Intellectual Issues in the History of Artificial Intelligence

Newell, Allen

doi:10.21236/ada125318

Cited by 56 publications

(47 citation statements)

References 45 publications

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“…One reason for the engineering community being slow in adopting these results is the cultural bias against emotions as a part of cognitive processes. Plato and Aristotle thought that emotions are "bad" for intelligence, this is a part of our cultural heritage ("one have to be cool to be smart"), and the founders of Artificial Intelligence repeated this truism about emotions [ 31 ]. Yet, as discussed in the next section, combining conceptual understanding with emotional evaluations is crucial for overcoming the combinatorial complexity as well as related difficulties of logic.…”

Section: Mind: Concepts and Emotionsmentioning

confidence: 99%

Phenomenology-Based Inverse Scattering for Sensor Information Fusion

Ding¹

2006

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S) AFRL-SN-HS SPONSOR/MONITOR'S REPORT NUMBER(S)Electromagnetics ABSTRACTFusion of sensor and communication data currently can only be performed at a late processing stage after sensor and textual information are formulated as logical statements at appropriately high level of abstraction. Contrary to this it seems, the human mind integrates sensor and language signals seamlessly, before signals are understood, at preconceptual level. Learning of conceptual contents of the surrounding world depends on language and vice versa. This paper uses phenomenology of the human mind and sensing processes to achieve high-level sensor fusion. We describe a phenomenology-based inverse scattering (PBIS) mathematical technique for such integration. It combines PBIS based on dynamic logic with dual cognitive-language models. The paper briefly discusses relationships between the proposed mathematical technique, working of the mind and applications to understanding-based search engines.

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Section: Mind: Concepts and Emotionsmentioning

confidence: 99%

Phenomenology-Based Inverse Scattering for Sensor Information Fusion

Ding¹

2006

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“…Fig. 13 cartoons a merging of two different approaches to artificial intelligence that have been evolving separately for decades [Newell 1983]. If we are able to combine the best features of both paradigms, and develop appropriate hardware and software, the scientific and practical consequences could be considerable.…”

Section: Applicationsmentioning

confidence: 99%

Computing with structured connectionist networks

et al. 1988

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Massively parallel (connectionist) computation is receivin~widespread attention. Both the theory of connectionist computation and its applications to a broad range of problems are advancing rapidly. Somewhat less attention has been paid to the experimental methodologies of designing and testing massively parallel networks. This paper describes a coordinated set of specification, simulation and monitoring tools that have proven to be quite useful in our research. Also included are an overview of the structured connectionist paradigm and several sample applications.

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“…Founders of artificial intelligence thought that formal logic was sufficient [31] and no specific mathematical techniques would be needed to describe the mind [29]. An opposite point of view is that there are few specific mathematical constructs, "the first principles" of the mind organization.…”

Section: Language and The Mindmentioning

confidence: 99%

“…One reason for engineering community being slow in adopting these results is the cultural bias against emotions as a part of thinking processes. Plato and Aristotle thought that emotions are "bad" for intelligence, this is a part of our cultural heritage ("one has to be cool to be smart"), and the founders of Artificial Intelligence repeated this truism about emotions [31]. Yet, as discussed in the next section, combining conceptual understanding with emotional evaluations is crucial for overcoming the combinatorial complexity as well as the related difficulties of logic.…”

Section: Mind: Concepts and Emotionsmentioning

confidence: 99%

“…Imagination was often considered to be a part of thinking processes; Kant [20] emphasized the role of imagination in the thought process, he called thinking "a play of cognitive functions of imagination and understanding". Whereas pattern recognition and artificial intelligence algorithms of recent past would not know how to relate to this [29,31], Carpenter and Grossberg's resonance model [3] and the MFT dynamics both describe imagination as an inseparable part of thinking: imagined patterns are top-down signals that prime the perception cortex areas (priming is a neural terminology for making neural cells to be more readily excited). In MFT, models M h give the imagined neural patterns.…”

Section: Imaginationmentioning

confidence: 99%

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Neural Networks, Fuzzy Models and Dynamic Logic

Perlovsky

Aspects of Automatic Text Analysis

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Summary. The paper discusses possible relationships between computational intelligence, known mechanisms of the mind, semiotics, and computational linguistics. Mathematical mechanisms of concepts, emotions, and goals are described as a part of information processing in the mind and are related to language and thought processes in which an event (signals from surrounding world, text corpus, or inside the mind) is understood as a concept. Previous attempts in artificial intelligence at describing thought processes are briefly reviewed and their fundamental (mathematical) limitations are analyzed. The role of emotional signals in overcoming these past limitations is emphasized. The paper describes mathematical mechanisms of concepts applicable to sensory signals and linguistics; they are based on measures of similarities between models and signals. Linguistic similarities are discussed that can utilize various structures and rules proposed in computational linguistic literature. A hierarchical structure of the proposed method is capable of learning and recognizing concepts from textual data, from the level of words and up to sentences, groups of sentences, and towards large bodies of text. I briefly discuss a role of concepts as a mechanism unifying thinking and language and their possible role in language acquisition. A thought process is related to semiotic notions of signs and symbols. It is further related to understanding, imagination, intuition, and other processes in the mind. The paper briefly discusses relationships between the mind and brain and applications to understanding-based search engines.

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Intellectual Issues in the History of Artificial Intelligence

Cited by 56 publications

References 45 publications

Phenomenology-Based Inverse Scattering for Sensor Information Fusion

Phenomenology-Based Inverse Scattering for Sensor Information Fusion

Computing with structured connectionist networks

Neural Networks, Fuzzy Models and Dynamic Logic

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