Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems with Hal Daumé III - #395
2020-07-27 · 63 min · episode 395 · 15 entities
Asserted relationships
-
→ appeared on The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence podcast0.68
evidence rules-v4
Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems with Hal Daumé III - #395
-
0.55
evidence rules-v4
Feed author/publisher: Sam Charrington
-
0.40
evidence rules-v4
Feed category: Science
-
0.40
evidence rules-v4
Feed category: Technology
-
0.40
evidence rules-v4
Feed category: News
-
0.40
evidence rules-v4
Feed category: Tech News
-
0.40
evidence rules-v4
Feed author/publisher: TWIML
Entities found in this episode
concepts 7
-
0.50
evidence rules-v4
Feed category: Science
-
0.50
evidence rules-v4
Feed category: Tech News
-
0.40
evidence rules-v4
Feed category: Science
-
0.40
evidence rules-v4
Feed category: News
-
0.40
evidence rules-v4
Feed category: Tech News
-
0.35
evidence rules-v4
NLP
-
0.35
evidence rules-v4
III
companys 4
-
0.70
evidence rules-v4
Feed author/publisher: TWIML
-
0.50
evidence rules-v4
Feed category: Technology
-
0.40
evidence rules-v4
Feed category: Technology
-
0.40
evidence rules-v4
Feed author/publisher: TWIML
persons 3
-
0.72
evidence rules-v4
Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems with Hal Daumé III - #395
-
0.70
evidence rules-v4
Feed author/publisher: Sam Charrington
-
0.55
evidence rules-v4
Feed author/publisher: Sam Charrington
podcasts 1
-
appeared on The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence podcast0.68
evidence rules-v4
Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems with Hal Daumé III - #395
Episode description as stored
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.