The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Exploring Causality and Community with Suzana Ilić - #419
Oct 16, 2020
Suzana Ilić, a computational linguist at Causaly and founder of Machine Learning Tokyo, discusses her unique journey in AI and causality extraction from biomedical text. She reveals the exponential growth of the MLT community and how it evolved beyond her personal project. The conversation covers balancing technical and strategic roles in product management while fostering community-driven research. Suzana also highlights the intersection of linguistics and machine learning, emphasizing collaboration in enhancing NLP technologies.
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Linguist in AI
- Suzana Ilić's background is in applied linguistics, not a traditional AI path.
- She transitioned into AI by working with increasingly larger datasets in NLP, leading her to explore machine learning.
Causality in Biomedicine
- Causaly uses causal modeling to extract causal relationships from biomedical texts.
- This allows researchers to quickly find causes, effects, treatments, and other relationships, which traditionally took days or weeks.
MLT Origins
- Machine Learning Tokyo (MLT) started with two people in a co-working space.
- It grew organically through weekly sessions and now has 7,000 members globally.