Unsupervised Learning with Jacob Effron Ep 47: Chief AI Scientist of Databricks Jonathan Frankle on Why New Model Architectures are Unlikely, When to Pre-Train or Fine Tune, and Hopes for Future AI Policy
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Nov 12, 2024 Jonathan Frankle, Chief AI Scientist at Databricks, brings deep insight into the fast-paced world of AI. He discusses the evolution of AI models, favoring transformers over LSTMs, and shares strategic insights from the merger of Mosaic and Databricks. Frankle emphasizes the importance of effective AI evaluation benchmarks and customer collaboration in developing AI solutions. Ethical considerations and responsible AI policy also take center stage, as he highlights the need for transparency and community engagement in the rapidly evolving landscape.
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Partner or Acquire For Innovation
- Partner openly with startups and integrate the best tools rather than aiming for a closed ecosystem.
- Combine strengths through partnerships or acquisitions to improve customer experience in AI infrastructure.
Focus Beyond Model Creation
- Meta and Allen Institute provide powerful open source models, making further model-building less critical.
- Biggest AI opportunities now lie in evaluation creation, fine-tuning, and bridging model usage with customer data.
Expect AI Surprises and Uncertainty
- Predicting AI's future breakthroughs is very challenging; many ideas and scaling efforts fail but some succeed unexpectedly.
- Being wrong is part of scientific progress and openness to surprises enables innovation.