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Research#llm📝 BlogAnalyzed: Dec 29, 2025 07:43

Daring to DAIR: Distributed AI Research with Timnit Gebru - #568

Published:Apr 18, 2022 16:00
1 min read
Practical AI

Analysis

This podcast episode from Practical AI features Timnit Gebru, founder of the Distributed Artificial Intelligence Research Institute (DAIR). The discussion centers on Gebru's journey, including her departure from Google after publishing a paper on the risks of large language models, and the subsequent founding of DAIR. The episode explores DAIR's goals, its distributed research model, the challenges of defining its research scope, and the importance of independent AI research. It also touches upon the effectiveness of internal ethics teams within the industry and examples of institutional pitfalls to avoid. The episode promises a comprehensive look at DAIR's mission and Gebru's perspective on the future of AI research.

Key Takeaways

Reference

“We discuss the importance of the “distributed” nature of the institute, how they’re going about figuring out what is in scope and out of scope for the institute’s research charter, and what building an institution means to her.”

PermalinkPractical AI
Research#llm📝 BlogAnalyzed: Dec 29, 2025 07:56

Machine Learning as a Software Engineering Enterprise with Charles Isbell - #441

Published:Dec 23, 2020 22:03
1 min read
Practical AI

Analysis

This article summarizes a podcast episode from Practical AI featuring Charles Isbell, discussing machine learning as a software engineering enterprise. The conversation covers Isbell's invited talk at NeurIPS 2020, the success of Georgia Tech's online Master's program in CS, and the importance of accessible education. It also touches upon the impact of machine learning, the need for diverse perspectives in the field, and the fallout from Timnit Gebru's departure. The episode emphasizes the shift from traditional compiler hacking to embracing the opportunities within machine learning.
Reference

“We spend quite a bit speaking about the impact machine learning is beginning to have on the world, and how we should move from thinking of ourselves as compiler hackers, and begin to see the possibilities and opportunities that have been ignored.”

PermalinkPractical AI
Technology#AI Ethics👥 CommunityAnalyzed: Jan 3, 2026 18:08

Timnit Gebru Resigns from Google

Published:Dec 3, 2020 18:18
1 min read
Hacker News

Analysis

The article reports the resignation of Timnit Gebru, a prominent AI researcher, from Google. This event is significant because Gebru was a leading voice in AI ethics and responsible AI development. Her departure raises questions about Google's commitment to these areas and the treatment of researchers who voice concerns.
Reference

“N/A - The provided text is a summary, not a direct quote.”

PermalinkHacker News
Research#AI Ethics📝 BlogAnalyzed: Dec 29, 2025 08:07

Trends in Fairness and AI Ethics with Timnit Gebru - #336

Published:Jan 6, 2020 20:02
1 min read
Practical AI

Analysis

This article summarizes a discussion with Timnit Gebru, a research scientist at Google's Ethical AI team, about trends in AI ethics and fairness in 2019. The conversation, recorded at NeurIPS, covered topics such as the diversification of NeurIPS through groups like Black in AI and WiML, advancements in the fairness community, and relevant research papers. The article highlights the importance of ethical considerations and fairness within the AI field, particularly focusing on the contributions of various groups working towards these goals.
Reference

“In our conversation, we discuss diversification of NeurIPS, with groups like Black in AI, WiML and others taking huge steps forward, trends in the fairness community, quite a few papers, and much more.”

PermalinkPractical AI
Research#AI Ethics📝 BlogAnalyzed: Dec 29, 2025 08:33

Using Deep Learning and Google Street View to Estimate Demographics with Timnit Gebru

Published:Dec 19, 2017 00:54
1 min read
Practical AI

Analysis

This article discusses a podcast interview with Timnit Gebru, a researcher at Microsoft Research, focusing on her work using deep learning and Google Street View to estimate demographics. The conversation covers the research pipeline, challenges faced in building the model, and the role of social awareness, including domain adaptation and fairness. The interview also touches upon the Black in AI group and Gebru's perspective on fairness research. The article provides a concise overview of the research and its implications, highlighting the intersection of AI, social impact, and ethical considerations.
Reference

“Timnit describes the pipeline she developed for this research, and some of the challenges she faced building and end-to-end model based on google street view images, census data and commercial car vendor data.”

PermalinkPractical AI