language as beyond special purpose wetware, what Nietzsche referred to as instinct. (1) 1.2 . . . In a special-purpose machine these instructions are an integral part of the device and constitute a part of its design structure. For an all-purpose machine it must be possible to instruct the device to carry out any computation that can be formulated in numerical terms. Hence there must be some organ capable of storing these program orders. There must, moreover, be a unit which can understand these instructions and order their execution.

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Finally, note anthropomorphized word choices understand for the CPUs ability to fetch and execute. (1) Hence there must be some organ capable of storing these program orders.

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Control organ differentiated from storage organ rather than building the two features into the same device. (1) 1.4 If the memory for orders is merely a storage organ there must exist an organ which can automatically execute the orders stored in the memory. We shall call this organ the Control.

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Early design decision to separate storage from control, while treated as ontological characteristic of stored program computers to this day, became fuzzy as soon as caches where added to CPUs. (1) 1.4 If the memory for orders is merely a storage organ there must exist an organ which can automatically execute the orders stored in the memory. We shall call this organ the Control.

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Situated problems guided situated cognition of response; analysis of this problem space, not computing in general, although today a workspace of 4000 40-bit, twelve decimal precision mathematical computations is completely inadequate for the problems for which computers are designed, nor is anything like distributed network control conceivable, means that problem space now encompasses legacy, to the extent that not dead, as well as 64 bit address and timer width distributed control (Galloway protocological) computing, again not computing in general. (2) 2.3 It is reasonable at this time to build a machine that can conveniently handle problems several orders of magnitude more complex than are now handled by existing machines, electronic or electro-mechanical. We consequently plan on a fully automatic electronic storage facility of about 4,000 numbers of 40 binary digits each. This corresponds to a precision of 2-40 0.9 x 10-12, i.e. of about 12 decimal. We believe that this memory capacity exceeds the capacities required for most problems that one deals with at present by a factor of about 10.

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Transfers into memory as total substitution of contents and partial substitutions of operators to orders (memory location numbers). (3) 3.3 To summarize, transfers into the memory will be of two sorts: Total substitutions, whereby the quantity previously stored is cleared out and replaced by a new number. Partial substitutions in which that part of an order containing a memory location-number--we assume the various positions in the memory are enumerated serially by memory location-numbers--is replaced by a new memory location-number.

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Three levels of memory as compromises between localization within working memory and responsiveness of long term memory. (4) 4.1 the availability time for a word in the memory should be 5 to 50 sec. It is equally desirable that words may be replaced with new words at about the same rate. It does not seem possible physically to achieve such a capacity. We are therefore forced to recognize the possibility of constructing a hierarchy of memories, each of which has greater capacity than the preceding but which is less quickly accessible.
(4-5) One is accordingly led to consider the possibility of storing electrical charges on a dielectric plate inside a cathode-ray tube.

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Sense of technical determinism based on selection of memory unit, although history demonstrated multiple, divergent solutions invented worldwide. (4) 3.7 We proceed now to a more detailed discussion of the machine. Inasmuch as our experience has shown that the moment one chooses a given component as the elementary memory unit, one has also more or less determined upon much of the balance of the machine, we start by a consideration of the memory organ.

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One feasible memory organ was dielectric plate inside a cathode-ray tube, putatively a two-dimensional matrix. (4)
(4-5) One is accordingly led to consider the possibility of storing electrical charges on a dielectric plate inside a cathode-ray tube.

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Parallel storage of memory words versus serial storage by EDVAC. (5) 4.3 We accordingly adopt the parallel procedure and thus are led to consider a so-called parallel machine, as contrasted with the serial principles being instituted for the EDVAC.

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Proposal for storing 4000 words using 40 Selectrons for two to the twelfth power forty binary digit words. (5) At the present time the Princeton Laboratories of the Radio Corporation of America are engaged in the development of a storage tube, the Selectron, of the type we have mentioned above.
(5) 4.2 To achieve a total electronic storage of about 4,000 words we propose to use 40 Selectrons, thereby achieving a memory of 212 words of 40 binary digits each.

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Secondary storage medium part of input-output system demonstrating additional fuzzy borders between the canonical division of the stored program computer. (7) 4.7 It is now clear that the secondary storage medium is really nothing other than a part of our input-output system.

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Away from human decimal standard towards what seems more appropriate for machine computation, 2, 8, 16 (binary, octal, hexadecimal); noted ambivalence of floating point capability as another human convenience. (8) We feel, however, that the base 10 may not even be a permanent feature in a scientific instrument and consequently will probably attempt to train ourselves to use numbers base 2 or 8 or 16.

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Mention of floating decimal point solutions proposed for other digital computers in America and England. (8) 5.3 Several of the digital computers being built or planned in this country and England are to contain a so-called "floating decimal point." This is a mechanism for expressing each word as a characteristic and a mantissa--e.g. 123.45 would be carried in the machine as (0.12345,03), where 3 is the exponent of 10 associated with the number.

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Principle of incorporating in physical circuits only the necessary or most frequently used logical concepts, such as Accumulator. (9) The reader may remark upon our alternate spells of radicalism and conservatism in deciding upon various possible features for our mechanism. We hope, however, that he will agree, on closer inspection, that we are guided by a consistent and sound principle in judging the merits of any idea. We wish to incorporate into the machine--in the form of circuits--only such logical concepts as are either necessary to have a complete system or highly convenient because of the frequency with which they occur and the influence they exert in the relevant mathematical situations.

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Twenty pages of detail on hard wiring of basic arithmetic operations attests to materiality of even stored program computation and hence all code; example of representing negative numbers by complementation invites variable ontology view where both negative numbers and complementation operations exist at same level, not one inscribing the other. (9) 5.7 Thus we have been led to the familiar representation of negative numbers by complementation.

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Basic machine operations of fetch and execute, store, and input/output beyond Selectron memory. (29) 6.1 the orders for this computer are less than half as long as a forty binary digit number, and hence the orders are stored in the Selectron memory in pairs.
(29) Among these orders we can immediately describe two major types: An order of the first type begins by causing the transfer of the number, which is stored at a specified memory location, from the Selectrons to the Selectron register. Next, it causes the arithmetical unit to perform some arithmetical operations on this number (usually in conjunction with another number which is already in the arithmetic unit), and to retain the resulting number in the arithmetic unit. The seonc type order causes the transfer of the number, which is held in the arithmetical unit, into the Selectron Register, and from there to a specified memory location in the Selectrons. . . .
An additonal type of order consists of the transfer orders of 3.5. Further orders control the inputs and the outputs of the machine.

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Tetrads as proto-bytes. (30) It is convenient, as will be seen in 6.8.2. and Chapter 9, Part II, to group these binary digits into tetrads, groups of 4 binary digits. . . . Outside the machine each tetrad can be expressed by a base 16 digit.

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Memory address decoder design reflected in command decoding, with strong sense of materiality of code by decoding machine operation numbers to physical circuits; clever use of error checking making connections to unused outputs. (30) The specification of the nature of the operation that is involved in an order occurs in binary form, so that another many-one or decoding function is required to decode the order. . . . Since there will not be 64 different orders, not all 64 outputs need be provided. However, it is perhaps worthwhile to connect the outputs corresponding to unused order possibilities to a checking circuit which will give an indication whenever a code word unintelligible to the control is received in the input flip-flops.
(31) The twelve flip-flops operating the four function tables used in selecting a Selectron position, and the six flip-flops operating the function table used for decoding the order, are referred to as the Function Table Register, FR.

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Strict enforcement of separation between internal operations and those involving input or output beyond the computer; many pages of detail on control, but not as detailed as that of the arithmetic unit, admittedly only an overview. (33) 6.6 The orders which the Control understands may be divided into two groups: Those that specify operations which are performed within the computer and those that specify operations involved in getting data into and out of the computer. . . . The internal operations which have been tentatively adopted are listed in Table 1. It has already been pointed out that not all of these operations are logically basic, but that many can be programmed by means of others.

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Serial transfer for starting up the machine; no concept of parallel (tetrad) long term memory. (39) Since, at the beginning of the problem, the computer is empty, facilities must be built into the control for reading a set of numbers from a wire when the operator presses a manual switch. . . . A detection circuit on CC will stop the process when the specified number of numbers has been placed in the memory, and the control will then be shifted to the orders located in the first position of the Selectron memory.

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Deterministic, lossless translation between binary and decimal representation of numbers. (40) 6.8.2 Since the computer operates in the binary system, some means of decimal-binary and binary-decimal conversions is highly desirable.

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Survey of evolution of high level programming languages based on unpublished source materials. (1) This paper surveys the evolution of high level programming languages during the first decade of computer programming activity. . . . The principal features of each contribution are illustrated; and for purposes of comparison, a particular fixed algorithm has been encoded (as far as possible) in each of the languages. This research is based primarily on unpublished source materials, and the authors hope that they have been able to compile a fairly complete picture of the early developments in this area.

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Objections answered: theological, heads in the sand, mathematical, argument from consciousness, argument from disabilities, Lady Lovelace objection, continuity of nervous system, informality of behavior, ESP. (1)

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Visual memory has greater storage requirements than audible memory and memory for other senses. (61) As I have explained, the problem is mainly one of programming. Advances in engineering will have to be made too, but it seems unlikely that these will not be adequate for the requirements. Estimates of the storage capacity of the brain vary from 1010 to 1015 binary digits. I incline to the lower value and believe that only a very small fraction is used for the higher types of thinking. Most of it is probably used for the retention of visual impressions.

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How does von Neumann musing about machine (and human) intelligence compare to Turing? (63)

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The markers are eventually reduced to zeros and ones, voltages. (6) In a decimal digital machine each number is represented in the same way as in conventional writing or printing, i.e. as a sequence of decimal digits. Each decimal digit, in turn, is represented by a system of markers.

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Binary comes after decimal awkwardness inherited from base ten numbering systems employed by humans: note the wastefulness of using a four bit register to represent each decimal digit. (7)

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Computing machines divided into super-analog and digital devices. (438) As you probably know, the main types of computing machines existing or being discussed or planned at this moment fall into two large classes: super-analog devices and digital devices.
(438) Roughly speaking, an analog calculation is one in which you look at some physical process which happens to have the same mathematical equations as the process youƒre interested in, and you investigate this physical process physically. You do not take the physical process which you are interested in, because that is your whole reason to calculate. You always look for something which is like it but not exactly the same thing.
(439) He discussed the components of digital machines (toothed wheels, electromechanical relays, vacuum tubes, and nerve cells), the speeds of these components (including both response time and recovery time), and the need for power amplification in these components. He stressed the role of the basic logical operations (such as sensing a coincidence) in control mechanisms, including "the most elaborate control mechanism known, namely, the human nervous system.

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Measuring complexity of automaton unclear until considering machines. (439) It is not completely obvious how to measure the complexity of an automaton. For computing machines, probably the reasonable way is to count how many vacuum tubes are involved.
(440) If you can repeat an elementary act like switching with a vacuum tube 1 million times per second, that does not mean of course that you will perform anything that is mathematically relevant 1 million times per second.

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Recognition that problem of memory distorts modus operandi of early computing. (442) You may have noticed that I have already introduced one distinction, namely, the total numerical material produced in a process. The other thing which matters is how much you need simultaneously. This is probably the most vexing problem in modern computing machine technology. Itƒs also quite a problem from the point of view of the human organism, namely, the problem of memory.
(442) Hence in both the computer and the human nervous system, the dynamic part (the switching part) of the automaton is simpler than the memory.
(443) He then estimated the memory capacity of an ordinary printed page to be about 20 thousand units, and remarked that this is about the memory capacity of the digital computers under consideration at that time.
(443) The planning may be difficult, input and output may be cumbersome, and so on, but the main trouble is that it has a phenomenally low memory for the computing to be done. The whole technique of computing will be completely distorted by this modus operandi.
(444) In comparing artificial with natural automata there is one very important thing we do not know: whether nature has ever been subject to this handicap, or whether natural organisms involve some much better memory device.

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References to fictitious mechanisms of McCulloch, Pitts, Turing. (446) Both of them [McCulloch and Pitts and Turing] show that their fictitious mechanisms are exactly co-extensional with formal logics; in other words, that what their automata can do can be described in logical terms and, conversely, anything which can be described rigorously in logical terms can also be done by automata.
(446) Iƒm going to describe both the work of McCulloch and Pitts and the work of Turing because they reflect two very important ways to get at the subject: the synthetic way, and the integral way. McCulloch and Pitts described structures which are built up from very simple elements, so that all you have to define axiomatically are the elements, and then their combination can be extremely complex. Turing started by axiomatically describing what the whole automaton is supposed to be, without telling what its elements are, just by describing how itƒs supposed to function.
(447) They believed that the extremely amputated, simplified, idealized object which they axiomatized possessed the essential traits of the neuron, and that all esle are incidental complications, which in a first analysis are better forgotten.
(448) No matter how you formulate your conditions, you can always put a neural network in the box which will realize these conditions, which means that the generality of neural systems is exactly the same as the generality of logics.
(448-449) You see that you can produce circuits which look complicated, but which are actually quite simple from the point of view of how they are synthesized and which have about the same complexity that they should have, namely, the complexity that grammar has.
(449) It certainly follows that anything that you can describe in words can also be done with the neuron method. And it follows that the nerves need not be supernaturally clever or complicated.

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Suggestion that frequency modulation scheme more reliable than digital. (451) He then raised the question "Why has the digital notation never been used in nature, as far as we know, and why has this pulse notation been used instead?" and said that this was the kind of question he was interested in. He suggested the answer: that the frequency modulation scheme is more reliable than the digital scheme.
(451) This is not the way to make a memory for the simple reason that to use a switching organ like a neuron, or six to a dozen switching organs, as you actually would have to use because of fatigue, in order to do as small a thing as remember one binary digit, is a terrible waste, because a switching organ can do vastly more than store.
(452) In the McCulloch and Pitts theory the conclusion was that actual automata, properly described and axiomatized, are equivalent to formal logics. In Turingƒs theory the conclusion is the reverse. Turing was interested in formal logics, not in automata. He was concerned to prove certain theorems about an important problem of formal logics, the so-called Entschiedungsproblem, the problem of decision. The problem is to determine, for a class of logical expressions or propositions, whether there is a mechanical method for deciding whether an expression of this class is true or false. Turingƒs discussion of automata was really a formal, logical trick to deal with this problem in a somewhat more transparent and more consistent way than it had been dealt with before.
(453) The importance of Turingƒs research is just this: that if you construct an automaton right, then any additional requirements about the automaton can be handled by sufficiently elaborate instructions. This is true only if A is sufficiently complicated, if it has reached a certain minimum level of complexity.
(454) Turing proved that there is something for which you cannot construct an automaton; namely, you cannot construct an automaton which can predict in how many steps another automaton which can solve a certain problem will actually solve it.

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Digitization clever trick to produce extreme precision from poor precision. (464) Digitization is just a very clever trick to produce extreme precision out of poor precision.
(465) It is not surprising that this new theory of information should be like formal logics, but it is surprising that it is likely to have a lot in common with thermodynamics.
(465) It is likely that you cannot define the function of an automaton, or its efficiency, without characterizing the milieu in which it works by means of statistical traits like the ones used to characterize a milieu in thermodynamics.
(466) Also, it is quite clear from the practice of building computing machines that the decisive properties of computing machines involve balance: balances between the speeds of various parts, balances between the speed of one part and the sizes of other parts, even balances between the speed ratio of two parts and the sizes of other parts. I mentioned this in the case of the hierarchic structure of memory. All of these requirements look like the balance requirements one makes in thermodynamics for the sake of efficiency.

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Estimate of relative complexity of human nervous system compared to large computing machines of the time necessarily equivocates aspects of human thought and computation. (468) So the human nervous system is roughly a million times more complicated than these large computing machines.
(468) Thus the nervous system has a million times as many components as these machines have, but each component of the machine is about 5 thousand times faster than a neuron.
(470) The remarkable thing, however, is the enormous gap between the thermodynamical minimum (3 X 10-14 ergs) and the energy dissipation per binary act in the neuron (3 X 10-3 ergs). The factor here is 1011.

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Acoustic delay line and cathode ray tube storage. (470) The actual devices which are used are of such a nature that the store is effected, not in a macroscopic object like a vacuum tube, but in something which is microscopic and has only a virtual existence.

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Suggests predominance of trans-continuous alternation between digital and analog mechanisms in all forms of transduction based on unsuitability of pure analog mechanisms. (472) This whole trans-continuous alternation between digital and analog mechanisms is probably characteristic of every field.
(473) Pure analog mechanisms are usually not suited for very complicated situations. The only way to handle a complicated situation with analog mechanisms is to break it up into parts and deal with the parts separately and alternately, and this is a digital trick.
(473) Our artificial systems are patchworks in which we achieve desirable electrical traits at the price of mechanically unsound things. . . . And so the differences in size between artificial and natural automata are probably connected essentially with quite radical differences in materials.

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If every error has to be caught no organism would run for a millisecond; natural automata better suited to their milieu. (474) Itƒs very likely that on the basis of the philosophy that every error has to be caught, explained, and corrected, a system of the complexity of the living organism would not run for a millisecond.
(474-475) To apply the philosophy underlying natural automata to artificial automata we must understand complicated mechanisms better than we do, we must have more elaborate statistics about what goes wrong, and we must have much more perfect statistical information about the milieu in which a mechanism lives than we now have.
(475) It makes an enormous difference whether a computing machine is designed, say, for more or less typical problems of mathematical analysis, or for number theory, or combinatorics, or for translating a text.
(475) What matters is that the statistical properties of problems of mathematical analysis are reasonably well known, and as far as we know, reasonably homogeneous.
(475-476) Natural automata are much better suited to their milieu than any artifacts we know. It is therefore quite possible that we are not too far from the limits of computation which can be achieved in artificial automata without really fundamental insights into a theory of information, although one should be very careful with such statements because they can sound awfully ridiculous 5 years later.

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Foreshadowing self-compilers considering automata outputing things like themselves, realizing automata shifted from physical instantiation to functional specification? (478) A complete discussion of automata can be obtained only by taking a broader view of these things and considering automata which can have outputs something like themselves. . . . Draw up a list of unambiguously defined elementary parts. Imagine that there is a practically unlimited supply of these parts floating around in a large container. One can then imagine an automaton functioning in the following manner: It also is floating around in this medium; its essential activity is to pick up parts and put them together, or, if aggregates of parts are found, to take them apart.

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Predilection for linear codes a literary, narrative habit rather than one based on maximal combinatorial cleverness. (487) There is reason to suspect that our predilection for linear codes, which have a simple, almost temporal sequence, is chiefly a literary habit, corresponding to our not particularly high level of combinatorial cleverness, and that a very efficient language would probably depart from linearity.

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Some observations about organization of natural organisms may be useful for constructing artificial automata. (391-392) Natural organisms are, as a rule, much more complicated and subtle, and therefore much less well understood in detail, than are artificial automata. Nevertheless, some regularities which we observe in the organization of the former may be quite instructive in our thinking and planning of the latter; and conversely, a good deal of our experiences and difficulties with our artificial automata can be to some extent projected on our interpretations of natural organisms.

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Divide problem into functioning of individual elements and overall organization, reminiscent of Socratic analysis in Phaedrus. (392) The organisms can be viewed as made up of parts which to a certain extent are independent, elementary units. We may, therefore, to this extent, view as the first part of the problem the structure and functioning of such elementary units individually. The second part of the problem consists of understanding how these elements are organized into a whole, and how the functioning of the whole is expressed in terms of these elements.

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Axiomatic procedure treats elements as black boxes with well-defined outside functional characteristics. (392) Axiomatizing the behavior of the elements means this: We assume that the elements have certain well-defined, outside, functional characteristics; that is, they are to be treated as "black boxes." They are viewed as automatisms, the inner structure of which need not be disclosed, but which are assumed to react to certain unambiguously defined stimuli, by certain unambiguously defined responses.

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Declaration of thousand to million order of magnitude of complexity reveals fantasy boundary of early computing theories. (393) With any reasonable definition of what constitutes an element, the natural organisms are very highly complex aggregations of these elements. . . . The number of neurons in the central nervous system is somewhere of the order of 1010. We have absolutely no past experience with systems of this degree of complexity. All artificial automata made by man have numbers of parts which by any comparably schematic count are of the order 103 to 106.

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Highest complexity due to length of chains of events. (394) The notion of using an automaton for the purpose of computing is relatively new. While computing automata are not the most complicated artificial automata from the point of view of the end results they achieve, they do nevertheless represent the highest degree of complexity in the sense that they produce the longest chains of events determining and following each other.
(394) The use of a fast computing machine is believed to be by and large justified when the computing task involves about a million multiplications or more in a sequence.
(394) The simplest way to estimate this degree of complexity is, instead of counting decimal places, to count the number of places that would be required for the same precision in the binary system of notation.

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Analogy principle that certain ranges of numbers represented by physical quantities; usefulness tied to control of noise level, signal to noise ratio. (396) A computing machine may be based on the principle that numbers are represented by certain physical quantities. . . . Operations like addition, multiplication, and integration may then be performed by finding various natural processes which act on these quantities in the desired way.
(396) The first well-integrated, large computing machine ever made was an analogy machine, V. Bushƒs Differential Analyzer. This machine, by the way, did the computing not with electrical currents, but with rotating disks.
(396) The guiding principle without which it is impossible to reach an understanding of the situation is the classical one of all "communication theory"-the "signal to noise ratio." That is, the critical question with every analogy procedure is this: How large are the uncontrollable fluctuations of the mechanism that constitute the "noise," compared to the significant "signals" that express the numbers on which the machine operates? The usefulness of any analogy principle depends on how low it can keep the relative size of the uncontrollable fluctuations-the "noise level.

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Digital machine represents numbers as aggregates of digits like human decimal system; always has small round off error. (397) A digital machine works with the familiar method of representing numbers as aggregates of digits. This is, by the way, the procedure which all of us use in our individual, non-mechanical computing, where we express numbers in the decimal system.
(398) What it produces when a product is called for is not that product itself, but rather the product plus a small extra term-the round-off error. This error is, of course, not a random variable like the noise in an analogy machine. It is, arithmetically, completely determined in every particular instance. Yet its mode of determination is so complicated, and its variations throughout the number of instances of its occurrence in a problem so irregular, that it usually can be treated to a high degree of approximation as a random variable.

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Consider living organisms as purely digital automata although mixed character admitted at level of organism as well as each neuronal element. (399) The nerve impulse seems in the main to be an all or none affair, comparable to a binary digit. . . . It is well known that there are various composite functional sequences in the organism which have to go through a variety of steps from the original stimulus to the ultimate effect, some of the steps being neural, that is, digital, and others humoral, that is, analogy.
(399) It is well known that such mixed (part neural and part humoral) feedback chains can produce processes of great importance.
(400) I shall consider the living organisms as if they were purely digital automata.

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Best to treat elements as black boxes with schematic descriptions, whether vacuum tubes or neurons. (401) By an all or none organ we should rather mean one which fulfills the following two conditions. First, it functions in the all or none manner under certain suitable operating conditions. Second, these operating conditions are the ones under which it is normally used; they represent the functionally normal state of affairs within the large organism, of which it forms a part. . . . I realize that this definition brings in rather undesirable criteria of "propriety" of "context," of "appearance" and "intention." I do not see, however, how we can avoid using them, and how we can forego counting on the employment of common sense in their application.
(401) Here [in the case of the vacuum tube], too, the purely electrical phenomena are accompanied by numerous other phenomena of solid state physics, thermodynamics, mechanics [as in case of neuron]. All of these are important to understand the structure of a vacuum tube, but are best excluded from the discussion, if it is to treat the vacuum tube as a "black box" with a schematic description.

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Neuron and vacuum tube functionally equivalent representatives of relay switching organs. (401-402) The neuron, as well as the vacuum tube, viewed under the aspects discussed above, are then two instances of the same generic entity, which it is customary to call a "switching organ" or "relay organ." (The electrochemical relay is, of course, another instance.) Such an organ is defined as a "black box," which responds to a specified stimulus or combination of stimuli by an energetically independent response.
(402) I shall, therefore, discuss computing machines solely from the point of view of aggregates of switching organs which are vacuum tubes.

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Theory of automata a chapter in formal logic; look at Out of Their Minds for how computer science has evolved since. (406) Everybody who has worked in formal logic will confirm that it is one of the technically most refractory parts of mathematics. The reason for this is that it deals with rigid, all-or-none concepts, and has very little contact with the continuous concept of the real or of the complex number, that is, with mathematical analysis. Yet analysis is the technically most successful and best elaborated part of mathematics. Thus formal logic is, by the nature of its approach, cut off from the best cultivated portions of mathematics, and forced onto the most difficult part of the mathematical terrain, into combinatorics.
(406) The theory of automata, of the digital, all-or-none type, as discussed up to now, is certainly a chapter in formal logic. It would, therefore, seem that it will have to share this unattractive property of formal logic. It will have to be, from the mathematical point of view, combinatorial rather than analytical.

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Importance of allowing and utilizing exceptions second key difference with formal logic. (407) 2. The operations of logic (syllogisms, conjunctions, disjunctions, negations, etc., that is, in the terminology that is customary for automata, various forms of gating, coincidence, anti-coincidence, blocking, etc., actions) will all have to be treated by procedures which allow exceptions (malfunctions) with low but non zero probabilities. . . . there are numerous indications to make us believe that this new system of formal logic will move closer to another discipline which has been little linked in the past with logic. This is thermodynamics, primarily in the form it was received from Boltzmann, and is that part of theoretical physics which comes nearest in some of its aspects to manipulating and measuring information. Its techniques are indeed much more analytical than combinatorial, which again illustrates the point that I have been trying to make above.

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Poor dealing with errors symptom of lack of logical theory of automata still reckoned with today; natural automata make errors inconspicuous, but must be overcautious in design of artificial ones. (408) It is unlikely that we could construct automata of a much higher complexity than the ones we now have, without possessing a very advanced and subtle theory of automata and information. A fortiori, this is inconceivable for automata of such enormous complexity as is possessed by the human central nervous system.
(408) This intellectual inadequacy certainly prevents us from getting much farther than we are now.
(408) A simple manifestation of this factor is our present relation to error checking.
(408-409) The basic principle of dealing with malfunctions in nature is to make their effect as unimportant as possible and to apply correctives, if they are necessary at all, at leisure. In our dealings with artificial automata, on the other hand, we require an immediate diagnosis. . . . natural organisms are constructed to make errors as inconspicuous, as harmless, as possible. Artificial automata are designed to make errors as conspicuous, as disastrous, as possible. . . . With our artificial automata we are moving much more in the dark than nature appears to be with its organisms. We are, apparently, at least at present, have to be, much more "scared" by the occurrence of an isolated error and by the malfunction which must be behind it. Our behavior is clearly that of overcaution, generated by ignorance.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (409) 20131019o 0 -3+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Single error principle basis of troubleshooting. (409) almost all our error diagnosing techniques are based on the assumption that the machine contains only one faulty component. In this case, iterative subdivisions of the machine into parts permit us to determine which portion contains the fault. As soon as the possibility exists that the machine may contain several faults, these, rather powerful, dichotomic methods of diagnosis are lost.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (410) 20131019p 0 -10+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Count versus decimal expression in signal transmission example of encoding pressure value. (410) Assume, for example, that a pressure (clearly a continuous quantity) is to be transmitted. It is well known how this trick is done. The nerve which does it still transmits nothing but individual all or none impulses. How does it [nerve] then express the continuously numerical value of pressure in terms of these impulses, that is, of digits? . . . The mechanisms which achieves this "encoding" is, therefore, essentially a frequency modulation system.
(410) It is very instructive, however, that it uses a "count" rather than a "decimal expansion" (or binary expansion, etc.) method.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (410-411) 20131019q 0 -11+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Living organisms prefer counting over symbolic expression method: instance of noein versus legein; Plato says something about only humans understand abstract forms. (410-411) The counting method is certainly less efficient than the expansion method. . . . On the other hand, the counting method has a high stability and safety from error. . . . Obviously, the simplest form of achieving safety by redundancy is to use the, per se, quite unsafe digital expansion notation, but to repeat every such message several times.
(411) On may, therefore, suspect that if the only demerit of the digital expansion system were its greater logical complexity, nature would not, for this reason alone, have rejected it. It is, nevertheless, true that we have nowhere an indication of its use in natural organisms.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (412) 20131019r 0 -5+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
McCulloch and Pitts model defined by singling out inputs and correlating to outputs. (412) McCulloch and Pitts have used these units to build up complicated networks which may be called formal neural networks. . . . The functioning of such a network may be defined by singling out some of the inputs of the entire system and some of its outputs, and then describing what original stimuli on the former are to cause what ultimate stimuli on the latter.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (412) 20131019s 0 -4+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Functional equivalence of what can be presented unambiguously in finite word sequence and what can be realized by formal neural network: coextensive concepts. (412) any functioning in this sense which can be defined at all logically, strictly, and unambiguously in a finite number of words can also be realized by such a formal neural network.
(413) there is no difference between the possibility of describing a real or imagined mode of behavior completely and unambiguously in words, and the possibility of realizing it by a finite formal neural network. The two concepts are co extensive. A difficulty of principle embodying any mode of behavior in such a network can exist only if we are also unable to describe that behavior completely.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (413) 20131019t 0 -3+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Problems of realization of neural networks of practical size and putting into words: humanities tends to focus on logocentrism debate, for example OHCO thesis, and ignore the former. (413) Thus the remaining problems are these two. First, if a certain mode of behavior can be effected by a finite neural network, the question still remains whether that network can be realized within a practical size, specifically, whether it will fit into the physical limitations of the organism in question. Second, the question arises whether every existing mode of behavior can really be put completely and unambiguously into words.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (414) 20131019u 0 -12+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Need precise verbal description of visual analogy to fulfill computationalist paradigm; possibility that logic morphs toward neurology rather than reverse becomes humanities battleground. (414) Nobody would attempt to describe and define within any practical amount of space the general concept of analogy which dominates our interpretation of vision. . . . We are dealing here with parts of logics with which we have practically no past experience. . . . It is, therefore, not at all unlikely that it is futile to look for a precise logical concept, that is, for a precise verbal description, of "visual analogy." It is possible that the connection pattern of the visual brain itself is the simplest logical expression or definition of this principle.
(414) All of this does not alter my belief that a new, essentially logical, theory is called for in order to understand high complication automata and, in particular, the central nervous system. It may be, however, that in this process logic will have to undergo a pseudomorphosis to neurology to a much greater extent than the reverse.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (415) 20131019v 0 -12+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Concept of complication never clearly formulated. (415) We are inclined to suspect in a vague way the existence of a concept of "complication." This concept and its putative properties have never been clearly formulated. We are, however, always tempted to assume that they will work in this way. When an automaton performs certain operations, they must be expected to be of a lower degree of complication than the automaton itself. . . . That is, if A can produce B, then A in some way must have contained a complete description of B. In order to make it effective, there must be, furthermore, various arrangements in A that see to it that this description is interpreted and that the constructive operations that it calls for are carried out. In this sense, it would therefore seem that a certain degenerating tendency must be expected, some decrease in complexity as one automaton makes another automaton.
(415) Although this has some indefinite plausibility to it, it is in clear contradiction with the most obvious things that go on in nature. Organisms reproduce themselves, that is, they produce new organisms with no decrease in complexity.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (416) 20131019w 0 -33+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Turing theory of computing automata a way to specify complication; here is von Neumann explaining Turing machine. (416) An automaton is a "black box," which will not be described in detail but is expected to have the following attributes. It possesses a finite number of states, which need be prima facie characterized only by stating their number, say n, and by enumerating them accordingly: 1, 2, . . . n. The essential operating characteristic of the automaton consists of describing how it is caused to change its state, that is, to go over from a state i into a state j . . . As far as the machine is concerned, let the whole outside world consist of a long paper tape. . . . On each field of this strip we may or may not put a sign, say, a dot, and it is assumed that it is possible to erase as well as to write in such a dot. A field marked with a dot will be called a "1," a field unmarked with a dot will be called a "0." . . . In describing the position of the tape relative to the automaton it is assumed that one particular field of the tape is under direct inspection by the automaton, and that the automaton has the ability to move the tape forward and backward, say, by one field at a time. In specifying this, let the automaton be in the state i (= 1, . . . , n), and let it see on the tape an e (= 0, 1). It will then go over into the state j (= 0, 1, . . . , n), move the tape by p fields (p = 0, +1, -1; +1 is a move forward, -1 is a move backward), and inscribe into the new field that it sees f ( = 0, 1; inscribing 0 means erasing; inscribing 1 means putting in a dot). Specifying j, p, f as functions of i, e is then the complete definition of the functioning of such an automaton.
(417) An automaton is able to "form" a certain sequence if it is possible to specify a finite length of tape, appropriately marked, so that, if this tape is fed to the automaton in question, the automaton will thereupon write the sequence on the remaining (infinite) free portion of the tape. . . . The finite, premarked, piece of tape constitutes the "instruction" of the automaton for this problem.
An automaton is "universal" if any sequence that can be produced by any automaton at all can also be solved by this particular automaton.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (417-418) 20131019x 0 -9+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Turing machine operation no more mysterious than following instructions for using words from reading dictionary and grammar seems like quite a challenge after all, demonstrating ignorance of semiotics (Edwards, Golumbia). (417-418) Turing observed that a completely general description of any conceivable automaton can be (in the sense of the foregoing definition) given in a finite number of words. This description will contain certain empty passages, those referring to the functions mentioned earlier (j, p, f in terms of i, e), which specify the actual functioning of the automaton. When these empty passages are filled in, we deal with a specific automaton. As long as they are left empty, this schema represents the general definition of the general automaton. Now it becomes possible to describe an automaton which has the ability to interpret such a definition. In other words, which, when fed the functions that in the sense described above define a specific automaton, will thereupon function like the object described. The ability to do this is no more mysterious than the ability to read a dictionary and a grammar and to follow their instructions about the uses and principles of combinations of words. This automaton, which is constructed to read a description and to imitate the object described, is then the universal automaton in the sense of Turing. To make it duplicate any operation that any other automaton can perform, it suffices to furnish it with a description of the automaton in question and, in addition, with the instructions which that device have required for the operation under consideration.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (418) 20131019y 0 -3+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Extend Turing machine to produce other automata becomes basis of bootstrapping and self-compiling. (418) His [Turingƒs] automata are purely computing machines. Their output is a piece of tape with zeros and ones on it. What is needed for the construction to which I referred is an automaton whose output is other automata.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (419) 20131019z 0 -10+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Derivation of theorem regarding self-reproduction by Turing machines pondered by Chun and others. (419) (a) Automaton A, which when furnished the description of any other automaton in terms of appropriate functions, will construct that entity. The description should in this case not be given in the form of a marked tape, as in Turingƒs case, because we will not normally choose a tape as a structural element. It is quite easy, however, to describe combinations of structural elements which have all the notational properties of a tape with fields that can be marked. A description in this sense will be called an instruction and denoted by a letter I.
(419) "Constructing" is to be understood in the same sense as before. . . . One need not worry about how a fixed automaton of this sort can produce others which are larger and more complex than itself. In this case the greater size and the higher complexity of the object to be constructed will be reflected in a presumably still greater size of the instructions I that have to be furnished.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (420) 20131109a 0 -5+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Arrival at self-reproduction in aggregate result E through definite chronological and logical order: Chun asks for the details of how the instructions actually work as early example of hiding vicissitudes of execution. (420) E is clearly self-reproductive. Note that no vicious circle is involved. The decisive step occurs in E, when the instruction ID, describing D, is constructed and attached to D. When the construction (the copying) of ID is called for, D exists already, and it is in no wise modified by the construction of ID. ID is simply added to form E.

4 1 1 (+) [-4+]mCQK von_neumann-theory_of_natural_and_artificial_automata (421) 20131109 0 -7+ progress/1998/01/notes_for_von_neumann-theory_of_natural_and_artificial_automata.html
Crude steps representing one particular in theory of automata direction based on complication. (421) All these are very crude steps in the direction of a systematic theory of automata. They represent, in addition, only one particular direction. This is, as I indicated before, the direction towards forming a rigorous concept of what constitutes "complication." . . . This fact, that complication, as well as organization, below a certain minimum level is degenerative, and beyond that level can become self- supporting and even increasing, will clearly play an important role in any future theory of the subject.


select Chapter, Heading, SubHeading, InterstitialSequence, RelevanceLevel, TextName, PositionStart, TimestampBookmarkExtra, CitationOffset, CitationSentences, Path, Lexia from Notes where Chapter=4 and Heading=1 and (SubHeading=0 or SubHeading=1) and (InterstitialSequence=0 or InterstitialSequence=100) order by Heading, SubHeading, InterstitialSequence desc, RelevanceLevel desc, TextName, cast((trim(leading '(' from substring_index(PositionStart, '-', 1))) as unsigned)

TOC 4.1 system engineers pioneers of babelization, distribued network visionaries, the new ontologists+

4.2 application developers beyond hard mastery and bricolage, auto-ethnographers of coding places

5.1 working code places

TOC 5.1 working code places+

5.2 programming philosophers

TOC 5.2 programming philosophers+

5.3 symposia, ensoniment

TOC 5.3 symposia, ensoniment+

5.4 tapoc, flossification

TOC 5.4 tapoc, flossification+

5.5 pmrek, machine embodiment

6.1 recommendations

TOC 6.1 recommendations+

6.2 future directions


TOC

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