
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.
Artificial perception, Complex systems, AI-systems, Artificial perception ;, Teknik och teknologier, Engineering and Technology
Artificial perception, Complex systems, AI-systems, Artificial perception ;, Teknik och teknologier, Engineering and Technology
| 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). | 3 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
