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Brian Deer's 1998 Lancet article was a pivotal investigation into the validity of a study by Andrew Wakefield et al., which suggested a link between the MMR (measles, mumps, and rubella) vaccine and autism. Deer's investigation revealed serious ethical violations and conflicts of interest associated with Wakefield's research. He uncovered evidence of financial interests and unethical conduct by Wakefield, including undisclosed payments and falsification of data. Deer's article played a significant role in discrediting Wakefield's study and highlighting the importance of scientific integrity and transparency in medical research.

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Jim Keller is a legendary microprocessor engineer, having worked at AMD, Apple, Tesla, and now Intel. He's known for his work on the AMD K7, K8, K12 and Zen microarchitectures, Apple A4, A5 processors, and co-author of the specifications for the x86-64 instruction set and HyperTransport interconnect.

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"Thinking, Fast and Slow" is a book by Daniel Kahneman, a Nobel Prize-winning psychologist, that explores the two systems that drive the way humans think: System 1 and System 2.

System 1: This is the fast, intuitive, and automatic mode of thinking. It operates effortlessly and quickly, relying on heuristics and intuition. However, it can also lead to biases and errors.

System 2: This is the slower, more deliberate, and analytical mode of thinking. It requires effort and conscious attention. It is responsible for logical reasoning and critical thinking.

Kahneman discusses various cognitive biases and heuristics that affect decision-making, such as anchoring, availability heuristic, and overconfidence bias. He explains how these biases arise from the inherent workings of our minds and influence our judgments and choices.

Moreover, he delves into prospect theory, which describes how people make decisions under uncertainty, and highlights the role of loss aversion in decision-making processes.

Overall, Kahneman's book provides valuable insights into the complexities of human cognition and offers practical implications for understanding decision-making, judgment, and behavior in various contexts, including economics, psychology, and everyday life.

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"Judgment Under Uncertainty: Heuristics and Biases" is a seminal paper co-authored by Daniel Kahneman, Amos Tversky, and Paul Slovic, published in the journal Science in 1974. This paper laid the groundwork for understanding the systematic biases and heuristics that affect human decision-making. Kahneman's contributions to the paper can be summarized as follows:

Identification of Heuristics: Kahneman, along with Tversky and Slovic, identified various cognitive heuristics that individuals employ when making judgments and decisions under uncertainty. These heuristics are mental shortcuts that simplify complex problems but can lead to systematic biases.

Description of Biases: The paper outlined several biases that result from the use of these heuristics, such as the availability heuristic, representativeness heuristic, and anchoring bias. Kahneman and his colleagues demonstrated how these biases can distort judgments and lead to systematic errors.

Experimental Evidence: Kahneman conducted numerous experiments to provide empirical evidence for the existence of these biases and heuristics. These experiments helped validate their theories and provided insights into the underlying mechanisms of human judgment and decision-making.

Theoretical Framework: The paper proposed a theoretical framework for understanding how individuals process information and make judgments under conditions of uncertainty. This framework has since become foundational in the fields of psychology, economics, and decision science.

Overall, Kahneman's contributions to "Judgment Under Uncertainty: Heuristics and Biases" helped shape our understanding of human decision-making and laid the groundwork for further research in behavioral economics and cognitive psychology.

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https://www.youtube.com/watch?v=4dC_nRYIDZU

Saw you posted Lex, might be a good jumping off one if you've never heard Sam. Easy(ish) listen for a jumping off point

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https://www.youtube.com/watch?v=cCl-5vC7aW8

Sam Harris is by far my favorite podcaster. A little heady if you haven't heard.

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Melanie Mitchell is a prominent figure in the fields of artificial intelligence (AI) and machine learning (ML). She is a Professor of Computer Science at Portland State University and an External Professor at the Santa Fe Institute. Mitchell's contributions span various areas within AI and ML, including complex systems, cognitive science, and computational biology. Some of her notable contributions include:

Complex Systems: Mitchell has conducted extensive research on complex systems, focusing on understanding emergent behaviors in systems composed of numerous interacting components. Her work has helped shed light on how simple rules can lead to complex behaviors, a concept often utilized in AI and ML algorithms.

Artificial Intelligence: Mitchell has explored the fundamental challenges of creating intelligent systems, including the limitations of current AI techniques such as deep learning and the need for more explainable and interpretable AI models.

Cognitive Science: Mitchell's research intersects with cognitive science, particularly in understanding human cognition and intelligence. She has investigated the parallels between biological and artificial intelligence, offering insights into how computational models can inform our understanding of the mind.

Computational Biology: Mitchell has applied AI and ML techniques to computational biology, particularly in the study of genetic regulatory networks and evolutionary dynamics. Her work in this area has contributed to advancements in understanding biological systems and processes.

Books and Publications: Melanie Mitchell is also known for her contributions as an author. Her book "Complexity: A Guided Tour" provides a comprehensive introduction to the field of complex systems, making it accessible to a wide audience. Additionally, she has published numerous research papers in top-tier conferences and journals in AI, ML, and related fields.

Overall, Melanie Mitchell's contributions have had a significant impact on the interdisciplinary study of AI, ML, complex systems, and cognitive science, helping to advance our understanding of intelligence and emergent phenomena in both artificial and natural systems.

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Jim talks with Katherine Gehl about her and Michael Porter's book, The Politics Industry: How Political Innovation Can Break Partisan Gridlock and Save Our Democracy. They discuss Jim's past familiarity with Michael Porter's work, Porter's five forces, the "what the hell is water" phenomenon, the Schoolhouse Rock problem, political industry theory, political payback for unhelpful activities, why political competitors are doing better as "customers" become more dissatisfied, the current American party system as a protected duopoly, nonprofit investments in things that have no chance, non-constitutional problems, the reversible accident of plurality voting, whether more parties are essential, how Ross Perot's 1992 election pressured the two parties to balance the budget, reforming the primary system, final-five voting, Alaska's experiment in final-four voting, instant runoffs, freeing players to make good strategic choices, lowering the barrier to entry for new thinking, and much more.