• Korean Journal of Cognitive Science
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• v.23 no.3
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• pp.367-388
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• 2012

The centennial of Alan Mathison Turing(23 June 1912 - 7 June 1954) is an appropriate occasion on which to assess his profound influence on the development of cognitive science. His contributions to and attitudes toward that field are discussed from the metamathematical perspective. This essay addresses (i)Turing's mathematical analysis of cognition, (ii)universal Turing machines, (iii)the limitations of universal Turing machines, (iv)oracle Turing machine beyond universal Turing machine, and (v)Turing test for cognitive science. Turing was a ground-breaker, eager to move on to new fields. He actually opened wider the scientific windows to the mind. The results show that first, by means of mathematical logic Turing discovered a new bridge between the mind and the physical world. Second, Turing gave a new formal analysis of operations of the mind. Third, Turing investigated oracle Turing machines and connectionist network machines as new models of minds beyond the limitations of his own universal machines. This paper explores why the cognitive scientist would be ever expecting a new Turing Test on the shoulder of Alan Turing.

• Journal for History of Mathematics
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• v.17 no.4
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• pp.27-36
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• 2004

This paper addresses G$\ddot{o}$del's critique of Turing's mechanism that a configuration of the Turing machine corresponds to each state of human mind. The first part gives a quick overview of Turing's analysis of cognition as computation and its variants. In the following part, we describe the concept of Turing machines, and the third part explains the computational limitations of Turing machines as a cognitive system. The fourth part demonstrates that Godel did not agree with Turing's argument, sometimes referred to as mechanism. Finally, we discuss an oracle Turing machine and its implications.

• Journal for History of Mathematics
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• v.22 no.3
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• pp.31-44
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• 2009

Anyone wishing to understand cognitive science, a converging science, need to become familiar with three major mathematical landmarks: Turing machines, Neural networks, and $G\ddot{o}del's$ incompleteness theorems. The present paper aims to explore the mathematical foundations of cognitive science, focusing especially on these historical landmarks. We begin by considering cognitive science as a metamathematics. The following parts addresses two mathematical models for cognitive systems; Turing machines as the computer system and Neural networks as the brain system. The last part investigates $G\ddot{o}del's$ achievements in cognitive science and its implications for the future of cognitive science.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2000.05a
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• pp.322-328
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• 2000

This study discusses cognition as a computable mapping in cognitive system and relates G del's Incompleteness Theorems to the computability of cognition from a metamathematical perspective. Understanding cognition as a from of computation requires not only Turing machine models but also neural network models. In previous studies of computation by cognitive systems, it is remarkable to note how little serious attention has been given to the issue of computation by neural networks with respect to G del's Incompleteness Theorems. To address this problem, first, we introduce a definition of cognition and cognitive science. Second, we deal with G del's view of computability, incompleteness and speed-up theorems, and then we interpret G del's disjunction on the mind and the machine. Third, we discuss cognition as a Turing computable function and its relation to G del's incompleteness. Finally, we investigate cognition as a neural computable function and its relation to G del's incompleteness. The results show that a second-order representing system can be implemented by a finite recurrent neural network. Hence one cannot prove the consistency of such neural networks in terms of first-order theories. Neural computability, theoretically, is beyond the computational incompleteness of Turing machines. If cognition is a neural computable function, then G del's incompleteness result does not limit the compytational capability of cognition in humans or in artifacts.

• IE interfaces
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• v.5 no.2
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• pp.87-100
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• 1992

This paper presents FMC prototypes developed to propagate manufacuting automation for small and medium sized enterprises in Korea. The system consists of two cells used to machine oil pump drive gears, turing cell and hobbing cell. Characteristics and processes of the fears are anaylzed for the cell design. The system configuration and functions of individual component machines are described. Furthermore, the developed system is compared with the current manual system in terms of manpower and cost reduction and productivity increase.

• Journal of Science and Technology Studies
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• v.17 no.1
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• pp.5-39
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• 2017

In March 2016, the computer Go program, AlphaGo, defeated Sedol Lee, a Korean professional Go player of 9-dan rank. This victory by AlphaGo shows the rise in popularity of artificial intelligence (AI). Not only was this game a testament to machine performance, it was the type of game that extended the Turing test. When the interrogator cannot differentiate between human being and machine, the machine has passed the test. This article examines the interactions between AI and human beings and studies the social nature of intelligence through the AlphaGo case. Collins insists that knowledge or intelligence is social and embodied, and the interrogators in the Turing test can identify the difference between native members and non-members through their knowledge only. Applying this concept, AlphaGo, as subject A of this test, fulfilled its role of stirring up the classical "truth of human." Meanwhile, Lee as subject B, played to speak the truth by revealing his own qualities. Here, it is also important role that interrogators judge what it is. Many spectators, as interrogators, have intervened to confirm the border between human beings and machines by using their embodied and social knowledge.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.814-819
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• 2004

Soft engineering, based on symbiotic coexistence of human, machines and environment, is a new engineering field to explore the proper technology and the proper way of engineering. To explore soft engineering intents easily, various robot projects at Pusan National University conducted are presented. Thought experiment, interactive e-leaning, rapid prototyping engineering, biomimciry, tangibility, and ubiquity are concepts to be explored. Thought experiments projects are organized and performed, which include robot assembly game, Turing test, and robotics in science fiction. "Junk robot project" and "ubiquitous Pusan National University (u-PNU) project" have been organized. Also, bug robot project, interactive robot project, and interactive emotional robot projects are introduced. Weekly science fiction films are shown and discussed.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.5
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• pp.622-629
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• 1999

This paper presents the development of an interactive knowledge-based CAM system for design-ing and manufacturing the mold. The system is composed of two functional parts. One is the geo-metric modeller that uses the feature-based models. The models include base plate step, hole, pocket, boss and slot, These are designed by interactive user interface. The other is the expert sys-tem module with inference engine and knowledge database of workpiece material tools manufac-turing machines process an working conditions. With two parts the final mold shape is generated with manufacturing information for effective production.

• Informatization Policy
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• v.23 no.4
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• pp.3-23
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• 2016

Since Turing (1950) proposed the development of a machine or system that could think and communicate with humans, many engineers and scientists have made endless efforts to create machines and systems that can replace humans. This effort made the field of artificial intelligence. Recently, as many people have been interested in the 4th Industrial Revolution, research on artificial intelligence technology has been actively carried out not only in the university laboratories but also in the companies as the core technology for realizing the 4th Industrial Revolution. As the artificial intelligence technology has been penetrated deeply into our lives, it is true that our lives have become much easier and more comfortable than in the past, but on the other hand, we have begun to have various negative effects. In this study, we review the social changes caused by artificial intelligence in terms of intelligent products and services. By analyzing positive effects and dysfunctions in various cases of daily life and work environment, we try to identify main policy issues.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.8
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• pp.307-324
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• 2018

Recently, recurrent neural networks have been attracting attention in solving prediction problem of sequential data through structure considering time dependency. However, as the time step of sequential data increases, the problem of the gradient vanishing is occurred. Long short-term memory models have been proposed to solve this problem, but there is a limit to storing a lot of data and preserving it for a long time. Therefore, research on memory-augmented neural network (MANN), which is a learning model using recurrent neural networks and memory elements, has been actively conducted. In this paper, we describe the structure and characteristics of MANN models that emerged as a hot topic in deep learning field and present the latest techniques and future research that utilize MANN.