How to Use Machine Learning to Inform Design Decisions and Make Predictions | by Kurt Klingensmith | Aug, 2024 | Towards Data Science #Decisions Predictions
Innovation Insight: Transfer Learning
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- Ignition Guide to Scoping a Machine Learning Project
- Simple, Powerful Machine Learning Pilot (Iron Mountain)
- Machine Learning Literacy for Business Partners (Micron)
Hi All,
I came across few interesting reads and thought would be relevant and useful for everyone:
An essay by Judea Pearl highlighting too much dependence on empirical learning, basing model development of historical data would create gaps and limit the use of model-based development:
https://ftp.cs.ucla.edu/pub/stat_ser/r502-reprint.pdf
Google research published an excellent paper - Machine Learning: The High-Interest Credit Card of Technical Debt. Amongst several other ideas, it brings up a point of technical debt due to data (similar to the idea of technical debt due to code)
https://storage.googleapis.com/pub-tools-public-publication-data/pdf/43146.pdf
And finally,
Stanford Institute for Human Centered AI published their latest version of AI Index Report. It was very interesting to see them highlight that AI model performance has improved substantially. It also points towards increased bias within language models.
https://aiindex.stanford.edu/report/
Happy reading!
Thanks, Sumit