Fall 2015: MAS.S66

Fall 2015:
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 

Slides (Opening Session, Joscha)

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

Slides (Possible Minds, Joscha)

September 28th: Agents within agents. The Society of Mind

Minsky, M. (1986): The Society of Mind
Singh, P. (2003): Examining the Society of Mind

Presenter: Manushaqe Muco

Slides (Society of Mind, Manushaqe)

October 5th: Are Deep Convolutional Learning Networks the Answer to Everything?

Guest speaker: Tomaso Poggio

Poggio, T.: What if…

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

LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444

Presenter: Mohammed AlQuraishi

Slides (Deep Learning, Mohammed)

October 26th: AI and Neuroscience

Frequently Asked Questions (FAQ) for Marcus, Marblestone and Dean. “The Atoms of Neural Computation”. Science. 31 Oct 2014. Vol. 346 No. 6209

Presenter: Adam Marblestone

Slides (What kind of Computer is the Brain?, Adam)

November 2nd: Universal intelligence. From Solomonoff induction to AIXI

Legg, S. and M. Hutter, Universal Intelligence: A Definition of Machine Intelligence. Minds and Machines, December 2007. 17(4): p. 391-444

Presenter: Colin McDonnell

Slides (AIXI, Colin)

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

Chomsky, N. (2014): Where Artificial Intelligence Went Wrong. The Atlantic, Nov. 1, 2012 http://www.chomsky.info/interviews/20121101.htm
Peter Norvig’s comments: http://norvig.com/chomsky.html

Presenter: Yoshihiko Suhara

Slides (Towards Mapping Contemporary AI, Yoshihiko)

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

Slides (Modeling Motivation in MicroPsi , Joscha)

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

Slides (Measuring the Progress of AI Benchmark Problems, Dhaval and Kane)

December 7th: Closing Discussion. Can we sketch a Map of Future AI research?