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Software, Hardware, and Progress in Machine Learning
The speakers discuss the relationship between software, hardware, and progress in machine learning, highlighting the importance of having the best models available. They also talk about the impact of software development on the community's ability to make progress and mention crucial tools like Jax and TPU support. Additionally, they emphasize the need for compute efficiency and the importance of focusing on specific benchmarks for community progress.