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Episode 22: LLMs, OpenAI, and the Existential Crisis for Machine Learning Engineering
Manage episode 386174356 series 3317544
Jeremy Howard (Fast.ai), Shreya Shankar (UC Berkeley), and Hamel Husain (Parlance Labs) join Hugo Bowne-Anderson to talk about how LLMs and OpenAI are changing the worlds of data science, machine learning, and machine learning engineering.
Jeremy Howard is co-founder of fast.ai, an ex-Chief Scientist at Kaggle, and creator of the ULMFiT approach on which all modern language models are based. Shreya Shankar is at UC Berkeley, ex Google brain, Facebook, and Viaduct. Hamel Husain has his own generative AI and LLM consultancy Parlance Labs and was previously at Outerbounds, Github, and Airbnb.
They talk about
- How LLMs shift the nature of the work we do in DS and ML,
- How they change the tools we use,
- The ways in which they could displace the role of traditional ML (e.g. will we stop using xgboost any time soon?),
- How to navigate all the new tools and techniques,
- The trade-offs between open and closed models,
- Reactions to the recent Open Developer Day and the increasing existential crisis for ML.
LINKS
34集单集
Manage episode 386174356 series 3317544
Jeremy Howard (Fast.ai), Shreya Shankar (UC Berkeley), and Hamel Husain (Parlance Labs) join Hugo Bowne-Anderson to talk about how LLMs and OpenAI are changing the worlds of data science, machine learning, and machine learning engineering.
Jeremy Howard is co-founder of fast.ai, an ex-Chief Scientist at Kaggle, and creator of the ULMFiT approach on which all modern language models are based. Shreya Shankar is at UC Berkeley, ex Google brain, Facebook, and Viaduct. Hamel Husain has his own generative AI and LLM consultancy Parlance Labs and was previously at Outerbounds, Github, and Airbnb.
They talk about
- How LLMs shift the nature of the work we do in DS and ML,
- How they change the tools we use,
- The ways in which they could displace the role of traditional ML (e.g. will we stop using xgboost any time soon?),
- How to navigate all the new tools and techniques,
- The trade-offs between open and closed models,
- Reactions to the recent Open Developer Day and the increasing existential crisis for ML.
LINKS
34集单集
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