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Creating agents that can interact naturally with humans is a common goal in artificial intelligence (AI) research.
“General Intelligence” objectively determined and measured
Charles Spearman · 1904
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Raven’s progressive matrices
John C Raven and JH Court · 1938
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Computing machinery and intelligence
Alan M Turing · 1950
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
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How to hold a turing test contest
Hugh Loebner · 2009
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How intelligence happens
John Duncan · 2010
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Grounded language learning in a simulated 3d world
Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, et al · 2017
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Mapping instructions to actions in 3d environments with visual goal prediction
Dipendra Kumar Misra, Andrew Bennett, Valts Blukis, Eyvind Niklasson, Max Shatkhin, and Yoav Artzi · 2018
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2019
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Executing instructions in situated collaborative interactions
Alane Suhr, Claudia Yan, Jacob Schluger, Stanley Yu, Hadi Khader, Marwa Mouallem, Iris Zhang, and Yoav Artzi · 2019
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Creating multimodal interactive agents with imitation and self-supervised learning
DeepMind Interactive Agents Team, Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Felix Fischer, Petko Georgiev, Alex Goldin, Tim Harley, et al · 2021
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Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al · 2021
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Dynabench: Rethinking benchmarking in nlp
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, et al · 2021
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MindCraft: Theory of mind modeling for situated dialogue in collaborative tasks
Cristian-Paul Bara, Sky CH-Wang, and Joyce Chai · 2021
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Jesse Thomason, Michael Murray, Maya Cakmak, and Luke Zettlemoyer · 2019
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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
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Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 2020
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Imitating interactive intelligence
Josh Abramson, Arun Ahuja, Iain Barr, Arthur Brussee, Federico Carnevale, Mary Cassin, Rachita Chhaparia, Stephen Clark, Bogdan Damoc, Andrew Dudzik, et al · 2020
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Aishwarya Padmakumar, Jesse Thomason, Ayush Shrivastava, Patrick Lange, Anjali Narayan-Chen, Spandana Gella, Robinson Piramuthu, Gokhan Tur, and Dilek Hakkani-Tur · 2021
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When should we prefer offline reinforcement learning over behavioral cloning?
Aviral Kumar, Joey Hong, Anikait Singh, and Sergey Levine · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al · 2022
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TEACh: Task-driven Embodied Agents that Chat
Aishwarya Padmakumar, Jesse Thomason, Ayush Shrivastava, Patrick Lange, Anjali Narayan-Chen, Spandana Gella, Robinson Piramithu, and Dilek Hakkani-Tur Gokhan Tur and · 2022
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