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Artificial Intelligence has historically relied on planning, heuristics, and handcrafted approaches designed by experts.
“A heuristic program to solve geometric-analogy problems”
Thomas Evans · 1964
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“The summer vision project”, 1966
Seymour Papert · 1966
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“Learning to predict by the methods of temporal differences”
Richard Sutton · 1988
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“Approximation by superpositions of a sigmoidal function”
George Cybenko · 1989
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“Responses of monkey dopamine neurons during learning of behavioral reactions”
Tomas Ljungberg, Paul Apicella and Wolfram Schultz · 1992
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“Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task”
Wolfram Schultz, Paul Apicella and Tomas Ljungberg · 1993
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“CYC: A large-scale investment in knowledge infrastructure”
Douglas Lenat · 1995
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“Sparse coding in the primate cortex”
Peter Foldiak · 2003
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“Imagenet classification with deep convolutional neural networks”
Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton · 2012
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“Towards biologically plausible deep learning”
Yoshua Bengio et al · 2015
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“Unsupervised learning of digit recognition using spike-timing-dependent plasticity”
Peter Diehl and Matthew Cook · 2015
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“Human-level control through deep reinforcement learning”
Volodymyr Mnih et al · 2015
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“Reinforcement Learning Applied to Single Neuron”
Zhipeng Wang and Mingbo Cai · 2015
Cited alongside, same era.
Greg Brockman et al · 2016
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Greg Brockman et al · 2016
Cited alongside, same era.
“Random synaptic feedback weights support error backpropagation for deep learning”
Timothy Lillicrap, Daniel Cownden, Douglas Tweed and Colin Akerman · 2016
Cited alongside, same era.
“Learning lexical features of programming languages from imagery using convolutional neural networks”
Jordan Ott et al · 2018
Later among the works it cites.
“Reinforcement learning: An introduction”
Richard Sutton and Andrew Barto · 2018
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“Solving the Rubik’s cube with deep reinforcement learning and search”
Forest Agostinelli, Stephen McAleer, Alexander Shmakov and Pierre Baldi · 2019
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“Questions to Guide the Future of Artificial Intelligence Research”
Jordan Ott · 2019
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“Learning in the Machine: To Share or Not to Share?”
Jordan Ott, Erik Linstead, Nicholas LaHaye and Pierre Baldi · 2019
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Adam Marblestone, Greg Wayne and Konrad Kording · 2016
Cited alongside, same era.
“Neuroscience-inspired artificial intelligence”
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield and Matthew Botvinick · 2017
Cited alongside, same era.
“Learning in the machine: Recirculation is random backpropagation”
Pierre Baldi and Peter Sadowski · 2018
Cited alongside, same era.
“Deep learning: A critical appraisal”
Gary Marcus · 2018
Cited alongside, same era.
“Neuron as an Agent”, 2018
Shohei Ohsawa et al · 2018
Cited alongside, same era.
“A deep learning approach to identifying source code in images and video”
Jordan Ott et al · 2018
Cited alongside, same era.
Colin Raffel et al · 2019
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“Do imagenet classifiers generalize to imagenet?”
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt and Vaishaal Shankar · 2019
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“Megatron-lm: Training multi-billion parameter language models using gpu model parallelism”
Mohammad Shoeybi et al · 2019
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“One pixel attack for fooling deep neural networks”
Jiawei Su, Danilo Vargas and Kouichi Sakurai · 2019
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“Training and inferring neural network function with multi-agent reinforcement learning”
Matthew Chalk, Gasper Tkacik and Olivier Marre · 2020
Closest in time.
“The Pretense of Knowledge On the insidious presumptions of Artificial Intelligence”, 2020
Jordan Ott · 2020
Closest in time.