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In this conversation, Autumn Phaneuf interviews Zach Weinersmith, a cartoonist and writer, about the feasibility and implications of space settlement. They discuss the challenges and misconceptions surrounding space colonization, including the idea that it will make us rich, mitigate war, and make us wiser. They explore the potential of the moon and Mars as settlement options, as well as the concept of rotating space stations. They also touch on the physiological effects of space travel and the need for further research in areas such as reproduction and ecosystem design. The conversation explores the challenges and implications of human settlement in space. It discusses the lack of data on the long-term effects of space travel on the human body, particularly for women. The conversation also delves into the need for a closed-loop ecosystem for sustainable space settlement and the legal framework surrounding space exploration and resource extraction. The main takeaways include the importance of addressing reproductive and medical challenges, the need for a better legal regime, and the debunking of misconceptions about space settlement.
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A City on Mars, Keywords space settlement, feasibility, challenges, misconceptions, moon, Mars, rotating space stations, reproduction, ecosystem design, space settlement, human reproduction, closed-loop ecosystem, space law, resource extraction, logistics, math.
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In this conversation, Autumn Phaneuf and Zach Weinersmith discusses his new book, A City on Mars, which takes a humorous look at the challenges of building a Martian society. He explores the misconceptions and myths surrounding space settlement and the feasibility of colonizing Mars. He argues that space is unlikely to make anyone rich and that the idea that space will mitigate war is unsupported. He also discusses the potential benefits and limitations of settling on the Moon and Mars, as well as the technical challenges involved.
Follow Zach Weinersmith on his website and Twitter
Subscribe to Breaking Math wherever you get your podcasts.
Become a patron of Breaking Math for as little as a buck a month
Follow Breaking Math on Twitter, Instagram, LinkedIn, Website
Follow Autumn on Twitter and Instagram
Folllow Gabe on Twitter.
email: [email protected]
A City on Mars, space settlement, Mars colonization, misconceptions, myths, feasibility, space myths, space economics, war, Moon settlement, technical challenges, logistics, math.
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Welcome to another engaging episode of the Breaking Math Podcast! Today's episode, titled "What is the Use?," features a fascinating conversation with the renowned mathematician and author, Professor Ian Stewart. As Professor Stewart discusses his latest book "What's the Use? How Mathematics Shapes Everyday Life," we dive deep into the real-world applications of mathematics that often go unnoticed in our daily technologies, like smartphones, and their unpredictable implications in various fields.
We'll explore the history of quaternions, invented by William Rowan Hamilton, which now play a critical role in computer graphics, gaming, and particle physics. Professor Stewart will also shed light on the non-commutative nature of quaternions, mirroring the complexities of spatial rotations, and how these mathematical principles find their correspondence in the natural world.
Furthermore, our discussion will encompass the interconnectivity within mathematics, touching upon how algebra, geometry, and trigonometry converge to paint a broader picture of this unified field. We also discuss the intriguing concept of "Fearful Symmetry" and how symmetrical and asymmetrical patterns govern everything from tiger stripes to sand dunes.
With references to his other works, including "Professor Stewart's Cabinet of Mathematical Curiosities" and "The Science of Discworld," Professor Stewart brings an element of surprise and entertainment to the profound impact of mathematics on our understanding of the world.
So stay tuned as we unlock the mysteries and the omnipresent nature of math in this thought-provoking episode with Professor Ian Stewart!
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Summary
Tom Chivers discusses his book 'Everything is Predictable: How Bayesian Statistics Explain Our World' and the applications of Bayesian statistics in various fields. He explains how Bayesian reasoning can be used to make predictions and evaluate the likelihood of hypotheses. Chivers also touches on the intersection of AI and ethics, particularly in relation to AI-generated art. The conversation explores the history of Bayes' theorem and its role in science, law, and medicine. Overall, the discussion highlights the power and implications of Bayesian statistics in understanding and navigating the world.
The conversation explores the role of AI in prediction and the importance of Bayesian thinking. It discusses the progress of AI in image classification and the challenges it still faces, such as accurately depicting fine details like hands. The conversation also delves into the topic of predictions going wrong, particularly in the context of conspiracy theories. It highlights the Bayesian nature of human beliefs and the influence of prior probabilities on updating beliefs with new evidence. The conversation concludes with a discussion on the relevance of Bayesian statistics in various fields and the need for beliefs to have probabilities and predictions attached to them.
Takeaways Bayesian statistics can be used to make predictions and evaluate the likelihood of hypotheses. Bayes' theorem has applications in various fields, including science, law, and medicine. The intersection of AI and ethics raises complex questions about AI-generated art and the predictability of human behavior. Understanding Bayesian reasoning can enhance decision-making and critical thinking skills. AI has made significant progress in image classification, but still faces challenges in accurately depicting fine details. Predictions can go wrong due to the influence of prior beliefs and the interpretation of new evidence. Beliefs should have probabilities and predictions attached to them, allowing for updates with new information. Bayesian thinking is crucial in various fields, including AI, pharmaceuticals, and decision-making. The importance of defining predictions and probabilities when engaging in debates and discussions. -
Summary
**Tensor Poster - If you are interested in the Breaking Math Tensor Poster on the mathematics of General Relativity, email us at [email protected]
In this episode, Gabriel Hesch and Autumn Phaneuf interview Steve Nadis, the author of the book 'The Gravity of Math.' They discuss the mathematics of gravity, including the work of Isaac Newton and Albert Einstein, gravitational waves, black holes, and recent developments in the field. Nadis shares his collaboration with Shing-Tung Yau and their journey in writing the book. They also talk about their shared experience at Hampshire College and the importance of independent thinking in education. In this conversation, Steve Nadis discusses the mathematical foundations of general relativity and the contributions of mathematicians to the theory. He explains how Einstein was introduced to the concept of gravity by Bernhard Riemann and learned about tensor calculus from Gregorio Ricci and Tullio Levi-Civita. Nadis also explores Einstein's discovery of the equivalence principle and his realization that a theory of gravity would require accelerated motion. He describes the development of the equations of general relativity and their significance in understanding the curvature of spacetime. Nadis highlights the ongoing research in general relativity, including the detection of gravitational waves and the exploration of higher dimensions and black holes. He also discusses the contributions of mathematician Emmy Noether to the conservation laws in physics. Finally, Nadis explains Einstein's cosmological constant and its connection to dark energy.
Chapters
00:00 Introduction and Book Overview
08:09 Collaboration and Writing Process
25:48 Interest in Black Holes and Recent Developments
35:30 The Mathematical Foundations of General Relativity
44:55 The Curvature of Spacetime and the Equations of General Relativity
56:06 Recent Discoveries in General Relativity
01:06:46 Emmy Noether's Contributions to Conservation Laws
01:13:48 Einstein's Cosmological Constant and Dark Energy
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Summary: The episode discusses the 10,000 year dilemma, which is a thought experiment on how to deal with nuclear waste in the future. Today's episode is hosted by guest host David Gibson, who is the founder of the Ray Kitty Creation Workshop. (Find out more about the Ray Kitty Creation Workshop by clicking here).
Gabriel and Autumn are out this week, but will be returning in short order with 3 separate interviews with authors of some fantastic popular science and math books including:
The Gravity of Math: How Geometry Rules the Universe by Dr. Shing-Tung Yau and Steve Nadis. This book is all about the history of our understanding of gravity from the theories of Isaac Newton to Albert Einstein and beyond, including gravitational waves, black holes, as well as some of the current uncertainties regarding a precise definition of mass. On sale now! EVERYTHING IS PREDICTABLE: How Bayesian Statistics Explain Our World by Tom Chivers. Published by Simon and Schuster. This book explains the importance of Baye's Theorem in helping us to understand why highly accurate screening tests can lead to false positives, a phenomenon we saw during the Covid-19 pandemic; How a failure to account for Bayes’ Theorem has put innocent people in jail; How military strategists using the theorem can predict where an enemy will strike next, and how Baye's Theorem is helping us to understang machine learning processes - a critical skillset to have in the 21st century.
Available 05/07/2024 A CITY ON MARS: Can we settle space, should we settle space, and have we really thought this through? by authors Dr. Kelly and Zach Weinersmith. Zach Weinersmith is the artist and creator of the famous cartoon strip Saturday Morning Breaking Cereal!
We've got a lot of great episodes coming up! Stay tuned. -
An interview with Prof. Marcus du Sautoy about his book Around the Wold in Eighty Games . . . .a Mathematician Unlocks the Secrets of the World's Greatest Games.
Topics covered in Today's Episode:1. Introduction to Professor Marcus du Sautoy and the Role of Games
- Impact of games on culture, strategy, and learning
- The educational importance of games throughout history
2. Differences in gaming cultures across regions like India and China
3. Creative Aspects of Mathematics
4. The surprising historical elements and banned games by Buddha
5. Historical and geographical narratives of games rather than rules
6. Game Theory and Education
7. Unknowable questions like thermodynamics and universe's infinity
8. Professor du Sautoy's Former Books and Collections
9. A preview of his previous books and their themes
10. Gaming Cultures and NFTs in Blockchain
11. Gamification in Education
12. The Role of AI in Gaming
13. Testing machine learning in mastering games like Go
14. Alphago's surprising move and its impact on Go strategies
15 . The future of AI in developing video game characters, plots, and environments
16. Conclusion and Giveaway Announcement
*Free Book Giveaway of Around The World in 88 Games . . . by Professor Marcus Du Sautory! Follow us on our socials for details:
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Summary
Brain Organelles, A.I. and Defining Intelligence in Nature-
In this episode, we continue our fascinating interview with GT, a science content creator on TikTok and YouTube known for their captivating - and sometimes disturbing science content.
GT can be found on the handle ‘@bearBaitOfficial’ on most social media channels.
In this episode, we resume our discussion on Brain Organelles - which are grown from human stem cells - how they are being used to learn about disease, how they may be integrated in A.I. as well as eithical concerns with them.
We also ponder what constitutes intelligence in nature, and even touch on the potential risks of AI behaving nefariously.
You won't want to miss this thought-provoking and engaging discussion.
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This episode is inspired by a correspondence the Breaking Math Podcast had with the editors of Digital Discovery, a journal by the Royal Society of Chemistry. In this episode the hosts review a paper about how the Lean Interactive Theorem Prover, which is usually used as a tool in creating mathemtics proofs, can be used to create rigorous and robust models in physics and chemistry.
Also - we have a brand new member of the Breaking Math Team! This episode is the debut episode for Autumn, CEO of Cosmo Labs, occasional co-host / host of the Breaking Math Podcast, and overall contributor who has been working behind the scenes on the podcast on branding and content for the last several months. Welcome Autumn!
Autumn and Gabe discuss how the paper explores the use of interactive theorem provers to ensure the accuracy of scientific theories and make them machine-readable. The episode discusses the limitations and potential of interactive theorem provers and highlights the themes of precision and formal verification in scientific knowledge. This episode also provide resources (listed below) for listeners interested in learning more about working with the LEAN interactive theorem prover.
Takeaways
Interactive theorem provers can revolutionize the way scientific theories are formulated and verified, ensuring mathematical certainty and minimizing errors. Interactive theorem provers require a high level of mathematical knowledge and may not be accessible to all scientists and engineers. Formal verification using interactive theorem provers can eliminate human error and hidden assumptions, leading to more confident and reliable scientific findings. Interactive theorem provers promote clear communication and collaboration across disciplines by forcing explicit definitions and minimizing ambiguities in scientific language. Lean Theorem Provers enable scientists to construct modular and reusable proofs, accelerating the pace of knowledge acquisition. Formal verification presents challenges in terms of transforming informal proofs into a formal language and bridging the reality gap. Integration of theorem provers and machine learning has the potential to enhance creativity, verification, and usefulness of machine learning models. The limitations and variables in formal verification require rigorous validation against experimental data to ensure real-world accuracy. Lean Theorem Provers have the potential to provide unwavering trust, accelerate innovation, and increase accessibility in scientific research. AI as a scientific partner can automate the formalization of informal theories and suggest new conjectures, revolutionizing scientific exploration. The impact of Lean Theorem Provers on humanity includes a shift in scientific validity, rapid scientific breakthroughs, and democratization of science.Help Support The Podcast by clicking on the links below:
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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
00:00 Introduction: Brain Organoids and Robots 00:39 Brain Organoids and Development 01:21 Ethical Implications of Brain Organoids 03:14 Summary and Introduction to Guest 03:41 Sentience and Consciousness in Brain Organoids 04:10 Neuron Count and Pain Receptors in Brain Organoids 05:00 Unanswered Questions and Discomfort 05:25 Psychological Discomfort in Brain Organoids 06:21 Early Videos and Brain Organoid Learning 07:20 Efficiency of Human Neural Networks 08:12 Sleep in Brain Organoids 09:13 Delta Brainwaves and Brain Organoids 10:11 Creating Brain Organoids with Specific Components 11:10 Genetic Memories in Brain Organoids 12:07 Efficiency and Learning in Human Brains 13:00 Sequential Memory and Chimpanzees 14:18 Different Thinking Skill Sets and Collaboration 16:13 ADHD and Hyperfocusing 18:01 Ethical Considerations in Brain Research 19:23 Understanding Genetic Mutations 20:51 Brain Organoids in Rat Bodies 22:14 Dendrite Growth in Brain Organoids 23:11 Duration of Dendrite Growth 24:26 Genetic Memory Transfer in Brain Organoids 25:19 Social Media Presence of Brain Organoid Companies 26:15 Brain Organoids Controlling Robot Spiders 27:14 Conclusion and Invitation to Part 2References:
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 [email protected]
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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.
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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
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Summary
#OpenAiSora #
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YouTubeTranscripts are available upon request. Email us at [email protected]
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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
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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 [email protected].
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
Help Support The Podcast by clicking on the links below:
Start YOUR podcast on ZenCastr!
Use my special link ZenCastr Discount to save 30% off your first month of any Zencastr paid plan
Visit our Patreon -
Help Support The Podcast by clicking on the links below:
Start YOUR podcast on ZenCastr!
Use my special link ZenCastr Discount to save 30% off your first month of any Zencastr paid plan
Visit our PatreonPart 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 [email protected].
Become a supporter of this podcast: https://www.spreaker.com/podcast/breaking-math-podcast--5545277/support.
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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.
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Transcripts of this episode are avialable upon request. Email us at [email protected].
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.Help Support The Podcast by clicking on the links below:
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How is Machine Learning being used to further original scientific discoveries? -
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. -
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[
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Use my special link to save 30% off your first month of any Zencastr paid plan Patreon YouTube Breaking Math WebsiteEmail us for copies of the transcript! -
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]
Help Support The Podcast by clicking on the links below:
Try out ZenCastr w/ 30% DiscountUse my special link to save 30% off your first month of any Zencastr paid plan Patreon YouTube Breaking Math WebsiteEmail us for copies of the transcript! - Visa fler
