Learning Bayesian Statistics Podcast Por Alexandre Andorra arte de portada

Learning Bayesian Statistics

Learning Bayesian Statistics

De: Alexandre Andorra
Escúchala gratis

Acerca de esta escucha

 Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Estonia. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)!Copyright Alexandre Andorra Ciencia
Episodios
  • BITESIZE | How AI is Redefining Human Interactions, with Tom Griffiths

    BITESIZE | How AI is Redefining Human Interactions, with Tom Griffiths
    May 21 2025

    Today’s clip is from episode 132 of the podcast, with Tom Griffiths.

    Tom and Alex Andorra discuss the fundamental differences between human intelligence and artificial intelligence, emphasizing the constraints that shape human cognition, such as limited data, computational resources, and communication bandwidth.

    They explore how AI systems currently learn and the potential for aligning AI with human cognitive processes.

    The discussion also delves into the implications of AI in enhancing human decision-making and the importance of understanding human biases to create more effective AI systems.

    Get the full discussion here.

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression Course (first 2 lessons free)

    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Transcript

    This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
    Más Menos
    22 m
  • #132 Bayesian Cognition and the Future of Human-AI Interaction, with Tom Griffiths

    #132 Bayesian Cognition and the Future of Human-AI Interaction, with Tom Griffiths
    May 13 2025

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression Course (first 2 lessons free)

    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Takeaways:

    • Computational cognitive science seeks to understand intelligence mathematically.
    • Bayesian statistics is crucial for understanding human cognition.
    • Inductive biases help explain how humans learn from limited data.
    • Eliciting prior distributions can reveal implicit beliefs.
    • The wisdom of individuals can provide richer insights than averaging group responses.
    • Generative AI can mimic human cognitive processes.
    • Human intelligence is shaped by constraints of data, computation, and communication.
    • AI systems operate under different constraints than human cognition. Human intelligence differs fundamentally from machine intelligence.
    • Generative AI can complement and enhance human learning.
    • AI systems currently lack intrinsic human compatibility.
    • Language training in AI helps align its understanding with human perspectives.
    • Reinforcement learning from human feedback can lead to misalignment of AI goals.
    • Representational alignment can improve AI's understanding of human concepts.
    • AI can help humans make better decisions by providing relevant information.
    • Research should focus on solving problems rather than just methods.

    Chapters:

    00:00 Understanding Computational Cognitive Science

    13:52 Bayesian Models and Human Cognition

    29:50 Eliciting Implicit Prior Distributions

    38:07 The Relationship Between Human and AI Intelligence

    45:15 Aligning Human and Machine Preferences

    50:26 Innovations in AI and Human Interaction

    55:35 Resource Rationality in Decision Making

    01:00:07 Language Learning in AI Models

    01:06:04 Inductive Biases in Language Learning

    01:11:55 Advice for Aspiring Cognitive Scientists

    01:21:19 Future Trends in Cognitive Science and AI

    Thank you to my Patrons for making this episode possible!

    Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz,...
    Más Menos
    1 h y 30 m
  • BITESIZE | Hacking Bayesian Models for Better Performance, with Luke Bornn

    BITESIZE | Hacking Bayesian Models for Better Performance, with Luke Bornn
    May 7 2025

    Today’s clip is from episode 131 of the podcast, with Luke Bornn.

    Luke and Alex discuss the application of generative models in sports analytics. They emphasize the importance of Bayesian modeling to account for uncertainty and contextual variations in player data.

    The discussion also covers the challenges of balancing model complexity with computational efficiency, the innovative ways to hack Bayesian models for improved performance, and the significance of understanding model fitting and discretization in statistical modeling.

    Get the full discussion here.

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression Course (first 2 lessons free)

    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Transcript

    This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
    Más Menos
    14 m
adbl_web_global_use_to_activate_T1_webcro805_stickypopup
Todavía no hay opiniones