The Behavioral Data Science Podcast

Jake and David chat about best practices and considerations for those building and using AI systems that leverage LLMs.

Jake and David chat about types of GenAI, and specifically how LLMs work—from input text or audio through the output you read.

Jake and I chat about current hot topics in the LLM space and what we would (and would not) trust an LLM with.

Jake and I chat about a forthcoming book chapter titled, "Welcome to the Era of Experience" by David Silver and Richard Sutton (link below). This—naturally—led other topics to surface, such as companies staffed entirely by AI agents (which turned out as well as that sounds); superintelligence (we might be legally required to reference this during the 2025 AI hype cycle); and how practical systems built on these ideas would even be architected (we both came in with different ideas here which was fun). Happy listening.

Links to things mentioned:

• Smith, D., & Sutton, R. S. (April 26, 2025). Welcome to the Era of Experience. Preprint of a chapter to appear in Designing an Intelligence. MIT Press.
• Sutton, R. S., & Barto, A. G. (2015). Reinforcement Learning: An Introduction. The MIT Press.
• Wilkins, J. (April 27, 2025). Professors Staffed a Fake Company Entirely With AI Agents, and You'll Never Guess What Happened. Who would have thought? [Friendly write-up of the agentic company work]
• Xu, F. F., Song, Y., Li, B., Tang, Y., Jain, K., Bao, M., ..., & Neubig, G. (2024). TheAgentCompany: Benchmarking LLM agents on consequential real world tasks. arXiv:2412.14161. [Actual study.]

In this episode, we chat with Dr. Beth Garrison about her journey in behavior analysis, what led her to pursue a PhD in artificial intelligence, and her thoughts on where this is all headed.

Links to the papers Dr. Garrison references:

• Exploring Engagement Opportunities for Autistic Children: Using AAC as a Controller in a Wizard-of-Oz Coloring Game: https://doi.org/10.1145/3701193
• Understanding the experience of neurodivergent workers in image and text data annotation: https://doi.org/10.1016/j.chbr.2023.100318

In this episode, David talks about the dataset he's been collecting on his own, daily behavior over the last 15 years; and, how behavior science + data science let him do neat things with it.

Jake talks about his backyard science project where he used computer vision to detect squirrels in his backyard.

In this episode, we dive into some basics around unsupervised machine learning and how behavior analysts might use it in their work.

Episode 010: Guest Chat with Zach Morford

Episode 009: What does it take to go end-to-end with an AI application? Part III - The Deployment Lifecycle