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Some argue scale is all what is needed to achieve AI, covering even causal models.
Über formal unentscheidbare sätze der principia mathematica und verwandter systeme i
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Clive R Charig, David R Webb, Stephen Richard Payne, and John E Wickham · 1986
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan · 2006
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The max-min hill-climbing bayesian network structure learning algorithm
Ioannis Tsamardinos, Laura E Brown, and Constantin F Aliferis · 2006
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Causality
Judea Pearl · 2009
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Chinese room argument
John Searle · 2009
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Bayesian artificial intelligence
Kevin B Korb and Ann E Nicholson · 2010
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How to grow a mind: Statistics, structure, and abstraction
Joshua B Tenenbaum, Charles Kemp, Thomas L Griffiths, and Noah D Goodman · 2011
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The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost
Suzana Herculano-Houzel · 2012
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Chocolate consumption, cognitive function, and nobel laureates
Franz H. Messerli · 2012
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Structural intervention distance for evaluating causal graphs
Jonas Peters and Peter Bühlmann · 2015
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Actual causality
Joseph Y Halpern · 2016
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Distinguishing cause from effect using observational data: methods and benchmarks
Joris M Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schölkopf · 2016
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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The 30-year cycle in the ai debate
Jean-Marie Chauvet · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Detecting non-causal artifacts in multivariate linear regression models
Dominik Janzing and Bernhard Schölkopf · 2018
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Cebab: Estimating the causal effects of real-world concepts on nlp model behavior
Eldar D Abraham, Karel D’Oosterlinck, Amir Feder, Yair Gat, Atticus Geiger, Christopher Potts, Roi Reichart, and Zhengxuan Wu · 2022
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Luminous language model
AlephAlpha · 2022
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On pearl’s hierarchy and the foundations of causal inference
Elias Bareinboim, Juan D Correa, Duligur Ibeling, and Thomas Icard · 2022
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Causalqa: A benchmark for causal question answering
Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Blübaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, and Martin Potthast · 2022
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Causal inference in natural language processing: Estimation, prediction, interpretation and beyond
Amir Feder, Katherine A Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E Roberts, et al · 2022
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Sanghack Lee and Elias Bareinboim · 2019
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The bitter lesson
Richard Sutton · 2019
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1on pearl’s hierarchy and
Elias Bareinboim, Juan D Correa, Duligur Ibeling, and Thomas Icard · 2020
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The scaling hypothesis
Gwern Branwen · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Probabilistic and causal inference: The works of judea pearl, 2022
Hector Geffner, Rina Dechter, and Joseph Y Halpern · 2022
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Causal bert: Language models for causality detection between events expressed in text
Vivek Khetan, Roshni Ramnani, Mayuresh Anand, Subhashis Sengupta, and Andrew E Fano · 2022
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Amortized causal discovery: Learning to infer causal graphs from time-series data
Sindy Löwe, David Madras, Richard Zemel, and Max Welling · 2022
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Efficient reinforcement learning with prior causal knowledge
Yangyi Lu, Amirhossein Meisami, and Ambuj Tewari · 2022
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Deep learning is hitting a wall
Gary Marcus · 2022
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Causalcity: Complex simulations with agency for causal discovery and reasoning
Daniel McDuff, Yale Song, Jiyoung Lee, Vibhav Vineet, Sai Vemprala, Nicholas Alexander Gyde, Hadi Salman, Shuang Ma, Kwanghoon Sohn, and Ashish Kapoor · 2022
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Selection bias induced spurious correlations in large language models
Emily McMilin · 2022
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Causality for machine learning
Bernhard Schölkopf · 2022
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Commonsenseqa 2.0: Exposing the limits of ai through gamification
Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, and Jonathan Berant · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Wikiwhy: Answering and explaining cause-and-effect questions
Matthew Ho, Aditya Sharma, Justin Chang, Michael Saxon, Sharon Levy, Yujie Lu, and William Yang Wang · 2023
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Can large language models infer causation from correlation?
Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona Diab, and Bernhard Schölkopf · 2023
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Causal reasoning and large language models: Opening a new frontier for causality
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan · 2023
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OpenAI · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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