Domain expertise has tremendous value in #explainable machine learning. Causality is the bridge to aligning this domain knowledge and understanding why models are guided to a particular response. In episode 12 of the #aiportfolio podcast Serg Masís and I discuss explainable ai and #llms. If you have watched Jon Krohn's show, Super Data Science Podcast, lots of the great technical questions come from Serg Masís, so his insights into #machinelearning in highly regulated environments can expand your knowledge on the topic. One thing that stuck out to me is that if you have an exploration mindset, you can leverage models to help you do better Exploratory Data Analysis vs just visualizing statistics on the data. Links to show are in the comments
The whole idea of performing EDA is for adding explanability to ML models and helping in ML model selection. How do you feel ML models can help in EDA ? Sounds weird
Thank you so much for sharing, Mark Moyou, PhD!
Very intuitive. Thanks for sharing.
Sr. Data Scientist @NVIDIA | Host @ AI Portfolio Podcast, Caribbean Tech Pioneers Podcast, Progress Guaranteed Podcast | Director @Optimized AI Conference
4moYoutube:https://youtu.be/88Hr9iBrfF8?si=QSrtcM90rUckxdCH Apple: https://podcasts.apple.com/us/podcast/serg-masis-agi-interpretable-machine-learning-upcoming/id1718453663?i=1000661670324 Spotify: https://open.spotify.com/episode/3msHYRad1n1hyRKB3OUGnn?si=e8cvTzogRaifwfqr-nuJ-Q