Alan Turing and Human-Like Intelligence

2021 ◽  
pp. 24-51
Author(s):  
Peter Millican
Keyword(s):  
Information Processing ◽  
Human Intelligence ◽  
Mathematical Concepts ◽  
Model Of Computation ◽  
Alan Turing ◽  
Verbal Behaviour ◽  
Ample Reason

Alan Turing’s model of computation (1936) is explicated in terms of the potential operations of a human “computer”, and his famous test for intelligence (1950) is based on indistinguishability from human verbal behaviour. But this chapter challenges the apparent human-centredness of the 1936 model, suggesting a focus instead on mathematical concepts, with human comparisons making an entrance only retrospectively. The 1950 account of intelligence also turns out to be far less human-centred than it initially appears to be, because the universality of computation makes human intelligence just one variety amongst many. It is only when Turing considers consciousness that he treats intelligence in a way that cannot properly be carried over to machines. But here he is mistaken, since his own work gave ample reason to reinterpret intelligence as sophisticated information processing for some purpose, and to divorce this from the subjective consciousness with which it is humanly associated.

scholarly journals Limits of Artificial Intelligence: a perspective from the psychobiological development

2016 ◽  
Author(s):  
Alejandro Londoño-Valencia
Keyword(s):  
Artificial Intelligence ◽  
Human Intelligence ◽  
Alan Turing ◽  
John Mccarthy ◽  
Everyday Problems ◽  
Complex Algorithm ◽  
Complex Computer

For several decades the term Artificial Intelligence, coined by John McCarthy in 1956, is being used to generate complex computer solutions to everyday problems and for the development of technologies based on the conceptualization about human intelligence, allowing imitate it the more closely possible. Although there have been major advances in this field, there has not been possible to create a computer or a sufficiently complex algorithm that allow to make undifferentiated the human intelligence of the artificial intelligence, such as proposed by Alan Turing in his famous test. For this reason it is important to reflect on the reasons for not been able to reach this ambitious goal, so an analytical and compared proposal is presented in this paper about the limits of AI paired against the psychobiological characteristics and processes that support the intelligence in humans.Keywords: artificial intelligence, human intelligence, adaptation, development, biology, evolution.

The Omnipresent Computing Menace to Information Society

2011 ◽  
Vol 15 (7) ◽  
pp. 786-792 ◽  
Author(s):  
Alfons Schuster ◽  
◽  
Daniel Berrar ◽  
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Genetic Code ◽  
Information Society ◽  
Machine Intelligence ◽  
Human Intelligence ◽  
Sensitive Information ◽  
Intelligent Computing ◽  
Carbon Based

Computers have evolved from mere number crunchers to systems demonstrating an astonishing degree of sophistication, decision-making ability, and autonomy. Silicon is no longer the only substrate facilitating information processing. Despite these progresses, machine intelligence is still far from rivaling human intelligence. Nonetheless, we might be all too ready to rely on inferior agents for decision making, to give away sensitive information without fully understanding the consequences involved, or to tinker with genetic code to program carbon-based machines without fully appreciating the risks. This article explores the potentials and risks that information societies may face in the wake of current and emerging intelligent computing paradigms.

The sleeping brain, the states of consciousness, and the human intelligence

2007 ◽  
Vol 30 (2) ◽  
pp. 159-159
Author(s):  
Roumen Kirov
Keyword(s):  
Information Processing ◽  
Brain Activation ◽  
Human Memory ◽  
Experimental Results ◽  
Human Intelligence ◽  
States Of Consciousness ◽  
Neuroimaging Data ◽  
Memory States

AbstractA large number of experimental results clearly indicate that sleep has an important role for human intelligence. Sleep-wake stages and their specific patterns of brain activation and neuromodulation subserve human memory, states of consciousness, and modes of information processing that strongly relate to intelligence. Therefore, human intelligence should be explained in a broader framework than is implicated by neuroimaging data alone.

Toward Everyday Language Computing - Computing from a Viewpoint of Linguistic Theory -

1997 ◽  
Vol 1 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Ichiro Kobayashi ◽  
◽  
Michio Sugeno ◽  
Keyword(s):  
Information Processing ◽  
Social Context ◽  
Main Tool ◽  
Linguistic Theory ◽  
Human Intelligence ◽  
Everyday Language ◽  
Everyday Lives ◽  
Linguistic System ◽  
Systemic Functional Linguistic ◽  
Intellectual Activities

This paper describes our approach toward everyday language computing and shows some examples. The basic idea in the computing is that human intelligence reflects the linguistic system they use in their everyday lives, i.e., everyday language. Everyday language is, therefore, thought to be the main tool for reasoning and thinking in human intellectual activities. To find out how to apply everyday language to computing, we apply systemic functional linguistic theory that discusses the linguistic system from the viewpoint of social context. Following the ideas from this linguistic theory, we analyze the organization of human intelligence and then propose ideas for information processing using language.

Toward a triarchic theory of human intelligence

1984 ◽  
Vol 7 (2) ◽  
pp. 269-287 ◽  
Author(s):  
Robert J. Sternberg
Keyword(s):  
Information Processing ◽  
External World ◽  
Human Intelligence ◽  
New Information ◽  
Person Environment Fit ◽  
The Everyday ◽  
Everyday World ◽  
Intelligent Behavior ◽  
The Individual ◽  
Selection Of

AbstractThis article is a synopsis of a triarchic theory of human intelligence. The theory comprises three subtheories: a contextual subtheory, which relates intelligence to the external world of the individual; a componential subtheory, which relates intelligence to the individual's internal world; and a two-facet subtheory, which relates intelligence to both the external and internal worlds. The contextual subtheory defines intelligent behavior in terms of purposive adaptation to, shaping of, and selection of real-world environments relevant to one's life. The normal course of intelligent functioning in the everyday world entails adaptation to the environment; when the environment does not fit one's values, aptitudes, or interests, one may attempt to shape the environment so as to achieve a better person-environment fit; when shaping fails, an attempt may be made to select a new environment that provides a better fit. The two-facet subtheory further constrains this definition by regarding as most relevant to the demonstration of intelligence contextually intelligent behavior that involves either adaptation to novelty, automatization of information processing, or both. Efficacious automatization of processing allows allocation of additional resources to the processing of novelty in the environment; conversely, efficacious adaptation to novelty allows automatization to occur earlier in one's experience with new tasks and situations. The componential subtheory specifies the mental mechanisms responsible for the learning, planning, execution, and evaluation of intelligent behavior. Metacomponents of intelligence control one's information processing and enable one to monitor and later evaluate it; performance components execute the plans constructed by the metacomponents; knowledge-acquisition components selectively encode and combine new information and selectively compare new information to old so as to allow new information to be learned.

scholarly journals A chronometric model of the relationship between frontal midline theta functional connectivity and human intelligence

2019 ◽  
Author(s):  
Anna-Lena Schubert ◽  
Dirk Hagemann ◽  
Christoph Löffler ◽  
Jan Rummel ◽  
Stefan Arnau
Keyword(s):  
Information Processing ◽  
Individual Differences ◽  
Cognitive Control ◽  
Cognitive Abilities ◽  
Time Course ◽  
Fluid Intelligence ◽  
Adaptive Modulation ◽  
Human Intelligence ◽  
Comparison Task ◽  
Control Processes

Individual differences in cognitive control have been suggested to act as a domain-general bottleneck constraining performance in a variety of cognitive ability measures including but not limited to fluid intelligence, working memory capacity, and processing speed. However, due to psychometric problems associated with the measurement of individual differences in cognitive control, it has been challenging to empirically test the assumption that individual differences in cognitive control underlie individual differences in cognitive abilities. In the present study, we addressed these issues by analyzing the chronometry of intelligence-related differences in mid-frontal global theta connectivity, which has been shown to reflect cognitive control functions. We demonstrate in a sample of 98 adults, who completed a cognitive control task while their EEG was recorded, that individual differences in mid-frontal global theta connectivity during stages of higher-order information-processing explained 65 percent of the variance in fluid intelligence. In comparison, task-evoked theta connectivity during earlier stages of information processing was not related to fluid intelligence. These results suggest that more intelligent individuals benefit from an adaptive modulation of theta-band synchronization during the time-course of information processing. Moreover, they emphasize the role of interregional goal-directed information-processing for cognitive control processes in human intelligence and support theoretical accounts of intelligence which propose that individual differences in cognitive control processes give rise to individual differences in cognitive abilities.

Turing’s model of the mind

2017 ◽  
Author(s):  
Mark Sprevak
Keyword(s):  
Human Brain ◽  
Cognitive Science ◽  
Essential Element ◽  
Human Intelligence ◽  
Complex Objects ◽  
Intelligent Machine ◽  
Alan Turing ◽  
Magic Trick ◽  
The Face ◽  
The Mind

This chapter examines Alan Turing’s contribution to the field that offers our best understanding of the mind: cognitive science. The idea that the human mind is (in some sense) a computer is central to cognitive science. Turing played a key role in developing this idea. The precise course of Turing’s influence on cognitive science is complex and shows how seemingly abstract work in mathematical logic can spark a revolution in psychology. Alan Turing contributed to a revolutionary idea: that mental activity is computation. Turing’s work helped lay the foundation for what is now known as cognitive science. Today, computation is an essential element for explaining how the mind works. In this chapter, I return to Turing’s early attempts to understanding the mind using computation and examine the role that Turing played in the early days of cognitive science. Turing is famous as a founding figure in artificial intelligence (AI) but his contribution to cognitive science is less well known. The aim of AI is to create an intelligent machine. Turing was one of the first people to carry out research in AI, working on machine intelligence as early as 1941 and, as Chapters 29 and 30 explain, he was responsible for, or anticipated, many of the ideas that were later to shape AI. Unlike AI, cognitive science does not aim to create an intelligent machine. It aims instead to understand the mechanisms that are peculiar to human intelligence. On the face of it, human intelligence is miraculous. How do we reason, understand language, remember past events, come up with a joke? It is hard to know how even to begin to explain these phenomena. Yet, like a magic trick that looks like a miracle to the audience, but which is explained by revealing the pulleys and levers behind the stage, so human intelligence could be explained if we knew the mechanisms that lie behind its production. A first step in this direction is to examine a piece of machinery that is usually hidden from view: the human brain. A challenge is the astonishing complexity of the human brain: it is one of the most complex objects in the universe, containing 100 billion neurons and a web of around 100 trillion connections.

Human and Artificial Intelligence: A Clarification

2021 ◽  
pp. 13-48
Author(s):  
Thomas Fuchs
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Human Person ◽  
The Other ◽  
Human Intelligence ◽  
The Difference ◽  
The One ◽  
And Robotics

The advances in artificial intelligence (AI) and robotics are increasingly calling into question the distinction between the simulation and reality of the human person. On the one hand, they suggest a computeromorphic understanding of human intelligence, and on the other, an anthropomorphic view of AI systems. In other words: we increasingly view ourselves as our machines, and conversely, our machines as ourselves. So, what is the difference between human and AI? And can AI achieve consciousness at some point? The chapter argues that an embodied view of consciousness and the person establishes a notion of intelligence that cannot be reduced to information processing.

scholarly journals Knowledge Representation as a Tool for Intelligence Augmentation

2011 ◽  
pp. 321-341 ◽  
Author(s):  
Auke J.J. van Breemen ◽  
Jozsef I. Farkas ◽  
Janos J. Sarbo
Keyword(s):  
Information Processing ◽  
Knowledge Representation ◽  
Preliminary Analysis ◽  
Cognitive Theory ◽  
Cognitive Model ◽  
Human Information Processing ◽  
Human Intelligence ◽  
Proposed Model ◽  
Computer Aided ◽  
Theory Of Information

The goal of Intelligence Augmentation (IA) is the development of tools that improve the efficiency of human intelligence. To this end, the authors of this chapter introduce a model of human conceptualization on the basis of a cognitive theory of information processing and a Peircean theory of signs. An account of two experiments is provided. The first concerns conceptualization by individuals, and the second describes how problem elicitation was approached by a team of participants. A preliminary analysis of the results shows that the proposed model is congruent with multi channel and multi purpose human information processing. This implies that the cognitive model can be used as a model for knowledge representation in various fields of human-computer interfacing such as computer aided problem solving and problem elicitation.

Language processing is not a race against time

2016 ◽  
Vol 39 ◽  
Author(s):  
Giosuè Baggio ◽  
Carmelo M. Vicario
Keyword(s):  
Information Processing ◽  
Human Brain ◽  
Language Processing ◽  
Cognitive Functions ◽  
Memory Systems ◽  
Language System ◽  
Moderating Factors

AbstractWe agree with Christiansen & Chater (C&C) that language processing and acquisition are tightly constrained by the limits of sensory and memory systems. However, the human brain supports a range of cognitive functions that mitigate the effects of information processing bottlenecks. The language system is partly organised around these moderating factors, not just around restrictions on storage and computation.

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