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Article . 2013
License: CC BY NC ND
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Article . 2013 . Peer-reviewed
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AIC–An AI-system for Combination of Senses

Authors: Håkansson, Anne;

AIC–An AI-system for Combination of Senses

Abstract

AbstractAI-complete systems developed today, are commonly used for solving different artificial intelligence problems. A problem is a typical image recognition or speech recognition, but it can also be language processing, as well as, other complex systems dealing with general problem solving. However, no AI-complete system, which models the human brain or behavior, can exist without looking at the totality of the whole situation and, and hence, incorporating an AI-computerized sensory systems into a totality that constitute a combination of senses. This paper proposes a combination of sensory systems to form a comprehensive AI-system by combining the different senses, called AIC –AI-system for a combination of senses. The AIC-system is not a complete system in the sense that it contains a total set of information or uses all kinds of digital sensory systems. Nonetheless, it is a system under self-development. It develops its own knowledge base, as experiences, which will be based on the different characteristics: images, sounds, smells, tastes, touches with emotions/feelings and expressions. The result is a kind of perception of the surrounding environment.

Country
Sweden
Related Organizations
Keywords

Artificial perception, Complex systems, AI-systems, Artificial perception ;, Teknik och teknologier, Engineering and Technology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Top 10%
Average
Green
gold
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