Large Language Models and the Reverse Turing Test
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[1] Alexander M. Rush,et al. Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models , 2022, IEEE Transactions on Visualization and Computer Graphics.
[2] W. Fitch,et al. Evolutionary loss of complexity in human vocal anatomy as an adaptation for speech , 2022, Science.
[3] James L. McClelland,et al. Language models show human-like content effects on reasoning , 2022, ArXiv.
[4] Peter R. Florence,et al. Inner Monologue: Embodied Reasoning through Planning with Language Models , 2022, CoRL.
[5] A. Weinstein,et al. Intuitive physics learning in a deep-learning model inspired by developmental psychology , 2022, Nature Human Behaviour.
[6] Xiao-Jing Wang,et al. Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition. , 2022, Annual review of neuroscience.
[7] A. Zador,et al. The application of artificial intelligence to biology and neuroscience , 2022, Cell.
[8] Saketh Reddy Karra,et al. AI Personification: Estimating the Personality of Language Models , 2022, ArXiv.
[9] Andrew M. Dai,et al. PaLM: Scaling Language Modeling with Pathways , 2022, J. Mach. Learn. Res..
[10] Lisa Anne Hendricks,et al. Training Compute-Optimal Large Language Models , 2022, ArXiv.
[11] T. Besiroglu,et al. Compute Trends Across Three Eras of Machine Learning , 2022, 2022 International Joint Conference on Neural Networks (IJCNN).
[12] Dale Schuurmans,et al. Chain of Thought Prompting Elicits Reasoning in Large Language Models , 2022, ArXiv.
[13] Renelito Delos Santos,et al. LaMDA: Language Models for Dialog Applications , 2022, ArXiv.
[14] J. Ngai. BRAIN 2.0: Transforming neuroscience , 2022, Cell.
[15] Jing Shuang Lisa Li. Internal Feedback in Biological Control: Locality and System Level Synthesis , 2021, ACC.
[16] Yuval Tassa,et al. From Motor Control to Team Play in Simulated Humanoid Football , 2021, Sci. Robotics.
[17] Ryan J. Low,et al. Geometry of abstract learned knowledge in the hippocampus , 2021, Nature.
[18] Tyler L. Hayes,et al. Replay in Deep Learning: Current Approaches and Missing Biological Elements , 2021, Neural Computation.
[19] T. Sejnowski,et al. Diversity-enabled sweet spots in layered architectures and speed–accuracy trade-offs in sensorimotor control , 2019, Proceedings of the National Academy of Sciences.
[20] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[21] Terrence J Sejnowski,et al. The unreasonable effectiveness of deep learning in artificial intelligence , 2020, Proceedings of the National Academy of Sciences.
[22] Peter M. Aronow,et al. The Book of Why: The New Science of Cause and Effect , 2020, Journal of the American Statistical Association.
[23] T. Sejnowski. Dopamine Made You Do It , 2019, Think Tank.
[24] Jakub W. Pachocki,et al. Dota 2 with Large Scale Deep Reinforcement Learning , 2019, ArXiv.
[25] Glenn Barenthin,et al. Churchland, Patricia. Conscience: The Origins of Moral Intuition , 2019, Researcher. European Journal of Humanities & Social Sciences.
[26] Peter L. Bartlett,et al. Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks , 2017, J. Mach. Learn. Res..
[27] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[28] Demis Hassabis,et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.
[29] Mélanie Frappier,et al. The Book of Why: The New Science of Cause and Effect , 2018, Science.
[30] Surya Ganguli,et al. A theory of multineuronal dimensionality, dynamics and measurement , 2017, bioRxiv.
[31] J. Tenenbaum,et al. Mind Games: Game Engines as an Architecture for Intuitive Physics , 2017, Trends in Cognitive Sciences.
[32] D. Hassabis,et al. Neuroscience-Inspired Artificial Intelligence , 2017, Neuron.
[33] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[34] R. Ivry,et al. The Cerebellum: Adaptive Prediction for Movement and Cognition , 2017, Trends in Cognitive Sciences.
[35] F. D. Waal. Are We Smart Enough to Know How Smart Animals Are , 2016 .
[36] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[37] Simone M. Ritter,et al. Creativity—the unconscious foundations of the incubation period , 2014, Front. Hum. Neurosci..
[38] H. Potter,et al. GREEK AND ROMAN MYTHOLOGIES ’ CHARACTERIZATIONS AS SIGN ASSOCIATED WITH ROWLING ’ S HARRY POTTER AND THE SORCERER ’ S STONE , 2014 .
[39] R. Lemon,et al. What We Know Currently about Mirror Neurons , 2013, Current Biology.
[40] Matthew D. Schultz,et al. Global Epigenomic Reconfiguration During Mammalian Brain Development , 2013, Science.
[41] T. Sejnowski,et al. The language of the brain. , 2012, Scientific American.
[42] P. Sterling. Allostasis: A model of predictive regulation , 2012, Physiology & Behavior.
[43] R. Needham,et al. Artificial Intelligence : A General Survey , 2012 .
[44] B. Horwitz,et al. Laryngeal Motor Cortex and Control of Speech in Humans , 2011, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[45] B. Skinner,et al. The Case Against B.F. Skinner , 2009 .
[46] David F. Bjorklund,et al. Why Youth is Not Wasted on the Young: Immaturity in Human Development , 2007 .
[47] Richard S. Sutton,et al. Learning to predict by the methods of temporal differences , 1988, Machine Learning.
[48] M. Ann. The Basal Ganglia and Cognitive Pattern Generators , 2005 .
[49] Yoshua Bengio,et al. Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.
[50] Ronald,et al. Learning representations by backpropagating errors , 2004 .
[51] Stephen R. Anderson,et al. The Language Organ: Linguistics as Cognitive Physiology , 2002 .
[52] S. Dehaene,et al. Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework , 2001, Cognition.
[53] A. Gopnik,et al. The Scientist in the Crib: What Early Learning Tells Us About the Mind , 2000 .
[54] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[55] Brenner. Francisco Crick in Paradiso , 1996, Current Biology.
[56] Gerald Tesauro,et al. Temporal difference learning and TD-Gammon , 1995, CACM.
[57] T. Sejnowski,et al. Beyond modularity: Neural evidence for constructivist principles in development , 1994, Behavioral and Brain Sciences.
[58] Terrence J. Sejnowski,et al. A Parallel Network that Learns to Play Backgammon , 1989, Artif. Intell..
[59] Terrence J. Sejnowski,et al. Parallel Networks that Learn to Pronounce English Text , 1987, Complex Syst..
[60] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[61] Movement and Cognition. , 1977 .
[62] Joseph Weizenbaum,et al. ELIZA—a computer program for the study of natural language communication between man and machine , 1966, CACM.
[63] Frank Rosenblatt,et al. PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .
[64] E. Abbott,et al. Flatland: a Romance of Many Dimensions , 1884, Nature.