Sessions and suggested reading (subject to change)

February 8th: Introduction: Mind as Machine
Boden, M. (2007): Mind as Machine, ch. 4, 16

Slides. Joscha Bach: Cognitive AI Orientation.ppt

Tuesday (shift due to President’s Day) February 16th: On the relationship between symbolic and connectionist AI
Carey, S., Gleitman, L., Marcus, G. F., Newport, E. L., Spelke, E. S. (2003): The Algebraic Mind, ch. 4

Slides. Ben Berman: Marcus Presentation.pptx, Adam Marblestone: SymbolicConnectionist_Marblestone, Henry Lieberman: Symbolic vs. Subsymbolic.pptx

February 22nd: Theories of Representation, Perception and Symbol Grounding
Sowa, J. R. (1987, 2015). Semantic Networks.

Harnad, S. (1990) The Symbol Grounding Problem. Physica D 42: 335-346.
Barsalou, L. (1999). Perceptual Symbol Systems. Behavioral and Brain Sciences 22, 577–660.
Baum, E. B. (2003). What is thought? MIT Press

Slides. Kenny Friedman: FutureAIPresentation

February 29th: Learning
Ullman, S., Harari, D., & Dorfman, N. (2012). From simple innate biases to complex visual concepts. Proceedings of the National Academy of Sciences of the United States of America, 109(44), 18215–20.
O’Reilly, R. C., Wyatte, D., & Rohrlich, J. (2014). Learning Through Time in the Thalamocortical Loops, 37. Neurons and Cognition.

Shimon Ullman, Liay Assif, Ethan Fetaya, Daniel Harari. (2016). Atoms of recognition in human and computer vision.
Slides. Colin McDonnell: Learning , Adam Marblestone: Feb29th2016_Marblestone

Reading guide (questions to think about while/after reading):
“How might human learning differ from back-propagation-based training of neural nets?”
“What kinds of heuristics can we rely on, to bootstrap learning about the human world?”
“How can we interrogate those differences scientifically?”

March 7th: The MicroPsi architecture
Bach, J. (2009). Principles of Synthetic Intelligence
Slides. Joscha Bach: MicroPsi FutureAI Spring 2016

March 14th: Social cognition and theory of mind
Saxe, R. (in press). The Neural Basis of Consciousness.,%20R.%20(in%20press).%20Theory%20of%20mind%20-%20neural%20basis%20(Encyclopedia%20of%20Consciousness).pdf
Zawidzki, T. W. (2013): Mindshaping. A New Framework for Understanding Human Social Cognition. MIT Press
Slides. Manushaqe Muco: Social Cognition and Theory of Mind , Joscha Bach: Social Cognition FutureAI March 14.ppt

March 28th: Cortical organization
Franzius, M., Sprekeler, H., & Wiskott, L. (2007). Slowness and sparseness lead to place, head-direction, and spatial-view cells. PLoS Computational Biology, 3(8), e166.
Dileep George, Jeff Hawkins. (2009) Towards a Mathematical Theory of Cortical Micro-Circuits.
Gary F. Marcus, Adam H. Marblestone, Thomas L. Dean. (2014). Frequently Asked Questions for: The Atoms of Neural Computation.
Hopfield, J. J. (2009). Neurodynamics of mental exploration. Proceedings of the National Academy of Sciences, 107(4), 1648–1653.
Slides. Adam Marblestone: cortical organization marblestone, Keeley Erhardt & Daniel Fitzgerald: MAS.S63 — Cortical Organization

April 4th: Computational models of cortical function
Hayworth, K. J. (2012). Dynamically partitionable autoassociative networks as a solution to the neural binding problem. Frontiers in Computational Neuroscience, 6, 73.
Kurach, K., Andrychowicz, M., & Sutskever, I. (2015). Neural Random-Access Machines, 13. Learning; Neural and Evolutionary Computing.
Kriete, T., Noelle, D. C., Cohen, J. D., & O’Reilly, R. C. (2013). Indirection and symbol-like processing in the prefrontal cortex and basal ganglia. Proceedings of the National Academy of Sciences of the United States of America, 110(41), 16390–5.

Slides. Eric Chu & Archana Ram: Computational Models of Cortical Function

April 11th: Modeling imagination and creativity in computational models
Schank, R. (1993): Making Machines Creative\_article,\_v2.html
McCormack, J. and d’Inverno, M. (eds.) (2012). “Computers and Creativity”. Springer, Berlin.
Veale, T., Feyaerts, K. and Forceville, C. (2013) “Creativity and the Agile Mind: A Multidisciplinary study of a Multifaceted phenomenon”. Mouton de Gruyter

Slides. Kane Hadley: CreativityFutureAI

April 25nd:  The Spaun cognitive architecture
Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition Oxford University Press, 2013, Chris Eliasmith
Adam Marblestone: SPAUN ,
Bret Fontecchio: Generative Memory

May 2nd: The Leabra architecture
O’Reilly, R.C. et al (2014). Computational Cognitive Neuroscience
O’Reilly, R. C., Hazy, T. E., Mollick, J., Mackie, P., & Herd, S. (2014). Goal-Driven Cognition in the Brain: A Computational Framework.
O’Reilly, R.C. (2006). Biologically Based Computational Models of High-Level Cognition. Science, 314, 91-94
O’Reilly, R.C., Bhattacharyya, R., Howard, M.D. & Ketz, N. (2014). Complementary Learning Systems. Cognitive Science, 38,1229-1248.

Michael Chang:Draft 9 Clean.pptx ,
Daniel Goodwin: LEABRA-Presentation-DRG

May 6th: The Design Space of Cognitive Architectures

May 9th: Final Student Presentations
Joscha Bach:
Cognitive AI Summary

Kane Hadley: Chinese Character Learning Assistant

Daniel Goodwin: Exploring Impact of Diversity in Multilayer, Multitask Neural Networks

Ben Berman: Realtime Deep Dream, Reenacting hallucinations in VR

Colin McDonnell: DefinitionLearning

Archana Ram: Visual Abstraction as a Means of Image Category Generalization and Recognition Under Partial Occlusion

Kenny Friedman: Escaping the Local Minimum

Michael Chang: Learning Predictive Models of Physics

Keeley Erhardt: An attempt at understanding the differences between the features used by deep convolutional neural nets and those used by humans for image identification

Eric Chu: GRID-LSTM.pptx