In this episode, Jake and David discuss articles from the ⁠recent issue of the Journal of Applied Behavior Analysis. They also discuss how the underlying technologies and concepts might be extended via data science techniques.

In this episode, Jake and David discuss articles from the recent issue of Artificial Intelligence in Medicine. They also discuss how the underlying technologies and concepts might be translated to behavioral health services.

OpenAI and Anthropic have recently released products where users can connect health-related data and receive healthcare advice and guidance.

Jake and David discuss these products, the potential benefits to ease of access to personalized healthcare-related information, and the potential harms from inaccurate (or dangerous) recommendations and data privacy concerns.

At the end of the episode, they each answer the question we all have to ask ourselves: "Will you be hooking your data up to these systems?"

Realistically, you can't build any model of behavior-environment relations if you can't (a) find the data you need and (b) integrate those data into a usable database.

In this episode of The Behavioral Data Science Podcast, we discuss the many considerations and decisions one needs to make. And, we do so by discussing a seven-year-long project Jake has been working on to build a usable database of all open-source articles published within five behavior-analytic journals.

Crucial to any behavioral data science project is identifying either (a) what behavior you want to analyze and how you'll get the data; or (b) what data you can get and what behaviors those data allow you to analyze well.

In this episode, we chat about these decisions in the context of the literally wild behavior of birds at backyard feeders.

For the interested, here's a link to the backyard ecology dashboard referenced during the episode: https://david-j-cox.github.io/backyard-ecology/

In each episode of Season 3, we take a claim from a news story, paper, or hot topic, and walk through how a behavioral data scientist would think about it: clarify the question, identify the operant and respondent principles potentially at play, design the data pipelines, choose the models, and turn the resulting insights into data-based behavior-change tools.

In this episode, we take on the claim that "late-night screen time disrupts sleep and leads teens to be more depressed".

As we close out Season 2 and our emphasis on LLMs, we had the distinct privilege of chatting with Dr. Elizabeth Garrison. She is one of the few people in the world with domain expertise spanning behavior analysis (BCBA) and artificial intelligence (PhD).

In this episode, we reflect on the state of AI research and industry work pre-ChatGPT and post-ChatGPT release, the shift in academic AI research when the transformer architecture became broadly available, and the differences between academia and industry in both behavior science and AI.

"Bubbles" are an economic phenomenon characterized by a rapid increase in asset prices that far exceed the asset's underlying fundamental value, driven by speculative buying and herd behavior rather than intrinsic worth.

In this episode, Jake and David ask, "Are we in an AI bubble?". And, if so, what might this mean for both individuals and organizations as they navigate the current AI strategic landscape?