Super Data Science: ML & AI Podcast with Jon Krohn
The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.
Super Data Science: ML & AI Podcast with Jon Krohn
797: Deep Learning Classics and Trends, with Dr. Rosanne Liu
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.
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
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