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The Data Exchange with Ben Lorica
Reflections from the First AI Conference in San Francisco
Nov 9, 2023
The hosts analyze takeaways from the inaugural AI conference in San Francisco, discussing the importance of empirical evidence. Experimenting and iterating in AI leads to improved results. The rise of open source and custom foundation models in AI is explored. The use of ensembles in machine learning and highlights from the AI conference are discussed, including generative AI for speech.
49:28
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Quick takeaways
- The AI and machine learning fields are in the exploration phase, particularly in relation to large language models, emphasizing the importance of trial and error and sharing learnings.
- The AI conference had a diverse demographic, with a significant representation of mid-career professionals and attendees from various industries and regions.
Deep dives
Takeaway 1: Empirical nature of AI and machine learning
In the podcast episode, the speaker discusses the empirical nature of AI and machine learning. They emphasize that these fields are still in the exploration phase, especially when it comes to large language models. Many speakers at the conference shared their experiments and observations, highlighting the importance of trying different approaches and sharing learnings with each other. The focus is on trial and error and learning from the results obtained.
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