References

Agrawal, Pulkit, Joao Carreira, and Jitendra Malik. “Learning to see by moving.” Proceedings of the IEEE International Conference on Computer Vision. 2015.

Agrawal, Pulkit, et al. “Learning to poke by poking: Experiential learning of intuitive physics.” arXiv preprint arXiv:1606.07419 (2016).

AI•ON. “The AI•ON collection of open research problems.” Online under

Allamanis, Miltiadis, Hao Peng, and Charles Sutton. “A convolutional attention network for extreme summarization of source code.” arXiv preprint arXiv:1602.03001 (2016).

Andrychowicz, Marcin, et al. “Learning to learn by gradient descent by gradient descent.” Advances in Neural Information Processing Systems

Blitzer, John, Mark Dredze, and Fernando Pereira. “Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification.” ACL. Vol. 7. 2007.

Brachman, Ronald J. “AI more than the sum of its parts.” AI Magazine 27.4 (2006): 19.

Brooks, R., et al. “Challenge problems for artificial intelligence.” Thirteenth National Conference on Artificial Intelligence-AAAI

Brown, Noam, and Tuomas Sandholm. “Safe and Nested Endgame Solving for Imperfect-Information Games.” Online under http://www.cs.cmu.edu/~noamb/papers/17-AAAI-Refinement.pdf(2017)

Cadena, Cesar, et al. “Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age.” IEEE Transactions on Robotics 32.6 (2016): 1309–1332.

Chang, Michael B., et al. “A compositional object-based approach to learning physical dynamics.” arXiv preprint arXiv:1612.00341 (2016).

Chen, Yutian, et al. “Learning to Learn for Global Optimization of Black Box Functions.” arXiv preprint arXiv:1611.03824 (2016).

Choi, Myung Jin, Antonio Torralba, and Alan S. Willsky. “Context models and out-of-context objects.” Pattern Recognition Letters 33.7 (2012): 853–862.

Ciocarlie, Matei. “Versatility in Robotic Manipulation: the Long Road to Everywhere.” Online under https://www.youtube.com/watch?v=wiTQ6qOR8o4(2015)

Commonsense Reasoning. “Commonsense reasoning Winograd schema challenge.” Online under http://commonsensereasoning.org/winograd.html(2016a)

Commonsense Reasoning. “Commonsense reasoning pronoun disambiguation problems” Online under http://commonsensereasoning.org/disambiguation.html (2016b)

Davies, Mark. The corpus of contemporary American English. BYE, Brigham Young University, 2008.

de Freitas, Nando. “Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016.

Degrave, Jonas, Michiel Hermans, and Joni Dambre. “A Differentiable Physics Engine for Deep Learning in Robotics.” arXiv preprint arXiv:1611.01652 (2016).

Deng, Jia, et al. “Imagenet: A large-scale hierarchical image database.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.

Denil, Misha, et al. “Learning to Perform Physics Experiments via Deep Reinforcement Learning.” arXiv preprint arXiv:1611.01843 (2016).

Duan, Yan, et al. “RL²: Fast Reinforcement Learning via Slow Reinforcement Learning.” arXiv preprint arXiv:1611.02779 (2016).

Ess, Andreas, et al. “Object detection and tracking for autonomous navigation in dynamic environments.” The International Journal of Robotics Research 29.14 (2010): 1707–1725.

Finn, Chelsea, and Sergey Levine. “Deep Visual Foresight for Planning Robot Motion.” arXiv preprint arXiv:1610.00696 (2016).

Fouhey, David F., and C. Lawrence Zitnick. “Predicting object dynamics in scenes.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.

Fragkiadaki, Katerina, et al. “Learning visual predictive models of physics for playing billiards.” arXiv preprint arXiv:1511.07404 (2015).

Garofolo, John, et al. “TIMIT Acoustic-Phonetic Continuous Speech Corpus LDC93S1.” Web Download. Philadelphia: Linguistic Data Consortium, 1993.

Gaunt, Alexander L., et al. “Terpret: A probabilistic programming language for program induction.” arXiv preprint arXiv:1608.04428 (2016).

Genesereth, Michael, Nathaniel Love, and Barney Pell. “General game playing: Overview of the AAAI competition.” AI magazine 26.2 (2005): 62.

Graves, Alex, et al. “Hybrid computing using a neural network with dynamic external memory.” Nature 538.7626 (2016): 471–476.

Hamrick, Jessica B., et al. “Imagination-Based Decision Making with Physical Models in Deep Neural Networks.” Online under http://phys.csail.mit.edu/papers/5.pdf(2016)

Han, Dongyoon, Jiwhan Kim, and Junmo Kim. “Deep Pyramidal Residual Networks.” arXiv preprint arXiv:1610.02915 (2016).

Harlow, Harry F. “The formation of learning sets.” Psychological review 56.1 (1949): 51.

Held, Richard, and Alan Hein. “Movement-produced stimulation in the development of visually guided behavior.” Journal of comparative and physiological psychology 56.5 (1963): 872.

Hernández-Orallo, José. “Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement.” Artificial Intelligence Review(2016a): 1–51.

Hernández-Orallo, José, et al. “Computer models solving intelligence test problems: progress and implications.” Artificial Intelligence 230 (2016b): 74–107.

Hernández-Orallo, José. “The measure of all minds.” Cambridge University Press, 2017.

IOCCC. “The International Obfuscated C Code Contest.” Online under http://www.ioccc.org(2016)

Kadlec, Rudolf, et al. “Finding a jack-of-all-trades: an examination of semi-supervised learning in reading comprehension.” Under review at ICLR 2017, online under https://openreview.net/pdf?id=rJM69B5xx

Kamper, Herman, Aren Jansen, and Sharon Goldwater. “Unsupervised word segmentation and lexicon discovery using acoustic word embeddings.” IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) 24.4 (2016): 669–679.

Kirkpatrick, James, et al. “Overcoming catastrophic forgetting in neural networks.” arXiv preprint arXiv:1612.00796 (2016).

Kondo, H. M., et al. “Auditory and visual scene analysis: an overview.” Philosophical transactions of the Royal Society of London. Series B, Biological sciences 372.1714 (2017).

Lahat, Dana, Tülay Adali, and Christian Jutten. “Multimodal data fusion: an overview of methods, challenges, and prospects.” Proceedings of the IEEE 103.9 (2015): 1449–1477.

Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. “Human-level concept learning through probabilistic program induction.” Science 350.6266 (2015): 1332–1338.

Lake, Brenden M., et al. “Building machines that learn and think like people.” arXiv preprint arXiv:1604.00289 (2016).

Lewicki, Michael S., et al. “Scene analysis in the natural environment.” Frontiers in psychology 5 (2014): 199.

Li, Wenbin, Aleš Leonardis, and Mario Fritz. “Visual stability prediction and its application to manipulation.” arXiv preprint arXiv:1609.04861 (2016).

Li, Zhizhong, and Derek Hoiem. “Learning without forgetting.” European Conference on Computer Vision. Springer International Publishing, 2016.

Loebner, Hugh. “Home page of the Loebner prize-the first Turing test.” Online under http://www.loebner.net/Prizef/loebner-prize.html (2016).

Lovett, Andrew, and Kenneth Forbus. “Modeling visual problem solving as analogical reasoning.” Psychological Review 124.1 (2017): 60.

Malik, Jitendra. “The Hilbert Problems of Computer Vision.” Online under https://www.youtube.com/watch?v=QaF2kkez5XU (2015)

McCarthy, John. “An example for natural language understanding and the AI Problems it raises.” Online under http://www-formal.stanford.edu/jmc/mrhug/mrhug.html(1976)

McLeod, John. “Card game rules — card games and tile games from around the world.” Online under https://www.pagat.com (2017)

Miller, George A. “WordNet: a lexical database for English.” Communications of the ACM 38.11 (1995): 39–41.

Mottaghi, Roozbeh, et al. ““What happens if…” Learning to Predict the Effect of Forces in Images.” European Conference on Computer Vision. Springer International Publishing, 2016.

Morency, Louis-Philippe. “Multimodal Machine Learning.” Online under https://www.youtube.com/watch?v=pMb_CIK14lU(2015)

Nair, Ashvin, et al. “Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation.” Online under http://phys.csail.mit.edu/papers/15.pdf (2016)

Nguyen, Anh, Jason Yosinski, and Jeff Clune. “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images.” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015.

Nilsson, Nils J. “Human-level artificial intelligence? Be serious!.” AI magazine 26.4 (2005): 68.

Pan, Sinno Jialin, and Qiang Yang. “A survey on transfer learning.” IEEE Transactions on knowledge and data engineering 22.10 (2010): 1345–1359.

Pang, Bo, and Lillian Lee. “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.” Proceedings of the 43rd annual meeting on association for computational linguistics. Association for Computational Linguistics, 2005.

Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. “Actor-mimic: Deep multitask and transfer reinforcement learning.” arXiv preprint arXiv:1511.06342 (2015).

Park, Alex S., and James R. Glass. “Unsupervised pattern discovery in speech.” IEEE Transactions on Audio, Speech, and Language Processing 16.1 (2008): 186–197.

Rajaram, Rakesh Nattoji, Eshed Ohn-Bar, and Mohan M. Trivedi. “An exploration of why and when pedestrian detection fails.” 2015 IEEE 18th International Conference on Intelligent Transportation Systems. IEEE, 2015.

Riccardi, Giuseppe, and Dilek Z. Hakkani-Tür. “Active and unsupervised learning for automatic speech recognition.” Interspeech. 2003.

Robo chat challenge. “Robo chat challenge 2014.” Online under http://www.robochatchallenge.com (2014)

Rosa, Marek, Jan Feyereisl, and The GoodAI Collective. “A Framework for Searching for General Artificial Intelligence.” arXiv preprint arXiv:1611.00685 (2016).

Schmidhuber, Jurgen. “Evolutionary principles in self-referential learning.” On learning how to learn: The meta-meta-… hook.) Diploma thesis, Institut f. Informatik, Tech. Univ. Munich (1987).

Silver, David, et al. “Mastering the game of Go with deep neural networks and tree search.” Nature 529.7587 (2016): 484–489.

Smith, Linda, and Michael Gasser. “The development of embodied cognition: Six lessons from babies.” Artificial life 11.1–2 (2005): 13–29.

Song, Shuran, Linguang Zhang, and Jianxiong Xiao. “Robot in a room: Toward perfect object recognition in closed environments.” CoRR (2015).

Stewart, Russell, and Stefano Ermon. “Label-free supervision of neural networks with physics and domain knowledge.” arXiv preprint arXiv:1609.05566 (2016).

Thrun, Sebastian, and Tom M. Mitchell. “Lifelong robot learning.” Robotics and autonomous systems 15.1–2 (1995): 25–46.

Thrun, Sebastian, and Lorien Pratt. “Learning to learn: Introduction and overview.” Learning to learn. Springer US, 1998. 3–17.

Verschae, Rodrigo, and Javier Ruiz-del-Solar. “Object detection: current and future directions.” Frontiers in Robotics and AI 2 (2015): 29.

Wang, Jane X., et al. “Learning to reinforcement learn.” arXiv preprint arXiv:1611.05763 (2016).

Yao, Bangpeng, Jiayuan Ma, and Li Fei-Fei. “Discovering object functionality.” Proceedings of the IEEE International Conference on Computer Vision. 2013.