Reinforcement learning through human feedback (RLHF) has come a long way. In this episode, research scientist Nathan Lambert talks to Jon Krohn about the technique’s origins of the technique. He also walks through other ways to fine-tune LLMs, and how he believes generative AI might democratize education.

This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au) and AWS Trainium (https://go.aws/3ycV6K0), and Crawlbase (https://crawlbase.com), the ultimate data crawling platform. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• Why it is important that AI is open [03:13]
• The efficacy and scalability of direct preference optimization [07:32]
• Robotics and LLMs [14:32]
• The challenges to aligning reward models with human preferences [23:00]
• How to make sure AI’s decision making on preferences reflect desirable behavior [28:52]
• Why Nathan believes AI is closer to alchemy than science [37:38]

Additional materials: www.superdatascience.com/791

The Super Data Science Podcast: Machine Learning | AI | Success

791: Reinforcement Learning from Human Feedback (RLHF), with Dr. Nathan Lambert

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.

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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.

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