Discussing Directions and Goals of Future AI Research
The commercial successes of major Artificial Intelligence applications, along with breakthrough results in machine learning and computer vision, have led to renewed conviction and support for building artificial minds. The so-called “Winter of AI” is over, but it is unlikely that the current set of methods and insights will carry us all the way. While most AI researchers recognize the need to integrate knowledge from the other cognitive sciences into a cohesive whole, our visions of how to do that are fragmented and incomplete.
This seminar will attempt to identify the white spots in AI’s map of the mind, by taking a bird’s eye perspective to past and current research in cognitive architectures, bridging the symbolic/connectionist divide, general learning, and representational paradigms.
We would like to encourage discussion and reflection, and welcome new insights through the interaction of participants. We will attempt to keep the schedule flexible to accommodate your suggestions and interests.
Sessions and reading, in Fall Term 2015
September 14th: Orientation
Origins: Artificial Intelligence, the very idea (opening discussion)
Turing, A. (1950): Computing Machinery and Intelligence
September 21st: Possibilities for artificial minds
Sloman, A., The Structure and Space of Possible Minds. The Mind and the Machine: philosophical aspects of Artificial Intelligence. 1984: Ellis Horwood LTD
Sloman, A. (1989). On designing a visual system: Towards a Gibsonian computational model of vision. Journal of Experimental and Theoretical AI, 1,4, 289-337 1989
September 28th: Agents within agents. The Society of Mind
Presenter: Manushaqe Muco
October 5th: Are Deep Convolutional Learning Networks the Answer to Everything?
Guest speaker: Tomaso Poggio
October 12th: Columbus Day (no session)
October 19th: The Neocognitron and Deep Learning
Fukushima, K. (1980): Neocognitron: A self-organizing neural net model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36, 193-202 (1980)
Bengio, J., LeCun Y. (2007): Scaling Learning Algorithms Towards AI: (in Bottou et al. (Eds) Large- Scale Kernel Machines, MIT Press
Presenter: Mohammed AlQuraishi
October 26th: AI and Neuroscience
Presenter: Adam Marblestone
November 2nd: Universal intelligence. From Solomonoff induction to AIXI
Presenter: Colin McDonnell
November 9th: Cognitive Architectures
Newell, A. 1990. Unified theories of cognition. Cambridge, Mass.: Harvard University Press.
Thorisson, K. R., Helgasson, H. P. (2012). Cognitive Architectures and Autonomy: A Comparative Review. Journal of Artificial General Intelligence 3(2) 1-30, 2012
Langley, P., Laird, J. E., Rogers, S. 2009. Cognitive Architectures: Research Issues and Challenges. Cognitive Systems Research, 10(2), 141-160
Presenter: Carolyn Saund, Alex Lenail
November 16th: Towards mapping contemporary AI. The Norvig/Chomsky debate
Presenter: Yoshihiko Suhara
November 23rd: Affect and Motivation
Marsella, S., Gratch, J. (2014). Computationally Modeling Human Emotion. Communications of the ACM, Vol. 57 No. 12, Pages 56-67
Bach, J. (2015). Modeling Motivation in MicroPsi 2. In Goertzel, B., Bieger, J. Potapov, A. (eds.): Proceedings of AGI 2015, LNAI 9205, 3-13
November 30th: Measuring the Progress of AI. Benchmark Problems
Adams, S. S., Bach, J., Coop, R., Hall, J. S., Schlesinger, S., Arel, I., Furlan, R., Samsonovich, A., Shapiro, S. C., Goertzel, B., Scheutz, M., Sowa, J. (2012). Mapping the AGI Landscape. Journal of Artificial Intelligence, 33(1):25-42
Presenters: Dhaval Adjodah, Kane Hadley
December 7th: Closing Discussion. Can we sketch a Map of Future AI research?