Podcasts about Category theory

In this conversation, the host Chris Glanden engages withguests Charlie Northrup and Keenan Hale to discuss advancements in AI, particularly focusing on large language models and their limitations. They explore the concept of Delta K, which refers to the transformation of knowledge, and how it relates to the predictive capabilities of AI. Thediscussion also delves into thin calculus and the category theory of things, emphasizing the need for an external truth to build sound mathematical systems. In this conversation, the speakers delve into the concepts of agentic calculusand Delta K, exploring their implications for artificial general intelligence (AGI) and the future of the economy. They discuss the observer-dependent nature of reality and how different perspectives can lead to varied interpretations ofthe same phenomena. The conversation also touches on the potential of the agentic economy to revolutionize ownership and economic dynamics, as well as the philosophical implications of waveform collapse in quantum mechanics.Overall, the discussion highlights the need for a new understanding of cognitive processing and the role of agents in shaping future interactions and economies. TIMESTAMPS:00:00 - Introduction to the Guests and Their Expertise02:16 - Recent Developments in AI and Technology04:50 - Understanding Large Language Models10:53 - Delta K and Its Limitations16:24 - Thin Calculus and the Category Theory of Things19:19 - Understanding Agentic Calculus22:27 - Delta K and Its Implications for AGI28:59 - Cognitive Processing and States of Being36:09 - The Agentic Economy: A New Paradigm40:25 - Waveform Collapse and Delta K43:20 The Future of Agentic Interactions SYMLINKS:[LinkedIn - Charlie Northrup] - https://www.linkedin.com/in/charlie-northrup-1b73b051Charlie Northrup is a technology innovator at Neewer Sciences, contributing pioneering research in agentic AI systems, thing calculus, and distributed digital ecosystems. He shares updates and insights about his work on LinkedIn.[LinkedIn - Keenan Hale] - https://www.linkedin.com/in/keenandewayne/Keenan Hale is recognized for his interdisciplinary contributions bridging theoretical mathematics and cryptographic systems. He connects with others inthe AI and cryptography communities through LinkedIn and shares research updates and discussions.[LinkedIn - Mike Elkins] - https://www.linkedin.com/in/elkinsmike/Mike Elkins is the Chief Human and Information Security Officer at Banffist, actively involved in discussions about cybersecurity, digital transformation, and AI-driven enterprise operations. He also speaks at major conferences like BlackHat, RSA, and SecureWorld.

Ryan Brewer is a college dropout who has an incredible blog about PL, Category Theory and Logic. He better define his goal as making Formal Theory more accessible outside the ivory tower of academia, and easier to put into practice where it matters. He has a couple of very interesting main projects, such as the first Cedille 2 Interpreter, Saber VM, and Arctic. In this episode we will talk about all of his projects. His trajectory becoming self-taught in PL, compilers and Formal Methods, and he shares with us the wealth of resources he used to navigate this sea of knowledge. We also have a brief but heated discussion on the ethics of Science. Links Ryan's Website Saber VM Arctic, which is built on top of Lustre Category Theory Wiki

Summary In this conversation, Jordan and Sophia discuss various topics including Sophia's background, her interest in functional programming and category theory, her experience with Kung Fu and philosophy, and her work at NuBank. They also touch on the challenges of teaching and learning, the importance of creating a supportive environment for growth, and the impact of different cultures on learning and programming. Keywords Sophia Velten, functional programming, category theory, Kung Fu, philosophy, NuBank, teaching, learning, growth, culture Takeaways Sophia's diverse background in programming, philosophy, and martial arts has influenced her approach to learning and problem-solving. She emphasizes the importance of creating a supportive environment for learning and growth, recognizing that different people have different ways of thinking and learning. Sophia's work at NuBank includes the development of libraries like Stateflow and Nodely, which aim to improve code quality and maintainability. She believes in the power of functional programming and the use of monads to separate pure code from side effects. Sophia's experiences with philosophy and Buddhism have shaped her perspective on life and learning, helping her to embrace the limitations of human knowledge and focus on personal growth. References https://github.com/nubank/state-flow https://github.com/nubank/nodely https://peter.gillardmoss.me.uk/blog/2023/10/10/on-forests-and-factories/ Sound Bites "Once you slip monads into things, it's controversial." "The atomic and a lot of our stack is not something that is familiar to many people." "Learning is growth. Growth is uncomfortable. Growth is hard."

Eric Daimler is the cofounder and CEO of Conexus AI, a data management platform that provides composable and machine-verifiable data integration. He was previously an assistant dean and assistant professor at Carnegie Mellon University. He was the founding partner of Hg Analytics and managing director at Skilled Science. He was also the White House Presidential Innovation Fellow for Machine Intelligence and Robotics. Eric's favorite book: ReCulturing (Author: Melissa Daimler) (00:00) Understanding Symbolic AI(02:42) Symbolic AI mirrors biological intelligence(06:01) Category Theory(08:42) Comparing Symbolic AI and Probabilistic AI(11:22) Symbolic Generative AI(14:19) Implementing Symbolic AI(18:25) Symbolic Reasoning(21:24) Explainability(24:39) Neuro Symbolic AI(26:41) The Future of Symbolic AI(30:43) Rapid Fire Round--------Where to find Prateek Joshi: Newsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-91047b19 Twitter: https://twitter.com/prateekvjoshi 

- Intel Vision 2024 Event, Intel Gaudi 3, Xeon 6 - Meta MTIA accelerator chip - Nvidia GPU shortage easing - Category Theory, Categorical Deep Learning, Geometric Deep Learning - China economic growth plans, high end manufacturing industrial policy [audio mp3="https://orionx.net/wp-content/uploads/2024/04/HPCNB_20240415.mp3"][/audio] The post HPC News Bytes – 20240415 appeared first on OrionX.net.

Stephen Wolfram unveils his new Observer Theory and explains the origins of the Second Law (Entropy) with Curt Jaimungal. This is Wolfram's first podcast on his new views on consciousness, and the deepest dive into Wolfram's mind.TIMESTAMPS:- 00:00:00 What is Observer Theory?- 00:12:42 Different Observers (Who are "YOU"?)- 00:19:32 The Universe Talking to Itself (Particles are "Concepts")- 00:20:10 Alien Minds and Communicating with ET- 00:34:32 Consciousness vs. Observation- 00:48:48 "Beliefs" Dictate the Laws of Physics- 01:05:49 The Most Insightful Breakthrough of Our Time- 01:22:50 Wolfram Teaches How to Research (Advice)- 01:33:08 Where is the Evidence for Wolfram's Physics?- 01:44:42 The "Ruliad" as an Observer- 01:51:36 The Largest "Myth" of Modern Science- 02:05:09 Non-Local Collections of Observers (is "society" an observer?)- 02:13:54 Wolfram's Model Changes How You Act- 02:20:16 Biological Theory of Everything- 02:27:38 Wolfram's Writing Process- 02:40:49 Curt's Next Project, Category Theory, & the Infinite Groupoid HUGE THANK YOU TO MARK FROM "LAST THEORY" and JONATHAN GORARD: https://www.youtube.com/@lasttheory NOTE: The perspectives expressed by guests don't necessarily mirror my own. There's a versicolored arrangement of people on TOE, each harboring distinct viewpoints, as part of my endeavor to understand the perspectives that exist.THANK YOU: To Mike Duffey for your insight, help, and recommendations on this channel. Support TOE: - Patreon: https://patreon.com/curtjaimungal (early access to ad-free audio episodes!) - Crypto: https://tinyurl.com/cryptoTOE- PayPal: https://tinyurl.com/paypalTOE- TOE Merch: https://tinyurl.com/TOEmerchFollow TOE: - *NEW* Get my 'Top 10 TOEs' PDF + Weekly Personal Updates: https://www.curtjaimungal.org- Instagram: https://www.instagram.com/theoriesofe...- TikTok: https://www.tiktok.com/@theoriesofeve...- Twitter: https://twitter.com/TOEwithCurt- Discord Invite: https://discord.com/invite/kBcnfNVwqs- iTunes: https://podcasts.apple.com/ca/podcast...- Pandora: https://pdora.co/33b9lfP- Spotify: https://open.spotify.com/show/4gL14b9...- Subreddit r/TheoriesOfEverything: https://reddit.com/r/theoriesofeveryt... Join this channel to get access to perks: https://www.youtube.com/channel/UCdWI...LINKS MENTIONED: - Stephen Wolfram's 1st TOE Podcast: https://youtu.be/1sXrRc3Bhrs- Stephen Wolfram's 2nd TOE Podcast: https://youtu.be/xHPQ_oSsJgg- Wolfram's "Alien Mind" Article: https://writings.stephenwolfram.com/2...- A New Kind of Science (Stephen Wolfram): https://amzn.to/49EBprD- Adventures of a Computational Explorer (Stephen Wolfram): https://amzn.to/3uFM7PQ- A Project to Find the Fundamental Theory of Physics (Stephen Wolfram): https://amzn.to/49K1S7t- Combinators: A Centennial View (Stephen Wolfram): https://amzn.to/3I2FTNf- Metamathematics and the Foundations of Mathematics (Stephen Wolfram): https://amzn.to/3uHwU0O- The Second Law (Stephen Wolfram): https://amzn.to/42IioCF- Introduction to Computational Thinking (Stephen Wolfram): https://amzn.to/3uCoszZ- Book on Predicting Eclipses (Stephen Wolfram): https://amzn.to/42IiiuN- Book about ChatGPT (Stephen Wolfram): https://amzn.to/42PGGuy

Math wants to be friends. Let mathematician, author and Abstract Mathematologist Dr. Eugenia Cheng introduce you to a secret world: the artsy and emotional side of math. Dr. Cheng helps answer the age-old and (recently viral) question, “IS MATH REAL?” We chat about Fibonacci sequences, golden ratios, common core, loving thy neighbor, slide rules vs. calculators, imaginary numbers, the nature of zero, infinite cookies, and more. Turns out that math can change your relationships and permeate your every thought.. if you let it. Also: wtf, Barbie?Visit Dr. Eugenia Cheng's website and follow her on TwitterBrowse Dr. Cheng's books including Is Math Real?: How Simple Questions Lead Us to Mathematics' Deepest Truths (2023), The Joy of Abstraction: An Exploration of Math, Category Theory, and Life (2022), and How to Bake Pi: An Edible Exploration of the Mathematics of Mathematics (2016)A donation went to Math Circles of ChicagoMore episode sources and linksSmologies (short, classroom-safe) episodesOther episodes you may enjoy: Quantum Ontology (WHAT IS REAL?), Dolorology (PAIN), Fearology (FEAR), Egyptology (ANCIENT EGYPT), Classical Archeology (ANCIENT ROME), Economic Sociology (MONEY/FREAKONOMICS), Tiktokology (THE TIKTOK APP)Sponsors of OlogiesTranscripts and bleeped episodesBecome a patron of Ologies for as little as a buck a monthOlogiesMerch.com has hats, shirts, hoodies, totes!Follow @Ologies on Twitter and InstagramFollow @AlieWard on Twitter and InstagramEditing by Mercedes Maitland of Maitland Audio ProductionsManaging Director: Susan HaleScheduling Producer: Noel DilworthTranscripts by Emily White of The WordaryWebsite by Kelly R. DwyerTheme song by Nick Thorburn

Read the full transcript here. What should the goals of math education be? What does it mean to "think well"? Is math real? Why are feelings of bewilderment or confusion so common in math classes but not as common in other subjects? Schools now generally offer reading and writing instruction separately — even though both are important for language use — because the skill sets they require can differ so widely; so how might math education benefit from drawing a similar distinction? What should math classes impart to students that will enable them to engage as citizens with complex or controversial issues? What does it mean to ask good questions in math? Can math teach empathy? What is category theory? Can most people learn most things if they just have the right teacher and/or educational materials?Eugenia Cheng is a mathematician, educator, author, public speaker, columnist, concert pianist, composer, and artist. She is Scientist In Residence at the School of the Art Institute of Chicago. She won tenure in Pure Mathematics at the University of Sheffield, UK, and is now Honorary Visiting Fellow at City, University of London. She has previously taught at the Universities of Cambridge, Chicago, and Nice, and holds a PhD in pure mathematics from the University of Cambridge. Alongside her research in Category Theory and undergraduate teaching, her aim is to rid the world of "math phobia". Eugenia was an early pioneer of math on YouTube, and her videos have been viewed around 15 million times to date. She has also written several books, including: How to Bake Pi (2015); Beyond Infinity (2017); The Art of Logic (2018); x + y : A Mathematician's Manifesto for Rethinking Gender (2020); The Joy of Abstraction: A Exploration of Math, Category Theory, and Life (2022); Is Math Real? How Simple Questions Lead Us to Mathematics' Deepest Truths (2023); and two children's books: Molly and the Mathematical Mysteries and Bake Infinite Pie with x + y. She also writes the "Everyday Math" column for the Wall Street Journal and has completed mathematical art commissions for Hotel EMC2, 6018 North, the Lubeznik Center, and the Cultural Center, Chicago. She is the founder of the Liederstube, an intimate oasis for art song based in Chicago. As a composer she has been commissioned by GRAMMY-nominated soprano Laura Strickling and is one of the composers for the LYNX Amplify series, setting work by autistic poets who are primarily non-speaking. Learn more about her at her website, eugeniacheng.com. [Read more]

Is math real? How to bake pi? And how much is x+y, really?  Many people don't like math because they find it too complicated or boring. But math can actually be a lot of fun, and we can find it everywhere in life, even in the most mundane things like baking. And it is through baking that today's guest, Eugenia Cheng, decided to rid the world of math phobia. Dr. Cheng is a renowned mathematician, educator, author, and concert pianist. She's a scientist in residence at the School of the Art Institute of Chicago, where she teaches mathematics to art students. She is an expert in category theory and has recently published a book, Is Math Real?, which we will discuss in detail today! Join Eugenia and me as we explore mathematics' deepest truths. Key Takeaways:  Intro (00:00) Judging a book by its cover: Is Math Real? (01:10) On the unreasonable power of mathematics in the physical sciences (04:05) If there were no physical world, would math exist? (08:14) The number zero (10:30) Is our brain a massive computer? (17:04) How to Bake Pi (22:38) Category theory (27:07) How to revitalize and modernize education (39:21) Is math created or discovered? (45:12) Outro (49:33) — Additional resources: 

Thimothy GowersChaire CombinatoireCollège de FranceAnnée 2023-2024A Dialogical Account of Proofs in Mathematical PracticeIntervenant(s)Catarina Dutilh Novaes, Vrije Universiteit Amsterdam et University of St Andrews

Array Cast - October 13, 2023 Show Notes[01] 00:02:40 Minnowbrook conference https://aplwiki.com/wiki/APL_Implementer%27s_Workshop Combinators https://en.wikipedia.org/wiki/Combinatory_logic#Examples_of_combinators Tacit Programming https://mlochbaum.github.io/BQN/doc/tacit.html Function Composition https://aplwiki.com/wiki/Function_composition Tacit Episodes of the ArrayCast Tacit #4 The Dyadic Hook https://www.arraycast.com/episodes/episode17-tacit4-the-dyadic-hook Tacit #3 And Other Topics https://www.arraycast.com/episodes/episode15-tacit-3-and-other-topics Tacit #2 Why Tacit? https://www.arraycast.com/episodes/episode11-why-tacit Tacit #1 Tacit Programming https://www.arraycast.com/episodes/episode-09-tacit-programming[02] 00:03:35 Arrayground https://apps.apple.com/us/app/arrayground/id6453522556 Conor's Uiua videos Uiua - A New Array Language https://www.youtube.com/watch?v=iTC1EiX5bM0 BQN vs. Uiua https://www.youtube.com/watch?v=pq1k5USZZ9A BQN vs. Uiua #2 https://www.youtube.com/watch?v=SpZJxbOf_jM[03] 00:05:41 Stanley Jordan https://en.wikipedia.org/wiki/Stanley_Jordan April, an APL Compiler for Common Lisp https://www.youtube.com/watch?v=AUEIgfj9koc Andrew Sengul Episode of the ArrayCast https://www.arraycast.com/episodes/episode23-andrew-sengul Uiua Episode of the ArrayCast https://www.arraycast.com/episodes/episode63-uiua Game videos in BQN Snake2 in 8 Minutes https://youtu.be/tOZde7zrsLM?si=N2jTdTZBlPEleCr0 https://youtu.be/wTIlQ1Ib-zE Snake (longer version) https://youtu.be/wTIlQ1Ib-zE A Game implemented in APL Draculark in APL https://medium.com/@solarbreeze69/draculark-a-mudlarking-vampire-hunting-game-bbf40361bf1a[04] 00:09:06 Forks https://code.jsoftware.com/wiki/Vocabulary/fork Before and After in BQN https://mlochbaum.github.io/BQN/doc/hook.html Invisible Modifiers https://code.jsoftware.com/wiki/Vocabulary/fork#invisiblemodifiers Peter Mikkelsonhttps://pmikkelsen.comhttps://www.dyalog.com/blog/2022/11/welcome-peter-mikkelsen/[05] 00:14:52 Atop in J https://code.jsoftware.com/wiki/Vocabulary/at Compose (Over) in J https://code.jsoftware.com/wiki/Vocabulary/ampv Atop and Over BQN https://mlochbaum.github.io/BQN/doc/compose.html[06] 00:17:04 Henry Rich Episodes on the ArrayCast Fold in J https://www.arraycast.com/episodes/episode50-fold Henry Rich - Threads in J9.4 https://www.arraycast.com/episodes/episode48-henry-rich Henry Rich presents J903 https://www.arraycast.com/episodes/episode18-henry-rich-presents-j903 Henry Rich's Deep Dive into J https://www.arraycast.com/episodes/episode-06-henry-richs-deep-dive-into-j Invisible Modifiers Table https://code.jsoftware.com/wiki/Vocabulary/fork#invisiblemodifiers Seymour Papert https://en.wikipedia.org/wiki/Seymour_Papert[07] 00:20:10 NuVoc https://code.jsoftware.com/wiki/Vocabulary Forks https://code.jsoftware.com/wiki/Vocabulary/fork Modifier Trains https://code.jsoftware.com/wiki/Vocabulary/fork#invisiblemodifiers Modifier Train Exploration https://code.jsoftware.com/wiki/Vocabulary/ModifierTrains Modifier Train Exploration Discussion https://code.jsoftware.com/wiki/Talk:Vocabulary/ModifierTrains[08] 00:24:23 Atop APL Paw glyph https://aplwiki.com/wiki/Atop_(operator) https://aplwiki.com/wiki/Over Over APL Hoof glyph https://aplwiki.com/wiki/Over[09] 00:44:44 Arity of functions https://en.wikipedia.org/wiki/Arity Conjugate Monadic + https://code.jsoftware.com/wiki/Vocabulary/plus Plus Dyadic + https://code.jsoftware.com/wiki/Vocabulary/plus#dyadic[10] 00:54:55 Forks in the KAP Programming Language https://kapdemo.dhsdevelopments.com/kap-comparison.html#_fork Forks modelled in the dfns workspace http://dfns.dyalog.com/n_fork.htm[11] 01:00:35 Kadane's Algorithm https://en.wikipedia.org/wiki/Maximum_subarray_problem[12] 01:06:24 Pepe's Trains - Past, Present and ... https://www.jsoftware.com/pipermail/programming/2017-October/049263.html[13] 01:10:47 Dyadic Or BQN https://mlochbaum.github.io/BQN/doc/logic.html Monadic Sort BQN https://mlochbaum.github.io/BQN/doc/order.html[14] 01:15:23 Signum Monadic * https://code.jsoftware.com/wiki/Vocabulary/star Times Dyadic * https://code.jsoftware.com/wiki/Vocabulary/star#dyadic[15] 01:18:20 Jelly programming language https://github.com/DennisMitchell/jellylanguage https://aplwiki.com/wiki/Jelly[16] 01:25:14 Zilde Empty https://aplwiki.com/wiki/Zilde BQN's Nothing https://mlochbaum.github.io/BQN/help/nothing.html Monadic and Dyadic Meanings https://aplwiki.com/wiki/Mnemonics#Pairing_monadic_and_dyadic_meanings Overloading in BQN https://mlochbaum.github.io/BQN/commentary/overload.html q Programming Language https://code.kx.com/q/ Overloading in q https://code.kx.com/q/ref/overloads q Unary Forms https://code.kx.com/q/basics/exposed-infrastructure/#unary-forms[17] 01:30:56 Michael Higginson Episode of ArrayCast https://www.arraycast.com/episodes/episode46-michael-higginson Lynn Sutherland Nial Episode of ArrayCast https://www.arraycast.com/episodes/episode61-lynn-sutherland-and-nial NARS2000 Programming Language https://www.nars2000.org/ Hyperators in NARS2000 https://wiki.nars2000.org/index.php?title=Anonymous_Functions/Operators/Hyperators Hyperators in Dyalog https://dfns.dyalog.com/n_hyperators.htm[18] 01:41:46 Category Theory https://en.wikipedia.org/wiki/Category_theory[19] 01:45:25 Contact AT ArrayCast DOT Com

Eric Daimler is the founder and CEO of Conexus, a groundbreaking solution for what is perhaps today's biggest information technology problem: data deluge. Eric is leading the development of CQL, a patent-pending platform founded upon category theory — a revolution in mathematics — to help companies manage the overwhelming and rapidly growing challenge of data integration and migration. In addition, Eric has over 20 years of experience as an entrepreneur, investor, technologist, and policymaker. He served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of the President. He was the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics.   Advertiser:  https://timezest.com/mspradio/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/ Support the show on Patreon:  https://patreon.com/mspradio/ Want our stuff?  Cool Merch?  Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on: Facebook: https://www.facebook.com/mspradionews/ Twitter: https://twitter.com/mspradionews/ Instagram: https://www.instagram.com/mspradio/ LinkedIn: https://www.linkedin.com/company/28908079/

In this episode I argue that intuition is more rigorous than precisely defined arguments, such as those constructed using mathematics. I talk about the cost of precision in real-world situations, offer an approach to formalize intuition, and suggest that when it comes to establishing the connection between our ideas and reality we should always let intuition lead.Support the showmedium.com/nontrivial

Covering Part 8 of Alain Badiou's Being and Event on “Theory of the Subject,” Alex and Andrew discuss the theory of subject and the event, and Badiou's wider work. Guest Andrei Rodin contextualizes Badiou's project through its relation to the wider philosophy of mathematics. Rodin is a mathematician and philosopher with affiliations in France, including the University of Lorraine and the University Paris-Cité, and in Russia at the Russian Academy of Sciences, Saint-Petersburgh State University, as well as the Russian Society for History and Philosophy of Science. He is the author of Axiomatic Method and Category Theory. Concepts related to the Theory of the Subject Badiou's Theory of the Subject, the Future Anterior of Truth, Paul Cohen's Forcing, Comments on Lacan, Event versus Language, Subject, The Outside, The Undocumented Family, State as Preventing the Event, Decolonize Badiou. Recap of Being and Event (Parts 1-3) normal and natural, being qua being, entities multiples sets void, ordinal chains, infinity (natural and real), being is the state and state of situation (form through set theory) (Part 4) turning point, there will always be sites that are presented but whose members are represented, gap, normal and abnormal, un- in- ex-, (Second Half of the Book) how things work, fidelity as a procedure that assigning belonging (temporal), quasi existentialism of the decision, against a construction which is an internal model that grinds through itself, construction always hits an impasse (errancy of the excess of the situation), external model, excess (End of the Book), fidelity to the event, not an act of construction, subtraction, the subtractive procedure is forcing (Cohen), the generic is a product of forcing (Cohen), the four truth procedures (love, art, science, politics) are for subjects, the subject is local configuration of event, fidelity, force, generic. Further Reading Manifesto for Philosophy (BE Explainer), Number and Numbers (math notes for BE), Conditions (Four Truth Procedures); BE Trilogy: (1) BE is both abstract and set theoretical, (2) LW is in the world and takes the perspective from world that truth interrupts, and IT (3) takes the perspective of truth to asks where everything else comes from (in favor of infinite against finite); Logic of Worlds is less heroic, undoes the eureka theory of event, more temporality and history, subjectivity as process, phenomenology, additional math theories, category theory; Immanence of Truths, back to set theory, transfinite mathematics and large cardinals, in the Gödel-Cohen debate “I choose Cohen” Interview with Andrei Rodin WVO Quine, Set Theory, Meta-Mathematics, Category Theory, Computation, ZFC and Paul Cohen, Constructivist Mathematics, Infinities and Georg Cantor, Euclid and Numbers, Big Numbers, Non-Countable Sets, Axioms, David Hilbert, Generic, Forcing Links Rodin page, http://philomatica.org/ Rodin papers, https://varetis.academia.edu/AndreiRodin Rodin texts, http://philomatica.org/my-stuff/my-texts/ Rodin, Review of Badiou's “Mathematics of the Transcendental,” http://philomatica.org/wp-content/uploads/2013/01/braspublished.pdf Rodin, Axiomatic Method and Category Theory, https://link.springer.com/book/10.1007/978-3-319-00404-4

Max describes category theory as a branch of mathematics, and asks whether it will become more important in the age of AI. We then discuss the issues of the day: - looks at John Stossel's Video on how governments responded to Covid, and where their decisions stand three years later - Google's Recent Moves in Response to ChatGPT as well as layoffs Probability Distribution of the Week: Beta Distribution

Please support our Patreon.  For early and ad-free episodes, members-only content, and more.Alexander Prähauser is an artist and mathematician living in Austria.  We discuss the usefulness of mathematics and category theory in developing scientific thought from and contributing to Hegelian Dialectics. Crew:Host: C. Derick VarnAudio Producer: Paul Channel Strip  ( @aufhebenkultur )Branding Design: Djene Bajalan and C. Derick VarnIntro and Outro Music by Bitter Lake.Intro Videos Design: Jason Myles, Dejene Balajan Support the show

Show Notes(02:15) Eric reflected on his early interest in computer science and his decision to study at Carnegie Mellon University in the early 90s.(05:40) Eric recalled his academic and overall college experience, emphasizing the importance of the people he was surrounded with.(08:22) Eric talked about his time working as a quant analyst early in his career, the moment he encountered the birth of the Mosaic browser, and his decision to join the tech industry.(13:01) Eric imparted wisdom learned from venture investing during the dot-com boom.(18:02) Eric talked about the next phase of his academic career - earning a Ph.D. in Computer Science from Carnegie Mellon and dropping out of a Ph.D. program at Stanford.(21:06) Eric discussed his academic research on Computational Economics for corporate malfeasance during his time as a Ph.D. student.(27:39) Eric shared different initiatives he worked on with Carnegie Mellon University - serving as the Assistant Dean and Assistant Professor of Software Engineering, launching CMU's Silicon Valley Campus, and founding CMU's Entrepreneurial Management program.(31:54) Eric described his journey in founding Hg Analytics, a hedge fund focused on statistical arbitrage, alongside other CMU's Computer Science PhDs.(37:36) Eric revisited his passion for AI and robotics, which eventually led to serving as a Presidential Innovation Fellow during the Obama Administration with the White House Office of Science and Technology Policy.(42:54) Eric shared his perspective on the role of AI in geopolitics and highlighted the challenges with data integration.(47:29) Eric explained his company Conexus, which develops a technology spin-off from MIT's Mathematics department using a branch of math called Category Theory.(50:55) Eric went over a customer case study that uses Conexus's solution to guarantee the semantics of data integrity during data transformation.(54:20) Eric showed his enthusiasm for the concept of data relationships.(56:59) Eric provided a sneak peek of his forthcoming book, "The Coming Composability: The roadmap for using technology to solve society's biggest problems."(58:38) Closing segment.Eric's Contact InfoTwitterLinkedInConexus' ResourcesWebsite | ResourcesMentioned ContentPeopleKai-Fu LeeAndrew NgEric XingBook"ReCulturing: Design Your Company Culture to Connect with Strategy and Purpose for Lasting Success" (by Melissa Daimler)About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf you're new, see the podcast homepage for the most recent episodes to listen to or browse the full guest list.

Princess Martha Louise of Norway is engaged to an American shaman, and the Norwegians have been proving very close-minded about the love between a royal and a healer who believes he descends from a reptilian species and that his $222 amulet can ward of Covid-19. Also, are historic emissions the best way to express which countries bear responsibility for climate change? Plus, Dr. Eugenia Cheng is a mathematician, educator, author (How To Bake Pi, Beyond Infinity, x + y: A Mathematician's Manifesto For Rethinking Gender), public speaker, columnist, concert pianist, and artist. She joins to talk about her new book, The Joy of Abstraction: An Exploration of Math, Category Theory, and Life. Produced by Joel Patterson and Corey Wara Email us at thegist@mikepesca.com To advertise on the show, visit: https://advertisecast.com/TheGist Learn more about your ad choices. Visit megaphone.fm/adchoices

Todays guest is CEO and co-founder of Conexus, the first spinoff of the MIT math department that takes discoveries in high level mathematics (category theory) and applies them to make databases intelligent across many industries.

Tai-Danae Bradley is a mathematician who received her Ph.D. in mathematics from the CUNY Graduate Center. She was formerly at Alphabet and is now at Sandbox AQ, a startup focused on combining machine learning and quantum physics. Tai-Danae is a visiting research professor of mathematics at The Master's University and the executive director of the Math3ma Institute, where she hosts her popular blog on category theory. She is also a co-author of the textbook Topology: A Categorical Approach that presents basic topology from the modern perspective of category theory. In this episode, we provide a compressed crash course in category theory. We provide definitions and plenty of basic examples for all the basic notions: objects, morphisms, categories, functors, natural transformations. We also discuss the first basic result in category theory which is the Yoneda Lemma. We conclude with a discussion of how Tai-Danae has used category-theoretic methods in her work on language modeling, in particular, in how the passing from syntax to semantics can be realized through category-theoretic notions. Originally published on July 20, 2022 on YouTube: https://youtu.be/Gz8W1r90olc   Timestamps: 00:00:00 : Introduction 00:03:07 : How did you get into category theory? 00:06:29 : Outline of podcast 00:09:21 : Motivating category theory 00:11:35 : Analogy: Object Oriented Programming 00:12:32 : Definition of category 00:18:50 : Example: Category of sets 00:20:17 : Example: Matrix category 00:25:45 : Example: Preordered set (poset) is a category 00:33:43 : Example: Category of finite-dimensional vector spaces 00:37:46 : Forgetful functor 00:39:15 : Fruity example of forgetful functor: Forget race, gender, we're all part of humanity! 00:40:06 : Definition of functor 00:42:01 : Example: API change between programming languages is a functor 00:44:23 : Example: Groups, group homomorphisms are categories and functors 00:47:33 : Resume definition of functor 00:49:14 : Example: Functor between poset categories = order-preserving function 00:52:28 : Hom Functors. Things are getting meta (no not the tech company) 00:57:27 : Category theory is beautiful because of its rigidity 01:00:54 : Contravariant functor 01:03:23 : Definition: Presheaf 01:04:04 : Why are things meta? Arrows, arrows between arrows, categories of categories, ad infinitum. 01:07:38 : Probing a space with maps (prelude to Yoneda Lemma) 01:12:10 : Algebraic topology motivated category theory 01:15:44 : Definition: Natural transformation 01:19:21 : Example: Indexing category 01:21:54 : Example: Change of currency as natural transformation 01:25:35 : Isomorphism and natural isomorphism 01:27:34 : Notion of isomorphism in different categories 01:30:00 : Yoneda Lemma 01:33:46 : Example for Yoneda Lemma: Identity functor and evaluation natural transformation 01:42:33 : Analogy between Yoneda Lemma and linear algebra 01:46:06 : Corollary of Yoneda Lemma: Isomorphism of objects = Isomorphism of hom functors 01:50:40 : Yoneda embedding is fully faithful. Reasoning about this. 01:55:15 : Language Category 02:03:10 : Tai-Danae's paper: "An enriched category theory of language: from syntax to semantics" Further Reading: Tai-Danae's Blog: https://www.math3ma.com/categories Tai-Danae Bradley. "What is applied category theory?" https://arxiv.org/pdf/1809.05923.pdf Tai-Danae Bradley, John Terilla, Yiannis Vlassopoulos. "An enriched category theory of language: from syntax to semantics." https://arxiv.org/pdf/2106.07890.pdf

In this episode, we're joined by Eric Daimler, CEO & co-founder of Conexus AI, Inc, an MIT spin out. We discuss the Conexus software platform, which is built on top of breakthroughs in the mathematics of Category Theory, and how it guarantees the integrity of universal data models. Eric shares real-world examples of applying this approach to various complex industries, such as transportation and logistics, avionics, and energy.Listen to this episode wherever you listen to podcasts. Eric Daimler: https://www.linkedin.com/in/ericdaimler/ Joey Dodds: https://www.linkedin.com/in/joey-dodds-4b462a41/ Rob Dockins: https://galois.com/team/robert-dockins/ Galois, Inc.: https://galois.com/ Contact us: podcast@galois.com

Highlights from this week's conversation include:Eric's background and career journey (3:30)Presenting to people without knowledge of AI (11:04)Why math was chosen over AI (19:03)From compilers to databases (25:42)The contribution of category theory (30:09)The Connexus customer experience (37:45)The primary user of Connexus (46:33)Interacting with 300,000 databases (51:07)When Connexus begins to add value (54:02)The best way to learn this mathematical approach (55:46)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Harry's guest Eric Daimler, a serial software entrepreneur and a former Presidential Innovation Fellow in the Obama Administration, has an interesting argument about math. If you're a young person today trying to decide which math course you're going to take—or maybe an old person who just wants to brush up—he says you shouldn't bother with trigonometry or calculus. Instead he says you should study category theory. An increasingly important in computer science, category theory is about the relationships between sets or structures. It can be used to prove that different structures are consistent or compatible with one another, and to prove that the relationships in a dataset are still intact even after the data has been transformed in some way. Together with two former MIT mathematicians, Daimler co-founded a company called Conexus that uses category theory to tackle the problem of data interoperability. Longtime listeners know that data interoperability in healthcare, or more often the lack of interoperability, is a repeating theme of the show. In fields from drug development to frontline medical care, we've got petabytes of data to work with, in the form of electronic medical records, genomic and proteomic data, and clinical trial data. That data could be the fuel for machine learning and other kinds of computation that could help us make develop drugs faster and make smarter decisions about care. The problem is, it's all stored in different databases and formats that can't be safely merged without a nightmarish amount of work. So when someone like Daimler says they have a way to use math to bring heterogeneous data together without compromising that data's integrity – well, it's time to pay attention. That's why on today's show, we're all going back to school for an introductory class in category theory.Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:1. Open the Podcasts app on your iPhone, iPad, or Mac. 2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.3. Scroll down to find the subhead titled "Ratings & Reviews."4. Under one of the highlighted reviews, select "Write a Review."5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.7. Once you've finished, select "Send" or "Save" in the top-right corner. 8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out. 9. After selecting a nickname, tap OK. Your review may not be immediately visible.That's it! Thanks so much.TranscriptHarry Glorikian: Hello. I'm Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.My guest today is Eric Daimler, a serial software entrepreneur and a former Presidential Innovation Fellow in the Obama Administration.And he has an interesting argument about math. Daimler says if you're a young person today trying to decide which math course you're going to take, or maybe an old person who just wants to brush up, you shouldn't bother with trigonometry or calculus.Instead he says you should study category theory.That's a field that isn't even part of the curriculum at most high schools. But it's increasingly important in computer science.Category theory is about the relationships between sets or structures. It can be used to prove that different structures are consistent or compatible with one another, and to prove that the relationships in a dataset are still intact even after you've transformed that data in some way.Together with two former MIT mathematicians, Daimler co-founded a company called Conexus that uses category theory to tackle the problem of data interoperability.Now…longtime listeners of the show know that data interoperability in healthcare, or more often the lack of interoperability, is one of my biggest hobby horses. In fields from drug development to frontline medical care, we've got petabytes of data to work with, in the form of electronic medical records, genomic and proteomic data, and clinical trial data.That data could be the fuel for machine learning and other kinds of computation that could help us make develop drugs faster and make smarter decisions about care. The problem is, it's all stored in different databases and formats that can't be safely merged without a nightmarish amount of work.So when someone like Daimler says they have a way to use math to bring heterogeneous data together without compromising that data's integrity – well, I pay attention.So on today's show, we're all going back to school for an introductory class in category theory from Conexus CEO Eric Daimler.Harry Glorikian: Eric, welcome to the show.Eric Daimler: It's great to be here.Harry Glorikian: So I was reading your varied background. I mean, you've worked in so many different kinds of organizations. I'm not sure that there is a compact way or even an accurate way to describe you. So can you describe yourself? You know, what do you do and what are your main interest areas?Eric Daimler: Yeah, I mean, the easiest way to describe me might come from my mother. Well, where, you know, somebody asked her, is that the doctor? And she says, Well, yes, but he's not the type that helps people. So I you know, I've been doing research around artificial intelligence and I from a lot of different perspectives around my research in graph theory and machine learning and computational linguistics. I've been a venture capitalist on Sand Hill Road. I've done entrepreneurship, done entrepreneurship, and I started a couple of businesses which I'm doing now. And most notably I was doing policy in Washington, D.C. is part of the Obama administration for a time. So I am often known for that last part. But my background really is rare, if not unique, for having the exposure to AI from all of those angles, from business, academia and policy.Harry Glorikian: Yeah. I mean, I was looking at the obviously the like you said, the one thing that jumped out to me was the you were a Presidential Innovation Fellow in the Obama administration in 2016. Can you can you give listeners an idea of what is what is the Presidential Innovation Fellowship Program? You know, who are the types of people that are fellows and what kind of things do they do?Eric Daimler: Sure, it was I guess with that sort of question, it's helpful then to give a broader picture, even how it started. There was a a program started during the Nixon administration that's colloquially known as the Science Advisers to the President, you know, a bipartisan group to give science advice to the president that that's called the OSTP, Office of Science and Technology Policy. There are experts within that group that know know everything from space to cancer, to be super specific to, in my domain, computer security. And I was the authority that was the sole authority during my time in artificial intelligence. So there are other people with other expertise there. There are people in different capacities. You know, I had the particular capacity, I had the particular title that I had that was a one year term. The staffing for these things goes up and down, depending on the administration in ways that you might be able to predict and guess. The people with those titles also also find themselves in different parts of the the executive branch. So they will do a variety of things that are not predicted by the the title of the fellow. My particular role that I happened to be doing was in helping to coordinate on behalf of the President, humbly, on behalf of the President, their research agenda across the executive branch. There are some very able people with whom I had the good fortune of working during my time during my time there, some of which are now in the in the Biden administration. And again, it's to be a nonpartisan effort around artificial intelligence. Both sides should really be advocates for having our research agenda in government be most effective. But my role was coordinating such things as, really this is helpful, the definition of robotics, which you might be surprised by as a reflex but but quickly find to be useful when you're thinking that the Defense Department's definition and use, therefore, of robotics is really fundamentally different than that of health and human services use and a definition of robotics and the VA and Department of Energy and State and and so forth.Eric Daimler: So that is we find to be useful, to be coordinated by the Office of the President and experts speaking on behalf. It was started really this additional impulse was started after the effects of, I'll generously call them, of healthcare.gov and the trip-ups there where President Obama, to his great credit, realized that we needed to attract more technologists into government, that we had a lot of lawyers to be sure we had, we had a ton of academics, but we didn't have a lot of business people, practical technologists. So he created a way to get people like me motivated to come into government for short, short periods of time. The the idea was that you could sit around a cabinet, a cabinet meeting, and you could you would never be able to raise your hand saying, oh, I don't know anything about economics or I don't know anything about foreign policy, but you could raise your hand and say, Oh, I don't know anything about technology. That needs to be a thing of the past. President Obama saw that and created a program starting with Todd. Todd Park, the chief technologist, the second chief technology officer of the United States, is fantastic to to start to start some programs to bring in people like me.Harry Glorikian: Oh, yeah. And believe me, in health care, we need we need more technologists, which I always preach. I'm like, don't go to Facebook. Come here. You know, you can get double whammy. You can make money and you can affect people's lives. So I'm always preaching that to everybody. But so if I'm not mistaken, in early 2021, you wrote an open letter to the brand new Biden administration calling for sort of a big federal effort to improve national data infrastructure. Like, can you summarize for everybody the argument in that piece and. Do you see them doing any of the items that you're suggesting?Eric Daimler: Right. The the idea is that despite us making some real good efforts during the Obama administration with solidifying our, I'll say, our view on artificial intelligence across the executive, and this continuing actually into the Trump administration with the establishment of an AI office inside the OSTP. So credit where credit is due. That extended into the the Biden administration, where some very well-meaning people can be focusing on different parts of the the conundrum of AI expressions, having various distortions. You know, the popular one we will read about is this distortion of bias that can express itself in really ugly ways, as you know, as individuals, especially for underrepresented groups. The point of the article was to help others be reminded of of some of the easy, low hanging fruit that we can that we can work on around AI. So, you know, bias comes in a lot of different ways, the same way we all have cognitive distortions, you know, cognitive biases. There are some like 50 of them, right. You know, bias can happen around gender and ethnicity and age, sexual orientation and so forth. You know, it all can also can come from absence of data. There's a type of bias that's present just by being in a developed, rich country in collecting, for example, with Conexus's customers, my company Conexus's customers, where they are trying to report on their good efforts for economic and social good and around clean, renewable energies, they find that there's a bias in being able to collect data in rich countries versus developing countries.Eric Daimler: That's another type of bias. So that was that was the point of me writing that open letter, to prioritize, these letters. It's just to distinguish what the low hanging fruit was versus some of the hard problems. The, some of tthe low hanging fruit, I think is available, I can say, In three easy parts that people can remember. One is circuit breakers. So we we can have circuit breakers in a lot of different parts of these automated systems. You know, automated car rolling down a road is, is the easiest example where, you know, at some point a driver needs to take over control to determine to make a judgment about that shadow being a person or a tumbleweed on the crosswalk, that's a type of circuit breaker. We can have those circuit breakers in a lot of different automated systems. Another one is an audit. And the way I mean is audit is having people like me or just generally people that are experts in the craft being able to distinguish the data or the biases can become possible from the data model algorithms where biases also can become possible. Right. And we get a lot of efficiency from these automated systems, these learning algorithms. I think we can afford a little bit taken off to audit the degree to which these data models are doing what we intend.Eric Daimler: And an example of a data model is that Delta Airlines, you know, they know my age or my height, and I fly to San Francisco, to New York or some such thing. The data model would be their own proprietary algorithm to determine whether or not I am deserving of an upgrade to first class, for example. That's a data model. We can have other data models. A famous one that we all are part of is FICO scores, credit scores, and those don't have to be disclosed. None of us actually know what Experian or any of the credit agencies used to determine our credit scores. But they they use these type of things called zero knowledge proofs, where we just send through enough data, enough times that we can get to a sense of what those data models are. So that's an exposure of a data model. A declarative exposure would be maybe a next best thing, a next step, and that's a type of audit.Eric Daimler: And then the third low hanging fruit, I'd say, around regulation, and I think these are just coming towards eventualities, is demanding lineage or demanding provenance. You know, you'll see a lot of news reports, often on less credible sites, but sometimes on on shockingly credible sites where claims are made that you need to then search yourself and, you know, people in a hurry just won't do it, when these become very large systems and very large systems of information, alert systems of automation, I want to know: How were these conclusions given? So, you know, an example in health care would be if my clinician gave me a diagnosis of, let's say, some sort of cancer. And then to say, you know, here's a drug, by the way, and there's a five chance, 5 percent chance of there being some awful side effects. You know, that's a connection of causation or a connection of of conclusions that I'm really not comfortable with. You know, I want to know, like, every step is like, wait, wait. So, so what type of cancer? So what's the probability of my cancer? You know, where is it? And so what drug, you know, how did you make that decision? You know, I want to know every little step of the way. It's fine that they give me that conclusion, but I want to be able to back that up. You know, a similar example, just in everyday parlance for people would be if I did suddenly to say I want a house, and then houses are presented to me. I don't quite want that. Although that looks like good for a Hollywood narrative. Right? I want to say, oh, wait, what's my income? Or what's my cash? You know, how much? And then what's my credit? Like, how much can I afford? Oh, these are houses you can kind of afford. Like, I want those little steps or at least want to back out how those decisions were made available. That's a lineage. So those three things, circuit breaker, audit, lineage, those are three pieces of low hanging fruit that I think the European Union, the State of New York and other other government entities would be well served to prioritize.Harry Glorikian: I would love all of them, especially, you know, the health care example, although I'm not holding my breath because I might not come back to life by how long I'd have to hold my breath on that one. But we're hoping for the best and we talk about that on the show all the time. But you mentioned Conexus. You're one of three co founders, I believe. If I'm not mistaken, Conexus is the first ever commercial spin out from MIT's math department. The company is in the area of large scale data integration, building on insights that come out of the field of mathematics that's called category algebra, categorical algebra, or something called enterprise category theory. And to be quite honest, I did have to Wikipedia to sort of look that up, was not familiar with it. So can you explain category algebra in terms of a non mathematician and maybe give us an example that someone can wrap their mind around.Eric Daimler: Yeah. Yeah. And it's important to get into because even though what my company does is, Conexus does a software expression of categorical algebra, it's really beginning to permeate our world. You know, the the way I tell my my nieces and nephews is, what do quantum computers, smart contracts and Minecraft all have in common? And the answer is composability. You know, they are actually all composable. And what composable is, is it's kind of related to modularity, but it's modularity without regard to scale. So the the easy analogy is in trains where, yeah, you can swap out a boxcar in a train, but mostly trains can only get to be a couple of miles long. Swap in and out boxcars, but the train is really limited in scale. Whereas the train system, the system of a train can be infinitely large, infinitely complex. At every point in the track you can have another track. That is the difference between modularity and composability. So Minecraft is infinitely self referential where you have a whole 'nother universe that exists in and around Minecraft. In smart contracts is actually not enabled without the ability to prove the efficacy, which is then enabled by categorical algebra or its sister in math, type theory. They're kind of adjacent. And that's similar to quantum computing. So quantum computing is very sexy. It gets in the press quite frequently with forks and all, all that. If it you wouldn't be able to prove the efficacy of a quantum compiler, you wouldn't actually. Humans can't actually say whether it's true or not without type theory or categorical algebra.Eric Daimler: How you think of kind categorical algebra you can think of as a little bit related to graph theory. Graph theory is those things that you see, they look like spider webs. If you see the visualizations of graph theories are graphs. Category theory is a little bit related, you might say, to graph theory, but with more structure or more semantics or richness. So in each point, each node and each edge, in the vernacular, you can you can put an infinite amount of information. That's really what a categorical algebra allows. This, the discovery, this was invented to be translating math between different domains of math. The discovery in 2011 from one of my co-founders, who was faculty at MIT's Math Department, was that we could apply that to databases. And it's in that the whole world opens up. This solves the problem that that bedeviled the good folks trying to work on healthcare.gov. It allows for a good explanation of how we can prevent the next 737 Max disaster, where individual systems certainly can be formally verified. But the whole plane doesn't have a mechanism of being formally verified with classic approaches. And it also has application in drug discovery, where we have a way of bringing together hundreds of thousands of databases in a formal way without risk of data being misinterpreted, which is a big deal when you have a 10-year time horizon for FDA trials and you have multiple teams coming in and out of data sets and and human instinct to hoard data and a concern about it ever becoming corrupted. This math and the software expression built upon it opens up just a fantastically rich new world of opportunity for for drug discovery and for clinicians and for health care delivery. And the list is quite, quite deep.Harry Glorikian: So. What does Conexus provide its clients? Is it a service? Is it a technology? Is it both? Can you give us an example of it?Eric Daimler: Yeah. So Conexus is software. Conexus is enterprise software. It's an enterprise software platform that works generally with very large organizations that have generally very large complex data data infrastructures. You know the example, I can start in health care and then I can I can move to an even bigger one, was with a hospital group that we work with in New York City. I didn't even know health care groups could really have this problem. But it's endemic to really the world's data, where one group within the same hospital had a particular way that they represented diabetes. Now to a layman, layman in a health care sense, I would think, well, there's a definition of diabetes. I can just look it up in the Oxford English Dictionary. But this particular domain found diabetes to just be easily represented as yes, no. Do they have it? Do they not? Another group within the same hospital group thought that they would represent it as diabetes, ow are we treating it? A third group would be representing it as diabetes, how long ago. And then a fourth group had some well-meaning clinicians that would characterize it as, they had it and they have less now or, you know, type one, type two, you know, with a more more nuanced view.Eric Daimler: The traditional way of capturing that data, whether it's for drug discovery or whether it's for delivery, is to normalize it, which would then squash the fidelity of the data collected within those groups. Or they most likely to actually just wouldn't do it. They wouldn't collect the data, they wouldn't bring the data together because it's just too hard, it's too expensive. They would use these processes called ETL, extract, transform, load, that have been around for 30 years but are often slow, expensive, fragile. They could take six months to year, cost $1,000,000, deploy 50 to 100 people generally from Accenture or Deloitte or Tata or Wipro. You know, that's a burden. It's a burden, you know, so the data wasn't available and that would then impair the researchers and their ability to to share data. And it would impair clinicians in their view of patient care. And it also impaired the people in operations where they would work on billing. So we work with one company right now that that works on 1.4 trillion records a year. And they just have trouble with that volume and the number of databases and the heterogeneous data infrastructure, bringing together that data to give them one view that then can facilitate health care delivery. Eric Daimler: The big example is, we work with Uber where they they have a very smart team, as smart as one might think. They also have an effectively infinite balance sheet with which they could fund an ideal IT infrastructure. But despite that, you know, Uber grew up like every other organization optimizing for the delivery of their service or product and, and that doesn't entail optimizing for that infrastructure. So what they found, just like this hospital group with different definitions of diabetes, they found they happen to have grown up around service areas. So in this case cities, more or less. So when then the time came to do analysis -- we're just passing Super Bowl weekend, how will the Super Bowl affect the the supply of drivers or the demand from riders? They had to do it for the city of San Francisco, separate than the city of San Jose or the city of Oakland. They couldn't do the whole San Francisco Bay Area region, let alone the whole of the state or the whole of the country or what have you. And that repeated itself for every business question, every organizational question that they would want to have. This is the same in drug discovery. This is the same in patient care delivery or in billing. These operational questions are hard, shockingly hard.Eric Daimler: We had another one in logistics where we had a logistics company that had 100,000 employees. I didn't even know some of these companies could be so big, and they actually had a client with 100,000 employees. That client had 1000 ships, each one of which had 10,000 containers. And I didn't even know like how big these systems were really. I hadn't thought about it. But I mean, they're enormous. And the question was, hey, where's our personal protective equipment? Where is the PPE? And that's actually a hard question to ask. You know, we are thinking about maybe our FedEx tracking numbers from an Amazon order. But if you're looking at the PPE and where it is on a container or inside of a ship, you know, inside this large company, it's actually a hard question to ask. That's this question that all of these organizations have. Eric Daimler: In our case, Uber, where they they they had a friction in time and in money and in accuracy, asking every one of these business questions. They went then to find, how do I solve this problem? Do I use these old tools of ETL from the '80s? Do I use these more modern tools from the 2000s? They're called RDF or OWL? Or is there something else? They discovered that they needed a more foundational system, this categorical algebra that that's now expressing itself in smart contracts and quantum computers and other places. And they just then they found, oh, who are the leaders in the enterprise software expression of that math? And it's us. We happen to be 40 miles north of them. Which is fortunate. We worked with Uber to to solve that problem in bringing together their heterogeneous data infrastructure to solve their problems. And to have them tell it they save $10 million plus a year in in the efficiency and speed gains from the solution we helped provide for them.[musical interlude]Harry Glorikian: Let's pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that's leave a rating and a review for the show on Apple Podcasts.All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. It'll only take a minute, but you'll be doing a lot to help other listeners discover the show.And one more thing. If you like the interviews we do here on the show I know you'll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.It's a friendly and accessible tour of all the ways today's information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.And now, back to the show.[musical interlude]Harry Glorikian: So your website says that your software can map data sources to each other so that the perfect data model is discovered, not designed. And so what does that mean? I mean, does that imply that there's some machine learning or other form of artificial intelligence involved, sort of saying here are the right pieces to put together as opposed to let me design this just for you. I'm trying to piece it together.Eric Daimler: Yeah. You know, the way we might come at this is just reminding ourselves about the structure of artificial intelligence. You know, in the public discourse, we will often find news, I'm sure you can find it today, on deep learning. You know, whatever's going on in deep learning because it's sexy, it's fun. You know, DeepMind really made a name for themselves and got them acquired at a pretty valuation because of their their Hollywood-esque challenge to Go, and solving of that game. But that particular domain of AI, deep learning, deep neural nets is a itself just a subset of machine learning. I say just not not not to minimize it. It's a fantastically powerful algorithm. But but just to place it, it is a subset of machine learning. And then machine learning itself is a subset of artificial intelligence. That's a probabilistic subset. So we all know probabilities are, those are good and bad. Fine when the context is digital advertising, less fine when it's the safety of a commercial jet. There is another part of artificial intelligence called deterministic artificial intelligence. They often get expressed as expert systems. Those generally got a bad name with the the flops of the early '80s. Right. They flopped because of scale, by the way. And then the flops in the early 2000s and 2010s from IBM's ill fated Watson experiment, the promise did not meet the the reality.Eric Daimler: It's in that deterministic A.I. that that magic is to be found, especially when deployed in conjunction with the probabilistic AI. That's that's where really the future is. There's some people have a religious view of, oh, it's only going to be a probabilistic world but there's many people like myself and not to bring up fancy names, but Andrew Ng, who's a brilliant AI researcher and investor, who also also shares this view, that it's a mix of probabilistic and deterministic AI. What deterministic AI does is, to put it simply, it searches the landscape of all possible connections. Actually it's difference between bottoms up and tops down. So the traditional way of, well, say, integrating things is looking at, for example, that hospital network and saying, oh, wow, we have four definitions of diabetes. Let me go solve this problem and create the one that works for our hospital network. Well, then pretty soon you have five standards, right? That's the traditional way that that goes. That's what a top down looks that looks like.Eric Daimler: It's called a Golden Record often, and it rarely works because pretty soon what happens is the organizations will find again their own need for their own definition of diabetes. In most all cases, that's top down approach rarely works. The bottoms up approach says, Let's discover the connections between these and we'll discover the relationships. We don't discover it organically like we depend on people because it's deterministic. I, we, we discover it through a massive, you know, non intuitive in some cases, it's just kind of infeasible for us to explore a trillion connections. But what the AI does is it explores a factorial number actually is a technical, the technical equation for it, a factorial number of of possible paths that then determine the map of relationships between between entities. So imagine just discovering the US highway system. If you did that as a person, it's going to take a bit. If you had some infinitely fast crawlers that robot's discovering the highway system infinitely fast, remember, then that's a much more effective way of doing it that gives you some degree of power. That's the difference between bottoms up and tops down. That's the difference between deterministic, really, we might say, and probabilistic in some simple way.Harry Glorikian: Yeah, I'm a firm believer of the two coming together and again, I just look at them as like a box. I always tell people like, it's a box of tools. I need to know the problem, and then we can sort of reach in and pick out which set of tools that are going to come together to solve this issue, as opposed to this damn word called AI that everybody thinks is one thing that they're sort of throwing at the wall to solve a problem.Harry Glorikian: But you're trying to solve, I'm going to say, data interoperability. And on this show I've had a lot of people talk about interoperability in health care, which I actually believe is, you could break the system because things aren't working right or I can't see what I need to see across the two hospitals that I need information from. But you published an essay on Medium about Haven, the health care collaboration between Amazon, JPMorgan, Berkshire Hathaway. Their goal was to use big data to guide patients to the best performing clinicians and the most affordable medicines. They originally were going to serve these first three founding companies. I think knowing the people that started it, their vision was bigger than that. There was a huge, you know, to-do when it came out. Fireworks and everything. Launched in 2018. They hired Atul Gawande, famous author, surgeon. But then Gawande left in 2020. And, you know, the company was sort of quietly, you know, pushed off into the sunset. Your essay argued that Haven likely failed due to data interoperability challenges. I mean. How so? What what specific challenges do you imagine Haven ran into?Eric Daimler: You know, it's funny, I say in the article very gently that I imagine this is what happened. And it's because I hedge it that that the Harvard Business Review said, "Oh, well, you're just guessing." Actually, I wasn't guessing. No, I know. I know the people that were doing it. I know the challenges there. But but I'm not going to quote them and get them in trouble. And, you know, they're not authorized to speak on it. So I perhaps was a little too modest in my framing of the conclusion. So this actually is what happened. What happens is in the same way that we had the difficulty with healthcare.gov, in the same way that I described these banks having difficulty. Heterogeneous databases don't like to talk to one another. In a variety of different ways. You know, the diabetes example is true, but it's just one of many, many, many, many, many, many cases of data just being collected differently for their own use. It can be as prosaic as first name, last name or "F.last name." Right? It's just that simple, you know? And how do I bring those together? Well, those are those are called entity resolutions. Those are somewhat straightforward, but not often 100 percent solvable. You know, this is just a pain. It's a pain. And, you know, so what what Haven gets into is they're saying, well, we're massive. We got like Uber, we got an effectively infinite balance sheet. We got some very smart people. We'll solve this problem. And, you know, this is some of the problem with getting ahead of yourself. You know, I won't call it arrogance, but getting ahead of yourself, is that, you think, oh, I'll just be able to solve that problem.Eric Daimler: You know, credit where credit is due to Uber, you know, they looked both deeper saying, oh, this can't be solved at the level of computer science. And they looked outside, which is often a really hard organizational exercise. That just didn't happen at Haven. They thought they thought they could they could solve it themselves and they just didn't. The databases, not only could they have had, did have, their own structure, but they also were stored in different formats or by different vendors. So you have an SAP database, you have an Oracle database. That's another layer of complication. And when I say that these these take $1,000,000 to connect, that's not $1,000,000 one way. It's actually $2 million if you want to connect it both ways. Right. And then when you start adding five, let alone 50, you take 50 factorial. That's a very big number already. You multiply that times a million and 6 to 12 months for each and a hundred or two hundred people each. And you just pretty soon it's an infeasible budget. It doesn't work. You know, the budget for us solving solving Uber's problem in the traditional way was something on the order of $2 trillion. You know, you do that. You know, we had a bank in the U.S. and the budget for their vision was was a couple of billion. Like, it doesn't work. Right. That's that's what happened Haven. They'll get around to it, but but they're slow, like all organizations, big organizations are. They'll get around to solving this at a deeper level. We hope that we will remain leaders in database integration when they finally realize that the solution is at a deeper level than their than the existing tools.Harry Glorikian: So I mean, this is not I mean, there's a lot of people trying to solve this problem. It's one of those areas where if we don't solve it, I don't think we're going to get health care to the next level, to sort of manage the information and manage people and get them what they need more efficiently and drive down costs.Eric Daimler: Yeah.Harry Glorikian: And I do believe that EMRs are. I don't want to call them junk. Maybe I'm going too far, but I really think that they you know, if you had decided that you were going to design something to manage patients, that is not the software you would have written to start. Hands down. Which I worry about because these places won't, they spent so much putting them in that trying to get them to rip them out and put something in that actually works is challenging. You guys were actually doing something in COVID-19, too, if I'm not mistaken. Well, how is that project going? I don't know if it's over, but what are you learning about COVID-19 and the capabilities of your software, let's say?Eric Daimler: Yeah. You know, this is an important point that for anybody that's ever used Excel, we know what it means to get frustrated enough to secretly hard code a cell, you know, not keeping a formula in a cell. Yeah, that's what happened in a lot of these systems. So we will continue with electronic medical records to to bring these together, but they will end up being fragile, besides slow and expensive to construct. They will end up being fragile, because they were at some point hardcoded. And how that gets expressed is that the next time some other database standard appears inside of that organization's ecosystem from an acquisition or a divestiture or a different technical standard, even emerging, and then the whole process starts all over again. You know, we just experience this with a large company that that spent $100 million in about five years. And then they came to us and like, yeah, we know it works now, but we know like a year from now we're going to have to say we're going to go through it again. And, it's not like, oh, we'll just have a marginal difference. No, it's again, that factorial issue, that one database connected to the other 50 that already exist, creates this same problem all over again at a couple of orders of magnitude. So what we discover is these systems, these systems in the organization, they will continue to exist.Eric Daimler: These fragile systems will continue to exist. They'll continue to scale. They'll continue to grow in different parts of the life sciences domain, whether it's for clinicians, whether it's for operations, whether it's for drug discovery. Those will continue to exist. They'll continue to expand, and they will begin to approach the type of compositional systems that I'm describing from quantum computers or Minecraft or smart contracts, where you then need the the discovery and math that Conexus expresses in software for databases. When you need that is when you then need to prove the efficacy or otherwise demonstrate the lack of fragility or the integrity of the semantics. Conexus can with, it's a law of nature and it's in math, with 100 percent accuracy, prove the integrity of a database integration. And that matters in high consequence context when you're doing something as critical as drug side effects for different populations. We don't want your data to be misinterpreted. You can't afford lives to be lost or you can't, in regulation, you can't afford data to be leaking. That's where you'll ultimately need the categorical algebra. You'll need a provable compositional system. You can continue to construct these ones that will begin to approach compositionality, but when you need the math is when you need to prove it for either the high consequence context of lives, of money or related to that, of regulation.Harry Glorikian: Yeah, well, I keep telling my kids, make sure you're proficient in math because you're going to be using it for the rest of your life and finance. I always remind them about finance because I think both go together. But you've got a new book coming out. It's called "The Future is Formal" and not tuxedo like formal, but like you're, using the word formal. And I think you have a very specific meaning in mind. And I do want you to talk about, but I think what you're referring to is how we want automated systems to behave, meaning everything from advertising algorithms to self-driving trucks. And you can tell me if that my assumption is correct or not.Eric Daimler: Though it's a great segue, actually, from the math. You know, what I'm trying to do is bring in people that are not programmers or research technology, information technology researchers day to day into the conversation around automated digital systems. That's my motivation. And my motivation is, powered by the belief that we will bring out the best of the technology with more people engaged. And with more people engaged, we have a chance to embrace it and not resist it. You know, my greatest fear, I will say, selfishly, is that we come up with technology that people just reject, they just veto it because they don't understand it as a citizen. That also presents a danger because I think that companies' commercial expressions naturally will grow towards where their technology is needed. So this is actually to some extent a threat to Western security relative to Chinese competition, that we embrace the technology in the way that we want it to be expressed in our society. So trying to bring people into this conversation, even if they're not programmers, the connection to math is that there are 18 million computer programmers in the world. We don't need 18 million and one, you know. But what we do need is we do need people to be thinking, I say in a formal way, but also just be thinking about the values that are going to be represented in these digital infrastructures.Eric Daimler: You know, somewhere as a society, we will have to have a conversation with ourselves to determine the car driving to the crosswalk, braking or rolling or slowing or stopping completely. And then who's liable if it doesn't? Is it the driver or is it the manufacturer? Is it the the programmer that somehow put a bug in their code? You know, we're entering an age where we're going to start experiencing what some person calls double bugs. There's the bug in maybe one's expression in code. This often could be the semantics. Or in English. Like your English doesn't make sense. Right? Right. Or or was it actually an error in your thinking? You know, did you leave a gap in your thinking? This is often where where some of the bugs in Ethereum and smart contracts have been expressed where, you know, there's an old programming rule where you don't want to say something equals true. You always want to be saying true equals something. If you get if you do the former, not the latter, you can have to actually create bugs that can create security breaches.Eric Daimler: Just a small little error in thinking. That's not an error in semantics. That level of thinking, you don't need to know calculus for, or category theory for that matter. You just need to be thinking in a formal way. You know, often, often lawyers, accountants, engineers, you know, anybody with scientific training can, can more quickly get this idea, where those that are educated in liberal arts can contribute is in reminding themselves of the broader context that wants to be expressed, because often engineers can be overly reductionist. So there's really a there's a push and pull or, you know, an interplay between those two sensibilities that then we want to express in rules. Then that's ultimately what I mean by formal, formal rules. Tell me exactly what you mean. Tell me exactly how that is going to work. You know, physicians would understand this when they think about drug effects and drug side effects. They know exactly what it's going to be supposed to be doing, you know, with some degree of probability. But they can be very clear, very clear about it. It's that clear thinking that all of us will need to exercise as we think about the development and deployment of modern automated digital systems.Harry Glorikian: Yeah, you know, it's funny because that's the other thing I tell people, like when they say, What should my kid take? I'm like, have him take a, you know, basic programming, not because they're going to do it for a living, but they'll understand how this thing is structured and they can get wrap their mind around how it is. And, you know, I see how my nephew thinks who's from the computer science world and how I think, and sometimes, you know, it's funny watching him think. Or one of the CTOs of one of our companies how he looks at the world. And I'm like you. You got to back up a little bit and look at the bigger picture. Right. And so it's the two of us coming together that make more magic than one or the other by themselves.Harry Glorikian: So, you know, I want to jump back sort of to the different roles you've had in your career. Like like you said, you've been a technology investor, a serial startup founder, a university professor, an academic administrator, an entrepreneur, a management instructor, Presidential Innovation Fellow. I don't think I've missed anything, but I may have. You're also a speaker, a commentator, an author. Which one of those is most rewarding?Eric Daimler: Oh, that's an interesting question. Which one of those is most rewarding? I'm not sure. I find it to be rewarding with my friends and family. So it's rewarding to be with people. I find that to be rewarding in those particular expressions. My motivation is to be, you know, just bringing people in to have a conversation about what we want our world to look like, to the degree to which the technologies that I work with every day are closer to the dystopia of Hollywood narratives or closer to our hopes around the utopia that's possible, that where this is in that spectrum is up to us in our conversation around what these things want to look like. We have some glimpses of both extremes, but I'd like people, and I find it to be rewarding, to just be helping facilitate the helping catalyze that conversation. So the catalyst of that conversation and whatever form it takes is where I enjoy being.Harry Glorikian: Yeah, because I was thinking about like, you know, what can, what can you do as an individual that shapes the future. Does any of these roles stand out as more impactful than others, let's say?Eric Daimler: I think the future is in this notion of composability. I feel strongly about that and I want to enroll people into this paradigm as a framework from which to see many of the activities going around us. Why have NFTs come on the public, in the public media, so quickly? Why does crypto, cryptocurrency capture our imagination? Those And TikTok and the metaverse. And those are all expressions of this quick reconfiguration of patterns in different contexts that themselves are going to become easier and easier to express. The future is going to be owned by people that that take the special knowledge that they've acquired and then put it into short business expressions. I'm going to call them rules that then can be recontextualized and redeployed. This is my version of, or my abstraction of what people call the the future being just all TikTok. It's not literally that we're all going to be doing short dance videos. It's that TikTok is is an expression of people creating short bits of content and then having those be reconfigured and redistributed. That can be in medicine or clinical practice or in drugs, but it can be in any range of expertise, expertise or knowledge. And what's changed? What's changed and what is changing is the different technologies that are being brought to bear to capture that knowledge so that it can be scalable, so it can be compositional. Yeah, that's what's changing. That's what's going to be changing over the next 10 to 20 years. The more you study that, I think the better off we will be. And I'd say, you know, for my way of thinking about math, you might say the more math, the better. But if I were to choose for my children, I would say I would replace trig and geometry and even calculus, some people would be happy to know, with categorical algebra, category theory and with probability and statistics. So I would replace calculus, which I think is really the math of the 20th century, with something more appropriate to our digital age, which is categorical algebra.Harry Glorikian: I will tell my son because I'm sure he'll be very excited to to if I told him that not calculus, but he's not going to be happy when I say go to this other area, because I think he'd like to get out of it altogether.Eric Daimler: It's easier than calculus. Yeah.Harry Glorikian: So, you know, it was great having you on the show. I feel like we could talk for another hour on all these different aspects. You know, I'm hoping that your company is truly successful and that you help us solve this interoperability problem, which is, I've been I've been talking about it forever. It seems like I feel like, you know, the last 15 or 20 years. And I still worry if we're any closer to solving that problem, but I'm hopeful, and I wish you great success on the launch of your new book. It sounds exciting. I'm going to have to get myself a copy.Eric Daimler: Thank you very much. It's been fun. It's good to be with you.Harry Glorikian: Thank you.Harry Glorikian: That's it for this week's episode. You can find a full transcript of this episode as well as the full archive of episodes of The Harry Glorikian Show and MoneyBall Medicine at our website. Just go to glorikian.com and click on the tab Podcasts.I'd like to thank our listeners for boosting The Harry Glorikian Show into the top three percent of global podcasts.If you want to be sure to get every new episode of the show automatically, be sure to open Apple Podcasts or your favorite podcast player and hit follow or subscribe. Don't forget to leave us a rating and review on Apple Podcasts. And we always love to hear from listeners on Twitter, where you can find me at hglorikian.Thanks for listening, stay healthy, and be sure to tune in two weeks from now for our next interview.

Joël Quenneville (Twitter)Elm's Universal Pattern episodeList.concatMap is the same pattern as andThen under a different nameandThen identity can be used to flatten somethingDillon's Combinators articleMartin Janiczek's elm-list-cartesian package gives two valid map2 implementions for ListMonoid - need a way of having something empty, and way to combine two things - for example addition for numbers starting with 0Jeroen's elm-review-simplify packageMore of Joël's distillation of category theory ideas:Running out of maps (applicatives)The Mechanics of Maybe (taking maybe apart and putting it back together)Two ways of looking at map functions (functors)Elm's universal pattern (applicatives)Inverting a binary tree (folding, catamorphisms)Joël's directory of blog posts on the ThoughtBot blog

This episode is about the journey of a programmer that converted himself into a Haskell developer after working with C/C++ for more than 10years. Here are a few questions that you'll find the answer to in this episode: What does he find so compelling about Haskell? Why did it make him dive deeper into the Theoretical Computer Science? Why did it make him learn Coq and Category Theory? How does Coq compare with ACL2? How do both Coq and ACL2 compares to TLA+? Did learning Coq make John a better programmer? Links John's Email: johnw@newartisans.com John's Twitter: @jwiegley

This episode is about the journey of a programmer that converted himself into a Haskell developer after working with C/C++ for more than 10years. Here are a few questions that you'll find the answer to in this episode: What does he find so compelling about Haskell? Why did it make him dive deeper into the Theoretical Computer Science? Why did it make him learn Coq and Category Theory? How does Coq compare with ACL2? How do both Coq and ACL2 compares to TLA+? Did learning Coq make John a better programmer? Links John's Email: johnw@newartisans.com John's Twitter: @jwiegley

This episode is about the journey of a programmer that converted himself into a Haskell developer after working with C/C++ for more than 10years. Here are a few questions that you'll find the answer to in this episode: What does he find so compelling about Haskell? Why did it make him dive deeper into the Theoretical Computer Science? Why did it make him learn Coq and Category Theory? How does Coq compare with ACL2? How do both Coq and ACL2 compares to TLA+? Did learning Coq make John a better programmer? Links John's Email: johnw@newartisans.com John's Twitter: @jwiegley

Today we're talking to Edward Kmett, the Head of Software Engineering at Groq. And we discuss how Edward came to be one of the most prominent members of the Haskell community. How Groq's chips are able to provide incredible processing capabilities for AI models, and how Category Theory gives us a different way to think about mathematics.  All of this right here, right now, on the ModernCTO Podcast!  To learn more about Groq, check them out at https://groq.com In case you missed it: check out our episode with Groq's Founder and CEO Jonathan Ross

All the way from San Fransisco, Ryan Wisnesky takes us on a journey of his career in Tech. Starting as a QA Engineer, moving to programming and eventually starting a company as a co-founder that was based on his final dissertation for his PhD at Harvard. Do Mathematicians need to know programming these days? How has his company, Conexus, helped Uber? What are the main responsibilities of a CTO and much more. Enjoy! and don't forget to leave a review on iTunes.Here are some links that Ryan shared with us.How academic start-ups differ: https://www.kauffman.org/wp-content/uploads/2019/12/fromlabbenchtoinnovation.pdf Work with Uber: paper: https://arxiv.org/abs/1909.04881 and community outreach: https://eng.uber.com/dragon-schema-integration-at-uber-scale/  and https://www.meetup.com/Category-Theory/events/zpvmgsyccfbhc/ General audience article on category theory: https://www.quantamagazine.org/with-category-theory-mathematics-escapes-from-equality-20191010/ 

Matteo Capucci is a PhD student at the University of Strathclyde in the MSP group, advised by Neil Ghani and Scott Cunningham. He studies Applied Category Theory (aka ACT), specifically Categorical Cybernetics and Applied Topos Theory. Today Matteo joined us to discuss the foundations of Categorical Cybernetics, in a wide-ranging conversation touching on lenses, feedback systems, dynamical systems, and more. The conversation extended these ideas to distributed systems, model checking, cyber-physical systems, program sketching, and quantum systems, among other things. This one was an absolute blast live and we hope you enjoy it after the fact in its audio form. You can read more about Matteo HERE. You can read more about the Boston Computation Club HERE. You can watch this presentation in video form on YouTube HERE.

What is Category Theory, and why is it called the math of the 21st century?We're fortunate to have the CEO and Co-Founder of Conexus, Eric Daimler, join us for this episode. Eric carries a wide breadth of experience, from Professional Investing to Policy Advisor and Author. He taught at Carnegie Mellon and worked under the Obama Administration.Eric emphatically speaks on the potent potential of Category Theory. When working with billions and even trillions of data points, this branch of math holds the key to faultless data.Eric provides real-world applications where Conexus is using Category Theory, including ride-sharing apps such as Uber, investing applications, and diabetes datasets. When looking at big data, there's a necessity for zero mistake data. With thousands of ambiguities, this becomes impractical.Category theory's relevance and applications are only taking off. Eric recommends his Co-Founder's book to learn more on the subject, which you can check out at the link below.Listen to the episode to learn more now!Don't forget to subscribe to the show on iTunes, Spotify, or wherever you get your podcasts. See you in the next episode! Resources:https://mitpress.mit.edu/books/category-theory-sciences 

Dr. Eugenia Cheng is Scientist In Residence at the School of the Art Institute of Chicago. She won tenure in Pure Mathematics at the University of Sheffield, UK, where she is now an Honorary Fellow. Alongside her research in Category Theory and undergraduate teaching her aim is to rid the world of “math phobia”. Eugenia is also math columnist for the Wall Street Journal and a concert pianist. Emotions are powerful. In newspaper headlines and on social media, they have become the primary way of understanding the world. With her new book "The Art of Logic: How to Make Sense in a World that Doesn't", Eugenia has set out to show how mathematical logic can help us see things more clearly - and know when politicians and companies are trying to mislead us. This talk, like the book, is filled with useful real-life examples of logic and illogic at work and an essential guide to decoding modern life. Originally published in August of 2018, watch the video of this event via g.co/TalksAtGoogle/TheArtOfLogic.

On this ID the Future from the vault, biologist Jonathan Wells rounds out his discussion with host Casey Luskin about his journal article “Membrane Patterns Carry Ontogenetic Information That Is Specified Independently of DNA.” In the first three episodes in this series, Dr. Wells showed that embryo development requires information carried by membrane patterns in embryonic cells. Today, Dr. Wells discusses what this means for future biological research and the challenge it poses to evolutionary theory. Source

*Episode Show Notes:* - Eric Daimler is the CEO & Co-Founder of Conexus.com. Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage. - Daimler believes the Obama administration made big efforts to bring in more technologists into government for innovation and digital modernization, and is optimistic that sensibility around a digitally native environment will be expressed inside of the Federal Government, and continue to trickle down into states' governments for the benefit of all. - Human failure has come before machines got trained on human failures. Therefore, technologists can't use massive amounts of data on every human problem and expect to come out with mind blowing results. So there's limitations on technology. What can be done is to transform these whole domains of knowledge and map them onto others through a new type of math. -There's a discovery in this domain called category theory. Categorical mathematics, category theory, is really at a level above all those other mathematics that transforms a problem from geometry, into another problem called safe set theory, applying it to databases. The math of category theory changes how we relate to data. This is “the math of the future”. -It's at a higher level of math, a level of abstraction to model the world in which companies operate their business, and make bigger decisions better and faster, reasoning large amounts of data at a higher level to power a whole new change in our environment, as business people, as academics, as citizens. -Daimler suggests three ways to solve data issues: matching data in a unified database, create a silo and then they sell a subscription to data silos and data interoperability math analysis through category theory. -AI definition has been misinterpreted over the years as algorithms that collect data and have machines do stuff, when in reality, AI should be understood as a system that senses plans, acts and learns from the experience. And it senses plans and acts from inputs that are given to it. -Not everyone needs to be a programmer in a basement. People need to be playing a multitude of roles. There's not just a choice between computer science or an English degree. What the current world of tech needs is policy considerations, places to get involved, and a way to focus educational efforts. Automation doesn't mean no human intervention. Societies benefit by that exchange of ideas and communication of values. *Shownotes Links:* https://www.linkedin.com/in/ericdaimler ** https://youtu.be/YP9kodLGvT8 https://youtu.be/jqn4wnSBKuE https://youtu.be/c92rK_UZaXU *About HumAIn Podcast* The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance. Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Bob Coercke is a celebrated physicist, he's been a Physics and Quantum professor at Oxford University for the last 20 years. He is particularly interested in Structure which is to say, Logic, Order, and Category Theory. He is well known for work involving compositional distributional models of natural language meaning and he is also fascinated with understanding how our brains work. Bob was recently appointed as the Chief Scientist at Cambridge Quantum Computing. Bob thinks that interactions between systems in Quantum Mechanics carries naturally over to how word meanings interact in natural language. Bob argues that this interaction embodies the phenomenon of quantum teleportation. Bob invented ZX-calculus, a graphical calculus for revealing the compositional structure inside quantum circuits - to show entanglement states and protocols in a visually succinct but logically complete way. Von Neumann himself didn't even like his own original symbolic formalism of quantum theory, despite it being widely used! We hope you enjoy this fascinating conversation which might give you a lot of insight into natural language processing. Tim Intro [00:00:00] The topological brain (Post-record button skit) [00:13:22] Show kick off [00:19:31] Bob introduction [00:22:37] Changing culture in universities [00:24:51] Machine Learning is like electricity [00:31:50] NLP -- what is Bob's Quantum conception? [00:34:50] The missing text problem [00:52:59] Can statistical induction be trusted? [00:59:49] On pragmatism and hybrid systems [01:04:42] Parlour tricks, parsing and information flows [01:07:43] How much human input is required with Bob's method? [01:11:29] Reality, meaning, structure and language [01:14:42] Replacing complexity with quantum entanglement, emergent complexity [01:17:45] Loading quantum data requires machine learning [01:19:49] QC is happy math coincidence for NLP [01:22:30] The Theory of English (ToE) [01:28:23] ... or can we learn the ToE? [01:29:56] How did diagrammatic quantum calculus come about? [01:31:04 The state of quantum computing today [01:37:49] NLP on QC might be doable even in the NISQ era [01:40:48] Hype and private investment are driving progress [01:48:34] Crypto discussion (moved to post-show) [01:50:38] Kilcher is in a startup (moved to post show) [01:53:40 Debrief [01:55:26]

“A way that math can make the world a better place is by making it a more interesting place to be a conscious being.” So says mathematician Emily Riehl near the start of this episode, and it’s a good summary of what’s to come. Emily works in realms of topology and category theory that are far away from practical applications, or even to some non-practical areas of theoretical physics. But they help us think about what is possible and how everything fits together, and what’s more interesting than that? We talk about what topology is, the specific example of homotopy — how things deform into other things — and how thinking about that leads us into groups, rings, groupoids, and ultimately to category theory, the most abstract of them all.Support Mindscape on Patreon.Emily Riehl received a Ph.D in mathematics from the University of Chicago. She is currently an associate professor of mathematics at Johns Hopkins University. Among her honors are the JHU President’s Frontier Award and the Joan & Joseph Birman Research Prize. She is author of Categorical Homotopy Theory, and co-author of the upcoming Elements of ∞-Category Theory. She competed on the United States women’s national Australian rules football team, where she served as vice-captain.Johns Hopkins web pageGoogle Scholar publicationsQuanta profileWikipediaTwitter

Category theory may strike you as intimidating, but trust us, you can (and after this episode, are probably itching to) talk applicative functors and parser combinators over afterwork drinks. Listen in to learn why Esko and Antti – both of whom started programming with dynamically typed languages – are so into category theory right now that they see applications of it everywhere.GuestAntti Holvikari is endlessly fascinated by pure functional programming languages such as Haskell and PureScript. Software quality and personal productivity are two things he’s constantly improving.HostEsko Lahti is an engineer who always wanted to learn about category theory in practice – but never knew where to start. Then he met Antti Holvikari.Episode linksPureScript: https://www.purescript.org/Parser Combinators, a Walkthrough: https://hasura.io/blog/parser-combinators-walkthrough/fp-ts: https://github.com/gcanti/fp-tsio-ts: https://github.com/gcanti/io-tsAlgebraic Data Types: https://dev.to/gcanti/functional-design-algebraic-data-types-36kfDiscriminated Unions in TypeScript: https://basarat.gitbook.io/typescript/type-system/discriminated-unionsMaybe Not, a talk by Rich Hickey: https://youtu.be/YR5WdGrpoug

From its more mainstream, business-focused and business-friendly “Lean In” variants, to more radical, critical and intersectional understandings of feminism, the past decade has seen a flourishing of discussion from those proposing and critiquing different schools of thought for the way we think about gender in society. Dr. Eugenia Cheng's addition to this conversation is x+y: A Mathematician's Manifesto for Rethinking Gender (Basic Books, 2020). She applies insights gained from her mathematical background to propose a new way to talk about gender and to propose an alternative: the terms “ingressive” and “congressive” behavior. In this interview, Dr. Cheng and I talk about what we gain from bringing a mathematical understanding to questions of social relations and structures. We talk about how she rethinks “gender”, and the new terms she proposes in her book. We end with a short discussion of whether these insights are applicable to conversations about other demographic and social identifiers. Dr. Eugenia Cheng is a mathematician and concert pianist. She is Scientist In Residence at the School of the Art Institute of Chicago and holds a PhD in pure mathematics from the University of Cambridge. Alongside her research in Category Theory and undergraduate teaching her aim is to rid the world of “math-phobia”. She was an early pioneer of math on YouTube and her videos have been viewed over 15 million times to date. Her other books are How to Bake Pi: An Edible Exploration of the Mathematics of Mathematics (Basic Books: 2016), which was featured on the Late Show with Stephen Colbert, Beyond Infinity: An Expedition to the Outer Limits of Mathematics (Basic Books: 2017) which was shortlisted for the Royal Society Science Book Prize in 2017 and The Art of Logic in an Illogical World (Basic Books: 2018) You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of x+y. Follow on Facebook or on Twitter at @BookReviewsAsia. Nicholas Gordon is a reviewer for the Asian Review of Books. In his day job, he's a researcher and writer for a think tank in economic and sustainable development. He is also a print and broadcast commentator on local and regional politics. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/asian-review

From its more mainstream, business-focused and business-friendly “Lean In” variants, to more radical, critical and intersectional understandings of feminism, the past decade has seen a flourishing of discussion from those proposing and critiquing different schools of thought for the way we think about gender in society. Dr. Eugenia Cheng’s addition to this conversation is x+y: A Mathematician's Manifesto for Rethinking Gender (Basic Books, 2020). She applies insights gained from her mathematical background to propose a new way to talk about gender and to propose an alternative: the terms “ingressive” and “congressive” behavior. In this interview, Dr. Cheng and I talk about what we gain from bringing a mathematical understanding to questions of social relations and structures. We talk about how she rethinks “gender”, and the new terms she proposes in her book. We end with a short discussion of whether these insights are applicable to conversations about other demographic and social identifiers. Dr. Eugenia Cheng is a mathematician and concert pianist. She is Scientist In Residence at the School of the Art Institute of Chicago and holds a PhD in pure mathematics from the University of Cambridge. Alongside her research in Category Theory and undergraduate teaching her aim is to rid the world of “math-phobia”. She was an early pioneer of math on YouTube and her videos have been viewed over 15 million times to date. Her other books are How to Bake Pi: An Edible Exploration of the Mathematics of Mathematics (Basic Books: 2016), which was featured on the Late Show with Stephen Colbert, Beyond Infinity: An Expedition to the Outer Limits of Mathematics (Basic Books: 2017) which was shortlisted for the Royal Society Science Book Prize in 2017 and The Art of Logic in an Illogical World (Basic Books: 2018) You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of x+y. Follow on Facebook or on Twitter at @BookReviewsAsia. Nicholas Gordon is a reviewer for the Asian Review of Books. In his day job, he’s a researcher and writer for a think tank in economic and sustainable development. He is also a print and broadcast commentator on local and regional politics. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

From its more mainstream, business-focused and business-friendly “Lean In” variants, to more radical, critical and intersectional understandings of feminism, the past decade has seen a flourishing of discussion from those proposing and critiquing different schools of thought for the way we think about gender in society. Dr. Eugenia Cheng’s addition to this conversation is x+y: A Mathematician's Manifesto for Rethinking Gender (Basic Books, 2020). She applies insights gained from her mathematical background to propose a new way to talk about gender and to propose an alternative: the terms “ingressive” and “congressive” behavior. In this interview, Dr. Cheng and I talk about what we gain from bringing a mathematical understanding to questions of social relations and structures. We talk about how she rethinks “gender”, and the new terms she proposes in her book. We end with a short discussion of whether these insights are applicable to conversations about other demographic and social identifiers. Dr. Eugenia Cheng is a mathematician and concert pianist. She is Scientist In Residence at the School of the Art Institute of Chicago and holds a PhD in pure mathematics from the University of Cambridge. Alongside her research in Category Theory and undergraduate teaching her aim is to rid the world of “math-phobia”. She was an early pioneer of math on YouTube and her videos have been viewed over 15 million times to date. Her other books are How to Bake Pi: An Edible Exploration of the Mathematics of Mathematics (Basic Books: 2016), which was featured on the Late Show with Stephen Colbert, Beyond Infinity: An Expedition to the Outer Limits of Mathematics (Basic Books: 2017) which was shortlisted for the Royal Society Science Book Prize in 2017 and The Art of Logic in an Illogical World (Basic Books: 2018) You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of x+y. Follow on Facebook or on Twitter at @BookReviewsAsia. Nicholas Gordon is a reviewer for the Asian Review of Books. In his day job, he’s a researcher and writer for a think tank in economic and sustainable development. He is also a print and broadcast commentator on local and regional politics. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sociology

From its more mainstream, business-focused and business-friendly “Lean In” variants, to more radical, critical and intersectional understandings of feminism, the past decade has seen a flourishing of discussion from those proposing and critiquing different schools of thought for the way we think about gender in society. Dr. Eugenia Cheng’s addition to this conversation is x+y: A Mathematician's Manifesto for Rethinking Gender (Basic Books, 2020). She applies insights gained from her mathematical background to propose a new way to talk about gender and to propose an alternative: the terms “ingressive” and “congressive” behavior. In this interview, Dr. Cheng and I talk about what we gain from bringing a mathematical understanding to questions of social relations and structures. We talk about how she rethinks “gender”, and the new terms she proposes in her book. We end with a short discussion of whether these insights are applicable to conversations about other demographic and social identifiers. Dr. Eugenia Cheng is a mathematician and concert pianist. She is Scientist In Residence at the School of the Art Institute of Chicago and holds a PhD in pure mathematics from the University of Cambridge. Alongside her research in Category Theory and undergraduate teaching her aim is to rid the world of “math-phobia”. She was an early pioneer of math on YouTube and her videos have been viewed over 15 million times to date. Her other books are How to Bake Pi: An Edible Exploration of the Mathematics of Mathematics (Basic Books: 2016), which was featured on the Late Show with Stephen Colbert, Beyond Infinity: An Expedition to the Outer Limits of Mathematics (Basic Books: 2017) which was shortlisted for the Royal Society Science Book Prize in 2017 and The Art of Logic in an Illogical World (Basic Books: 2018) You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of x+y. Follow on Facebook or on Twitter at @BookReviewsAsia. Nicholas Gordon is a reviewer for the Asian Review of Books. In his day job, he’s a researcher and writer for a think tank in economic and sustainable development. He is also a print and broadcast commentator on local and regional politics. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/mathematics

From its more mainstream, business-focused and business-friendly “Lean In” variants, to more radical, critical and intersectional understandings of feminism, the past decade has seen a flourishing of discussion from those proposing and critiquing different schools of thought for the way we think about gender in society. Dr. Eugenia Cheng’s addition to this conversation is x+y: A Mathematician's Manifesto for Rethinking Gender (Basic Books, 2020). She applies insights gained from her mathematical background to propose a new way to talk about gender and to propose an alternative: the terms “ingressive” and “congressive” behavior. In this interview, Dr. Cheng and I talk about what we gain from bringing a mathematical understanding to questions of social relations and structures. We talk about how she rethinks “gender”, and the new terms she proposes in her book. We end with a short discussion of whether these insights are applicable to conversations about other demographic and social identifiers. Dr. Eugenia Cheng is a mathematician and concert pianist. She is Scientist In Residence at the School of the Art Institute of Chicago and holds a PhD in pure mathematics from the University of Cambridge. Alongside her research in Category Theory and undergraduate teaching her aim is to rid the world of “math-phobia”. She was an early pioneer of math on YouTube and her videos have been viewed over 15 million times to date. Her other books are How to Bake Pi: An Edible Exploration of the Mathematics of Mathematics (Basic Books: 2016), which was featured on the Late Show with Stephen Colbert, Beyond Infinity: An Expedition to the Outer Limits of Mathematics (Basic Books: 2017) which was shortlisted for the Royal Society Science Book Prize in 2017 and The Art of Logic in an Illogical World (Basic Books: 2018) You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of x+y. Follow on Facebook or on Twitter at @BookReviewsAsia. Nicholas Gordon is a reviewer for the Asian Review of Books. In his day job, he’s a researcher and writer for a think tank in economic and sustainable development. He is also a print and broadcast commentator on local and regional politics. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/anthropology

From its more mainstream, business-focused and business-friendly “Lean In” variants, to more radical, critical and intersectional understandings of feminism, the past decade has seen a flourishing of discussion from those proposing and critiquing different schools of thought for the way we think about gender in society. Dr. Eugenia Cheng’s addition to this conversation is x+y: A Mathematician's Manifesto for Rethinking Gender (Basic Books, 2020). She applies insights gained from her mathematical background to propose a new way to talk about gender and to propose an alternative: the terms “ingressive” and “congressive” behavior. In this interview, Dr. Cheng and I talk about what we gain from bringing a mathematical understanding to questions of social relations and structures. We talk about how she rethinks “gender”, and the new terms she proposes in her book. We end with a short discussion of whether these insights are applicable to conversations about other demographic and social identifiers. Dr. Eugenia Cheng is a mathematician and concert pianist. She is Scientist In Residence at the School of the Art Institute of Chicago and holds a PhD in pure mathematics from the University of Cambridge. Alongside her research in Category Theory and undergraduate teaching her aim is to rid the world of “math-phobia”. She was an early pioneer of math on YouTube and her videos have been viewed over 15 million times to date. Her other books are How to Bake Pi: An Edible Exploration of the Mathematics of Mathematics (Basic Books: 2016), which was featured on the Late Show with Stephen Colbert, Beyond Infinity: An Expedition to the Outer Limits of Mathematics (Basic Books: 2017) which was shortlisted for the Royal Society Science Book Prize in 2017 and The Art of Logic in an Illogical World (Basic Books: 2018) You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of x+y. Follow on Facebook or on Twitter at @BookReviewsAsia. Nicholas Gordon is a reviewer for the Asian Review of Books. In his day job, he’s a researcher and writer for a think tank in economic and sustainable development. He is also a print and broadcast commentator on local and regional politics. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/gender-studies

01:41 - Descartes was wrong: ‘a person is a person through other persons’ (https://web.ics.purdue.edu/~drkelly/AeonMagBirhaneDescartesWasWrongPersonsAreSocial2017.pdf) * Abeba Birhane on a person is a person through other persons (https://www.youtube.com/watch?v=1RVscNkiTq0) * Cartesian Thinking (https://www.youtube.com/watch?v=1RVscNkiTq0) * Individualism (https://www.youtube.com/watch?v=1RVscNkiTq0) 13:59 - Predicting How People Behave and Act via Machine Learning is Ethically Flawed * “Measuring” People * Simon’s Ant (https://medium.com/@seannewmanmaroni/simons-ant-2c7693335ff9) * Abstraction * Greater Than Code Episode 038: Category Theory for Normal Humans with Dr. Eugenia Cheng (https://www.greaterthancode.com/category-theory-for-normal-humans) * Order Out of Chaos by Ilya Prigogine and Isabelle Stengers (https://www.amazon.com/Order-Out-Chaos-Ilya-Prigogine/dp/0553343637) * Collecting Data * Confirmation Bias (https://en.wikipedia.org/wiki/Confirmation_bias) 34:21 - Examining Machine Learning Models and Data * Means Testing (https://en.wikipedia.org/wiki/Means_test) * Generalized Empathy “When you get rid of what you don’t want, you do not necessarily get what you do want and you may get something you want a lot less. It is that simple…..anyone that ever watches television knows that!” – Russell L. Ackoff (http://fearlessrevival.com/russell-ackoff/) “Scoring” People Perpetuates Stereotypes Acurracy Confirms Bias 50:09 - Important Ideosyncracies and Contaminating Factors * Seeing and appreciating the potential to be different in every person in every situation. * The ability to tease apart existing cultural ideas around identity and humanity. * Taking concepts from different but related fields and seeing their connectedness and bringing them together into a whole that is more than the sum of their parts. * Seeing consequences that don’t belong to any one cause. Reflections: Mando: Cartesian thinking and worldview is embedded in us. Avdi: “Contaminating factors.” “Dive into yourself to find yourself.” Rein: Jainism (https://en.wikipedia.org/wiki/Jainism) has gotten this right for centuries. * The Elephant and the Blindfolded Men Parable * The Theory of Conditioned Predication or Contigency * The Theory of Partial Standpoints Jessica: Giving the Cartesian program credit for what it’s good for: using science as a way to break things down into parts and studying them deeply; we’ve learned a lot. Abeba: It’s not all bad. BUT, we forget to put the pieces back together and acknowledge reality. This episode was brought to you by @therubyrep (https://twitter.com/therubyrep) of DevReps, LLC (http://www.devreps.com/). To pledge your support and to join our awesome Slack community, visit patreon.com/greaterthancode (https://www.patreon.com/greaterthancode) To make a one-time donation so that we can continue to bring you more content and transcripts like this, please do so at paypal.me/devreps (https://www.paypal.me/devreps). You will also get an invitation to our Slack community this way as well.) Special Guest: Abeba Birhane.

Jakob and Todd discuss category theory, an important field in modern mathematics that focuses on the relations (morphisms) between mathematical objects. We discuss the importance of abstraction and the development in the history of mathematics beyond solving particular problems to studying the general nature of mathematical structures as such, the kinds of problems that can and can't be solved, their properties, etc. We also consider the significance of a relation-centered approach to other fields, how things like languages, theories, and beliefs can be analyzed by the relations between their constituent elements.

pdepodcast.episodios().stream().map(TocadorPodcast::play); Não entendeu? Aperte o play! Participantes Marcio Frayze David marcio@segunda.tech https://segunda.tech https://masto.donte.com.br/web/accounts/138458 https://twitter.com/marciofrayze Julianno Martins Silva juliannoms@gmail.com Livros: Learn You a Haskell for Great Good: http://learnyouahaskell.com/ Functional Thinking, Neal Ford: https://www.goodreads.com/book/show/18492332-functional-thinking Programming with Types: https://www.manning.com/books/programming-with-types Category Theory for Programmers, Bartosz Milewski: https://bartoszmilewski.com/2014/10/28/category-theory-for-programmers-the-preface/ Links interessantes: Higher Order Functions: https://www.youtube.com/watch?v=BMUiFMZr7vk Javadoc do Optional: https://docs.oracle.com/javase/8/docs/api/java/util/Optional.html Javadoc da api funcional do Java: https://docs.oracle.com/javase/8/docs/api/java/util/function/package-summary.html Biblioteca funcional pro Java: https://www.vavr.io/ Curso de Category Theory: https://www.youtube.com/user/DrBartosz/playlists

04:31 - Amy’s Superpower: Search Algorithms and Finding Things * Finding Things in Code * Visual vs Spacial Awareness 08:39 - Toxic Masculinity and Hierarchies in Engineering Roles * I’m not in security but whenever I hear people taking about who is more technical all I see is folks jockeying for status.(not blaming, that’s the culture we have made in software generally). But from outside whatever y’all mean when you say technical is completely opaque. (https://twitter.com/amynewell/status/1298957052694409218) * “Soft Skills” vs “Technical” 14:22 - Measuring Skill Advancement * The Individual Contributor (IC) vs Manager Track * Management vs Mentorship 21:02 - Congressive vs Ingressive * x + y: A Mathematician's Manifesto for Rethinking Gender by Eugenia Cheng (https://www.amazon.com/Mathematicians-Manifesto-Rethinking-Gender/dp/1541646509) * GTC Episode 038: Category Theory for Normal Humans with Dr. Eugenia Cheng (https://www.greaterthancode.com/category-theory-for-normal-humans) 22:43 - Ways Toxicity Shows Up in The Workplace * Doing/Recognizing “Real Work” * Letting Go of Past Baggage * Amy Newell - Lessons from Bipolar Disorder (https://www.youtube.com/watch?v=Qf8TYuAZY8g) (The first time Amy said the word “Patriarchy”) * Microaggressions 29:07 - Unlearning and Psychological Safety 37:07 - The Word “Nontechnical” * Respecting Expertise * Skilled/Unskilled Labor: All Labor is Skilled Labor! 40:41 - Recognizing and Feeling Value * Being Your Authentic Self * Making Culture Better for Everyone; Supporting People & Making Space * Counting Emotional Energy * Enough Leaning In. Let’s Tell Men to Lean Out. (https://www.nytimes.com/2019/10/10/opinion/sunday/feminism-lean-in.html) * Yes, and-ing Reflections: Jessica: Every person has a unique set of ways they can have an impact and best contribute. Jamey: Feeling like you belong and realizing that others might not. Being aware of both mindsets. Amy: The onus really should be on people who have power and privilege in any conversation to be doing most of the work to be aware of what I see vs what they see. Special Guest: Amy Newell.

Category theory is well-known for abstraction—concepts and tools from diverse fields being recognized as specific cases of more foundational structures—though the field has always been driven and shaped by the needs of applications. Moreover, category theory is rarely introduced even to undergraduate math majors, despite its unifying role in theory and its flexibility in application. Postdoctoral Associate Brendan Fong and Research Scientist David I. Spivak, both at MIT, have written a marvelous and timely new textbook that, as its title suggests, invites readers of all backgrounds to explore what it means to take a compositional approach and how it might serve their needs. An Invitation to Applied Category Theory: Seven Sketches in Compositionality (Cambridge University Press, 2019) has few mathematical prerequisites and is designed in part as a gateway to a wide range of more specialized fields. It also centers its treatment on applications, motivating several key developments in terms of real-world use cases. In this interview we discussed their views on the promise of category theory inside and outside mathematics, their motivations for writing this book, several of the accessible examples and remarkable payoffs included in its chapters, and their aspirations for the future of the field. Suggested companion works: --Tai-Danae Bradley, Math3ma --Eugenia Cheng, The Catsters --Saunders Mac Lane, Mathematics Form and Function --F. William Lawvere & Stephen H. Schanuel, Conceptual Mathematics: A First Introduction to Categories --Eugenia Cheng, x + y: A Mathematician's Manifesto for Rethinking Gender Cory Brunson (he/him) is a Research Assistant Professor in the Laboratory for Systems Medicine at the University of Florida.

Category theory is well-known for abstraction—concepts and tools from diverse fields being recognized as specific cases of more foundational structures—though the field has always been driven and shaped by the needs of applications. Moreover, category theory is rarely introduced even to undergraduate math majors, despite its unifying role in theory and its flexibility in application. Postdoctoral Associate Brendan Fong and Research Scientist David I. Spivak, both at MIT, have written a marvelous and timely new textbook that, as its title suggests, invites readers of all backgrounds to explore what it means to take a compositional approach and how it might serve their needs. An Invitation to Applied Category Theory: Seven Sketches in Compositionality (Cambridge University Press, 2019) has few mathematical prerequisites and is designed in part as a gateway to a wide range of more specialized fields. It also centers its treatment on applications, motivating several key developments in terms of real-world use cases. In this interview we discussed their views on the promise of category theory inside and outside mathematics, their motivations for writing this book, several of the accessible examples and remarkable payoffs included in its chapters, and their aspirations for the future of the field. Suggested companion works: --Tai-Danae Bradley, Math3ma --Eugenia Cheng, The Catsters --Saunders Mac Lane, Mathematics Form and Function --F. William Lawvere & Stephen H. Schanuel, Conceptual Mathematics: A First Introduction to Categories --Eugenia Cheng, x + y: A Mathematician's Manifesto for Rethinking Gender Cory Brunson (he/him) is a Research Assistant Professor in the Laboratory for Systems Medicine at the University of Florida. Learn more about your ad choices. Visit megaphone.fm/adchoices

Category theory is well-known for abstraction—concepts and tools from diverse fields being recognized as specific cases of more foundational structures—though the field has always been driven and shaped by the needs of applications. Moreover, category theory is rarely introduced even to undergraduate math majors, despite its unifying role in theory and its flexibility in application. Postdoctoral Associate Brendan Fong and Research Scientist David I. Spivak, both at MIT, have written a marvelous and timely new textbook that, as its title suggests, invites readers of all backgrounds to explore what it means to take a compositional approach and how it might serve their needs. An Invitation to Applied Category Theory: Seven Sketches in Compositionality (Cambridge University Press, 2019) has few mathematical prerequisites and is designed in part as a gateway to a wide range of more specialized fields. It also centers its treatment on applications, motivating several key developments in terms of real-world use cases. In this interview we discussed their views on the promise of category theory inside and outside mathematics, their motivations for writing this book, several of the accessible examples and remarkable payoffs included in its chapters, and their aspirations for the future of the field. Suggested companion works: --Tai-Danae Bradley, Math3ma --Eugenia Cheng, The Catsters --Saunders Mac Lane, Mathematics Form and Function --F. William Lawvere & Stephen H. Schanuel, Conceptual Mathematics: A First Introduction to Categories --Eugenia Cheng, x + y: A Mathematician's Manifesto for Rethinking Gender Cory Brunson (he/him) is a Research Assistant Professor in the Laboratory for Systems Medicine at the University of Florida. Learn more about your ad choices. Visit megaphone.fm/adchoices

Seed idea: Can category theory help us frame how things that are different, but that perform the same function, are somehow “the same.” With links to affordances, cross-category competition, and jobs to be done. Plus thoughts on the trade-offs between data ownership and convexity effects on social media.

Welcome to The Brew! This podcast series is about providing a platform for entrepreneurs, artists, athletes, and thought leaders to spark conversations about business and culture. We deep dive into topics that have and will continue shaping the business world and society as a whole. Episode #7 is with ARKS, a warehouse automation startup based in Riverside, CA. Two of its co-founders are brothers, Xavier and Oscar Hernandez, and we are pleased to have them join the show to share their knowledge on automation and technology. Topics discussed in this episode include: 1. Warehouse Automation 2. Social Entrepreneurship 3. Category Theory 4. Space Mining 5. Elon Musk and Living on Mars 6. The 5G Connection Race 7. Advice on building a startup Spotify: https://open.spotify.com/show/3ZQEnlMOJUINZGvYJWTwI5 Website: https://www.freelogicmedia.com/ Instagram: https://www.instagram.com/freelogicmedia/ Facebook: https://www.facebook.com/FreeLogicMedia/ Filmed and Produced By: Nick Saldivar Instagram: https://www.instagram.com/nsaldivar95/ Business Instagram: https://www.instagram.com/victoriammedia/ Website: https://www.victoriammedia.com/ Music: “Nesting" by Birocratic (http://www.birocratic.com) The songs used in this video were licensed via Birocratic License v05.2016. For info on how you can use this music in your project, check out http://www.birocratic.com/license-app. To download Birocratic’s 60+ song discography, visit http:// birocratic.bandcamp.com.

More about the structured recusion scheme known as the catamorphism. Basic idea of functors.

Review of basic application of category theory for functional programming. Recursion schemes are combinators that let you write point-free recursions.

A few very basic ideas of categories and combinators. Also, the problem of understanding very concise code.

Relation of point-free functional programming to category theory.

Brandon Shapiro is a graduate student in Mathematics at Cornell University studying under the supervision of Inna Zakharevich. Brandon studies higher category theory and how they relate to topology and computer science. He is currently focused on comparing how different shape structures describe higher categories and models of univalent type theory. Brandon also runs a blog called Blogularwhich is linked below.https://blogular8.wordpress.com/Check out Brandons website here:http://pi.math.cornell.edu/~bts82/Support the show (https://www.patreon.com/sensemakesmath)

Today Adam talks to Bartosz Milewski. He is the author of a famous blog series, lecture series and now book on Category Theory for programmers. The world of functional programming is rife with terminology imported from abstract algebra and Category Theory. In fact, it may be one of the most valid criticisms of functional programming is the use of Category-Theoretic terminology that can be unwelcoming to newcomers. Category theory can also be a tool to teach us to see software development in a different light and it can teach us to build better software. Bartosz is also just an interesting person, if you haven't heard of him yet, you are in for a treat. Bartosz's Website Blog Series Book Lecture Series https://corecursive.com/035-bartosz-milewski-category-theory/

0:34 ScalaConf, Moscow, Russia, 26 Nov, @ScalaConfRu 1:01 Become a patron 1:22 Scala in the City, London, UK, 28 Aug, @Scalainthecity 1:32 Scala World, The Lake District, UK, 31 Aug – 4 Sep, @scalaworldconf 1:58 Rúnar Bjarnason - Icelander. Eudaimonist. Individualist. Cofounder, Unison Computing. Author of Functional Programming in Scala. 2:05 Evolution 4:30 A Gentle Introduction to Haskell 5:00 Clean programming language 5:18 "Why Functional Programming Matters" by John Hughes 6:05 FP on JVM 8:45 Functional Programming in Scala a.k.a. the Red book 17:32 FP in Dotty 18:28 Standard library is a double-edged sword? 20:11 Missing features 21:51 Category theory 23:27 "Category Theory for Programmers" by Bartosz Milewski 23:51 "Category Theory for the Sciences" by David I. Spivak 23:58 "Category Theory" by Steve Awodey 24:05 "Category Theory in Context" by Emily Riehl 25:05 "Categories for the Working Mathematician" by Saunders Mac Lane 26:20 Unison programming language 28:42 Inspiration for Unison 30:28 The central idea of Unison 36:03 How to update Unison programs 41:45 Unison types 41:59 "Complete and Easy Bidirectional Typechecking for Higher-Rank Polymorphism" by Joshua Dunfield, Neelakantan R. Krishnaswami 42:30 Lennart Augustsson: Better type-error messages 44:02 Frank programming language 44:12 "Do be do be do" by Sam Lindley, Conor McBride, and Craig McLaughlin 52:58 Compile once! 56:53 All things that Unison doesn't do because they are old and busted 64:37 Why Unison? 65:00 Puccini's La bohème 65:55 Puccini's Tosca 65:58 Verdi's La Traviata 66:24 Why opera? 67:20 Why philosophy? 70:02 What is love? Follow us on Twitter @scala_love Have a lovely day! ❤️

Dr. John Carlos Baez, an American Mathematical Physicist and a Professor of Mathematics at the University of California, Riverside, based in the United States participate in Risk Roundup to discuss The Promise of Category Theory. Category Theory Category theory has become a key driver for modern mathematics, theoretical computer science and is beginning to be […] The post The Promise Of Category Theory appeared first on Risk Group.

01:24 - Saron’s Superpower: Being able to figure out what people need + read people. 02:26 - Codeland Conference (http://codelandconf.com/) July 22nd, 2019 in New York City! Episode 026: Codeland, Capitalism, and Creating Inclusive Spaces with Saron Yitbarek (https://www.greaterthancode.com/codeland-capitalism-and-creating-inclusive-spaces) 03:23 - Offering Free Onsite Childcare at Conferences 07:02 - CodeNewbie (https://www.codenewbie.org/) Base.cs Podcast (https://www.codenewbie.org/basecs) The CodeNewbie Podcast (https://www.codenewbie.org/podcast) 08:18 - Expertise in Newbie-ism 14:55 - Learning and Teaching Conversation Theory (https://en.wikipedia.org/wiki/Conversation_theory) Virtual Reality (https://en.wikipedia.org/wiki/Virtual_reality) Oculus (https://www.oculus.com/?locale=en_US) 22:27 - Encouraging Asking, Psychological Safety (https://en.wikipedia.org/wiki/Psychological_safety), and Being Comfortable with Being Uncomfortable 29:48 - Building a Supportive Community and Advice for Code Newbies Dev.to (https://dev.to/) 34:12 - Dealing with Bad Actors Episode 038: Category Theory for Normal Humans with Dr. Eugenia Cheng (Congressive/Ingressive Behavior Conversation) (https://www.greaterthancode.com/category-theory-for-normal-humans) 42:35 - Coding as a Reflection of People 49:23 - Lexicon and Creating a Shared Language Mitigated Speech (https://en.wikipedia.org/wiki/Mitigated_speech) Reflections: Chanté: Everyone is a Newbie, and the living room metaphor. Rein: How much ego investment in our work is appropriate? Arty: Growing the magical living room. Saron: Thinking about mitigation speech. Outliers: The Story of Success by Malcolm Gladwell (https://www.amazon.com/gp/product/0316017930/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=therubyrep-20&creative=9325&linkCode=as2&creativeASIN=0316017930&linkId=7d8a44b649d1a6249361310db4c2a8ac) This episode was brought to you by @therubyrep (https://twitter.com/therubyrep) of DevReps, LLC (http://www.devreps.com/). To pledge your support and to join our awesome Slack community, visit patreon.com/greaterthancode (https://www.patreon.com/greaterthancode) To make a one-time donation so that we can continue to bring you more content and transcripts like this, please do so at paypal.me/devreps (https://www.paypal.me/devreps). You will also get an invitation to our Slack community this way as well. Amazon links may be affiliate links, which means you’re supporting the show when you purchase our recommendations. Thanks! Special Guest: Saron Yitbarek.

00:03:35 - Благодарности Патронам 00:04:00 - шаблон для pdf презентаций из emacs org-mode 00:10:47 - впечатления Oli о Конференциях (SFScala, Lambda World) Видяхи с Lambda World 00:27:32 - Fury opensourced 00:40:39 - обсуждаем книги 00:52:45 - отчет Oli об FP тренинге John De Goes 00:58:28 - Scala version of Category Theory for Programmers 00:59:58 - Scala-дайджест #9 01:02:38 - Spark Release 2.4.0 w 2.12 01:13:54 - Typed-sql Поддержи подкаст https://www.patreon.com/scalalalaz Голоса выпуска: Ольга Махасоева, Вадим Челышов, Григорий Помадчин, Евгений Токарев

00:00:34 - Праздник Суккот 00:01:17 - Начало карьеры Игаля на Scala 00:04:00 - ФП в мире Дотнета 00:14:40 - Коммьюнити в Израиле Митап Underscore Конференция Scalapeno 00:21:50 - Переход из .Net на Scala 00:24:50 - Миграция на Bazel в Wix.com поддержка Scala для bazel (bazel.build) 00:45:08 - Зоопарк технологий в Wix 00:51:50 - Почему Scala в Wix 00:56:50 - Шарполаз 01:07:56 Purely Functional I/O Runar Bjarnason "Propositions as Types" by Philip Wadler 01:16:37 - Category Theory for Programmers Epub version https://github.com/typelevel/CTfromProgrammers.scala 01:30:05 - пост John de Goes про нейминг 01:43:00 - митап в Москве Поддержи подкаст https://www.patreon.com/scalalalaz Голоса выпуска: Евгений Токарев, Алексей Фомкин, Ольга Махасоева, Igal Tabachnik

At first glance chocolate brownies, puff pastry and Battenberg cake don’t seem to have a great deal in common with theoretical maths, but Eugenia Cheng has harnessed her love of cooking in order to tackle the fear of maths so many of us share – and has published a book about it called How to Bake Pi. Her mission is to rid the world of "maths phobia", and to this end she gave up her secure job teaching at Sheffield University to open up the world of maths to students from other disciplines as Scientist in Residence at the School of the Art Institute of Chicago, which also gives her the opportunity to pursue her own research in Category Theory - the purest form of maths. And she’s a highly accomplished pianist, performing in concert halls around the world, as well as founding Liederstube - a popular venue for lieder and art song in Chicago which has hosted performers such as Gerald Finley and Richard Wiegold. Eugenia explains to Michael how chocolate brownies and pure maths are related; how she prefers to work in cafes and bars with pen and paper rather than on a computer, and how her intensely emotional response to music is a release from the intensely ordered world of pure mathematics. And they dismantle stereotypes about Chinese ‘tiger mothers’, girls and maths, and the idea that people who are good at maths are automatically good at music. Eugenia chooses music from Bach’s Matthew Passion, Rachmaninov’s Second Piano Concerto – which she herself has played – and from Mahler’s Resurrection Symphony and Janacek’s opera The Makropulos Case, which take her on an emotional and philosophical journey towards a reconciliation with mortality. Producer: Jane Greenwood A Loftus production for BBC Radio 3

Taras Mankovski: tarasm In this episode, Taras and Charles talk about a project that they work on together: Funcadelic - a Functional Programming and Category Theory for Everyday JavaScript Development. Funcadelic takes the simple idea that a single function can operate on many different data structures (typeclass oriented programming) and brings it to JavaScript. Yes, there are a lot of FP libraries out there, but this one is geared towards unlocking the magical powers of functional programming while always maintaining a tangible and accessible experience for JavaScript developers. Because if you're a JavaScript developer today, you're already using most of the structures in funcadelic! Transcript: CHARLES: Hello everybody and welcome to The Frontside Podcast Episode 99. My name is Charles Lowell, developer here at The Frontside and your podcast host-in-training. And with me today is Mr. Taras Mankovski. Welcome. TARAS: Thank you, Charles. It's a pleasure to be here. CHARLES: Yeah. So, you are ubiquitous in the JavaScript world. You do a lot of stuff with mentoring and you are involved in a bunch of different interesting projects. I think you're one of those developers who's difficult to classify, which is – that's definitely one of my favorite kind of developers. I wanted to have you on the show today because there's been a project that we've been collaborating on. And there have been some interesting ideas to come out of that and solidify through that project, at least in my head. And yeah, I thought we could maybe just talk about that a little bit. TARAS: Yeah, sounds good. It's going to be fun. : The thing that we are going to be talking about is a project called Funcadelic. It's more than really just a library, a JavaScript library on GitHub. It's kind of a different way of thinking about your code. And so, I know for me, where this really became part of my workflow was, when was it? It was about three months ago or something like that? Six months ago? TARAS: Oh, I think yeah, I think it's probably more six months ago. I think it's probably what, two months, I think probably December maybe? CHARLES: Okay. But it's hard now to imagine working without this tool on my workbench. It's been probably the biggest game-changer for me in the last, I don't know, definitely in the last several years. TARAS: Yeah, it's pretty impressive how little, how small of a library can have such a big impact in what we do day-to-day. Because it definitely makes me think differently about how I can solve problems that I solve on a daily basis when I work with React. So, it's been pretty interesting. I think for me, having worked with this library, I think what I'm getting is an understanding of how things work in a way, and a perspective on how React works, in a way that I don't think was available to [inaudible] Funcadelic. The funny thing is it's not a React library, right? It's not designed for React. It's just that… CHARLES: I don't even think that – it helps you think about React, but I don't even think it's the way that the React developers think about React, right? TARAS: Yeah, I don't think so, either. I think a lot of people are on the spectrum of understanding functional programming. And I think a lot of people use, people learn how to use React, but they don't really – I don't think a lot of people have traveled very far. I'm talking about general, majority. There's definitely people who know functional programming really well. And there's a lot of really good libraries in the JavaScript space for doing functional programming in JavaScript. But I don't think the general public, the general people that on a daily basis go into – write a render function and do ‘this.' or like ‘product.map' and then return an array of components. I don't think those people necessarily think about or get the context within which they use this tool. CHARLES: Right. And I think that's actually kind of one of the reasons I think a library like Funcadelic is so important and fills kind of a missing piece in the ecosystem, is because it really is predicated on the idea that programmers use these concepts all the time. They really are, they're foundational. But we only kind of see them out of the corner of our eye, in our peripheral vision, as being like a formal concept, like mapping. And giving a name to that. You know what I mean? Like you do the mapping, but you're not thinking about: how do I generalize over it? And I think that that for me, certainly in my journey with functional programming, I thought that it was mostly about functions. Not to say that it isn't, but that was kind of the end of the story. It's like, keep your functions pure so that the outputs are only dependent on the inputs. And away you go. And understand closures and higher-order functions, functions that return functions or take functions, and that's it. But I really feel that that's only half the story. TARAS: Part of it I think is that for people, even if you look at the kind of content that's available around functional programming, it tends to be – trying to kind of [reach] people into this idea of thinking of map, filter, reduce, kind of operations. And I think that's a place where everybody starts. But I think what happens is that you really are missing – and I think for most people. And it wasn't for me, it wasn't until you wrote the readme for Funcadelic and then I read it – up until that point I didn't really, I was also the same. I didn't know how these things were related to each other. Because there's this history and wealth of conceptual depth that connects all these things together. And these things are well-understood by people who don't – they're probably not writing JavaScript on a daily basis. They might be like Haskell programmers or Lisp programmers or ClojureScript or something like it. In other worlds, not JavaScript world. So there is all this understanding about how functional programming works but I don't think it's really leaked to the general masses of the JavaScript community. CHARLES: Yeah. TARAS: You know? And it wasn't until I started reading the readme – I'm like, “There's so many answered questions that I didn't even know these questions were asked.” You know? CHARLES: Yeah, yeah. Yeah, no. And I think you're absolutely right. It isn't accessible to the general JavaScript community. And part of that is because one person's – like when you read about these things, when I would go read about these kind of higher-order concepts, of basically classifying functions, not just of saying, “Yeah, what is the essence of a map operation? What's the essence of an apply operation?” you know, “What's the essence of concatenation?” things like that, I go read the documentation in Haskell or in Clojure. And first of all, it's hard to distinguish when you're not programming in those day-to-day, am I reading reference documentation or explanatory documentation? But even in the explanatory documentation, they're using what seems like incredibly self-referential and abstract examples. And I don't think that's necessarily a knock against those communities. I think what it is, is what's concrete to one person is abstract to another. And it's like, if you're working with those things, you're working with those sets of analogies, and then you're working with those abstractions every day, then they're concrete to you. In a sense that once it clicks in your mind and your mind kind of accepts it and rationalizes over it, then it moves from being, “Yes, it's an abstraction. But it's a concrete abstraction,” in the sense that you can have a conversation with somebody and use that abstraction as an example, as a counterpoint, and a method triangulate and reveal other abstractions. But if you're talking to somebody for whom those abstractions haven't clicked yet, then it's just, it's opaque. And it's not helpful. And so, I think that one of the things that I realized is like we are using these abstractions, we just don't have names for them. And so, I wanted to give them names and put them in the hands. And the names are weird, but they are really useful. And so yeah, maybe we could talk about some of those right now. Because I think that maybe now's a good time to actually introduce some of those abstractions. So for example, if you don't know what a functor is, it's worthless to talk about a monad, in my opinion. So, that was critical piece of information for me. Because that is like missing in every monad tutorial you ever read. At least, I must have read a thousand monad tutorials. And they kind of glossed over functor or didn't mention it. Whatever. Maybe they did but I wasn't looking. And that needs to be put front and center, that there is a natural sequence to these things. It's like, some of these abstractions are built on other abstractions and you have to – you can't skip to monad. You have to start with functor. Again, I realize that's probably gibberish gobbledy-goop to a lot of people. So really, this is what Funcadelic is about, what this conversation is about, is just saying – talking through it in real-world examples to make those abstractions concrete, so it doesn't feel strange anymore. TARAS: Yeah. It's definitely giving names to things. I think it's really helpful. One thing I really like about Funcadelic is that you're not giving names that you made up. These are names that existed for 50 years. They're historic. And so, when you talk to somebody who is familiar with functional programming and if you say ‘functor', all of a sudden, I feel much smarter. And we're actually referencing the same thing, because we are – because alternatively, you can say something like, “Something that is mappable,” right? Like a functor is essentially describing something that you can map over. CHARLES: Right. That's all it is. TARAS: Right. But you know, having a name for it, it allows you to just describe it exactly as it is. CHARLES: So yeah, there are tons of things that you can map over, right? Most of the time, we think about arrays as something that we can map over. TARAS: One thing I found really interesting in starting to use Funcadelic is that when you start thinking about things as they are like abilities – like you know with an array you can map over an array. It's something we've all been doing for a while – but then something that you end up doing a lot of. When you get familiar with an array, being able to use object map, mapping an object, becomes something you want to do at some point. Most of the time, what happens is that you're like, “Oh, I don't actually – well, I have to write this thing. How do I write this thing?” and then by the time you do this, you're like, “I'm just going to go to Lodash and I'll just get the map thing that will map an object.” At the end of the day, it doesn't quite necessarily feel right because a lot of these libraries – like, Lodash has map but it feels like there is always some kind of a compromise with how these things are implemented. It's not consistent. CHARLES: Right. TARAS: And I don't remember exactly what the problem with Lodash map was. I know for a fact there are, like there's different ways that you can map things. There are different functions available for mapping different things in Lodash. CHARLES: There's ‘map to' and ‘map in' and blah, blah, blah, blah, blah. TARAS: Yeah. All those different variations. But I think it's been really interesting. We've been using Funcadelic on a project we've been working on, on microstates. And just being able to use one function map that is going to map an array or is going to map an object and it's going to do it the same, use the same function, and it's going to do the same thing. CHARLES: Yeah. You have one function that maps over the values. And that's the thing, is you realize you can map over arrays. You can map over objects. You can map over observables. You can map over promises. You can map over trees. You can map – there's literally thousands of things that you can map over. And realizing that all of these fragmented interfaces can be coalesced into a single interface. And so, it really is, I think the biggest thing is like the power of polymorphism for a function. Because that's basically the problem that I think – it's not I want to say basically the problem. I think it is a problem that a lot of the functional programming libraries suffer from, is that the only polymorphism is object polymorphism, which is kind of the native polymorphism in JavaScript. Whereas in systems like Haskell and like Clojure, you can have a function be polymorphic. And so, one function can operate on many different kinds of data, provided it has an interface. So, when we're working in microstates, we use literally one function: map. The same, the actual same function, reference to the same function we use everywhere when we map. And we're just mapping a bunch of different things. So, I think that that's one of the reasons that I prefer Rambda, for example, over Lodash, is because it has a form of polymorphism. Most of the things like maps and lenses and applicatives and stuff, almost everything works on both objects and arrays. That's actually kind of nice. So, Rambda has a basic polymorphism. But I think one of the other things that is really empowering about Funcadelic is that it allows you to make the map function work on any data structure that you happen to come up with. Anything that you want. You can make it mappable and map will work on it. TARAS: Yeah. I think for people, it's probably quite abstract, what that actually looks like. I think one thing that's interesting is that – so for listeners, the way that would look is you have a class, you have an ES6 class, which gives name to a certain piece of data that you have in your application. And then what you can do with Funcadelic, you can then say, “This is how you would implement – if you were to map this kind of an object, if you had an instance of this object, if you wanted to map that object, you can specify: what is the function you would use?” So, even though you would use the same – you would import map from Funcadelic and you would use that map to map whatever that object is and whatever type it is, but there's a way for you to say, “For instances of this type, I would like to use this specific implementation of a map to map that object,” but use one function to do it. So, it's going to use one function that you call. But you can specify, under the hood it can specify how the actual, what is actually used to map that instance. CHARLES: Right. And then that's nice, because then you can – anything that uses a mappable object, there's a couple of reasons that that's nice. Any time you can have some sort of higher-order mechanism that just requires that something be mappable, that it be a functor. And then it's really nice because then you can have higher-order operations that they just need something that's mappable. But you don't have to use that one shot of actually having a map function as a property of the object. You can actually, you can kind of define wrapper classes or whatever, that then introduce a unique way that this object can be mapped. So, you can have the same structure and map it three different ways. Whereas you're kind of constrained by that using normal OO inheritance because you have to have a map property on your object. TARAS: Yeah. There's something else actually, when you start thinking about this. For me personally, I think the first step when we start working with objects, having mappable objects, it was the first thing that was really helpful. But then I think really quickly, right after that, I think my second thing that I started using and I think is probably my favorite feature now, is append. I think it's actually – yeah, so append is an implementation of a semigroup but I think it's simpler. A simplest metaphor would be something like object assign. So, object assign is an implementation of a semigroup, except that object assign mutates the object that you pass into it. Right? CHARLES: Right. TARAS: It doesn't create a new object for you. CHARLES: Right. So again, getting to this idea that there are some universal operations. Like there's this idea that you can take two things – I don't want to say object because that's an actual concrete type in JavaScript – two things and you can smoosh them together. And I think there was actually – wasn't there literally a big controversy about this? TARAS: Yes. CHARLES: About like, array smoosh? I think this is nice – smooshing, right? But you can smoosh things together. And with addition, I can smoosh two integers together or two numbers together and get a third number, right? Or with objects, I can take two objects and smoosh them together by merging their keys. Or I can take two strings and I can smoosh them together and I can end up with a string that's concatenated. Or I can take two arrays and smoosh them together and I've got now an array that's been concatenated. So, what's interesting is these are all very, very different types that I've just described. And yet, there is some fundamental operation about them. And I think this is actually something that's bad about JavaScript is there's five different names for all those operations that we talked about, but it's really one unifying concept. And that might seem like a small thing, but when you have five different interfaces for one fundamental construct, that leads to fragmentation and you can't treat that data uniformly. And it ends up like, paper cuts, paper cuts everywhere. Whereas if you can unify all of these into a small, one thing, which is like we can append two objects, and then we're going to have an implementation of append for array. And behind the scenes it's going to call smoosh. And we've got a universal – we've got an implementation of append for object, which is going to assign the keys in an immutable way and return a new object. Or we're going to have an implementation of append for string which is going to concatenate the strings. You might even, I don't know a hardcore FP nerd would have to probably correct me because this is just totally conjecture, although I know it's probably a solved problem – is maybe you could say we append two functions together. And that returns a function which is composed, right? That might be a way that you could say – what would it look like? Is function a semigroup? Can we append two functions together? And maybe you end up with like a pipe or a function composition or something like that. But I think that highlights, when you have these universal interfaces, because literally I feel like most of the stuff that we have been working with, there's literally five, there's like five interfaces. And everything is one of these five interfaces. And it kind of flips you on your head, because the classic programming wisdom that I have certainly have espoused for at least the last 10 years is that you don't want to race to find abstractions. That's dangerous. Because you can get locked into the wrong abstraction, right? Wait. Let the abstraction emerge. And I'm a lot less bullish on that concept now that I've discovered these things, because that wisdom is cultivated in a world where there are [billions] of abstractions, if you're giving unique names to everything and the combinations between them. But if you are coming up in a world where there's five basic abstractions, then it actually pays off to ask the question, “Is this thing a functor? Is it a semigroup? Is it a monad? What would that look like?” It's a nice thought experiment that doesn't require that much investment. You can think about it for a couple of minutes. And usually, you can come up and say, “Yes, yes. It totally is,” and I can start using it. And now I've introduced this really powerful abstraction. Or you say like, “No, no it's not.” And then that information is just as valuable. And so, it's very low-cost to experiment with abstractions. And so, I kind of think of it as – I know I'm on a little bit of a rant here but this has kind of been a major revelation for me – is that when you have very few abstractions which you compose in myriad ways versus having a whole bunch of abstractions that can't be composed very much, the cost for experimenting with abstraction and making the wrong abstraction is several orders of magnitude lower. And so, you don't have to be as cautious. And you can actually use trying on abstractions as a tool, rather than a very, very high risky undertaking. And just to kind of close that thought out, I think that – I don't know if anybody else but me remembers the world before we decided we were going to make all of our web services RESTful – like when people first started building all these web services, we were just going crazy with the endpoints. And there was no rhyme or reason. Kind of weird arbitrary levels of nesting. Sometimes, you'd throw in an ID as a query param, sometimes you'd throw it in as part of the path. And then I definitely remember, it was probably around, I don't know, 2010 for me where I listened to a podcast where James Edward Gray was talking about S3 and ‘Was it a RESTful interface?' and the O'Reilly book. And it really clicked for me. And realizing that if you constrain yourself to thinking about your API at least as these fundamental operation of manipulating resources, and you were constrained to four verbs and everything, you want to have ID-based URLs and resources and as flat as possible, those constraints actually are very enabling for consumers of your API and for actually authoring an API. And I think it's the same principle at work here. Anyway, so I'll end that rant. [Laughs] TARAS: Yeah. I think it's, I think people could probably, for those who haven't been in programming as long as Charles has been, it's probably easier – Charles I think people could probably relate to what's happening with components now, I'd imagine. Because having components essentially look the same across every framework, they all have props and they all render, return some DOM, or some variation of that. But it's kind of the same thing. You take some data and then you return something that is going to become DOM. And I think having that as a rule for what a component is, you can then make really complicated applications using these fundamental building blocks. And then you don't have to – there's not really much thinking on, “Well, how am I? What interface is this component going to have?” Okay, well you know it's going to accept props. And you know it's going to render some DOM when you actually render it, right? CHARLES: Right. TARAS: That simplicity I think is really helpful. And I think it's one of the things that – I think one of the things about Funcadelic that I really like is there's kind of a really small set of rules that are really helpful. And these rules are actually, they make it predictable. Because one thing that I find really challenging with using Lodash or using Rambda is that because there are so many functions, it's difficult to know what is actually going to happen when I do something. So, a good example would be like if you use omit from Lodash, Lodash omit will then, it will actually – one thing you can do is you can materialize your getters. So, if you have getters in your objects, those getters can become values on the new object that's created. So, that's one thing that could happen. Or if you use omit, your symbol, if you have values… CHARLES: What does omit do, by the way? I'm actually… TARAS: Omit is a pretty popular way to exclude functions. Basically like a filter equivalent for an object. It's usually used to remove some props that are coming into a component. But it can do some – it can actually change the type of the object. One thing you know for sure is if you use something like omit or if you use assign, if you have an instance of something, guaranteed, working with that object in a mutable way is going to cause some really strange things. I think with omit, if you were to have an instance, it would definitely not give you an instance of the same type, like an object of the same type. It will give you just a regular [inaudible]. And there is no real way – you could create an instance. I don't know what assign would do. I'm guessing that it would just take an instance and would put things on it. But it's really not – I think this kind of ambiguity doesn't work very well when you're trying to build something, when you really just need to know exactly how your tool behaves. I think that's one thing that with Funcadelic, because it's such a small API and because you know for a fact that the library is designed to be immutable and it's designed to preserve type information, then you know that if you use one of the operations, you will get most likely the same kind of object. And you're not – well, you will the same kind of object and it's not going to mutate that object. And so, there are some of these guarantees that are actually really helpful and [inaudible] is liberating, I think. CHARLES: Right. TARAS: Especially when you're trying to do more challenging things, not trivial, just copy some properties from one object to another. But when you're actually doing more sophisticated things, in those use cases, having these kind of rules is extremely powerful. CHARLES: Yeah. And I've definitely resisted and I think will continue to resist expanding the API surface area that much. Because it is, I think there's only five or six functions in there. But what you get for those functions is extremely powerful and extremely predictable. I think it might help to give a concrete example. Like when you were talking about object assignment. Like if you have a car class in your application and you want to do an immutable object assign, well the kind of stereotypical way to do that or the typical way to do that is you assign. You create an empty object and then you assign one car to it and then you assign the next car to it. And now you've merged these two car things, right? But then the problem is, you're going to get an object out of that, not a car. It's just going to be a vanilla object. Now, it's going to have all the properties of both, but it's not going to be a car. And that could be a problem if you've got any custom methods on the prototype, any computed properties on the prototype. It's going to be a problem. Whereas Funcadelic, you can append two cars together and you're going to know that it's going to have both of the properties. You're going to know that it's going to be of type car. And you don't have to worry about running the constructor or anything like that. It's just going to have – the properties are going to be carried over properly. If you're using a library like Lodash or Rambda or something that doesn't account for type, because in order to append two things or map something, the implementation actually lives with the type, not with the function. The function is polymorphic, but the implementation lives with the type. You can then actually, you can always return the proper type. Because it's ultimately – like if your map operation or your filter operation or your what have you operation doesn't take type into account, then there's no way to actually preserve type. But because we delegate in Funcadelic, it's core to the concept. And so, it's actually a very trivial thing to do, which is why you get that repeatability. TARAS: One of my favorite things about append and semigroup implementation for object is that you can overload getters on an existing instance. So, what you get is you get a new instance with the getter that you passed to append applied to that instance. And this is kind of trippy but it's actually really powerful because – so, let's say you have an instance of an object and that instance has some getters on it. And those getters use some properties of this object to compute their value. And so, when you want – if I need to create a copy of that object in such a way that the getters still continue to work properly but I need to override how one specific getter works for one specific instance, one specific scenario, one specific use case in my code, then I can just use append. So, the first argument is the original instance, second argument is an object that has the getters that I want that I want to overload the getters on the original object. And then append will squish those things together, smoosh those things together, and give me a new instance that has the getters that I passed in, in the second argument. And all the same things that the first object had. And that object will work. This new object will be a fully-functional object just like the original object that I still have a copy of, that I can use. But this is really interesting because one thing that I'm finding with having these kind of tools in my toolset is that I've had features that I needed to implement on a project. And the people that I work with are really technical. So, they know where problems are going to be. And so, the would write requirements for how something – for a feature that needs to be implemented. And knowing the problems, they're like, “Oh, by the way. You're going to have a problem in this area when implementing this kind of specific functionality.” And for me, I'm like, having this toolset, I'm like, “I don't see it as a problem at all.” It's so easy for me. Because I know that if I need to implement – so if I have something that has expected behavior, but I need to create something that behaves very similarly but slightly different in one particular use case, I can always just copy that object and then overload its behavior. And it's still going to be a fully-functional object. And I think that alone is just not something that you can do usually. It's not something that's available. CHARLES: Yeah. It's a technique that I think was discovered. Maybe it's not original to us. But it's just, the tools enabled it in the sense that it's like having a flashlight in a dark room. It's like, that technique was always there. It's just when it becomes so concise that it's so easy to just append one more computed property to a thing, then you just wouldn't have thought to do it otherwise. TARAS: What's interesting about this too, for me, is that – and this goes back to the context conversation that we had earlier – is that I think React brought into our lives functional programming kind of, in a big way. Because part of programming React applications is working with functional components and working with functional concepts. And a lot of the things, like Redux, a lot of these things are powered by functional programming. And they work together. They compose well together because they're all from the same paradigm. But the problem is that there are all these concepts developed together over time. And they've been tested together and they've been formulated together in languages like Haskell. But the ideas that make all of these stuff work really well together, they haven't really become available in the JavaScript community. And it feels like, it's like we're all using a language that we don't fully understand. And it's like we're all – a lot of people in the JavaScript world who works, when it comes to functional programming, it's kind of like having English as a second language. It's like, you can use the words but you don't understand the humor. And it's kind of like, you can't make a joke. It's kind of like that. You can't really express yourself correctly when you don't have full grasp of the language. And I feel like how we use functional programming in JavaScript is a little bit like that. And by starting to bring these ideas, moving the wealth of knowledge from the source into the realm where we actually need to use it now, we can actually start to take advantage – leverage all these insights that actually enables all of this. It's not like – so, the idea of how to write React and think in a reactive way or think in a functional way, those things are not just owned by the React core team or they're not owned by an elite group of developers who really understand how functional programming works. It's just available to everyone. And all you need to do is just learn some of these concepts that glue all these ideas together that are fundamental pieces of how functional programming works. CHARLES: Right. That's a great point. And it points back to kind of the original reason that I wrote Funcadelic and then started and then continued to work on it with you, is that it really was – it was actually meant as a – it started out as an educational exercise. What would these things look like if they were translated into JavaScript? And it turned out that it rapidly became core to my workflow and way of thinking. And so, it really is, there are weird names to these universal concepts. It is true. It is a foreign language. I really like that analogy. But foreign languages sound weird, and when people are talking in a foreign language, you can feel excluded. And the really, the reason that we wrote Funcadelic and the reason it's there is to make them accessible, these things accessible to you. So that those abstract foreign words can over time turn into concrete concepts that you're completely comfortable with, just like any word in any language. At some point, you approached it having no idea what that sound represented. And so, it really is trying to – the emphasis there is not to noodle about and dwell on the names of the concepts but to take the real things that you are actually doing, give them names and formalize them, to enable you to participate in this new functional world that you're describing. Because I love that sentiment. It does not belong to the React core team. It doesn't belong to an elite set of developers on this project or that project. It literally is a universal tool that is 100% achievable. And people don't even realize how close – if they've been working with JavaScript for a couple of years, how close they actually are. TARAS: Yeah. I think they're really – for most people, if they were to read the readme and then – well, I think one of the problems, it kind of works with the language metaphor, is that you need an immersion, right? I think one of the reasons why Funcadelic really stuck and functors and semigroups and [filterable] and all of these things really stuck for me, and I'm thinking about how using monads and monadic operations or applicatives and all that stuff – the reason why it all stuck for me is because I've been able to talk to you about it. And I think for people, finding a network that will allow them to practice immersively, to think about functional programming, not just occasionally – I mean, you could do it occasionally as well, it just takes much longer. But once you really, once you have a few conversations where you try to dissect a problem in a functional way and you think about what these pieces are made of, it becomes very natural, very quickly. CHARLES: Yeah. I think that's actually a really great point. And the thing is, you can immerse yourself incrementally. So you can just say, “You know what? I'm just going to start using append.” Anywhere that I would concat two strings or I concat two arrays or I would do object assign – screw that. You can even just say, “Instead of using object.assign, I'm going to use append,” and start there. Or to say, “I'm just going to start with mapping.” I think also the thing that's nice about it too, is the buy-in can be incremental. But you're right. You do need immersion. You do need practice. You need to actually use, you need to use the functions and you need to be able to use them one at a time, so that your mind can close over them. So then, you can kind of move onto the next one. TARAS: Yeah. CHARLES: So, that might be one way, is to say – because you know, I don't think I really understood. I was already using append and map ubiquitously before I really understand applicative/apply. So, you don't have to grok it all at once. You can definitely bite it in chunks. And the best way to do that is to start with one concept and really just attack it mercilessly. And then also understand that there's a natural sequence there. TARAS: I would add a little bit of a caveat to that. I think there's a thing about using – doing something for learning purposes and there's another thing about shipping things. What's interesting with Funcadelic and what's interesting about a lot of these ideas from functional programming is that I think they give you benefits that you might have not previously thought. Like for example, if you're going to concat two strings together, doing it with append is probably the most robust way of doing it, relative to just being able to use [inaudible]. CHARLES: Yeah, that's true. You wouldn't want to use a string [inaudible], wouldn't you? TARAS: Yeah. But there are areas when using – there are times when things are just not possible otherwise. Like for example, if you wanted to treat an instance of a class in an immutable way, this is simply not possible in any way. So, if you're going to say, “I'm going to work with a bunch of these instances of ES6. And I want to keep them as instances, because they have certain behaviors that I want to have. I want to have a getter, or I want to have a method on it. And I want to keep these things fully full instances, not broken, not be turned into objects. I want them to be normal instances but I want to work with them immutably. When I need to make a change, I want to get a new object and not modify that object.” So, if you set yourself that goal and you say, “This is what I'm going to do,” then you really are not left with very many options. You only really have, you have to use append from Funcadelic. And because alternatively, you're going to implement something yourself. You might as well just use append. And [I think] that's a good place. I think if you're starting to, if you need to make something lazy, if you need to delay an execution of something – so, instead of pushing that execution, instead of using object assign and then computing everything ahead of time, you can use append. And you can create a getter and you can delay the computation of that value until the point when the user actually reaches for that value. If you want to start doing that kind of stuff, you really are not left with very many options. And append is the way to do it. But that's the thing, is when you start to set these kinds of standards for yourself, you level up in a way that is very significant. I think it's like a natural progression of learning. You start off learning and anything goes, as long as you can make this website work, it's like, “I'm happy.” And then over time you get better and better at it. And then when you get good at building applications, your next step might be like, “What if I was stricter about it? What if I could actually – what would that open up for me? What would that allow me to do?” I personally think about it that way. CHARLES: Yeah. I think that's a good way to think about it. You mentioned having a network for discussing these concepts and trying to internalize them. Let me first and foremost offer myself as somebody. If this is a hill that you are interested in climbing, and I think it is a very worthwhile hill to climb because of the perspective that you will gain from its summit, please reach out to me. You can contact me at cowboyd on Twitter or cowboyd@frontside.io. I'd be happy to discuss these kinds of things, because I think that these tools are just incredibly powerful and will improve you. So, if folks want to get in touch with you, Taras, where would that be? TARAS: I'm tarasm@gmail.com and tarasm on Twitter. CHARLES: Alright. Well, thank you everybody for listening. And as always, if you want to get in contact with us at Frontside, you can just email us at contact@frontside.io or give us a shout on Twitter at @TheFrontside. Thanks everybody. We'll see you all next time for episode 100.

Podlodka #44 – Функциональное программирование Мы используем его принципы каждый день, но можем даже об этом не подозревать. Мы можем отвергать его, но в то же время использовать его лучшие подходы. Функциональное программирование шагает по планете и уже во всю стучится в уютный мирок мобильных разработчиков, а значит будем про это говорить! В гостях у нас практикующий Haskell-программист, сооснователь сообщества ruHaskell - Денис Шевченко. Поддержи лучший подкаст про мобильную разработку: www.patreon.com/podlodka Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях!
 Telegram-чат: https://t.me/podlodka Telegram-канал: https://t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: https://twitter.com/PodlodkaPodcast Содержание: - 00:01:00 - Знакомство с гостем - 00:01:46 - История ФП - 00:05:18 - Математика - простейший ЯП - 00:07:50 - Основная идея - 00:16:28 - Сложность входа - 00:24:10 - ФП стоит на плечах математики - 00:31:50 - Идея типизации - 00:50:30 - Организация кода - 01:01:37 - Дебаг - 01:16:02 - Проблемы ленивого вычисления - 01:26:35 - Войти в ФП - 01:52:05 - Особенности поиска работы - 02:15:29 - Подводим черту Полезные ссылки: - Сообщество ruHaskell https://ruhaskell.org/ - Книга для самых маленьких https://www.ohaskell.guide/ - Haskell-проект Cardano https://www.cardanohub.org - То самое видео про булы, инты и историю ФП https://www.youtube.com/watch?v=XrNdvWqxBvA - List of companies using Haskell https://gist.github.com/sigrlami/769f5e6674adbd399f00 - Поиск ФП-работы FunctionalWorks - Учебный веб-проектик для диабетиков https://github.com/denisshevchenko/breadu.info https://breadu.info/ - Презентация о скриптах на Haskell https://www.youtube.com/watch?v=WWz1VE94bAM - Haskell is Not For Production and Other Tales https://youtu.be/mlTO510zO78 - Functional Programming and Modern DevOps https://www.youtube.com/watch?v=ybSBCVhVWs8 - THE DOWNFALL OF IMPERATIVE PROGRAMMING https://www.fpcomplete.com/blog/2012/04/the-downfall-of-imperative-programming - Category Theory for Programmers https://github.com/hmemcpy/milewski-ctfp-pdf - The Joy and Agony of Haskell in Production http://www.stephendiehl.com/posts/production.html

Nothing annoys Eugenia Cheng more than the suggestion that there is no creativity in mathematics. Doing mathematics is not about being a human calculator, she says. She doesn't spend her time multiplying big numbers in her head. She sits in hotel bars drawing (mainly arrows) with a fine quill pen, thinking about how ideas from different areas of mathematics relate to one another and hoping to reveal a unifying, underlying logic to the whole of mathematics. Her area of research, Category Theory, makes algebra seem superficial. And if that makes your head hurt a little, don't worry. Feeling confused is an essential part of doing mathematics. 'You can't make progress without it' Eugenia says. Jim asks Eugenia what drove her to such a high level of abstraction and learns more about her mission to rid the world of maths phobia, by baking. Producer: Anna Buckley Photo credit: Paul Crisanti, PhotoGetGo.

Podlodka #39 – Итоги 2017 года Хоть мы еще и маленький подкаст, но итоги года подводить можем. В специальном новогоднем выпуске мы, поедая мандарины, обсуждали топы всего, что только можно – компаний, книг, статей, конференций, выпусков подкаста, перспективных технологий. Если вы хотите составить план по прокачке своих навыков на новогодние каникулы, то этот выпуск точно для вас. А в качестве бонуса выяснили, кто же лучший ведущий подкаста, рассказали историю его создания и закопали все, что попалось под руку. Поддержи лучший подкаст про мобильную разработку: www.patreon.com/podlodka Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях!
 Telegram-чат: https://t.me/podlodka Telegram-канал: https://t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: https://twitter.com/PodlodkaPodcast Содержание: - 00:00:40 - Про содержание выпуска - 00:01:10 - История создания подкаста - 00:03:25 - Лучшие выпуски подкаста - 00:17:32 - Про Call for Papers - 00:17:55 - Статистика по слушателям подкаста - 00:27:35 - Куда уходят деньги с Patreon - 00:29:40 - Топ компаний - 00:45:27 - Топ книг - 00:59:35 - Топ статей - 01:03:02 - Топ конференций и митапов - 01:11:32 - Про эмпатию в IT - 01:12:25 - Какие технологии не взлетели и должны остаться в 2к17 - 01:14:55 - Какие технологии выстрелят в 2к18 - 01:20:32 - Какие навыки прокачивать в 2к18 - 01:23:50 - Топ ведущих подкаста - 01:26:30 - Ответы на вопросы слушателей - 01:32:10 - Отзывы слушателей о подкасте - 01:36:20 - Речетатив с подписчиками на Patreon - 01:38:40 - Поздравления с Новым годом от ведущих Полезные ссылки: - Набор стикеров Podlodka Podcast для Telegram https://t.me/addstickers/podlodka - 45 татуировок менеджера https://www.mann-ivanov-ferber.ru/books/paperbook/tattoos/ - Continuous Deployment of Mobile Software at Facebook https://research.fb.com/wp-content/uploads/2017/02/fse-rossi.pdf - Uber Engineering Blog https://eng.uber.com/ - AvitoTech GitHub https://github.com/avito-tech - Microservices: From Design to Deployment https://www.nginx.com/blog/microservices-from-design-to-deployment-ebook-nginx/ - Управляя изменениями https://www.mann-ivanov-ferber.ru/books/upravljaja_izmenenijami/ - Работа рулит! https://www.mann-ivanov-ferber.ru/books/rabota-rulit/ - Гарри Поттер и методы рационального мышления http://hpmor.ru/ - Демиан https://www.livelib.ru/book/1000312732-demian-german-gesse - Джедайские техники https://www.mann-ivanov-ferber.ru/books/dzhedajskie-texniki/ - Программист-прагматик https://www.ozon.ru/context/detail/id/1657382/ - Whither Swift http://lapcatsoftware.com/articles/whither-swift.html - You fired your top talent. I hope you’re happy https://startupsventurecapital.com/you-fired-your-top-talent-i-hope-youre-happy-cf57c41183dd - SoundCloud Microfeatures https://github.com/microfeatures/guidelines - Applying Conway's Law to improve your software development https://www.thoughtworks.com/insights/blog/applying-conways-law-improve-your-software-development - Concurrency in Swift: One approach https://gist.github.com/lattner/31ed37682ef1576b16bca1432ea9f782 - Category Theory for Programmers: The Preface https://bartoszmilewski.com/2014/10/28/category-theory-for-programmers-the-preface/

01:08 – Christina’s Background and Superpower: Multitasking and Automation See Also: GTC 056: Systematize Your Hustle with Kronda Adair 04:42 – Automation Processes: Discovery and Reconnaissance, and When Human Judgement and Input is Necessary 10:03 – Multitasking Timescales and Context Switching 16:39 – Decision-making Functions 23:28 – Being Kind to Your Busy Self and Choosing What NOT To Do We’re Going to Need More Wine: Stories That Are Funny, Complicated, and True by Gabrielle Union (https://www.amazon.com/gp/product/0062693980/ref=as_li_qf_sp_asin_il_tl?ie=UTF8&tag=therubyrep-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0062693980&linkId=36d5c466dd58a3659f9dc2c31e7c8554) 32:03 – Making Accomplishments Visible to Yourself and Having a Culture of Acknowledgement For more discussion on congressive/ingressive behavior, see also: GTC Episode 038: Category Theory for Normal Humans with Dr. Eugenia Cheng Operant Conditioning Chamber (Skinner Box) (https://en.wikipedia.org/wiki/Operant_conditioning_chamber) Reflections: Rein: Being self-aware of how much is on your plate, how you’re feeling about it, and then being able to say no, which is really saying yes to what YOU want to do, what YOU want to spend time on, and how YOU want to live YOUR life. Janelle: Taking the time to remember and that remembering takes time. Jessica: Choosing the group that you’re being generative with. Christina: UX is everything and everywhere. This episode was brought to you by @therubyrep (https://twitter.com/therubyrep) of DevReps, LLC (http://www.devreps.com/). To pledge your support and to join our awesome Slack community, visit patreon.com/greaterthancode (https://www.patreon.com/greaterthancode). To make a one-time donation so that we can continue to bring you more content and transcripts like this, please do so at paypal.me/devreps (https://www.paypal.me/devreps). You will also get an invitation to our Slack community this way as well. Amazon links may be affiliate links, which means you’re supporting the show when you purchase our recommendations. Thanks!

[00:01:54] - Lightbend, Twitter stack. Разговор с Виктором (внезапное возвращение спустя год) [00:27:35] - Путешествие в Java и обратно Ожидания от Scala в будущем, чуть-чуть экспериментов с GO [00:37:13] - Немного о стриминге: Storm, Akka Streams [00:41:01] - Немного о базах: MongoDB, PSQL [00:43:27] - Методы деплоя [00:47:00] - Монорепы https://finelydistributed.io/monorepo-for-small-teams-part-1-9-why-you-havent-heard-of-pants-28358b12f0cb https://finelydistributed.io/monorepo-for-small-teams-part-2-1-modularity-with-pants-82182996c98f [00:55:00] - Рекламная пауза от Виктора, краткое описание чем занимается (контекст обсуждения выше). [01:12:32] - Odersky - higher-kinded language import and declaring higher-kinded types officially unsound. [01:15:35] - JaneStreet - Ironing out your development style [01:27:37] - John De Goes - Excited About Scalaz 8 [01:31:32] - A note on Kentucky Mule and Twitter’s Scala compiler announcement [01:35:51] - Bootstrapping the Web with Scala Native https://www.spantree.net/blog/2017/08/29/bootstrapping-web-scala-native.html https://www.spantree.net/blog/2017/08/29/bootstrapping-web-scala-native.html https://twitter.com/RichardWhaling/status/902921953438257152 https://twitter.com/RichardWhaling/status/902921953438257152 [01:40:38] - Bartosz Milewski's - "Category Theory for Programmers" has been finished! [01:42:34] - 5 New features in Akka (Streams) 2.5.4 you may have missed [01:43:06] - circe-fs2 0.9.0-M1 is out! https://github.com/circe/circe-fs2/releases/tag/v0.9.0-M1 [01:46:11] - Lagom 1.3.7 is released! [01:48:42] - scala-pet-store https://github.com/pauljamescleary/scala-pet-store https://twitter.com/pauljamescleary/status/901162097563893766 [01:50:10] - sbt-compat Если вас заинтересовала беседа с Виктором и вы хотите поучавствовать в развитии его проектов (https://whisk.com), то можно обращаться к нему в твиттер, или писать на почту. Голоса выпуска: Виктор Тараненко, Евгений Токарев, Алексей Фомкин, Григорий Помадчин

In this episode, Frank and Andy talk to Kevin Hazzard. Links: Sponsor: Audible.com (http://thedatadrivenbook.com) – Get a free audio book when you sign up for a free trial! Sponsor: Enterprise Data & Analytics (http://entdna.com) Kevin’s blog: Developer Journey (http://devjourney.com) Notable Quotes: Frank has a treadmill desk. ([2:35]) “It’s all about collecting the data.” Health is secondary. ([3:25]) On Python… ([5:00]) We love our R listeners! ([6:30]) Never count JavaScript out. ([8:20]) Book reference: Our Magnificent Bastard Tongue (https://www.audible.com/pd/History/Our-Magnificent-Bastard-Tongue-Audiobook/B002V1OF16?source_code=PDTGBPD060314004R) ([8:50]) Book reference: “grok” from Stranger in a Strange Land (https://www.audible.com/pd/Sci-Fi-Fantasy/Stranger-in-a-Strange-Land-Audiobook/B002V8MUYI?source_code=PDTGBPD060314004R) ([10:45]) How would databases be different if we’d started with unlimited memory? ([12:00]) How to use data to drive web traffic. ([17:45]) Generic Activity Tracker architecture ([18:30]) Self-subscribing and auto-expiring microservices ([20:30]) Movie reference: Dune (http://www.imdb.com/title/tt0087182) ([21:47]) The Walmart-Amazon wars ([22:00]) “Data hunted me down and almost killed me!” ([24:25]) Category theory ([25:30]) Functional, then categorical, then both. ([26:50]) “Sharpening chansaws.” ([28:05]) Redis Cache (https://redis.io/) ([30:15]) “I’d love to not have a screen.” ([32:30]) On serving our community ([33:00]) Book recommendation: The Alchemist (https://www.audible.com/pd/Fiction/The-Alchemist-Audiobook/B002V0Q4LG?source_code=PDTGBPD060314004R) ([33:40]) “If you’re a technologist, we sorely need you to serve.” ([35:40]) Richmond.Net (https://www.meetup.com/Richmond-NET-User-Group/) ([36:45]) @KevinHazzard (https://twitter.com/KevinHazzard)

00:16 – Welcome to “Shopping is Hard; Let’s Do Math!” …we mean, “Greater Than Code!”; Eugenia’s Introduction Books: How to Bake Pi: An Edible Exploration of the Mathematics of Mathematics (https://www.amazon.com/gp/product/0465097677/ref=as_li_qf_sp_asin_il_tl?ie=UTF8&tag=therubyrep-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0465097677&linkId=0d495c591e20d50802c3fa80ef30775d) Beyond Infinity: An Expedition to the Outer Limits of Mathematics (https://www.amazon.com/gp/product/0465094813/ref=as_li_qf_sp_asin_il_tl?ie=UTF8&tag=therubyrep-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0465094813&linkId=1c8d256e9484319b7631615ccc857fd1) YouTube Channels: TheCatsters (https://www.youtube.com/user/TheCatsters/featured) TheMathsters Articles: Eugenia Cheng Makes Math a Piece of Cake (https://www.nytimes.com/2016/05/03/science/eugenia-cheng-math-how-to-bake-pi.html) Everyday Math (https://www.wsj.com/news/types/everyday-math) 01:54 – Getting Into Math: Is math useful? Is that the point? A Mathematician’s Lament by Paul Lockhart (https://www.maa.org/external_archive/devlin/LockhartsLament.pdf) 20:17 – Category Theory (https://en.wikipedia.org/wiki/Category_theory) Textbooks: Categories for the Working Mathematician (https://www.amazon.com/gp/product/0387984038/ref=as_li_qf_sp_asin_il_tl?ie=UTF8&tag=therubyrep-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0387984038&linkId=fb86951beaddc97589ea491f060216ce) Category Theory (Oxford Logic Guides) (https://www.amazon.com/gp/product/0199237182/ref=as_li_qf_sp_asin_il_tl?ie=UTF8&tag=therubyrep-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0199237182&linkId=770e13783e791421d55ca1d5fa69038e) Conceptual Mathematics: A First Introduction to Categories (https://www.amazon.com/gp/product/052171916X/ref=as_li_qf_sp_asin_il_tl?ie=UTF8&tag=therubyrep-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=052171916X&linkId=522bb5da8da065199edb56d83edb1f8a) 38:17 – Changing the Terminology Around Gender to Focus on Character Traits Instead: Congressive and Ingressive Behavior The Prisoner’s Dilemma (https://en.wikipedia.org/wiki/Prisoner%27s_dilemma) This episode was brought to you by @therubyrep (https://twitter.com/therubyrep) of DevReps, LLC (http://www.devreps.com/). To pledge your support and to join our awesome Slack community, visit patreon.com/greaterthancode (https://www.patreon.com/greaterthancode). To make a one-time donation so that we can continue to bring you more content and transcripts like this, please do so at paypal.me/devreps (https://www.paypal.me/devreps). You will also get an invitation to our Slack community this way as well. Amazon links may be affiliate links, which means you’re supporting the show when you purchase our recommendations. Thanks! Special Guest: Dr. Eugenia Cheng.

   iTunes & Stitcher subscribers! Be sure to go to the blog to read the description easier & check out links for this episode! Returning guest NewtypeLady is back to talk with me about the 1995 smash-hit, Neon Genesis Evangelion--another Gainax series that takes place in the “distant future of 2015″ (and celebrates it's 20th anniversary this year!) Even with this extra-long episode, we only touch the tip of the iceberg on what makes this series tick & why it's considered such an important piece of work. Listen as we discuss why it's a mecha show that's not a mecha show, our feelings towards these character's feelings, and the many reasons why we wish this series was not currently out of print in the US. Stream the episode above or [Direct Download]You can also now subscribe on itunes & Stitcher! Relevant links: The original Evangelion TV series DVDs The Evangelion manga (also available in ebook format on Amazon & Comixology!) The Evangelion Rebuild movies Insignificant Direction by Moyocco Anno Read more theory & analysis on the many layers of Eva at Evageeks Also, more detailed Gainax references (& cross-references) in Evangelion As well as Tributes to other works found within in Evangelion As always, feel free to leave me your thoughts and ideas for future episodes here, in a DM or reblog, or email directly at AnimeNostalgiaPodcast@gmail.com. Thanks for listening!

Trademarks I