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Avoiding AI's "Moore's Law": Why we are building a ladder to the moon

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If you have a question about this talk, please contact Michael Schlichtkrull .

An informal talk around LLM at scale, efficiency and open ML problems we are currently working on at Cohere For AI. I will present some background, some grumpy thoughts about how we can get away from the painfully inefficient formula of just scaling capacity. I’ll plan on leaving plenty of time for discussion.

Sara Hooker leads Cohere For AI, a non-profit research lab that seeks to solve complex machine learning problems. Cohere For AI supports fundamental research that explores the unknown, and is focused on creating more points of entry into machine learning research. With a long track-record of impactful research at Google Brain, Sara brings a wealth of knowledge from across machine learning. Her work has focused on model efficiency training techniques and optimizing for models that fulfil multiple desired criteria—interpretable, efficient, fair and robust. Before Cohere For AI, she was the founder of Delta Analytics, a non-profit that brings together researchers, data scientists, and software engineers to volunteer their skills for non-profits around the world.

This talk is part of the NLIP Seminar Series series.

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