From Shiny to Strategic: The Maturation of AI Across Industries // David Cox // #303
Apr 7, 2025
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David Cox, VP of Data Science at RethinkFirst, dives into the maturation of AI across industries. He discusses the shift from merely adopting shiny new technologies to analyzing ROI and focusing on market differentiation. The conversation covers how AI can enhance clinical decision-making and address behavioral change by leveraging data. Cox emphasizes the importance of user agency in AI's potential to empower healthier choices, alongside the need to navigate distractions that technology introduces into daily habits.
AI discussions have shifted from implementing new technologies to analyzing their ROI and optimizing tech stacks for better outcomes.
The podcast emphasizes the importance of 'nudging' through environmental changes to improve decision-making and promote healthier lifestyle choices.
Deep dives
Behavioral Insights from Machine Learning
The podcast discusses the integration of behavioral science and artificial intelligence, particularly through unsupervised machine learning to enhance decision-making in everyday life. This approach focuses on understanding human behavior within specific environments, examining stimuli that influence actions, such as why individuals may choose unhealthy food options over healthier choices. For instance, instead of merely detecting health metrics like physical activity or dietary habits, this methodology seeks to analyze the reasons behind those choices, offering insights into how environmental factors can be adjusted to promote healthier behaviors. By leveraging large sets of data from wearable technology, the application of these insights aims to help individuals make better decisions that lead to improved well-being.
Reinforcement Learning and Behavior Change
Reinforcement learning principles are explored as a framework for predicting and influencing human behavior by analyzing antecedent-behavior-consequence chains. This approach, familiar from biological studies, applies technological algorithms to human behavior, such as monitoring social media interactions, in order to understand what drives certain actions and how to encourage healthier choices. For example, the podcast highlights how platforms like Twitter engage users through tailored notifications that can easily distract from positive activities. By identifying the rewards and reinforcers linked to various behaviors, interventions can be designed to make healthy activities more rewarding and thus promote better lifestyle choices.
Data-Driven Personalization in Therapy
The conversation emphasizes the potential of using big data within clinical settings to create personalized interventions for improving mental and physical health. Unsupervised machine learning can help identify patient profiles and behaviors, enabling healthcare professionals to tailor therapeutic approaches that align with each individual’s needs. The challenge lies in collecting relevant data that accurately reflects a patient's lifestyle and choices, which could include tracking daily activities, screen time, and their engagement with health-related goals. By harnessing this data effectively, clinicians can enhance their decision-making processes and foster better outcomes for their patients, ultimately leading to healthier living.
Nudging Towards Better Decisions
The podcast introduces the concept of 'nudging' as a way to help individuals make better choices that align with their long-term health and wellness goals. Drawing from behavioral economics, the discussion illustrates how minor changes in the environment can significantly influence decision-making, such as placing healthier snacks within easier reach while making less healthy options harder to get. With reference to practical strategies, like increasing the effort needed to engage in undesirable behaviors, listeners are encouraged to employ simple yet effective tactics in their daily lives to facilitate healthier habits. This aligns with the broader goal of integrating data-driven insights into user-friendly applications to empower individuals in managing their health.
From Shiny to Strategic: The Maturation of AI Across Industries // MLOps Podcast #303 with David Cox, VP of Data Science; Assistant Director of Research at RethinkFirst; Institute of Applied Behavioral Science.
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// Abstract
Shiny new objects are made available to artificial intelligence(AI) practitioners daily. For many who are not AI practitioners, the release of ChatGPT in 2022 was their first contact with modern AI technology. This led to a flurry of funding and excitement around how AI might improve their bottom line. Two years on, the novelty of AI has worn off for many companies but remains a strategic initiative. This strategic nuance has led to two patterns that suggest a maturation of the AI conversation across industries. First, conversations seem to be pivoting from "Are we doing [the shiny new thing]" to serious analysis of the ROI from things built. This reframe places less emphasis on simply adopting new technologies for the sake of doing so and more emphasis on the optimal stack to maximize return relative to cost. Second, conversations are shifting to emphasize market differentiation. That is, anyone can build products that wrap around LLMs. In competitive markets, creating products and functionality that all your competitors can also build is a poor business strategy (unless having a particular thing is industry standard). Creating a competitive advantage requires companies to think strategically about their unique data assets and what they can build that their competitors cannot. // Bio
Dr. David Cox can formally lay claim to being a bioethicist (master's degree), a board-certified behavior analyst at the doctoral level, a behavioral economist (post-doc training), and a full-stack data scientist (post-doc training). He has worked in behavioral health for nearly 20 years as a clinician, academic researcher, scholar, technologist, and all-around behavior science junky. He currently works as the Assistant Director of Research for the Institute of Applied Behavioral Science at Endicott College and the VP of Data Science at RethinkFirst. David also likes to write, having published 60+ peer-reviewed articles, book chapters, and a few books. When he's not doing research or building tools at the intersection of artificial intelligence and behavioral health, he enjoys spending time with his wife and two beagles in and around Jacksonville, FL.
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