Women in AI Research (WiAIR)
Women in AI Research (WiAIR)
Podcast Description
Women in AI Research (WiAIR) is a podcast dedicated to celebrating the remarkable contributions of female AI researchers from around the globe. Our mission is to challenge the prevailing perception that AI research is predominantly male-driven. Our goal is to empower early career researchers, especially women, to pursue their passion for AI and make an impact in this rapidly growing field. You will learn from women at different career stages, stay updated on the latest research and advancements, and hear powerful stories of overcoming obstacles and breaking stereotypes.
Podcast Insights
Content Themes
The podcast focuses on topics such as bias in AI, the limitations of transformer models, and the personal journeys of women in AI research. Episodes include discussions on the social implications of AI, technical challenges in language models, and the overall impact of diverse voices in the AI field.
Women in AI Research (WiAIR) is a podcast dedicated to celebrating the remarkable contributions of female AI researchers from around the globe. Our mission is to challenge the prevailing perception that AI research is predominantly male-driven. Our goal is to empower early career researchers, especially women, to pursue their passion for AI and make an impact in this rapidly growing field. You will learn from women at different career stages, stay updated on the latest research and advancements, and hear powerful stories of overcoming obstacles and breaking stereotypes.
Can open-source large language models really outperform closed ones like Claude 3.5? 🤔
In this episode of the Women in AI Research podcast, Jekaterina Novikova and Malikeh Ehghaghi engage with Valentina Pyatkin, a postdoctoral researcher at the Allen Institute for AI.
We dive deep into the future of open science, LLM research, and extending model capabilities.
🔑 Topics we cover:
- Why open-source LLMs sometimes beat closed models
- The value of releasing datasets, recipes, and training infrastructure
- The role of open science in accelerating NLP innovation
- Insights from Valentina’s award-winning research journey
REFERENCES:
- Valentina's Google Scholar profile
- Olmo: Accelerating the science of language models
- Tulu 3: Pushing Frontiers in Open Language Model Post-Training
- open-instruct
- Generalizing Verifiable Instruction Following
- RewardBench 2: Advancing Reward Model Evaluation
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