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Computation and Hypercomputation
 MINDS AND MACHINES
, 2003
"... Does Nature permit the implementation of behaviours that cannot be simulated computationally? We consider the meaning of physical computationality in some detail, and present arguments in favour of physical hypercomputation: for example, modern scientific method does not allow the specification o ..."
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Does Nature permit the implementation of behaviours that cannot be simulated computationally? We consider the meaning of physical computationality in some detail, and present arguments in favour of physical hypercomputation: for example, modern scientific method does not allow the specification of any experiment capable of refuting hypercomputation. We consider the implications of relativistic algorithms capable of solving the (Turing) Halting Problem. We also reject as a fallacy the argument that hypercomputation has no relevance because noncomputable values are indistinguishable from sufficiently close computable approximations. In addition to
On Alan Turing's Anticipation Of Connectionism
, 1996
"... It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuronlike elements connected together into networks in a largely random manner. Turing called his networks `unorganised machines'. By the application of what he described ..."
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It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuronlike elements connected together into networks in a largely random manner. Turing called his networks `unorganised machines'. By the application of what he described as 'appropriate interference, mimicking education' an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of 'neurons' is sufficient. Turing proposed simulating both the behaviour of the network and the training process by means of a computer program. We outline Turing's connectionist project of 1948.
Alan Turing and the Mathematical Objection
 Minds and Machines 13(1
, 2003
"... Abstract. This paper concerns Alan Turing’s ideas about machines, mathematical methods of proof, and intelligence. By the late 1930s, Kurt Gödel and other logicians, including Turing himself, had shown that no finite set of rules could be used to generate all true mathematical statements. Yet accord ..."
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Abstract. This paper concerns Alan Turing’s ideas about machines, mathematical methods of proof, and intelligence. By the late 1930s, Kurt Gödel and other logicians, including Turing himself, had shown that no finite set of rules could be used to generate all true mathematical statements. Yet according to Turing, there was no upper bound to the number of mathematical truths provable by intelligent human beings, for they could invent new rules and methods of proof. So, the output of a human mathematician, for Turing, was not a computable sequence (i.e., one that could be generated by a Turing machine). Since computers only contained a finite number of instructions (or programs), one might argue, they could not reproduce human intelligence. Turing called this the “mathematical objection ” to his view that machines can think. Logicomathematical reasons, stemming from his own work, helped to convince Turing that it should be possible to reproduce human intelligence, and eventually compete with it, by developing the appropriate kind of digital computer. He felt it should be possible to program a computer so that it could learn or discover new rules, overcoming the limitations imposed by the incompleteness and undecidability results in the same way that human mathematicians presumably do. Key words: artificial intelligence, ChurchTuring thesis, computability, effective procedure, incompleteness, machine, mathematical objection, ordinal logics, Turing, undecidability The ‘skin of an onion ’ analogy is also helpful. In considering the functions of the mind or the brain we find certain operations which we can express in purely mechanical terms. This we say does not correspond to the real mind: it is a sort of skin which we must strip off if we are to find the real mind. But then in what remains, we find a further skin to be stripped off, and so on. Proceeding in this way, do we ever come to the ‘real ’ mind, or do we eventually come to the skin which has nothing in it? In the latter case, the whole mind is mechanical (Turing, 1950, p. 454–455). 1.
Computing machines can’t be intelligent (...and Turing said so
 In Minds and Machines
, 2002
"... According to the conventional wisdom, Turing (1950) said that computing machines can be intelligent. I don’t believe it. I think that what Turing really said was that computing machines – computers limited to computing – can only fake intelligence. If we want computers to become genuinely intelligen ..."
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According to the conventional wisdom, Turing (1950) said that computing machines can be intelligent. I don’t believe it. I think that what Turing really said was that computing machines – computers limited to computing – can only fake intelligence. If we want computers to become genuinely intelligent, we will have to give them enough “initiative ” (Turing, 1948, p. 21) to do more than compute. In this paper, I want to try to develop this idea. I want to explain how giving computers more “initiative ” can allow them to do more than compute. And I want to say why I believe (and believe that Turing believed) that they will have to go beyond computation before they can become genuinely intelligent. 1. What I Think Turing Said People who try to make computers more intelligent say they are trying to produce “Artificial Intelligence ” (or “AI”). Presumably, they want the word “artificial ” to suggest that the intelligence they are trying to create will – like artificial vanilla – not have developed naturally. But some of their critics are convinced that anything that looks like intelligence in a computer will have to be artificial in another sense – the sense in which an artificial smile is artificial. Which is to say fake. Computers, they believe, cannot be genuinely intelligent because they lack a certain je ne sais quoi that genuine intelligence requires. The more extreme of these critics believe that what computers lack is fundamental. Perhaps they believe that intelligence requires an immortal soul. Perhaps they feel that it can only be implemented in flesh and blood. Perhaps they believe that it requires human experiences or human emotions. Such critics believe that computers cannot be genuinely intelligent, period. Other critics of AI are a bit more generous. They believe that computers cannot be genuinely intelligent until … Perhaps they believe that computers cannot be genuinely intelligent until they have access to better parallel processing or to special neural
The Colossus
 History of Computing in the Twentieth Century
, 1980
"... In October 1975, after an official silence lasting thirtytwo years, the British Government made a set of captioned photographs of COLOSSUS available at the Public Record Office, These confirm that a series of programmable electronic digital computers was built in Britain during World War II, the fi ..."
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In October 1975, after an official silence lasting thirtytwo years, the British Government made a set of captioned photographs of COLOSSUS available at the Public Record Office, These confirm that a series of programmable electronic digital computers was built in Britain during World War II, the first being operational in 1943. It is stated that COLOSSUS incorporated 1500 valves, and operated in parallel arithmetic mode at 5000 pulses per second. A number of its features are disclosed, including the fact that it had 5000 character per second punched paper tape inputs, electronic circuits for counting, binary arithmetic and Boolean logic operations, "electronic storage registers changeable by an automatically controlled. sequence of operations", "conditional (branching) logic", "logic functions preset by patchpanels or switches, or conditionally selected by telephone relays", and typewriter output. Professor M.H.A. Newman is named as being responsible for formulating the requirement for COLOSSUS, and Mr. T.H. Flowers as leading the team which developed the machine. An indication is given that the design of COLOSSUS was influenced by the prewar work on computability by Alan Turing, who was employed in the same department of the British Government as Newman. The partial relaxation of the official secrecy surrounding COLOSSUS has made it possible to obtain interviews with a number of people involved in the project. The present paper is, in the main, based on these interviews, but supplemented by material already in the public domain. It attempts to document as fully as is presently permissible the story of the development of COLOSSUS. Particular attention is paid to interactions between the COLOSSUS project and other work carried out elsewhere on digital techniques and c...
Towards a theory of intelligence
 Theoretical Computer Science
"... In 1950, Turing suggested that intelligent behavior might require “a departure from the completely disciplined behaviour involved in computation”, but nothing that a digital computer could not do. In this paper, I want to explore Turing’s suggestion by asking what it is, beyond computation, that int ..."
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In 1950, Turing suggested that intelligent behavior might require “a departure from the completely disciplined behaviour involved in computation”, but nothing that a digital computer could not do. In this paper, I want to explore Turing’s suggestion by asking what it is, beyond computation, that intelligence might require, why it might require it and what knowing the answers to the first two questions might do to help us understand artificial and natural intelligence.
Such order from confusion sprung: Adaptive competence and affect regulation
, 1999
"... How do people process information to accomplish adaptive competence, that is, to maintain useful order, despite the confusion and unpredictability of the changing circumstances in which they live? Human adaptive competence does not hinge primarily on cognition, despite the substantial advantages co ..."
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How do people process information to accomplish adaptive competence, that is, to maintain useful order, despite the confusion and unpredictability of the changing circumstances in which they live? Human adaptive competence does not hinge primarily on cognition, despite the substantial advantages cognition confers. Rather, affects appraise adaptive pressures in terms of circumstances ’ changing contingencies (fact of affect onset), urgency (intensity of affect and rate of affect onset), category (“basic ” affect type), harm (intensity of negative valence), benefit (intensity of positive valence) and uncertainty (intensity of anxiety vs. confidence). Affects are realtime control signals in an adaptive control framework, organizing and motivating the avoidance of what are expected to be avoidable negative affects, worst affects first, and the pursuit of what are expected to be attainable positive affects. As long as affects are an adequately accurate appraisal of adaptive pressures, using realized and expected affects favorably to regulate future affects accomplishes adequate adaptive competence. ii © Copyright 1999 by
Logic and learning: Turing's legacy
 In
, 1994
"... Turing's best known work is concerned with whether universal machines can decide the truth value of arbitrary logic formulae. However, in this paper it is shown that there is a direct evolution in Turing's ideas from his earlier investigations of computability to his later interests in mac ..."
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Turing's best known work is concerned with whether universal machines can decide the truth value of arbitrary logic formulae. However, in this paper it is shown that there is a direct evolution in Turing's ideas from his earlier investigations of computability to his later interests in machine intelligence and machine learning. Turing realised that machines which could learn would be able to avoid some of the consequences of Godel's and his results on incompleteness and undecidability. Machines which learned could continuously add new axioms to their repertoire. Inspired by a radio talk given by Turing in 1951, Christopher Strachey went on to implement the world's first machine learning program. This particular first is usually attributed to A.L. Samuel. Strachey's program, which did rote learning in the game of Nim, preceded Samuel's checker playing program by four years. Neither Strachey's nor Samuel's system took up Turing's suggestion of learning logical formulae. Developments in t...
Comparative Analysis of Hypercomputational Systems
, 2006
"... In the 1930s, Turing suggested his abstract model for a practical computer, hypothetically visualizing the digital programmable computer long before it was actually invented. His model formed the foundation for every computer made today. The past few years have seen a change in ideas where philosoph ..."
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In the 1930s, Turing suggested his abstract model for a practical computer, hypothetically visualizing the digital programmable computer long before it was actually invented. His model formed the foundation for every computer made today. The past few years have seen a change in ideas where philosophers and scientists are suggesting models of hypothetical computing devices which can outperform the Turing machine, performing some calculations the latter is unable to. The ChurchTuring Thesis, which the Turing machine model embodies, has raised discussions on whether it could be possible to solve undecidable problems which Turing’s model is unable to. Models which could solve these problems, have gone further to claim abilities relating to quantum computing, relativity theory, even the modeling of natural biological laws themselves. These so called ‘hypermachines’ use hypercomputational abilities to make the impossible possible. Various models belonging to different disciplines of physics, mathematics and philosophy, have been suggested for these theories. My (primarily researchoriented) project is based on the study and review of these different hypercomputational models and attempts to compare the different models in terms of computational power. The project focuses on the ability to compare these models of different disciplines on similar grounds and
21. Notes on memory (1945) Universal Turing Machine
"... The implications of “smart ” information have been farreaching and pervasive. After years of unproven research into scatter/gather I/O, we confirm the deployment of fiberoptic cables. In order to realize this goal, we investigate how 64 bit architectures can be applied to the deployment of rasteri ..."
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The implications of “smart ” information have been farreaching and pervasive. After years of unproven research into scatter/gather I/O, we confirm the deployment of fiberoptic cables. In order to realize this goal, we investigate how 64 bit architectures can be applied to the deployment of rasterization. I.