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.
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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.
Is English just one of the languages you speak? If so, the AI tools you use might miss things that makes your voice multilingual.
In this episode of Women in AI Research, Jekaterina Novikova speaks with Dr. Annie En-Shiun Lee about her work on multilingual and multicultural AI — from the widening language gap and the lack of benchmarks for underrepresented languages, to why domain-specific data matters more than just scaling up models.
We talk about the limits of cross-lingual transfer, the risks of English-centric reasoning in AI, and the technical, ethical, and cultural challenges of building models that truly serve global communities.
References:
- SIB-200: A simple, inclusive, and big evaluation dataset for topic classification in 200+ languages and dialects
- URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base
- mR3: Multilingual Rubric-Agnostic Reward Reasoning Models
- ProxyLM: Predicting language model performance on multilingual tasks via proxy models
- ATAIGI: An AI-Powered Multimodal Learning App Leveraging Generative Models for Low-Resource Taiwanese Hokkien
- Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems
- AlignFreeze: Navigating the Impact of Realignment on the Layers of Multilingual Models Across Diverse Languages
- Irokobench: A new benchmark for african languages in the age of large language models
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