Ackerman, E. (2014). Robots playing ping pong: What's real, what's not? IEEE Spectrum
Amazon (2013). Amazon Prime Air.
Arshad, I. (2012). People and machines: Competitors or collaborators in the emerging world of work?
Chalmers, D. (1996). The conscious mind: In search of a fundamental theory. Oxford, UK: Oxford University Press.
Cheah, C. C., Liu, C. & Slotine, J. J. E. (2006). Adaptive tracking control for robots with unknown kinematic and dynamic properties. The International Journal of Robotics Research, 25 (3), 283-296. SAGE Publications.
Choudhary, S., Sloan, S., Fok, S., Neckar, A., Trautmann, E., Gao, P., Stewart, T., Eliasmith, C. & Boahen, K. (2012). Silicon neurons that compute. In A. E. P. Villa, W. Duch, P. Érdi, F. Masulli and G. Palm (Eds.) Artificial neural networks and machine learning - ICANN 2012 (pp. 121-128). Berlin, GER: Springer. 10.1007/978-3-642-33269-2_16
Corradi, F., Eliasmith, C. & Indiveri, G. (2014). Mapping arbitrary mathematical functions and dynamical systems to neuromorphic VLSI circuits for spike-based neural computation. IEEE International Symposium on Circuits and Systems (ISCAS) 2014 (pp. 269-272). IEEE. 10.1109/ISCAS.2014.6865117
Crawford, E., Gingerich, M. & Eliasmith, C. (2013). Biologically plausible, human-scale knowledge representation. In M. Knauff, M. Pauen, N. Sebanz and I. Wachsmuth (Eds.) 35th Annual Conference of the Cognitive Science Society (pp. 412-417). Austin, TX: Cognitive Science Society.
Davies, S., Stewart, T., Eliasmith, C. & Furber, S. (2013). Spike-based learning of transfer functions with the SpiNNaker neuromimetic simulator. The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1832-1839). IEEE. 10.1109/IJCNN.2013.6706962
De Garis, H., Shuo, C., Goertzel, B. & Ruiting, L. (2010). A world survey of artificial brain projects, Part I: Large-scale brain simulations. Neurocomputing, 74 (1-3), 3-29. 10.1016/j.neucom.2010.08.004
Eliasmith, C. & Anderson, C.H. (1999). Developing and applying a toolkit from a general neurocomputational framework. Neurocomputing, 26-27 (0), 1013-1018. 10.1016/S0925-2312(99)00098-3
— (2003). Neural engineering: Computation, representation and dynamics in neurobiological systems. Cambridge, MA: MIT Press.
Eliasmith, C. & Trujillo, O. (2013). The use and abuse of large-scale brain models. Current Opinion in Neurobiology, 25, 1-6. 10.1016/j.conb.2013.09.009
Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition. New York, NY: Oxford University Press.
Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y. & Rasmussen, D. (2012). A large-scale model of the functioning brain. Science, 338 (6111), 1202-1205. 10.1126/science.1225266
Esser, S. K., Andreopoulos, A., Appuswamy, R., Datta, P., Barch, D., Amir, A., Arthur, J., Cassidy, A., Flickner, M., Merolla, P., Chandra, S., Basilico, N., Carpin, S., Zimmerman, T., Zee, F., Alvarez-Icaza, R., Kusnitz, J. A., Wong, T. M., Risk, W. P., McQuinn, E., Nayak, T. K., Singh, R. & Modha, D. S. (2013). Cognitive computing systems: Algorithms and applications for networks of neurosynaptic cores. The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-10). IEEE. 10.1109/IJCNN.2013.6706746
Fodor, J. A. & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28 (1-2), 3-71. 10.1016/0010-0277(88)90031-5
Fox, D. (2009). IBM reveals the biggest artificial brain of all time. Popular Mechanics
Galluppi, F., Denk, C., Meiner, M. C., Stewart, T., Plana, L. A., Eliasmith, C., Furber, S. & Conradt, J. (2014). Event-based neural computing on an autonomous mobile platform. Proceedings of IEEE international conference on robotics and automation (ICRA). IEEE.
Goodman, W. C. (1996). Software and engineering industries: Threatened by technological change? Monthly Labor Review, 119 (8), 37-45. Bureau of Labor Statistics, U.S. Department of Labor. 10.2307/41844604
Graves, A., Mohamed, A. & Hinton, G. E. (2013). speech recognition with deep recurrent neural networks. IEEE international conference on acoustic speech and signal processing (ICASSP) (pp. 6645-6649). Vancouver, Canada: IEEE.
Hasler, J. & Marr, H. B. (2013). Finding a roadmap to achieve large neuromorphic hardware systems. Frontiers in Neuroscience, 7 (118), 1-29. 10.3389/fnins.2013.00118
Jackendoff, R. (2002). Foundations of language: Brain, meaning, grammar, evolution. Oxford, UK: Oxford University Press.
Khan, M. M., Lester, D. R., Plana, L. A., Rast, A., Jin, X., Painkras, E. & Furber, S. B. (2008). SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor. IEEE. (pp. 2849-2856). 10.1109/IJCNN.2008.4634199
Khatib, O. (1987). A unified approach for motion and force control of robot manipulators: The operational space formulation. IEEE Journal of Robotics and Automation, 3 (1), 43-53.
Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou and K. Q. Weinberger (Eds.) Advances in Neural Information Processing Systems 25 (p. 4).
Kumar, S. (2013). Introducing qualcomm zeroth processors: Brain-inspired computing. http://www.qualcomm.com/media/blog/2013/10/10/introducing-qualcomm-zeroth-processors-brain-inspired-computing
Kurzweil, R. (2005). The singularity is near. New York, NY: Penguin Books.
Leung, E. L., Cao, Z., Jiang, Z., Zhou, H. & Liu, L. (2013). Network-based drug discovery by integrating systems biology and computational technologies. Briefings in bioinformatics, 14 (4), 491-505. Oxford, UK: Oxford University Press.
Li, C., Delbruck, T. & Liu, S. (2012). Real-time speaker identification using the AEREAR2 event-based silicon cochlea. 2012 IEEE International Symposium on Circuits and Systems (pp. 1159-1162). 10.1109/ISCAS.2012.6271438
Lichtsteiner, P., Posch, C. & Delbruck, T. (2008). Temporal contrast vision sensor. IEEE Journal of Solid-State Circuits, 43 (2), 566-576.
Litt, A., Eliasmith, C., Kroon, F., Weinstein, S. & Thagard, P. (2006). Is the brain a quantum computer? Cognitive Science, 30 (3), 593-603.
Maass, W., Natschläger, T. & Markram, H. (2002). Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural computation, 14 (11), 2531-2560. Cambridge, MA: MIT Press.
Malone, P. (2014). Wealthy need to share the spoils of automation. http://www.canberratimes.com.au/comment/wealthy-need-to-share-the-spoils-of-automation-20140301-33sp6.html
Manyika, J., Chui, M., Bughin, J. & Dobbs, R. (2013). Disruptive technologies: Advances that will transform life, business, and the global economy. McKinsey Global Institute.
Marcus, G. (2013). Is "Deep Learning" a revolution in artificial intelligence? The New Yorker.
Metzinger, T. (2013). Two principles for robot ethics. In E. Hilgendorf and J. P. Günther (Eds.) Robotik und Gesetzgebung (pp. 263-302). Baden-Baden, GER: Nomos.
Newquist, H. P. (1994). The brain makers. Indianapolis, IN: Sams Publishing.
Norman, D. A. (1986). Reflection on cognition and parallel distributed processing. In J. L. McClelland and D. E. Rumelhart (Eds.) Parallel distributed processing: Exploration inthe microstructure of cognition (p. 531). Cambridge, MA: MIT Press.
Padbury, P., Christensen, S., Wilburn, G., Kunz, J. & Cass-Beggs, D. (2014). MetaScan 3: Emerging technologies. MetaScan 3: Emerging technologies (p. 45). Cambridge, MA: Policy Horizons Canada. http://www.horizons.gc.ca/eng/content/metascan-3-emerging-technologies-0
Pinker, S. & Prince, A. (1988). On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition, 28 (1-2), 73-193. 10.1016/0010-0277(88)90032-7
Plewes, T. J. (1990). Labor force data in the next century. Monthly Labor Review, 113 (4). Bureau of Labor Statistics, U.S. Department of Labor.
Print Edition (2014). The future of jobs: The onrushing wave. The Economist.
Robertson, J., Sheppard, T. & Sarnes, S. (2005). Workers compensation claim frequency continues to decline, particularly for smaller claims. NCCI Research Brief, 2
Rumelhart, D. E. (1989). The architecture of mind: A connectionist approach. In M. I. Poser (Ed.) The architecture of mind: A connectionist approach (pp. 133-159). Cambridge, MA: MIT Press.
Rutkin, A. H. (2013). Report suggests nearly half of U.S. jobs are vulnerable to computerization. MIT Technology Review.
Schaal, S., Mohajerian, P. & Ijspeert, A. (2007). Dynamics systems vs. optimal control: A unifying view. Progress in brain research, 165 (1), 425-45. 10.1016/S0079-6123(06)65027-9
Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3 (03), 417-424. Cambridge, UK: Cambridge University Press. 10.1017/S0140525X00005756
Simon, H. A. & Newell, A. (1958). Heuristic problem solving: The next advance in operations research. Operations Research, 6 (1), 1-10.
Stevenson, I. H. & Kording, K. P. (2011). How advances in neural recording affect data analysis. Nature neuroscience, 14 (2), 139-42. 10.1038/nn.2731
Stunt, V. (2014). Why Google is buying a seemingly crazy collection of companies. CBC News.
Sussillo, D., Abbott, L. F. (2009). Generating coherent patterns of activity from chaotic neural networks. Neuron, 63 (4), 544-57. 10.1016/j.neuron.2009.07.018
Thagard, P. (2010). The brain and the meaning of life. Princeton, NJ: Princeton University Press.
— (2014). Explanatory identities and conceptual change. Science & Education, 23 (7), 1531-1548. 10.1007/s11191-014-9682-1
Todorov, E. (2008). Optimal control theory. In K. Doya (Ed.) Bayesian brain: Probabilistic approaches to neural coding (pp. 269-298). Cambridge, MA: MIT Press.
— (2009). Parallels between sensory and motor information processing. In M. S. Gazzaniga (Ed.) The cognitive neurosciences (pp. 613-624). Cambridge, MA: MIT Press.
Ulam, S. (1958). Tribute to John von Neumann. Bulletin of the American Mathematical Society, 64 (3), 5.
Vinge, V. (1993). The coming technological singularity: How to survive in the post-human era. Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace (pp. 11-22). Westlake, OH: NASA Conference Publication 10129.