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Beyond Bayes: Paths Towards Universal Reasoning Systems
Zenna Tavares · Emily Mackevicius · Elias Bingham · Nan Rosemary Ke · Talia Ringer · Armando Solar-Lezama · Nada Amin · John Krakauer · Robert O Ness · Alexis Avedisian

Fri Jul 22 05:45 AM -- 03:00 PM (PDT) @ Ballroom 2
Event URL: http://beyond-bayes.github.io »

A long-standing objective of AI research has been to discover theories of reasoning that are general: accommodating various forms of knowledge and applicable across a diversity of domains. The last two decades have brought steady advances toward this goal, notably in the form of mature theories of probabilistic and causal inference, and in the explosion of reasoning methods built upon the deep learning revolution. However, these advances have only further exposed gaps in both our basic understanding of reasoning and in limitations in the flexibility and composability of automated reasoning technologies. This workshop aims to reinvigorate work on the grand challenge of developing a computational foundation for reasoning in minds, brains, and machines.

Author Information

Zenna Tavares (Columbia University / Basis)
Emily Mackevicius (Basis Research Institute and Columbia University)
Elias Bingham (Broad Institute & Basis)
Nan Rosemary Ke (Deepmind, Mila)
Talia Ringer (University of Illinois at Urbana-Champaign)
Armando Solar-Lezama (MIT)
Nada Amin (Harvard University)
John Krakauer (John Hopkins University)
Robert O Ness (Altdeep.ai)

Robert didn't start in machine learning. He started his career by becoming fluent in Mandarin Chinese and moving to Tibet to do developmental economics fieldwork. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies. After switching to the tech industry, Robert's interests shifted to modeling data. He attained his Ph.D. in mathematical statistics from Purdue University, and then he worked as a research engineer in various AI startups. He has published in journals and venues across these spaces, including RECOMB and NeurIPS, on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems. In addition to startup work, he is a machine learning professor at Northeastern University.

Alexis Avedisian (Columbia University)

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