Home > Uncategorized > Weekly QuEST Discussion Topics and News, 13 Jan

Weekly QuEST Discussion Topics and News, 13 Jan

QuEST 13 Jan 2017

This week we want to several things – we want to continue our discussion of the Kabrisky lecture – What is QueST – and along that line we want to clean up some concern on our use in our Theory of Consciousness of the word ‘situated’.  As our use of the term is not consistent with some usage (for example in the Journal of Cognitive Engineering and Decision Making – article in Press by our QuEST colleagues Patterson and Eggleston) – we want to reconcile the terms.  The other item on the agenda is Cap has to give several talks coming up and generate some material on historical perspectives in neural science and also computational models associated with machine learning and artificial intelligence so we will have some discussion along those lines.

Specifically – in one recent study cap presented at it was concluded that:

Operationally AI, it can be defined as those areas of R&D practiced by computer scientists who identify with one or more of the following academic sub-disciplines: Computer Vision, Natural Language Processing (NLP), Robotics (including Human-Robot Interactions), Search and Planning, Multi-agent Systems, Social Media Analysis (including Crowdsourcing), and Knowledge Representation and Reasoning (KRR).  In contradistinction to artificial general intelligence:

  • Artificial General Intelligence (AGI) is a research area within AI, small as measured by numbers of researchers or total funding, that seeks to build machines that can successfully perform any task that a human might do. Where AI is oriented around specific tasks, AGI seeks general cognitive abilities. On account of this ambitious goal, AGI has high visibility, disproportionate to its size or present level of success, among futurists, science fiction writers, and the public.

We will want to pull on these threads with respect to the breakthroughs in deep learning and the promise of other approaches to include unsupervised learning, reinforcement learning …


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