Podcasts about why the new science

Steven Forth is Ibbaka's Co-Founder, CEO, and Partner. Ibbaka is a strategic pricing advisory firm. He was CEO of LeveragePoint Innovations Inc., a SaaS business designed to help companies create and capture value. In this episode, Steven advocates for proactive scenario planning, encouraging businesses to identify critical uncertainties and fortify their pricing strategies for the uncertainties of the future.   Why you have to check out today's podcast: Understand the significance of pricing as a strategic element often overlooked in planning, and recognize its pivotal role in post-COVID economic landscapes Acknowledge the shift to a sounder economic period, where capital has a tangible cost, emphasizing the importance of net present value as a cornerstone of planning assumptions Prioritize fixing issues strategically, considering both short-term and long-term plays, and embrace scenario planning for effective pricing strategies in a dynamic environment   "I think we are settling into a sounder economic period where capital has a cost, net present value matters, and we need to have that as a planning assumption." - Steven Forth   Topics Covered: 01:38 - An observation about pricing being overlooked in strategic planning for 2024 and pricing being just an afterthought 04:20 - The need to strategically approach pricing in the context of the next three years post-COVID and thoughts on the monetization of generative AI 07:24 - Important thoughts on what kind of impact will AI have in businesses in the years ahead in comparison to what blockchain years ago 09:32 - From low interest rates to normal range, the importance of capital costs and net present value as part of planning assumptions. 13:05 - The need to take realistic steps to investments in AI, impact of non-zero interest rates on capital costs, the stabilization of buying behaviors into 2024 and how all these are considered in pricing planning in 2024 18:47 - Prioritizing what needs to be fixed first rather than fixing all at once and risk messing up everything 19:52 - How often should one conduct a pricing strategy 22:25 -Two key things in mind when planning for 2024: first establish baselines and trends, then aligning pricing with the overall strategic goals of the company 27:13 - What it means to have a portfolio point of view when making pricing planning and how to implement a faster cadence to reach your pricing goals 30:09 - Attributing business results to pricing changes and introducing the concept of causal analysis   Key Takeaways: "I think we are settling into a sounder economic period where capital has a cost, net present value matters and we need to have that as a planning assumption." - Steven Forth "You can't really do strategic planning if you don't understand where you are and how you got there." - Steven Forth "I would encourage people to at least consider looking at scenario planning where you plan for more than one scenario. You identify critical uncertainties and you plan for each of the critical uncertainties. That approach would make a lot of sense for pricing." - Steven Forth   People / Resources Mentioned: Judea Pearl: https://en.wikipedia.org/wiki/Judea_Pearl The Book of Why: The New Science of Cause and Effect: https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X   Connect with Steven Forth: LinkedIn: https://www.linkedin.com/in/stevenforth/ Email: steven@ibbaka.com   Connect with Mark Stiving: LinkedIn: https://www.linkedin.com/in/stiving/ Email: mark@impactpricing.com  

The world is changing fast. Technology can be used to empower us -- and also to hack our brains & our lives. What laws do we need to protect our freedoms? Rahul Matthan joins Amit Varma in episode 360 of The Seen and the Unseen to share his work on privacy -- and on a new, subtle approach towards data governance. (FOR FULL LINKED SHOW NOTES, GO TO SEENUNSEEN.IN.)   Also check out: 1. Rahul Matthan on Twitter, Instagram, LinkedIn, Trilegal, Substack and his own website. 2. Privacy 3.0: Unlocking Our Data-Driven Future -- Rahul Matthan. 3. The Third Way: India's Revolutionary Approach to Data Governance -- Rahul Matthan. 4. The Life and Times of KP Krishnan -- Episode 355 of The Seen and the Unseen. 5. Sudhir Sarnobat Works to Understand the World -- Episode 350 of The Seen and the Unseen. 6. Roam Research. 7. Zettelkasten on Wikipedia. 8. Tana, Obsidian and Notion. 9. Getting Things Done -- David Allen. 10. The Greatest Productivity Mantra: Kaator Re Bhaaji! -- Episode 11 of Everything is Everything. 11. Hallelujah (Spotify) (YouTube) -- Leonard Cohen. 12. Hallelujah (Spotify) (YouTube) -- Jeff Buckley. 13. The Holy or the Broken: Leonard Cohen, Jeff Buckley, and the Unlikely Ascent of "Hallelujah" -- Alan Light. 14. Hallelujah on Revisionist History by Malcolm Gladwell. 15. Bird by Bird: Some Instructions on Writing and Life -- Anne Lamott. 16. The New Basement Tapes. (Also Wikipedia.) 17. Kansas City -- Marcus Mumford. 18. The Premium Mediocre Life of Maya Millennial -- Venkatesh Rao. 19. Vitalik Buterin Fights the Dragon-Tyrant — Episode 342 of The Seen and the Unseen. 20. Paul Graham on Twitter and his own website. (His essays are extraordinary.) 21. Ribbonfarm by Venkatesh Rao. 22. The Network State --  Balaji Srinivasan. 23. Marc Andreessen on Twitter. 24. The Techno-Optimist Manifesto -- Marc Andreessen. 25. Siddhartha Mukherjee and Carlo Rovelli on Amazon. 26. For the Lord (Spotify) (YouTube) -- Rahul Matthan. 27. Predicting the Future -- Rahul Matthan (on Asimov's concept of Psychohistory etc). 28. Gurwinder Bhogal Examines Human Nature — Episode 331 of The Seen and the Unseen. 29. The Looking-Glass Self. 30. Panopticon. 31. Danish Husain and the Multiverse of Culture -- Episode 359 of The Seen and the Unseen. 32. A Scientist in the Kitchen — Episode 204 of The Seen and the Unseen (w Krish Ashok). 33. We Are All Amits From Africa — Episode 343 of The Seen and the Unseen (w Krish Ashok and Naren Shenoy). 34. Nothing is Indian! Everything is Indian! — Episode 12 of Everything is Everything. 35. The Right to Privacy -- Samuel D Warren and Louis D Brandeis. 36. John Locke on Britannica, Stanford Encyclopedia of Philosophy, Wikipedia and Econlib. 37. Build for Tomorrow -- Jason Feifer. 38. Ex Machina -- Alex Garland. 39. Arrival -- Denis Villeneuve. 40. The Great Manure Crisis of 1894 -- Rahul Matthan. 41. Climate Change and Our Power Sector — Episode 278 of The Seen and the Unseen (w Akshay Jaitley and Ajay Shah). 42. The Book of Why: The New Science of Cause and Effect -- Judea Pearl. 43. The New World Upon Us — Amit Varma on Alpha Zero. 44. Brave New World -- Vasant Dhar's podcast, produced by Amit Varma. 45. Human and Artificial Intelligence in Healthcare -- Episode 4 of Brave New World (w Eric Topol). 46. The Colonial Constitution -- Arghya Sengupta. 47. Beyond Consent: A New Paradigm for Data Protection -- Rahul Matthan. 48. The Puttaswamy case. 49. Judicial Reforms in India -- Episode 62 of The Seen and the Unseen (w Alok Prasanna Kumar.) 50. Accidental Feminism: Gender Parity and Selective Mobility among India's Professional Elite --  Swethaa S Ballakrishnen. 51. Magic Fruit: A Poetic Trip -- Vaishnav Vyas. 52. Hermanos Gutiérrez and Arc De Soleil on Spotify. 53. The Travelling Salesman Problem. 54. The Twenty-Six Words That Created the Internet -- Jeff Kosseff. 55. Code: And Other Laws of Cyberspace -- Lawrence Lessig. 56. Financial Inclusion and Digital Transformation in India -- Suyash Rai. 57. No Time for False Modesty -- Rahul Matthan. 58. In Service of the Republic: The Art and Science of Economic Policy -- Vijay Kelkar and Ajay Shah. 59. Once Upon a Prime -- Sarah Hart. 60. The Greatest Invention -- Silvia Ferrara. 61. Surveillance State -- Josh Chin and Liza Lin. 62. Surveillance Valley -- Yasha Levine. 63. Sex Robots and Vegan Meat -- Jenny Kleeman. 64. How to Take Smart Notes -- Sönke Ahrens. 65. The Creative Act -- Rick Rubin. 66. How to Write One Song -- Jeff Tweedy. 67. Adrian Tchaikovsky and NK Jemisin on Amazon. 68. Snarky Puppy. on Spotify and YouTube. 69. Empire Central -- Snarky Puppy. 70. Polyphia on Spotify and YouTube. 71. The Lazarus Project on Jio Cinema. This episode is sponsored by the Pune Public Policy Festival 2024, which takes place on January 19 & 20, 2024. The theme this year is Trade-offs! Amit Varma and Ajay Shah have launched a new video podcast. Check out Everything is Everything on YouTube. Check out Amit's online course, The Art of Clear Writing. And subscribe to The India Uncut Newsletter. It's free! Episode art: ‘Protocol' by Simahina.

With a background as a classroom teacher, a master's in educational neuroscience, and a doctorate in special education, Dr. Neena Saha has seen all facets of education. In her work, she noticed a gap in the research-to-practice workflow for early literacy and dedicated herself to streamlining the process of finding and disseminating the best educational research for educators. Together, Susan Lambert and Neena discuss the need for reading researchers to work together and collaborate in a more focused and concerted group effort, the challenges of implementation, and how educators can best keep up with research that often feels overwhelming.Show notes:Listen: Our recent episode with Claude GoldenbergRead: Neena's monthly reading research updateWatch: Neena's July video about a Bayesian network meta-analysisWatch: Celebrating the Legacy of Dr. Bud RoseWebsite: Center for Research Use in EducationRead: “Survey of Evidence in Education for Schools Descriptive Report”Read: “The Book of Why: The New Science of Cause and Effect” by Judea PearlRead: Reading Research Recap—If you want to start receiving monthly notifications for this series, please register or sign in to your Lexile & Quantile Hub account and join the Reading Research mailing list.Quotes:"What I did was focus really on dissemination, right? Getting rid of that hurdle of, you know, there's so many journals out there." —Dr. Neena Saha"You have to look at the full body, you're like cherry picking stuff if you're going to social media and the person with the biggest megaphone wins or whoever has the most interesting way of presenting it." —Dr. Neena Saha"We need a more concerted effort. There needs to be a bunch of researchers that come together and hash it out. It can't just be single ones here and there." —Dr. Neena Saha"Teachers or educators out there right now, when you're feeling overwhelmed and you can't figure out how to find the evidence, or some evidence, guess what? We're affirming for you that there's no easy way to do it...this is more of a systemic problem." —Dr. Neena Saha"It's not enough to do the science. You have to make sure it gets out there." —Dr. Neena Saha

In this week's episode, host Kristin Hayes talks with Nafisa Lohawala, a fellow at Resources for the Future who researches the effects of government policies on the transportation sector. Lohawala discusses the findings of a recent report that explores efforts to electrify medium- and heavy-duty vehicle fleets, the opportunities and challenges of electrification as a pathway toward lower transportation-sector emissions, and policies that could aid electrification. References and recommendations: “Medium- and Heavy-Duty Vehicle Electrification: Challenges, Policy Solutions, and Open Research Questions” by Beia Spiller, Nafisa Lohawala, and Emma DeAngeli; https://www.rff.org/publications/reports/medium-and-heavy-duty-vehicle-electrification-challenges-policy-solutions-and-open-research-questions/ Special series on the Common Resources blog: Electrifying Large Vehicles by Emma DeAngeli, Nafisa Lohawala, and Beia Spiller; https://www.resources.org/special-series-electrifying-large-vehicles/ “The Book of Why: The New Science of Cause and Effect” by Judea Pearl and Dana Mackenzie; https://www.hachettebookgroup.com/titles/judea-pearl/the-book-of-why/9780465097616/

Not many forces on Earth are comparable in power to a resilient spirit combined with a strong work ethic, and Dr. Glen Ferguson embodies this dynamic! Tune-in to this episode to hear how Dr. Ferguson went from a young man whose high school guidance counselor discouraged him from even applying to college, to enlisting in the U.S. Navy, to completing a PhD in physical chemistry, to becoming a data science individual contributor, to his present day role as Director of AI & ML at Huckleberry Labs! It is a remarkable story of resilience in the face of adversity and triumph against daunting odds!   FEATURED GUESTS: Name: Glen Ferguson Email: gallenferguson@gmail.com LinkedIn: https://www.linkedin.com/in/glenferguson/   SUPPORT THE DATA CANTEEN (LIKE PBS, WE'RE LISTENER SUPPORTED!): Donate: https://vetsindatascience.com/support-join   EPISODE LINKS: The Book of Why: The New Science of Cause and Effect (book recommendation): https://www.amazon.com/The-Book-of-Why-audiobook/dp/B07CYJ4G2L Deep Learning with Python 2nd Ed (book recommendation): https://www.amazon.com/Audible-Deep-Learning-Python-Second/dp/B09RN7QLT3 Deep Learning (book recommendation): https://www.deeplearningbook.org/ Pattern Recognition and Machine Learning (book recommendation): https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738 Introduction to Statistical Learning 2nd Ed (book recommendation): https://hastie.su.domains/ISLR2/ISLRv2_website.pdf   PODCAST INFO: Host: Ted Hallum Website: https://vetsindatascience.com/thedatacanteen Apple Podcasts: https://podcasts.apple.com/us/podcast/the-data-canteen/id1551751086 YouTube: https://www.youtube.com/channel/UCaNx9aLFRy1h9P22hd8ZPyw Stitcher: https://www.stitcher.com/show/the-data-canteen   CONTACT THE DATA CANTEEN: Voicemail: https://www.speakpipe.com/datacanteen   VETERANS IN DATA SCIENCE AND MACHINE LEARNING: Website: https://vetsindatascience.com/ Join the Community: https://vetsindatascience.com/support-join Mentorship Program: https://vetsindatascience.com/mentorship   OUTLINE: 00:00:07​ - Introduction 00:01:22 - Glen's first touch point with VDSML 00:03:15 - Glen's military background, college, & grad school 00:12:10 - Glen's first civilian career as a physical chemist working in materials science 00:15:31 - The shift to pursue data science 00:17:37 - A sabbatical in winemaking 00:20:25 - Glen's experience attending the NYC Data Science Academy 00:28:18 - Glen publishes about one of his data science projects in a peer reviewed journal 00:29:15 - Glen's first roles in data science as an individual contributor 00:38:37 - Glen moves into data science management 00:44:12 - Glen's overview of roles in the datasphere and military occupational specialties that map well to those roles 00:55:58 - Glen's current learning focus 00:57:29 - Glen's favorite learning resources 00:59:01 - The best way to contact Glen 01:00:03 - Farewells

Por que algo acontece? O que causou isso ou aquilo? Como entender a causa dos fenômenos que acontecem na natureza e na nossa sociedade? Para explorar o mundo complexo da causalidade trouxemos um especialista na área, o pesquisador Marcel Ribeiro-Dantas. Neste episódio, exploramos o que seria causalidade, se a inferência causal varia conforme a complexidade de cada área, se ela é sempre probabilística, se a plausibilidade é importante para entender a causalidade e os perigos de se politizar a ciência. Marcel é engenheiro de Computação e Automação, especialista em Big Data e mestre em Bioinformática pela Universidade Federal do Rio Grande do Norte. Aluno de doutorado na Universidade Sorbonne, em Paris, onde estuda causalidade no contexto de pacientes com câncer. Atualmente é pesquisador no Instituto Curie, mas foi membro co-fundador do Laboratório de Inovação Tecnológica em Saúde (LAIS) do Hospital Universitário Onofre Lopes (HUOL-UFRN), onde participou de atividades de pesquisa por 9 anos nas áreas de informática em saúde, dispositivos médicos, telemonitoramento de pacientes, telerradiologia, sistemas de recursos humanos em saúde e inteligência artificial. Participou também de atividades de pesquisa em âmbito internacional frutos de cooperações, como com a Universidade de Harvard e o MIT. Atualmente, tem interesse nos seguintes temas: inferência causal, redes biológicas, bioinformática e inteligência artificial. -----------REFERÊNCIAS DO EPISÓDIO---------- Mais informações sobre Marcel RIbeiro-Dantas: http://mribeirodantas.me/ Artigos Publicados por Marcel RIbeiro-Dantas: Google Scholar Livro - The Book of Why: The New Science of Cause and Effect https://amzn.to/3snKnGM ------------Cursos com Desconto------------ http://www.universogeneralista.com.br/curadoria-de-cursos/ ------------------Apoie o Canal------------ https://apoia.se/universogeneralista ------------------Youtube------------------ https://www.youtube.com/c/UniversoGeneralista ------------------Redes Sociais------------ https://www.instagram.com/universogeneralista/ https://twitter.com/UGeneralista -------- Tratamento de áudio ----------- Allan Spirandelli - https://www.instagram.com/allanspirandelli/ Spotify - https://sptfy.se/7mFh --------ASSUNTOS DO EPISÓDIO------- (0:00) Introdução (1:37) Currículo do convidado (2:42) Histórico do convidado (9:15) De onde vem a “causalidade”? (15:39) O que é causalidade e inferência causal? (22:24) Probabilidade e interdisciplinaridade (33:18) Método quantitativo através da história (38:58) Tipos de estudo, causalidade e correlação (45:05) Estudo clínico randomizado e seus desafios (1:05:07) Importância da Plausibilidade (1:17:25) Plausibilidade baixa em estudo positivo: o que acontece? (1:25:32) Critérios de Hill e suas limitações (1:33:16) Modelos qualitativos e quantitativos (1:37:59) Causalidade e estudos escassos (1:42:21) As limitações da ciência e o raciocínio evolutivo (1:45:33) Plausibilidade extrema (1:48:22) A popularização da ciência de dados (1:54:09) Identificação e estimação de causas (1:56:57) Politização da ciência e seus perigos --- Send in a voice message: https://anchor.fm/universogeneralista/message

To say that event A causes event B is to not only make a claim about our actual world, but about other possible worlds — in worlds where A didn't happen but everything else was the same, B would not have happened. This leads to an obvious difficulty if we want to infer causes from sets of data — we generally only have data about the actual world. Happily, there are ways around this difficulty, and the study of causal relations is of central importance in modern social science and artificial intelligence research. Judea Pearl has been the leader of the “causal revolution,” and we talk about what that means and what questions remain unanswered.Support Mindscape on Patreon.Judea Pearl received a Ph.D. in electrical engineering from the Polytechnic Institute of Brooklyn. He is currently a professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA. He is a founding editor of the Journal of Causal Inference. Among his awards are the Lakatos Award in the philosophy of science, The Allen Newell Award from the Association for Computing Machinery, the Benjamin Franklin Medal, the Rumelhart Prize from the Cognitive Science Society, the ACM Turing Award, and the Grenander Prize from the American Mathematical Society. He is the co-author (with Dana MacKenzie) of The Book of Why: The New Science of Cause and Effect.Web siteGoogle Scholar publicationsWikipediaAmazon author pageTwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Dr. Jerry Smith welcomes you to another episode of AI Live and Unbiased to explore the breadth and depth of Artificial Intelligence and to encourage you to change the world, not just observe it!   Dr. Jerry is talking today about questions and answers in the world of data science machinery and artificial intelligence.   Key Takeaways: What are Dr. Jerry's favorite AI design tools? Dr, Jerry shares his four primary tools: MATLAB. Is a commercial product. It has a home, academic, and enterprise version. MATLAB has toolkits and applications. The Predictive Maintenance Toolbox at MATLAB, especially the preventive failure model is of great value when we want to know why things fail, also by measuring systems performance and predicting the useful life of a product. Mathematical Modeling with Symbolic Math Toolbox is useful for algorithm-based environments. It is built on solid mathematics. R Programming is Dr. Jerry's favorite free tool for programming with statistical and math perspectives. R is an open and free source and comes with a lot of applications. Python is a great tool for programming and is as capable as R programming to assist us in problem-solving. Python is very useful when you know your work is directed to an enterprise level. Does Dr. Jerry have any recommended books for causality? The Book of Why is foundational for both the businessperson and the data scientist. It provides a historical review of what causality is and why it is important. For a deeper understanding of causality, Dr. Jerry recommends Causal Inference in Statistics: A Primer.   Counterfactuals and Causal Inferences: Methods and Principles it is a great tool to think through the counterfactual analysis.   Behavioral Data Analysis with R and Python is an awesome book for the practitioner who wants to know what behaviors are, how they show up in data, the causal characteristics, and how to abstract behavioral aspects from data. Dr. Jerry recommends Designing for Behavior Change, it talks about the three main strategies that we use to help people change their behaviors. The seven rules of human behavior can be found in Eddie Rafii's latest book: Behaviology, New Science of Human Behavior. Dr. Jerry shares his favorite tools for casual analysis: Compellon allows us to do performance analysis, showing the fundamental causal chains in your target of interest. It can be used by analysts. It allows users to do “what-if” analysis. Compellon is a commercial product.   Causal Nexus is an open-source package in Python that has a much deeper look at causal models than Compellon. BayesiaLab is a commercial tool that is one of the higher-end tools an organization can have. It allows you to work on casual networks and counterfactual events. It is used in AI research.   What skills are needed for data science machinery and AI developers? Capabilities can be segmented into Data-oriented, Information-oriented, Knowledge, and Intelligence. These different capabilities are used in many roles according to several levels of maturity.   Stay Connected with AI Live and Unbiased: Visit our website AgileThought.com Email your thoughts or suggestions to Podcast@AgileThought.com or Tweet @AgileThought using #AgileThoughtPodcast!   Learn more about Dr. Jerry Smith   Mentioned in this episode: MATLAB MATLAB Mathematical Modeling Python Artificial Intelligence with R Compellon Causal Nex BayesiaLab   Dr. Jerry's Book Recommendations: The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie   Causal Inference in Statistics: A Primer, Madelyn Glymour, Judea Pearl, and Nicholas P. Jewell   Counterfactuals and Causal Inferences: Methods and Principles,  Stephen L. Morgan and Christopher Winship   Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results, Florent Buisson   Designing for Behavior Change: Applying Psychology and Behavioral Economics, Stephen Wendel   Behaviology, New Science of Human Behavior, Eddie Rafii

新型コロナウイルスの世界的蔓延により、これまで以上に様々な変化が生まれた2021年。 そんな変化の大きい時代において、次にくるものとは一体何か。 パロアルトインサイトCEOである石角友愛と、CTOの長谷川貴久、そして日本を代表する投資ファンドであるインテグラルで 多くの企業支援を行ってきた山崎壯が、それぞれの角度から2022年を予測する。 ※本エピソードを記事化したものはこちらから※ 【出演者】 石角友愛 / 長谷川貴久 / 山崎壯 【2022年はこれがくる！】 ・テスラの自動運転 ・Apple M1チップ ・Web3と仮想通貨 ・非接触で冷凍食品販売できる自販機「ど冷えもん」 【今週のおすすめコンテンツ（長谷川貴久）】 「The Book of Why: The New Science of Cause and Effect by Judea Pearl」 統計的および哲学的な観点から、因果関係と因果推論についてまとめた一冊。 DX推進担当者や経営者の方にもおすすめです。 =================================== その他、ご質問や感想、取り上げて欲しいテーマなどあればお気軽にご連絡ください。 メール : info@paloaltoinsight.com 石角友愛のTwitter : @tomoechama （ハッシュタグ「#レベル5」をつけて投稿お願いします） パロアルトインサイトHP : www.paloaltoinsight.com =================================== 楽曲提供：Atsu (beatmaker and rapper from Zenarchy) https://twitter.com/atsu_izm 「Transform」Level 5 テーマソング https://m.soundcloud.com/atsuizm/transform --- Send in a voice message: https://anchor.fm/level5/message

Show notes:- On the Measure of Intelligence by François Chollet - Part 1: Foundations (Paper Explained) [YouTube](https://www.youtube.com/watch?v=3_qGr...)- [2108.07258 On the Opportunities and Risks of Foundation Models](https://arxiv.org/abs/2108.07258)- [2005.11401 Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)- Negative Data Augmentation: https://arxiv.org/abs/2102.05113- Beyond Accuracy: Behavioral Testing of NLP models with CheckList: [2005.04118 Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://arxiv.org/abs/2005.04118)- Symbolic AI vs Deep Learning battle https://www.technologyreview.com/2020...- Dense Passage Retrieval for Open-Domain Question Answering https://arxiv.org/abs/2004.04906- Data Augmentation Can Improve Robustness https://arxiv.org/abs/2111.05328- Contrastive Loss Explained. Contrastive loss has been used recently… | by Brian Williams | Towards Data Science https://towardsdatascience.com/contra...- Keras Code examples https://keras.io/examples/- https://you.com/ -- new web search engine by Richard Socher- The Book of Why: The New Science of Cause and Effect: Pearl, Judea, Mackenzie, Dana: 9780465097609: Amazon.com: Books https://www.amazon.com/Book-Why-Scien...- Chelsea Finn: https://twitter.com/chelseabfinn- Jeff Clune: https://twitter.com/jeffclune- Michael Bronstein (Geometric Deep Learning): https://twitter.com/mmbronstein https://arxiv.org/abs/2104.13478- Connor's Twitter: https://twitter.com/CShorten30- Dmitry's Twitter: https://twitter.com/DmitryKan

Ekrem Aksoy joins the adventure to discuss transformers and the method of helping Machine Learning algorithms focus on the important parts of an image to determine what to do. Panel Ben Wilson Charles Max Wood Daniel Svoboda Francois Bertrand Guest Ekrem Aksoy Sponsors Dev Influencers Accelerator Links Attention to Transformers Attention in the Human Brain and Its Applications in ML See, Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving Ekrem Aksoy - Medium Ekrem Aksoy, PhD - Gradient LinkedIn: Ekrem Aksoy Picks Ben- Read all the blog posts in this episode Charles- Accounting software | Xero Charles- The Prosperous Coach Daniel- Debt - Updated and Expanded: The First 5,000 Years  Ekrem- The Book of Why: The New Science of Cause and Effect Ekrem- Attention in Psychology, Neuroscience, and Machine Learning Contact Ben: Databricks GitHub | BenWilson2/ML-Engineering GitHub | databrickslabs/automl-toolkit LinkedIn: Benjamin Wilson Contact Charles: Devchat.tv DevChat.tv | Facebook Twitter: DevChat.tv ( @devchattv ) Contact Francois: Francois Bertrand GitHub | fbdesignpro/sweetviz

Jessica Young, PhD is a biostatistician in the Department of Population Medicine at Harvard Medical School who joins the show to discuss the ins and outs of her interesting and important work. Tune in to learn the following: How confounding factors in a study can influence the findings of the study, and how/why the gold standard of randomized trials can address this What is meant by the “fundamental challenge of causal inference” and how this explains why assumptions are always necessary in order to claim that a statistical analysis is unbiased Why large subject numbers or data points can't overwhelm biases; why bias is a function of the thing being studied Dr. Young's job is two-fold: she works on both the applications of statistical methods for public health and clinical medicine, and also on the development of methods in these areas. She focuses on causal inference, which is the formal process of understanding how to estimate causal effect from data collected in real-world studies. Through examples including a longitudinal study on nurses starting in the 1970s to present day studies revolving around the coronavirus pandemic, Dr. Young discusses confounding factors in studies and the effect they have on interpretations of findings, the importance of randomization, the presence of bias regardless of how statistically significant a finding is, meta-analyses, where she sees the field of biostatistics heading in the near future, and more.   To learn about the basics of causal inference, Dr. Young recommends reading The Book of Why: The New Science of Cause and Effect. Visit https://www.populationmedicine.org/JYoung to learn more about her work and publications.

So, what’s the deal with chiropractors? Are they all just full of it? Ian Kaplan is the COO of Hybrid Performance Method (Stefi Cohen’s training company) and soon to be doctor of chiropractic, and he’s also one of the most thoughtful and skeptical people in the fitness space. In this conversation, Ian breaks down how he thinks about uncertainty and providing treatment options when a lot of the research shows that most things that we talk about in the fitness and rehab space - well - don’t actually work. He also lays into some of the most common issues he sees with other chiros. And, of course, we spend awhile getting into the weeds and discussing pain science, Bayesian reasoning, and the future of artificial intelligence in guiding treatment protocols. Check out the full conversation with Ian if you want to hear two dorks talk about things like "epistemic humility" - or if you want to hear one of the smartest fellas in fitness explain how he thinks about pain. Check out more from Ian and Hybrid Performance Method here: Website: www.hybridperformancemethod.com Instagram: @kaplanfitness.hybrid | @hybridperformancemethod | @hybridperformancemethod | @steficohen Podcast: Hybrid Unlimited If you're enjoying the show, the best way to support it is by sharing with your friends. If you don't have any friends, why not a leave a review? It makes a difference in terms of other people finding the show. You can also subscribe to receive my e-mail newsletter at www.toddnief.com. Most of my writing never makes it to the blog, so get on that list. Show Notes: [01:04] Ian’s beefs with the field of chiropractic – and what it means to be “evidence-based” in a field with so much uncertainty. [11:50] How should someone actually think about treatment given the inherent uncertainty in dealing with complex systems? How does Ian weigh the costs and benefits of a potential treatment? [18:46] Why are clinicians so easy to fool: regression toward the mean and threshold effects. And, how to give patients hope without lying to them. [25:46] Is it better to try to treat pain with targeted tissue interventions or is it better to focus on the brain? [33:31] The role of artificial intelligence in developing precision medicine models for treating pain patients [39:51] What are the barriers to effectively analyzing treatment data from chiropractors and physical therapists? [44:50] A brief summary of Bayesian inference and its value for treatments, pain science and making business decisions [56:56] How does Ian think about Bayesian inference as a unifying principle for weird stuff we see in pain science like placebo effects and extreme pain sensitivity [01:07:54] How to check out more from Ian Links and Resources Mentioned Humorism What’s the Difference Between a “Straight” Chiropractor and a “Mixer”? from Gutierrez Chiropractic Sensitivity and specificity Threshold effect Opportunity cost Regression toward the mean Bean machine “Arthroscopic Partial Meniscectomy versus Sham Surgery for a Degenerative Meniscal Tear” from the New England Journal of Medicine “Null hypothesis significance testing: A short tutorial” from F1000 Research Confidence interval “Learn About Lookalike Audiences” from Facebook Tempus Externality Introduction to Bayesian networks Information theory “The Bayesian brain: the role of uncertainty in neural coding and computation” from CellPress Frequentist probability “Frequentist And Bayesian Approaches In Statistics” from Probabilistic World “How to take the ‘outside view’” from McKinsey “The Book of Why: The New Science of Cause and Effect” by Judea Pearl Neural network “To Make Sense of the Present, Brains May Predict the Future” from Quanta Magazine Greg Lehman

In this episode, I talk with Irineo Cabreros about causality. We discuss why causality matters, what does and does not imply causality, and two different mathematical formalizations of causality: potential outcomes and directed acyclic graphs (DAGs). Causal models are usually considered external to and separate from statistical models, whereas Irineo’s new paper shows how causality can be viewed as a relationship between particularly chosen random variables (potential outcomes). Links: Causal models on probability spaces (Irineo Cabreros, John D. Storey) The Book of Why: The New Science of Cause and Effect (Judea Pearl, Dana Mackenzie)

Sam Harris speaks with Judea Pearl about his work on the mathematics of causality and artificial intelligence. They discuss how science has generally failed to understand causation, different levels of causal inference, counterfactuals, the foundations of knowledge, the nature of possibility, the illusion of free will, artificial intelligence, the nature of consciousness, and other topics. Judea Pearl is a computer scientist and philosopher, known for his work in AI and the development of Bayesian networks, as well as his theory of causal and counterfactual inference. He is a professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA. In 2011, he was awarded with the Turing Award, the highest distinction in computer science. He is the author of The Book of Why: The New Science of Cause and Effect (coauthored with Dana McKenzie) among other titles. Twitter: @yudapearl

Mark Leonard speaks with Mark Schieritz from Die Zeit and ECFR's Sebastian Dullien about a new framework for transatlantic relations. The podcast was recorded on 6 September 2018. Bookshelf: Crashed by Adam Tooze https://www.penguinrandomhouse.com/books/301357/crashed-by-adam-tooze/9780670024933/ The Book of Why: The New Science of Cause and Effect by Judea Pearl https://www.penguin.co.uk/books/289825/the-book-of-why/#mJDZe5QqZFKGwC7k.99 The German barrier to a global euro by Sebastian Dullien https://www.ecfr.eu/article/commentary_german_barrier_global_euro_maas Weg vom Dollar by Mark Schieritz https://www.zeit.de/wirtschaft/2018-09/transatlantische-beziehungen-zahlungsverkehr-europa-usa-heiko-maas Es reicht! by Tina Hildebrandt, Kerstin Kohlenberg, Jörg Lau, Mark Schieritz und Michael Thumann https://www.zeit.de/2018/36/aussenpolitik-handelsstreit-donald-trump-heiko-maas Picture credit: Dollars and euros background by Petr Krachtovil via Public Domain Pictures https://www.publicdomainpictures.net/en/view-image.php?image=20851&picture=dollars-and-euros-background, CC-BY-0.

A new project out of the Center for Open Science in Charlottesville, Virginia, found that of all the experimental social science papers published in Science and Nature from 2010–15, 62% successfully replicated, even when larger sample sizes were used. What does this say about peer review? Host Sarah Crespi talks with Staff Writer Kelly Servick about how this project stacks up against similar replication efforts, and whether we can achieve similar results by merely asking people to guess whether a study can be replicated. Podcast producer Meagan Cantwell interviews Emily Brodsky of the University of California, Santa Cruz, about her research report examining why earthquakes occur as far as 10 kilometers from wastewater injection and fracking sites. Emily discusses why the well-established mechanism for human-induced earthquakes doesn't explain this distance, and how these findings may influence where we place injection wells in the future. In this month's book podcast, Jen Golbeck interviews Judea Pearl and Dana McKenzie, authors of The Book of Why: The New Science of Cause and Effect. They propose that researchers have for too long shied away from claiming causality and provide a road map for bringing cause and effect back into science. This week's episode was edited by Podigy.

A new project out of the Center for Open Science in Charlottesville, Virginia, found that of all the experimental social science papers published in Science and Nature from 2010–15, 62% successfully replicated, even when larger sample sizes were used. What does this say about peer review? Host Sarah Crespi talks with Staff Writer Kelly Servick about how this project stacks up against similar replication efforts, and whether we can achieve similar results by merely asking people to guess whether a study can be replicated. Podcast producer Meagan Cantwell interviews Emily Brodsky of the University of California, Santa Cruz, about her research report examining why earthquakes occur as far as 10 kilometers from wastewater injection and fracking sites. Emily discusses why the well-established mechanism for human-induced earthquakes doesn't explain this distance, and how these findings may influence where we place injection wells in the future. In this month's book podcast, Jen Golbeck interviews Judea Pearl and Dana McKenzie, authors of The Book of Why: The New Science of Cause and Effect. They propose that researchers have for too long shied away from claiming causality and provide a road map for bringing cause and effect back into science. This week's episode was edited by Podigy. Download a transcript of this episode (PDF) Listen to previous podcasts. About the Science Podcast [Image: Jens Lambert, Shutterstock; Music: Jeffrey Cook]

A new project out of the Center for Open Science in Charlottesville, Virginia, found that of all the experimental social science papers published in Science and Nature from 2010–15, 62% successfully replicated, even when larger sample sizes were used. What does this say about peer review? Host Sarah Crespi talks with Staff Writer Kelly Servick about how this project stacks up against similar replication efforts, and whether we can achieve similar results by merely asking people to guess whether a study can be replicated. Podcast producer Meagan Cantwell interviews Emily Brodsky of the University of California, Santa Cruz, about her research report examining why earthquakes occur as far as 10 kilometers from wastewater injection and fracking sites. Emily discusses why the well-established mechanism for human-induced earthquakes doesn’t explain this distance, and how these findings may influence where we place injection wells in the future. In this month’s book podcast, Jen Golbeck interviews Judea Pearl and Dana McKenzie, authors of The Book of Why: The New Science of Cause and Effect. They propose that researchers have for too long shied away from claiming causality and provide a road map for bringing cause and effect back into science. This week’s episode was edited by Podigy. Download a transcript of this episode (PDF) Listen to previous podcasts. About the Science Podcast [Image: Jens Lambert, Shutterstock; Music: Jeffrey Cook]

Billions of years ago, Mars probably hosted many water features: streams, rivers, gullies, etc. But until recently, water detected on the Red Planet was either locked up in ice or flitting about as a gas in the atmosphere. Now, researchers analyzing radar data from the Mars Express mission have found evidence for an enormous salty lake under the southern polar ice cap of Mars. Daniel Clery joins host Sarah Crespi to discuss how the water was found and how it can still be liquid—despite temperatures and pressures typically inhospitable to water in its liquid form. Read the research. Sarah also talks with science journalist Katherine Kornei about her story on changing athletic performance after gender transition. The feature profiles researcher Joanna Harper on the work she has done to understand the impacts of hormone replacement therapy and testosterone levels in transgender women involved in running and other sports. It turns out within a year of beginning hormone replacement therapy, transgender women plateau at their new performance level and stay in a similar rank with respect to the top performers in the sport. Her work has influenced sports oversight bodies like the International Olympic Committee. In this month’s book segment, Jen Golbeck interviews Andrew Lawler about his book The Secret Token: Myth, Obsession, and the Search for the Lost Colony of Roanoke. Next month’s book will be The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie. Write us at sciencepodcast@aaas.org or tweet to us @sciencemagazine with your questions for the authors. This week’s episode was edited by Podigy. Download a transcript of this episode (PDF) Listen to previous podcasts. [Image: Henry Howe; Music: Jeffrey Cook]

Billions of years ago, Mars probably hosted many water features: streams, rivers, gullies, etc. But until recently, water detected on the Red Planet was either locked up in ice or flitting about as a gas in the atmosphere. Now, researchers analyzing radar data from the Mars Express mission have found evidence for an enormous salty lake under the southern polar ice cap of Mars. Daniel Clery joins host Sarah Crespi to discuss how the water was found and how it can still be liquid—despite temperatures and pressures typically inhospitable to water in its liquid form. Read the research. Sarah also talks with science journalist Katherine Kornei about her story on changing athletic performance after gender transition. The feature profiles researcher Joanna Harper on the work she has done to understand the impacts of hormone replacement therapy and testosterone levels in transgender women involved in running and other sports. It turns out within a year of beginning hormone replacement therapy, transgender women plateau at their new performance level and stay in a similar rank with respect to the top performers in the sport. Her work has influenced sports oversight bodies like the International Olympic Committee. In this month's book segment, Jen Golbeck interviews Andrew Lawler about his book The Secret Token: Myth, Obsession, and the Search for the Lost Colony of Roanoke. Next month's book will be The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie. Write us at sciencepodcast@aaas.org or tweet to us @sciencemagazine with your questions for the authors. This week's episode was edited by Podigy. Download a transcript of this episode (PDF) Listen to previous podcasts. [Image: Henry Howe; Music: Jeffrey Cook]