Hal Daumé's guest appearances

Hal Daumé

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

NLP Highlights

The BIS: Business Integrity School

Burr Settles

Sebastian Ruder

Lukas Biewald

Hal Daumé III

Ines Montani

Alona Fyshe

Thomas Wolf

Emily Bender

Sameer Singh

Allyson Ettinger

Tim Rocktäschel

Saqib Shaikh

Samuel Castro

Karim Beguir

Anima Anandkumar

OwlTail

Cindy Moehring chats with Hal Daumé, senior principal researcher at Microsoft and professor of computer science at the University of Maryland, to explore the intersection of ethics and technology. Their discussion covers racial bias in artificial intelligence, the complexities of ethical machine learning, and who is responsible for ethical oversight at large tech firms. Learn more about the Business Integrity Leadership Initiative by visiting our website at https://walton.uark.edu/business-integrity/  Links from the episode:  Stanford’s “The Race Gap in Speech Recognition Technology”: https://fairspeech.stanford.edu/ Microsoft’s AI Fairness Checklist: https://www.microsoft.com/en-us/research/project/ai-fairness-checklist/ AI Now Institute: https://ainowinstitute.org/ Coded Bias by Shalini Kantayya: https://www.codedbias.com/ Race After Technology by Ruha Benjamin: https://www.ruhabenjamin.com/race-after-technology 

Cindy Moehring chats with Hal Daumé, senior principal researcher at Microsoft and professor of computer science at the University of Maryland, to explore the intersection of ethics and technology. Their discussion covers racial bias in artificial intelligence, the complexities of ethical machine learning, and who is responsible for ethical oversight at large tech firms. Learn more about the Business Integrity Leadership Initiative by visiting our website at <a href="https://walton.uark.edu/business-integrity/" rel="nofollow" target="_blank">https://walton.uark.edu/business-integrity/</a> Links from the episode: Stanford’s “The Race Gap in Speech Recognition Technology”: <a href="https://fairspeech.stanford.edu/" rel="noopener noreferrer nofollow" target="_blank">https://fairspeech.stanford.edu/</a> Microsoft’s AI Fairness Checklist: <a href="https://www.microsoft.com/en-us/research/project/ai-fairness-checklist" rel="noopener noreferrer nofollow" target="_blank">https://www.microsoft.com/en-us/research/project/ai-fairness-checklist</a>/ AI Now Institute: <a href="https://ainowinstitute.org/" rel="noopener noreferrer nofollow" target="_blank">https://ainowinstitute.org/</a> Coded Bias by Shalini Kantayya: <a href="https://www.codedbias.com/" rel="noopener noreferrer nofollow" target="_blank">https://www.codedbias.com/</a> Race After Technology by Ruha Benjamin: <a href="https://www.ruhabenjamin.com/race-after-technology" rel="noopener noreferrer nofollow" target="_blank">https://www.ruhabenjamin.com/race-after-technology</a>

Human Imperfections in AI with Hal Daumé

Today we’re joined by Hal Daume III, professor at the University of Maryland and Co-Chair of the 2020 ICML Conference. We had the pleasure of catching up with Hal ahead of this year's ICML to discuss his research at the intersection of bias, fairness, NLP, and the effects language has on machine learning models, exploring language in two categories as they appear in machine learning models and systems: (1) How we use language to interact with the world, and (2) how we “do” language.

Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems with Hal Daumé III - #395

In this episode, we invite Hal Daumé to continue the discussion on reinforcement learning, focusing on how it has been used in NLP. We discuss how to reduce NLP problems into the reinforcement learning framework, and circumstances where it may or may not be useful. We discuss imitation learning, roll-in and roll-out, and how to approximate an expert with a reference policy. DAgger: https://www.semanticscholar.org/paper/A-Reduction-of-Imitation-Learning-and-Structured-to-Ross-Gordon/17eddf33b513ae1134abadab728bdbf6abab2a05?navId=citing-papersRESLOPE: http://legacydirs.umiacs.umd.edu/~hal/docs/daume18reslope.pdf

In this episode, we invite Hal Daumé to continue the discussion on reinforcement learning, focusing on how it has been used in NLP. We discuss how to reduce NLP problems into the reinforcement learning framework, and circumstances where it may or may not be useful. We discuss imitation learning, roll-in and roll-out, and how to approximate an expert with a reference policy. DAgger: <a href="https://www.semanticscholar.org/paper/A-Reduction-of-Imitation-Learning-and-Structured-to-Ross-Gordon/17eddf33b513ae1134abadab728bdbf6abab2a05?navId=citing-papers" rel="nofollow" target="_blank">https://www.semanticscholar.org/paper/A-Reduction-of-Imitation-Learning-and-Structured-to-Ross-Gordon/17eddf33b513ae1134abadab728bdbf6abab2a05?navId=citing-papers</a> RESLOPE: <a href="http://legacydirs.umiacs.umd.edu/~hal/docs/daume18reslope.pdf" rel="nofollow" target="_blank">http://legacydirs.umiacs.umd.edu/~hal/docs/daume18reslope.pdf</a>

75 - Reinforcement / Imitation Learning in NLP, with Hal Daumé III