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AI Models Boost Transparency in Robot Plan Execution

AI Models Boost Transparency in Robot Plan Execution
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Multiple AI models help robots execute complex plans more transparently

MIT's HiP system helps robots complete long-horizon goals using three foundation models: a large language model, a video diffusion model, and an egocentric action model. Iterative refinement improves the plan at each step for household and manufacturing tasks. ....

Joshua Tenenbaum , Prakash Srivastava , Pulkit Agrawal , Abhishek Gupta , Anurag Ajay , Seungwook Han , University Of Washington , Compositional Foundation Models For Hierarchical Planning Hi , Improbable Ai Lab , National Science Foundation , Us Defense Advanced Research Projects Agency , Artificial Intelligence Laboratory , Watson Ai Lab , Department Of Electrical Engineering , Us Army Research Office , Zai Lab , Computer Science , Compositional Foundation Models , Hierarchical Planning , Bing Chat , Jim Fan , Action Diffuser , Electrical Engineering , Tommi Jaakkola , Leslie Pack Kaelbling , Shuang Li Phd ,

What does the future hold for generative AI?

MIT faculty members, including keynote speaker Rodney Brooks, discussed the power and potential pitfalls of generative AI tools like ChatGPT at the “Generative AI: Shaping the Future” Symposium, the kickoff event of the Institute’s Generative AI Week. ....

Sally Kornbluth , Dina Katabi , Rodney Brooks , Daniela Rus , Joshua Bennett , Google Bard , Jacob Andreas , Joshua Tenenbaum , Williamt Freeman , Max Tegmark , Antonio Torralba , Cognitive Sciences , Institute Kresge Auditorium , Delta Electronics Professor , Department Of Electrical Engineering , Mcgovern Institute For Brain Research , Department Of Brain , Artificial Intelligence Laboratory , Computer Science , Kresge Auditorium , Axim Collaborative , Erna Viterbi Professor , Electrical Engineering , Distinguished Chair , Star Trekwith , Mcgovern Institute ,

"Building Machines That Learn and Think Like People", 7 years later

In the post about GPT-4V, I mentioned the paper “Building Machines That Learn and Think Like People” (https://arxiv.org/abs/1604.00289) by Brenden Lake, Tomer Ullman, Joshua Tenenbaum, and Samuel Gershman. In that seminal paper the authors suggested that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. ....

Andrey Karpathy , Joshua Tenenbaum , Samuel Gershman , Machines That Learn , Think Like , Brenden Lake , Tomer Ullman , Wild West , Chance Saloon ,

Multi-AI collaboration helps reasoning and factual accuracy in large language models

Multi-AI collaboration helps reasoning and factual accuracy in large language models
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Igor Mordatch , Anca Dragan , Joshua Tenenbaum , Antonio Torralba , Google Deepmind , Computer Sciences , Artificial Intelligence Laboratory , Electrical Engineering ,