Episodes (Page 2)
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Review emerging research on how interacting with LLMs impacts human skills and cognitive abilities both positively and negatively
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Examine AI ethics topics beyond typical discussions of data security, bias, transparency, and explainability
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Define Explainable AI (XAI) as techniques to help developers and users understand why inputs produce specific outputs
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Explain that prompt engineering extends far beyond simple interface strategies and is grounded in peer-reviewed research
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Highlight that LLM output evaluation is more critical than prompts, context, and inputs
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Jake and David discuss best practices for building and using AI systems that leverage LLMs
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Jake and David demystify types of generative AI and how LLMs specifically function
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Jake and David discuss current hot topics and debates in the LLM space
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Discuss forthcoming book chapter 'Welcome to the Era of Experience' by David Silver and Richard Sutton
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Interview with Dr. Beth Garrison on her journey in behavior analysis and pursuit of AI PhD
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David discusses a 15-year personal dataset of his own daily behavior and quantified self practices
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Jake describes a backyard science project using computer vision to detect and track squirrel behavior
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Explores fundamentals of unsupervised machine learning and its applications in behavioral analysis
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Guest interview episode featuring Zach Morford
Chat with Zach Morford
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Concludes three-part series on end-to-end AI application development
The Deployment Lifecycle
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Part two of end-to-end AI application development series
The Model Lifecycle
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Part one of end-to-end AI application development series
The Data Lifecycle
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Identifies common barriers preventing research findings from reaching practice
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Examines whether large language models exhibit verbal behavior properties
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Explains vector embeddings and their mathematical foundations