Logo
    Search

    About this Episode

    The history of mathematics, in many ways, begins with counting. Things that needed, initially, to be counted were, and often still are, just that; things. We can say we have twelve tomatoes, or five friends, or that eleven days have passed. As society got more complex, tools that had been used since time immemorial, such as string and scales, became essential tools for counting not only concrete things, like sheep and bison, but more abstract things, such as distance and weight based on agreed-upon multiples of physical artifacts that were copied. This development could not have taken place without the idea of a unit: a standard of measuring something that defines what it means to have one of something. These units can be treated not only as counting numbers, but can be manipulated using fractions, and divided into arbitrarily small divisions. They can even be multiplied and divided together to form new units. So where does the idea of a unit come from? What's the difference between a unit, a dimension, and a physical variable? And how does the idea of physical dimension allow us to simplify complex problems? All of this and more on this episode of Breaking Math.

    Distributed under a CC BY-SA 4.0 International License. For full text, visit: https://creativecommons.org/licenses/by-sa/4.0/

    [Featuring: Sofía Baca; Millicent Oriana, Jacob Urban]

    Become a supporter of this podcast: https://www.spreaker.com/podcast/breaking-math-podcast--5545277/support.

    Recent Episodes from Breaking Math Podcast

    89. Brain Organelles, AI, and the Other Scary Science - An Interview with GT (Part I)

    89.  Brain Organelles, AI, and the Other Scary Science - An Interview with GT (Part I)
    Summary
    This conversation explores the topic of brain organoids and their integration with robots. The discussion covers the development and capabilities of brain organoids, the ethical implications of their use, and the differences between sentience and consciousness. The conversation also delves into the efficiency of human neural networks compared to artificial neural networks, the presence of sleep in brain organoids, and the potential for genetic memories in these structures. The episode concludes with an invitation to part two of the interview and a mention of the podcast's Patreon offering a commercial-free version of the episode.

    Takeaways
    • Brain organoids are capable of firing neural signals and forming structures similar to those in the human brain during development.
    • The ethical implications of using brain organoids in research and integrating them with robots raise important questions about sentience and consciousness.
    • Human neural networks are more efficient than artificial neural networks, but the reasons for this efficiency are still unknown.
    • Brain organoids exhibit sleep-like patterns and can undergo dendrite growth, potentially indicating learning capabilities.
    • Collaboration between scientists with different thinking skill sets is crucial for advancing research in brain organoids and related fields.
    Chapters
    1. 00:00 Introduction: Brain Organoids and Robots
    2. 00:39 Brain Organoids and Development
    3. 01:21 Ethical Implications of Brain Organoids
    4. 03:14 Summary and Introduction to Guest
    5. 03:41 Sentience and Consciousness in Brain Organoids
    6. 04:10 Neuron Count and Pain Receptors in Brain Organoids
    7. 05:00 Unanswered Questions and Discomfort
    8. 05:25 Psychological Discomfort in Brain Organoids
    9. 06:21 Early Videos and Brain Organoid Learning
    10. 07:20 Efficiency of Human Neural Networks
    11. 08:12 Sleep in Brain Organoids
    12. 09:13 Delta Brainwaves and Brain Organoids
    13. 10:11 Creating Brain Organoids with Specific Components
    14. 11:10 Genetic Memories in Brain Organoids
    15. 12:07 Efficiency and Learning in Human Brains
    16. 13:00 Sequential Memory and Chimpanzees
    17. 14:18 Different Thinking Skill Sets and Collaboration
    18. 16:13 ADHD and Hyperfocusing
    19. 18:01 Ethical Considerations in Brain Research
    20. 19:23 Understanding Genetic Mutations
    21. 20:51 Brain Organoids in Rat Bodies
    22. 22:14 Dendrite Growth in Brain Organoids
    23. 23:11 Duration of Dendrite Growth
    24. 24:26 Genetic Memory Transfer in Brain Organoids
    25. 25:19 Social Media Presence of Brain Organoid Companies
    26. 26:15 Brain Organoids Controlling Robot Spiders
    27. 27:14 Conclusion and Invitation to Part 2




    References:

    Muotri Labs (Brain Organelle piloting Spider Robot)

    Cortical Labs (Brain Organelle's trained to play Pong)

    *For a copy of the episode transcript, email us at breakingmathpodcast@gmail.com


    Become a supporter of this podcast: https://www.spreaker.com/podcast/breaking-math-podcast--5545277/support.

    88. Can OpenAi's SORA learn and model real-world physics? (Part 1 of n)

    88.  Can OpenAi's SORA learn and model real-world physics? (Part 1 of n)
    All content is available commercial free on patreon as well as on our Spreaker Supporters Club


    Enjoy this content? Would you like to support us? The best ways to support us are currently to subscribe to our Yourube Channel

    This is a follow up on our previous episode on OpenAi's SORA. We attempt to answer the question, "Can OpenAi's SORA model real-world physics?"

    We go over the details of the technical report, we discuss some controversial opinoins by experts in the field at Nvdia and Google's Deep Mind.

    The transcript for episode is avialable below upon request.

    87. OpenAi SORA, Physics-Informed ML, and a.i. Fraud- Oh My!

    87.  OpenAi SORA, Physics-Informed ML, and a.i. Fraud- Oh My!
    Become a supporter of this podcast: Spreaker Supporters Club

    All episodes are available commercial free on patreon!

    Visit our website at breakingmath.wtf

    Contact us at breakingmathpodcast@gmail.com

    Summary

    OpenAI's Sora, a text-to-video model, has the ability to generate realistic and imaginative scenes based on text prompts. This conversation explores the capabilities, limitations, and safety concerns of Sora. It showcases various examples of videos generated by Sora, including pirate ships battling in a cup of coffee, woolly mammoths in a snowy meadow, and golden retriever puppies playing in the snow. The conversation also discusses the technical details of Sora, such as its use of diffusion and transformer models. Additionally, it highlights the potential risks of AI fraud and impersonation. The episode concludes with a look at the future of physics-informed modeling and a call to action for listeners to engage with Breaking Math content.


    Takeaways

    • OpenAI's Sora is a groundbreaking text-to-video model that can generate realistic and imaginative scenes based on text prompts.
    • Sora has the potential to revolutionize various industries, including entertainment, advertising, and education.
    • While Sora's capabilities are impressive, there are limitations and safety concerns, such as the potential for misuse and the need for robust verification methods.
    • The conversation highlights the importance of understanding the ethical implications of AI and the need for ongoing research and development in the field.

    Chapters

    00:00 Introduction to OpenAI's Sora
    04:22 Overview of Sora's Capabilities
    07:08 Exploring Prompts and Generated Videos
    12:20 Technical Details of Sora
    16:33 Limitations and Safety Concerns
    23:10 Examples of Glitches in Generated Videos
    26:04 Impressive Videos Generated by Sora
    29:09 AI Fraud and Impersonation
    35:41 Future of Physics-Informed Modeling
    36:25 Conclusion and Call to Action

    #OpenAiSora #

    86. Math, Music, and Artificial Intelligence - Levi McClain Interview (Final Part)

    86.  Math, Music, and Artificial Intelligence - Levi McClain Interview (Final Part)
    All episodes are available commercial Free for supporters on Spreaker and Patreon

    Transcripts are available upon request. Email us at BreakingMathPodcast@gmail.com
    Follow us on X (Twitter)
    Follow us on Social Media Pages (Linktree)

    Visit our guest Levi McClain's Pages:
    youtube.com/@LeviMcClain
    levimcclain.com/

    Summary
    Levi McClean discusses various topics related to music, sound, and artificial intelligence. He explores what makes a sound scary, the intersection of art and technology, sonifying data, microtonal tuning, and the impact of using 31 notes per octave. Levi also talks about creating instruments for microtonal music and using unconventional techniques to make music. The conversation concludes with a discussion on understanding consonance and dissonance and the challenges of programming artificial intelligence to perceive sound like humans do.


    Takeaways:

    • The perception of scary sounds can be analyzed from different perspectives, including composition techniques, acoustic properties, neuroscience, and psychology.
    • Approaching art and music with a technical mind can lead to unique and innovative creations.
    • Sonifying data allows for the exploration of different ways to express information through sound.
    • Microtonal tuning expands the possibilities of harmony and offers new avenues for musical expression.
    • Creating instruments and using unconventional techniques can push the boundaries of traditional music-making.
    • Understanding consonance and dissonance is a complex topic that varies across cultures and musical traditions.
    • Programming artificial intelligence to understand consonance and dissonance requires a deeper understanding of human perception and cultural context.


    Chapters
    00:00 What Makes a Sound Scary
    03:00 Approaching Art and Music with a Technical Mind
    05:19 Sonifying Data and Turning it into Sound
    08:39 Exploring Music with Microtonal Tuning
    15:44 The Impact of Using 31 Notes per Octave
    17:37 Why 31 Notes Instead of Any Other Arbitrary Number
    19:53 Creating Instruments for Microtonal Music
    21:25 Using Unconventional Techniques to Make Music
    23:06 Closing Remarks and Questions
    24:03 Understanding Consonance and Dissonance
    25:25 Programming Artificial Intelligence to Understand Consonance and Dissonance

    85. Math, Music, Neuroscience, and Fear - an Interview with Musician Levi McClain

    85. Math, Music, Neuroscience, and Fear - an Interview with Musician Levi McClain
    Listen to episodes commercial Free on Patreon at patreon.com/breakingmath

    We are joined today by content creator Levi McClain to discuss the mathematics behind music theory, neuroscience, and human experiences such as fear as they relate to audio processing.

    For a copy of the episode transcript, email us at BreakingMathPodcast@gmail.com.

    For more in depth discussions on these topics and more, check out Levi's channels at:

    Patreon.com/LeviMcClain

    youtube.com/@LeviMcClain

    Tiktok.com/@levimcclain

    Instagram.com/levimcclainmusic

    Become a supporter of this podcast: https://www.spreaker.com/podcast/breaking-math-podcast--5545277/support.

    84. (Part 2) Intelligence in Nature v. Machine Learning - an Interview with Brit Cruise

    84. (Part 2) Intelligence in Nature v. Machine Learning - an Interview with Brit Cruise

    Part 2/2 of the interview with Brit Cruise, creator of the YouTube channel "Art of the Problem," about interesting mathematics,, electrical and computer engineering problems.

    In Part 1, we explored what 'intelligence' may be defined as by looking for examples of brains and proto-brains found in nature (including mold, bacteria, fungus, insects, fish, reptiles, and mammals).

    In Part 2, we discuss aritifical neural nets and how they are both similar different from human brains, as well as the ever decreasing gap between the two.

    Brit's YoutTube Channel can be found here: Art of the Problem - Brit Cruise

    Transcript will be made available soon! Stay tuned. You may receive a transcript by emailing us at breakingmathpodcast@gmail.com.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/breaking-math-podcast--5545277/support.

    83. Intelligence in Nature v. Machine Learning-An Interview with Brit Cruise - Part 1 of 2

    83. Intelligence in Nature v. Machine Learning-An Interview with Brit Cruise - Part 1 of 2
    In this episode (part 1 of 2), I interview Brit Cruise, creator of the YouTube channel 'Art of the Problem.' On his channel, he recently released the video "ChatGPT: 30 Year History | How AI learned to talk." We discuss examples of intelligence in nature and what is required in order for a brain to evolve at the most basic level. We use these concepts to discuss what artificial intelligence - such as Chat GPT - both is and is not.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/breaking-math-podcast--5545277/support.

    82. A.I. and Materials Discovery - an Interview with Taylor Sparks

    82.  A.I. and Materials Discovery - an Interview with Taylor Sparks
    How is Machine Learning being used to further original scientific discoveries?

    Transcripts of this episode are avialable upon request. Email us at BreakingMathPodcast@gmail.com.

    A link to the paper discussed in this episode can be found here-->

    Digital Discovery - Generative adversarial networks and diffusion models in material discovery

    In this episode Gabriel Hesch interviews Taylor Sparks, a professor of material science and engineering, about his recent paper on the use of generative modeling a.i. for material disovery. The paper is published in the journal Digital Discovery and is titled 'Generative Adversarial Networks and Diffusion MOdels in Material Discovery. They discuss the purpose of the call, the process of generative modeling, creating a representation for materials, using image-based generative models, and a comparison with Google's approach. They also touch on the concept of conditional generation of materials, the importance of open-source resources and collaboration, and the exciting developments in materials and AI. The conversation concludes with a discussion on future collaboration opportunities.
    Takeaways
    • Generative modeling is an exciting approach in materials science that allows for the prediction and creation of new materials.
    • Creating a representation for materials, such as using the crystallographic information file, enables the application of image-based generative models.
    • Google's approach to generative modeling received attention but also criticism for its lack of novelty and unconditioned generation of materials.
    • Open-source resources and collaboration are crucial in advancing materials informatics and machine learning in the field of materials science.


    Become a supporter of this podcast: https://www.spreaker.com/podcast/breaking-math-podcast--5545277/support.

    In Memory of Sofia Baca, Cofounder and cohost of Breaking Math

    In Memory of Sofia Baca, Cofounder and cohost of Breaking Math
    In October of 2023, Sofia Baca passed away unexpectedly from natural causes. Sofia was one of the founders and cohosts of the Breaking Math Podcast. In this episode, host Gabriel Hesch interviews Diane Baca, mother of Sofia Baca as we talk about her passions for creativity, mathematics, science, and discovering what it means to be human.


    Sofia lived an exceptional life with explosive creativity, a voracious passion for mathematics, physics, computer science, and creativity. Sofia also struggled immensely with mental health issues which included substance abuse as well as struggling for a very long time understand the source of their discontent. Sofia found great happiness in connecting with other people through teaching, tutoring, and creative expression. The podcast will continue in honor of Sofia. There are many folders of ideas that Sofia left with ideas for the show or for other projects. We will continue this show with sharing some of these ideas, but also with sharing stories of Sofia - including her ideas and her struggles in hopes that others may find solace in that they are not alone in their struggles. But also in hopes that others may find inspiration in what Sofia had to offer.

    We miss you dearly, Sofia.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/breaking-math-podcast--5545277/support.

    81: Correct. Now Try Again (Multiple Approaches to the Same Problem)

    81: Correct. Now Try Again (Multiple Approaches to the Same Problem)
    Join Sofía Baca and her guests, the host and co-host of the Nerd Forensics podcast, Millicent Oriana and Jacob Urban, as they explore what it means to be able to solve one problem in multiple ways.

    This episode is distributed under a Creative Commons Attribution-ShareAlike 4.0 International License. For full text, visit: https://creativecommons.org/licenses/by-sa/4.0/

    [Featuring: Sofía Baca; Millicent Oriana, Jacob Urban[

    Become a supporter of this podcast: https://www.spreaker.com/podcast/breaking-math-podcast--5545277/support.
    Breaking Math Podcast
    enJuly 24, 2023