6,800 research outputs found
Ethics of Artificial Intelligence Demarcations
In this paper we present a set of key demarcations, particularly important
when discussing ethical and societal issues of current AI research and
applications. Properly distinguishing issues and concerns related to Artificial
General Intelligence and weak AI, between symbolic and connectionist AI, AI
methods, data and applications are prerequisites for an informed debate. Such
demarcations would not only facilitate much-needed discussions on ethics on
current AI technologies and research. In addition sufficiently establishing
such demarcations would also enhance knowledge-sharing and support rigor in
interdisciplinary research between technical and social sciences.Comment: Proceedings of the Norwegian AI Symposium 2019 (NAIS 2019),
Trondheim, Norwa
The challenge of complexity for cognitive systems
Complex cognition addresses research on (a) high-level cognitive processes β mainly problem solving, reasoning, and decision making β and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods β analytical, empirical, and engineering methods β which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition β complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
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