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We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models.
Learning exploration policies for navigation
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Vrkitchen: an interactive 3d virtual environment for task-oriented learning
Gao, X., Gong, R., Shu, T., Xie, X., Wang, S., and Zhu, S.-C · 1903
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A deeper look at facial expression dataset bias
Li, S. and Deng, W · 1904
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Movement-produced stimulation in the development of visually guided behavior
Held, R. and Hein, A · 1964
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The Ecological Approach to Visual Perception
Gibson, J. J · 1979
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Metaphors we live by
Lakoff, G. and Johnson, M · 1979
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Bayesian Approach to Global Optimization: Theory and Applications
Mockus, J · 1989
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Intelligence without representation
Brooks, R. A · 1991
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The Embodied Mind: Cognitive Science and Human Experience
Varela, F. J., Thompson, E., and Rosch, E · 1991
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A Probabilistic Theory of Pattern Recognition
Devroye, L., Györfi, L., and Lugosi, G · 1996
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Multitask learning
Caruana, R · 1997
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Being There: Putting Brain, Body, and World Together Again
Clark, A · 1997
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The extended mind
Clark, A. and Chalmers, D · 1998
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Statistical Learning Theory
Vapnik, V · 1998
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Philosophy in the flesh : the embodied mind and its challenge to western thought
Lakoff, G. and Johnson, M. L · 1999
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2001
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An exploration of embodied visual exploration
Ramakrishnan, S. K., Jayaraman, D., and Grauman, K · 2001
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How should control and body systems be coupled? a robotic case study
Ishiguro, A. and Kawakatsu, T · 2004
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Embodied artificial intelligence: Trends and challenges
Pfeifer, R. and Iida, F · 2004
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2005
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The development of embodied cognition: Six lessons from babies
Smith, L. and Gasser, M · 2005
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Probabilistic Robotics
Thrun, S., Burgard, W., and Fox, D · 2005
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Comparative chemosensation from receptors to ecology
Bargmann, C. I · 2006
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Dealing with non-stationary environments using context detection
da Silva, B. C., Basso, E. W., Bazzan, A. L. C., and Engel, P. M · 2006
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How the Body Shapes the Way We Think: A New View of Intelligence
Pfeifer, R. and Bongard, J · 2006
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Threedworld: A platform for interactive multi-modal physical simulation
Gan, C., Schwartz, J., Alter, S., Mrowca, D., Schrimpf, M., Traer, J., De Freitas, J., Kubilius, J., Bhandwaldar, A., Haber, N., et al · 2007
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The epoch-greedy algorithm for contextual multi-armed bandits
Langford, J. and Zhang, T · 2007
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What is intrinsic motivation? a typology of computational approaches
Oudeyer, P.-Y. and Kaplan, F · 2007
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Quantization noise: roundoff error in digital computation, signal processing, control, and communications
Widrow, B. and Kollár, I · 2008
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Dataset Shift in Machine Learning
Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D. (eds.) · 2009
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The free energy principle: A unified brain theory?
Friston, K · 2010
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A survey on transfer learning
Pan, S. J. and Yang, Q · 2010
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A Brief Guide to Embodied Cognition: Why You Are Not Your Brain, 2011
McNearney, S · 2011
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Embodied Cognition
Shapiro, L · 2011
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Discourse on method
Descartes, R · 2012
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The transferability approach: Crossing the reality gap in evolutionary robotics
Koos, S., Mouret, J.-B., and Doncieux, S · 2012
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Addiction by Design: Machine Gambling in Las Vegas
Schüll, N. D · 2012
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Cognitive architectures
Thagard, P · 2012
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Relevance realization and the emerging framework in cognitive science
Vervaeke, J., Lillicrap, T. P., and Richards, B. A · 2012
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Counterfactual reasoning and learning systems: The example of computational advertising
Bottou, L., Peters, J., Quiñonero-Candela, J., Charles, D. X., Chickering, M., Portugaly, E., Ray, D., Simard, P., and Snelson, E · 2013
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Bias in algorithmic filtering and personalization
Bozdag, E · 2013
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Doing Data Science
O’Neil, C. and Schutt, R · 2013
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Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Provost, F. and Fawcett, T · 2013
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Exploring the filter bubble: the effect of using recommender systems on content diversity
Nguyen, T. T., Hui, P.-M., Harper, F. M., Terveen, L., and Konstan, J. A · 2014
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Machine learning for targeted display advertising: transfer learning in action
Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O., and Provost, F · 2014
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Exposure to ideologically diverse news and opinion on facebook
Bakshy, E., Messing, S., and Adamic, L. A · 2015
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Deep neural networks for youtube recommendations
Covington, P., Adams, J., and Sargin, E · 2016
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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
O’Neil, C · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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Closing the simulation-to-reality gap for deep robotic learning
Bousmalis, K. and Levine, S · 2017
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Domain adaptation for visual applications: A comprehensive survey
Csurka, G · 2017
Cited alongside, same era.
Generalization in deep learning
Kawaguchi, K., Kaelbling, L. P., and Bengio, Y · 2017
Cited alongside, same era.
Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning
Salvato, E., Fenu, G., Medvet, E., and Pellegrino, F. A · 2021
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Computer vision: the last fifty years
Shapiro, L. G · 2021
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Removing biased data to improve fairness and accuracy
Verma, S., Ernst, M., and Just, R · 2021
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Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
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A survey of embodied ai: From simulators to research tasks
Duan, J., Yu, S., Tan, H. L., Zhu, H., and Tan, C · 2022
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The Markov blankets of life: autonomy, active inference and the free energy principle
Kirchhoff, M., Parr, T., Palacios, E., Friston, K., and Kiverstein, J · 2017
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
Cited alongside, same era.
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T · 2017
Cited alongside, same era.
Robust Deep Reinforcement Learning with Adversarial Attacks, December 2017
Pattanaik, A., Tang, Z., Liu, S., Bommannan, G., and Chowdhary, G · 2017
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P · 2017
Cited alongside, same era.
Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments
Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., Reid, I., Gould, S., and Van Den Hengel, A · 2018
Cited alongside, same era.
Towards artificial general intelligence via a multimodal foundation model
Fei, N., Lu, Z., Gao, Y., Yang, G., Huo, Y., Wen, J., Lu, H., Song, R., Gao, X., Xiang, T., et al · 2022
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Algorithmic amplification of politics on twitter
Huszár, F., Ktena, S. I., O’Brien, C., Belli, L., Schlaikjer, A., and Hardt, M · 2022
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The purpose of qualia: What if human thinking is not (only) information processing?
Korth, M · 2022
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Illustrating reinforcement learning from human feedback (rlhf)
Lambert, N., Castricato, L., von Werra, L., and Havrilla, A · 2022
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Technological approach to mind everywhere: an experimentally-grounded framework for understanding diverse bodies and minds
Levin, M · 2022
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Model-based reinforcement learning with multi-step plan value estimation
Lin, H., Sun, Y., Zhang, J., and Yu, Y · 2022
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Orhan, P., Boubenec, Y., and King, J.-R · 2022
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Training language models to follow instructions with human feedback, 2022
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
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Guided safe shooting: model based reinforcement learning with safety constraints
Paolo, G., Gonzalez-Billandon, J., Thomas, A., and Kégl, B · 2022
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A review of generalized zero-shot learning methods
Pourpanah, F., Abdar, M., Luo, Y., Zhou, X., Wang, R., Lim, C. P., Wang, X.-Z., and Wu, Q. J · 2022
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Robust speech recognition via large-scale weak supervision, 2022
Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., and Sutskever, I · 2022
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Hierarchical text-conditional image generation with clip latents, 2022
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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Learning to walk in minutes using massively parallel deep reinforcement learning
Rudin, N., Hoeller, D., Reist, P., and Hutter, M · 2022
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Embodied ai-driven operation of smart cities: A concise review
Shenavarmasouleh, F., Mohammadi, F. G., Amini, M. H., and Reza Arabnia, H · 2022
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Skill-based model-based reinforcement learning
Shi, L. X., Lim, J. J., and Lee, Y · 2022
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Sivaraman, V., Wu, Y., and Perer, A · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Ai embodiment through 6g: Shaping the future of agi
Bariah, L. and Debbah, M · 2023
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., Florence, P., Fu, C., Arenas, M. G., Gopalakrishnan, K., Han, K., Hausman, K., Herzog, A., Hsu, J., Ichter, B., Irpan, A., Joshi, N., Julian, R., Kalashnikov, D., Kuang, Y., Leal, I., Lee, L., Lee, T.-W. E., Levine, S., Lu, Y., Michalewski, H., Mordatch, I., Pertsch, K., Rao, K., Reymann, K., Ryoo, M., Salazar, G., Sanketi, P., Sermanet, P., Singh, J., Singh, A., Soricut, R., Tran, H., Vanhoucke, V., Vuong, Q., Wahid, A., Welker, S., Wohlhart, P., Wu, J., Xia, F., Xiao, T., Xu, P., Xu, S., Yu, T., and Zitkovich, B · 2023
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Pangu-agent: A fine-tunable generalist agent with structured reasoning
Christianos, F., Papoudakis, G., Zimmer, M., Coste, T., Wu, Z., Chen, J., Khandelwal, K., Doran, J., Feng, X., Liu, J., et al · 2023
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A trust region approach for few-shot sim-to-real reinforcement learning, 2023
Daoudi, P., Prieur, C., Robu, B., Barlier, M., and Santos, L. D · 2023
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Sim-to-real transfer and reality gap modeling in model predictive control for autonomous driving
Daza, I. G., Izquierdo, R., Martínez, L. M., Benderius, O., and Llorca, D. F · 2023
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Learning rate schedules in the presence of distribution shift
Fahrbach, M., Javanmard, A., Mirrokni, V., and Worah, P · 2023
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What if gpt4 became autonomous: The auto-gpt project and use cases
FIRAT, M. and Kuleli, S · 2023
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The free energy principle made simpler but not too simple
Friston, K., Da Costa, L., Sajid, N., Heins, C., Ueltzhöffer, K., Pavliotis, G. A., and Parr, T · 2023
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Generalized Teacher Forcing for Learning Chaotic Dynamics, October 2023
Hess, F., Monfared, Z., Brenner, M., and Durstewitz, D · 2023
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Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., and Liu, T · 2023
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Ai alignment: A comprehensive survey
Ji, J., Qiu, T., Chen, B., Zhang, B., Lou, H., Wang, K., Duan, Y., He, Z., Zhou, J., Zhang, Z., et al · 2023
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Dinov2: Learning robust visual features without supervision, 2023
Oquab, M., Darcet, T., Moutakanni, T., Vo, H. V., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Howes, R., Huang, P.-Y., Xu, H., Sharma, V., Li, S.-W., Galuba, W., Rabbat, M., Assran, M., Ballas, N., Synnaeve, G., Misra, I., Jegou, H., Mairal, J., Labatut, P., Joulin, A., and Bojanowski, P · 2023
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Artificial Empathy - A Roadmap for Human Aligned Artificial General Intelligence
Perez, C · 2023
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Large language model alignment: A survey
Shen, T., Jin, R., Huang, Y., Liu, C., Dong, W., Guo, Z., Wu, X., Liu, Y., and Xiong, D · 2023
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Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al · 2023
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Chain-of-thought prompting elicits reasoning in large language models, 2023
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D · 2023
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The rise and potential of large language model based agents: A survey, 2023
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., Zheng, R., Fan, X., Wang, X., Xiong, L., Zhou, Y., Wang, W., Jiang, C., Zou, Y., Liu, X., Yin, Z., Dou, S., Weng, R., Cheng, W., Zhang, Q., Qin, W., Zheng, Y., Qiu, X., Huang, X., and Gui, T · 2023
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Continual learning in an easy-to-hard manner
Yifan, C., Yulu, C., Yadan, Z., and Wenbo, L · 2023
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Autort: Embodied foundation models for large scale orchestration of robotic agents
Ahn, M., Dwibedi, D., Finn, C., Arenas, M. G., Gopalakrishnan, K., Hausman, K., Ichter, B., Irpan, A., Joshi, N., Julian, R., et al · 2024
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Retrieval-augmented generation for large language models: A survey, 2024
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Guo, Q., Wang, M., and Wang, H · 2024
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Mortal computation: A foundation for biomimetic intelligence, 2024
Ororbia, A. and Friston, K · 2024
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Mentoring the Machines
Vervaeke, J. and Coyne, S · 2024
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Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms, 2024
Xiong, M., Hu, Z., Lu, X., Li, Y., Fu, J., He, J., and Hooi, B · 2024
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HAZARD challenge: Embodied decision making in dynamically changing environments, 2024
Zhou, Q., Chen, S., Wang, Y., Xu, H., Du, W., Zhang, H., Du, Y., Tenenbaum, J. B., and Gan, C · 2024
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