Advancements in Zero-Shot Image Classification and Text Encoding for Generative Models

Dr. Rosanne Liu, Research Scientist at Google DeepMind and co-founder of the ML Collective, shares her journey and the mission to democratize AI research. She explains her pioneering work on intrinsic dimensions in deep learning and the advantages of curiosity-driven research. Jon and Dr. Liu also explore the complexities of understanding powerful AI models, the specifics of character-aware text encoding, and the significant impact of diversity, equity, and inclusion in the ML community. With publications in NeurIPS, ICLR, ICML, and Science, Dr. Liu offers her expertise and vision for the future of machine learning.

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In this episode you will learn:

• How the ML Collective came about [03:31]

• The concept of a failure CV [16:12]

• ML Collective research topics [19:03]

• How Dr. Liu's work on the “intrinsic dimension” of deep learning models inspired the now-standard LoRA approach to fine-tuning LLMs [21:28]

• The pros and cons of curiosity-driven vs. goal-driven ML research [29:08]

• Discussion on Dr. Liu's research and papers [33:17]

• Character-aware vs. character-blind text encoding [54:59]

• The positive impacts of diversity, equity, and inclusion in the ML community [57:51]

Additional materials: www.superdatascience.com/797