L149 Machine Learning Capabilities for Applications
Автор: Phil Koopman
Загружено: 2025-05-10
Просмотров: 171
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Abstract: While most discussions of machine learning concentrate on the underlying mechanics, this talk discusses their capabilities, strengths, and weaknesses at the level of how they can be applied to a wide variety of real-world applications. Capabilities discussed are classification, end-to-end behavior, generative outputs, and foundation model applications. Challenges discussed include bias, validation, edge cases, hallucinations, autonowashing, safety, and accountability. The evergreen concept of the 90/10 principle cuts both ways for AI, with solving the last 10% of building dependable systems likely to make the difference between winning and losing bets on chip application areas.
Webinar recording from 9/11/2024, Business of Semiconductor Summit talk
For full set of play lists see:
https://users.ece.cmu.edu/~koopman/le...
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