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Programmatic AI data development, Multimodal AI, False dichotomy of fine-tuning vs RAG, Compute-optimal LLMs | Alex Ratner, CEO of Snorkel AI
Manage episode 377281014 series 3370867
Content provided by Prateek Joshi. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Prateek Joshi or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Alex Ratner is the CEO of Snorkel AI, a platform that provides programmatic data labeling and foundation models to enable companies to build AI applications. They've raised $135M so far from amazing investors such as Addition, Greylock, Google Ventures, and Lightspeed. He was previously the cofounder and CEO of SiftPage. He has a bachelors degree from Harvard and a PhD from Stanford.
In this episode, we cover a range of topics including:
- Making AI data development first-class and programmatic
- The data-centric step for every model-centric step
- False dichotomy of fine tuning vs RAG
- Foundation model dynamics: winner take all vs diverse models
- Training compute-optimal LLMs
- Designing multimodal datasets (DataComp)
- Distilling Step-by-Step
- 'GPT-You' for every enterprise
Alex's favorite books: Foundation series books (Author: Isaac Asimov)
--------
Where to find Prateek Joshi:
Newsletter: https://prateekjoshi.substack.com
Website: https://prateekj.com
LinkedIn: https://www.linkedin.com/in/prateek-joshi-91047b19
Twitter: https://twitter.com/prateekvjoshi
152 episodes
Share
Manage episode 377281014 series 3370867
Content provided by Prateek Joshi. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Prateek Joshi or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Alex Ratner is the CEO of Snorkel AI, a platform that provides programmatic data labeling and foundation models to enable companies to build AI applications. They've raised $135M so far from amazing investors such as Addition, Greylock, Google Ventures, and Lightspeed. He was previously the cofounder and CEO of SiftPage. He has a bachelors degree from Harvard and a PhD from Stanford.
In this episode, we cover a range of topics including:
- Making AI data development first-class and programmatic
- The data-centric step for every model-centric step
- False dichotomy of fine tuning vs RAG
- Foundation model dynamics: winner take all vs diverse models
- Training compute-optimal LLMs
- Designing multimodal datasets (DataComp)
- Distilling Step-by-Step
- 'GPT-You' for every enterprise
Alex's favorite books: Foundation series books (Author: Isaac Asimov)
--------
Where to find Prateek Joshi:
Newsletter: https://prateekjoshi.substack.com
Website: https://prateekj.com
LinkedIn: https://www.linkedin.com/in/prateek-joshi-91047b19
Twitter: https://twitter.com/prateekvjoshi
152 episodes
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