Podcasts about Data science

Python in 2025 is in a delightfully refreshing place: the GIL's days are numbered, packaging is getting sharper tools, and the type checkers are multiplying like gremlins snacking after midnight. On this episode, we have an amazing panel to give us a range of perspectives on what matter in 2025 in Python. We have Barry Warsaw, Brett Cannon, Gregory Kapfhammer, Jodie Burchell, Reuven Lerner, and Thomas Wouters on to give us their thoughts. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Talk Python Courses Links from the show Python Software Foundation (PSF): www.python.org PEP 810: Explicit lazy imports: peps.python.org PEP 779: Free-threaded Python is officially supported: peps.python.org PEP 723: Inline script metadata: peps.python.org PyCharm: www.jetbrains.com JetBrains: www.jetbrains.com Visual Studio Code: code.visualstudio.com pandas: pandas.pydata.org PydanticAI: ai.pydantic.dev OpenAI API docs: platform.openai.com uv: docs.astral.sh Hatch: github.com PDM: pdm-project.org Poetry: python-poetry.org Project Jupyter: jupyter.org JupyterLite: jupyterlite.readthedocs.io PEP 690: Lazy Imports: peps.python.org PyTorch: pytorch.org Python concurrent.futures: docs.python.org Python Package Index (PyPI): pypi.org EuroPython: tickets.europython.eu TensorFlow: www.tensorflow.org Keras: keras.io PyCon US: us.pycon.org NumFOCUS: numfocus.org Python discussion forum (discuss.python.org): discuss.python.org Language Server Protocol: microsoft.github.io mypy: mypy-lang.org Pyright: github.com Pylance: marketplace.visualstudio.com Pyrefly: github.com ty: github.com Zuban: docs.zubanls.com Jedi: jedi.readthedocs.io GitHub: github.com PyOhio: www.pyohio.org Watch this episode on YouTube: youtube.com Episode #532 deep-dive: talkpython.fm/532 Episode transcripts: talkpython.fm Theme Song: Developer Rap

This week's Questions of the Week episode gets DEEP.You all sent in some of the most emotional, complex, and honest questions we've ever answered, especially around:What to do when you don't like your partner's friendsWhether we ever regret only dating each otherHow to know when it's time to leave a relationshipHow our finances changed after becoming entrepreneursNavigating alcohol, health changes, and lifestyle shiftsThe “are we official?” conversationOur age gap and how it's affected our relationshipThe funniest moments, biggest purchases, and dream NYC businessThis episode is personal, vulnerable, and full of advice we wish we had years ago.So settle in, get cozy, and grab your chai.

As a data science professional, I know firsthand how challenging it can be to navigate the job market and prepare for interviews. That's why I started this podcast - to provide valuable resources to those looking to break into the field of data science.Running a podcast takes a lot of time and effort, so please consider supporting us. Become a Paid Subscriber: ⁠⁠⁠⁠⁠https://podcasters.spotify.com/pod/show/data-science-interview/subscribe⁠

Was für ein Jahr! Im Christmas Special von MY DATA IS BETTER THAN YOURS blickt Jonas Rashedi auf die spannendsten Gesprächsfetzen, inspirierendsten Anekdoten und unterhaltsamsten Momente aus dem Podcastjahr 2025 zurück. Mit dabei: ein Projekt bei Douglas mit dem Titel „Bällebad“, Insights aus der Physik, Diskussionen über Datenschutz und die Frage, was ein guter Tech Stack wirklich leisten muss. Außerdem werfen wir nochmal einen Blick auf smarte Einlegesohlen mit 3D-Bewegungsdaten und sprechen über das Spannungsfeld zwischen Freude an Daten und Verantwortung in der Anwendung. Ein großes Dankeschön an alle meine Gäste für ihre Offenheit, Expertise und den gemeinsamen Blick über den Tellerrand. Ich wünsche euch frohe Weihnachten, entspannte Tage – und einen starken Start ins neue Jahr!

Topics covered in this episode: Has the cost of building software just dropped 90%? More on Deprecation Warnings How FOSS Won and Why It Matters Should I be looking for a GitHub alternative? Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. HEADS UP: We are taking next week off, happy holiday everyone. Michael #1: Has the cost of building software just dropped 90%? by Martin Alderson Agentic coding tools are collapsing “implementation time,” so the cost curve of shipping software may be shifting sharply Recent programming advancements haven't been that great of a true benefit: Cloud, TDD, microservices, complex frontends, Kubernetes, etc. Agentic AI's big savings are not just code generation, but coordination overhead reduction (fewer handoffs, fewer meetings, fewer blocks). Thinking, product clarity, and domain decisions stay hard, while typing and scaffolding get cheap. Is it the end of software dev? Not really, see Jevons paradox: when production gets cheaper, total demand can rise rather than spending simply falling. (Historically: the efficiency of coal use led to the increased consumption of coal) Pushes back on “only good for greenfield” by arguing agents also help with legacy code comprehension and bug-fixing. I 100% agree. #Legacy code for the win. Brian #2: More on Deprecation Warnings How are people ignoring them? yep, it's right in the Python docs: -W ignore::DeprecationWarning Don't do that! Perhaps the docs should give the example of emitting them only once -W once::::DeprecationWarning See also -X dev mode , which sets -W default and some other runtime checks Don't use warn, use the @warnings.deprecated decorator instead Thanks John Hagen for pointing this out Emits a warning It's understood by type checkers, so editors visually warn you You can pass in your own custom UserWarning with category mypy also has a command line option and setting for this --enable-error-code deprecated or in [tool.mypy] enable_error_code = ["deprecated"] My recommendation Use @deprecated with your own custom warning and test with pytest -W error Michael #3: How FOSS Won and Why It Matters by Thomas Depierre Companies are not cheap, companies optimize cost control. They do this by making purchasing slow and painful. FOSS is/was a major unlock hack to skip procurement, legal, etc. Example is months to start using a paid “Add to calendar” widget! It “works both ways”: the same bypass lowers the barrier for maintainers too, no need for a legal entity, lawyers, liability insurance, or sales motion. Proposals that “fix FOSS” by reintroducing supply-chain style controls (he name-checks SBOMs and mandated processes) risk being rejected or gamed, because they restore the very friction FOSS sidesteps. Brian #4: Should I be looking for a GitHub alternative? Pricing changes for GitHub Actions The self-hosted runner pricing change caused a kerfuffle. It's has been postponed But… if you were to look around, maybe pay attention to These 4 GitHub alternatives are just as good—or better Codeburg, BitBucket, GitLab, Gitea And a new-ish entry, Tangled Extras Brian: End of year sale for The Complete pytest Course Use code XMAS2025 for 50% off before Dec 31 Writing work on Lean TDD book on hold for holidays Will pick up again in January Michael: PyCharm has better Ruff support now out of the box, via Daniel Molnar This is from the release notes of 2025.3: "PyCharm 2025.3 expands its LSP integration with support for Ruff, ty, Pyright, and Pyrefly.” If you check out the LSP section it will land you on this page and you can go to Ruff. The Ruff doc site was also updated. Previously it was only available external tools and a third party plugin, this feels like a big step. Fun quote I saw on ExTwitter: May your bug tracker be forever empty. Joke: Try/Catch/Stack Overflow Create a super annoying linkedin profile - From Tim Kellogg, submitted by archtoad

Funderar du på en framtid inom Data, AI och IT-konsulteri? I detta avsnitt får du "inside information" om Sogetis prisbelönta traineeprogram, CareerBooster, med fokus på uppstarten i mars 2026.Jag tar dig med bakom kulisserna och går igenom vad som faktiskt krävs för att lyckas som konsult idag. Det handlar inte bara om att kunna koda – det handlar om att förstå affären, kommunicera lösningar och bygga förtroende.

In this episode, we welcome Moritz Frenzel, Global Technical Director for Engineering, Data Science, and Automation at Altair. Together, we dive into the exciting intersection of engineering, creativity, and AI, exploring how democratized tools and simulation technologies are unlocking new possibilities for innovation.Moritz shares his vision for the future of engineering, reflecting on how AI enhances rather than replaces human creativity, and why he believes that “building is more than consuming.” From bold predictions about the next decade to lessons learned at the forefront of technology, this conversation is an inspiring reminder that innovation thrives on curiosity, collaboration, and the courage to rethink what's possible.

Have you ever thought about getting your small product into production, but are worried about the cost of the big cloud providers? Or maybe you think your current cloud service is over-architected and costing you too much? Well, in this episode, we interview Michael Kennedy, author of "Talk Python in Production," a new book that guides you through deploying web apps at scale with right-sized engineering. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Christopher Trudeau - guest host: www.linkedin.com Michael's personal site: mkennedy.codes Talk Python in Production Book: talkpython.fm glances: github.com btop: github.com Uptimekuma: uptimekuma.org Coolify: coolify.io Talk Python Blog: talkpython.fm Hetzner (€20 credit with link): hetzner.cloud OpalStack: www.opalstack.com Bunny.net CDN: bunny.net Galleries from the book: github.com Pandoc: pandoc.org Docker: www.docker.com Watch this episode on YouTube: youtube.com Episode #531 deep-dive: talkpython.fm/531 Episode transcripts: talkpython.fm Theme Song: Developer Rap

As the retail industry heads into 2026, innovation is no longer theoretical — it's operational. In this special episode of the Rethink Retail Predictions Podcast, we spoke directly with retail leaders who are actively building the future of commerce. From omnichannel growth and AI-driven personalization to pricing pressure, trust, and the rise of machine-assisted shopping, these experts share unfiltered insights into what's actually changing inside retail organizations.

Eliza Lochner is a seasoned marketing leader with experience spanning Fortune 500 companies and high-growth startups. She leads global marketing for Airbnb's real estate development partnerships and new supply initiatives, including the Airbnb Friendly Apartments program, which helps renters earn supplemental income while giving property owners transparency, controls, and new revenue opportunities. Passionate about building human connections that fuel business growth, Eliza focuses on partnerships at the intersection of housing affordability, flexibility, and real estate innovation.(01:30) - Airbnb-friendly Apartments (02:55) - Addressing Housing Affordability(04:34) - Owner & Property Manager Controls(06:28) - Program Success & Expansion(09:25) - Impact on Resident & Investor Attraction(14:24) - Revenue Sharing & Incentives(18:56) - Building Trust with Property Managers(21:14) - Blueprint - The Future of Real Estate - Register for 2026: The Premier Event for Industry Executives, Real Estate & Construction Tech Startups and VC's, at The Venetian, Las Vegas on September 22nd-24th, 2026. As a friend of Tangent, you can save $300 on your All-Access pass(22:05) - Channel Partners & Distribution Strategy(24:00) - Boutique Hotels Partnerships(25:45) - Major Events: World Cup and Olympics(29:43) - Future of Airbnb Friendly Buildings Program(31:26) - Collaboration Superpower: Michelle Obama & Eumaeus (Wiki)

Afrooz Ansaripour, Director of Data Science at Walmart, joins the show to explain how global leaders are shifting from simple historical tracking to predicting psychological triggers and customer intent. This episode explores the evolution of customer intelligence and how Generative AI is turning massive data sets into personalized, value driven experiences. Listeners will learn how to balance hyper personalization with foundational privacy to build lasting consumer trust.Key InsightsPredict intent rather than just reporting past transactions to understand why a customer is with the brand.Use Generative AI as an explainability layer to transform complex data platforms from black boxes into conversational tools.Prioritize customer trust as a critical part of the user experience rather than just a legal requirement.Integrate digital and physical signals to create a 360 degree view that reveals insights which would otherwise be invisible.Focus on rapid technology adoption and curiosity as the primary drivers of success in modern AI teams.Timestamped Highlights01:51 Identifying the challenges and opportunities when managing millions of real time signals.06:43 Strategies for showing genuine value to the customer without making them feel like just a part of a sale.09:51 How LLMs are fundamentally changing the way data teams interpret unstructured feedback and behavioral patterns.14:42 Managing privacy and ethical data practices while building personalized conversational AI.19:14 Stitching together the online and offline journey to create a seamless customer experience.22:52 The necessary evolution of data science skills toward storytelling and execution bias.A Powerful Thought"Personalization should never come at the expense of customer trust." Tactical StepsCombat the garbage in garbage out problem by refining cleaning processes to handle modern AI requirements.Build an interactive layer or chatbot on top of data products to make insights instantly accessible and automated.Translate technical insights into real world decisions to ensure customers actually benefit from data models.Next StepsSubscribe to the show for more insights into the future of tech. Share this episode with a peer who is currently navigating the complexities of customer data.

While the transition from academia to industry can be brutal for data scientists, academics don't show up in industry empty-handed. They bring powerful transferable skills that many industry-trained data scientists never develop.In this Value Boost episode, Dr. Sayli Javadekar joins Dr. Genevieve Hayes to flip the script on their previous conversation, exploring the valuable skills that academic-trained data scientists bring to industry and how any data scientist can develop these same strengths.You'll learn:The most valuable skills academics bring to industry [01:30]Why the experimental mindset matters so much [03:43]The hidden benefit of extended research projects [04:54]How mentorship can work both ways for mutual benefit [07:06]Guest BioDr Sayli Javadekar is a data scientist at Thoughtworks, with experience at the World Bank and UNAIDS. Before this, she was an Assistant Professor at the University of Bath and holds a PhD in Econometrics from the University of Geneva.LinksConnect with Sayli on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Together, Leon and Oscar share how applied EDA practices remain the backbone of trustworthy analytics pipelines in both academic and industry settings. Their discussion highlights the challenges and lessons learned from building the EDA Toolkit, and why reproducible workflows are more important than ever in the age of AI and ML.Key Highlights:Reproducible EDA: How to standardize exploratory data analysis workflows for consistent and trustworthy insights.Open-Source Innovation: The design and impact of the EDA Toolkit, bridging research, healthcare, and education.Best Practices for Analytics: Lessons learned from creating tools that make EDA more intuitive and scalable across projects.The Future of Data Science Workflows: Why reproducibility and standardization matter in modern AI/ML pipelines.

Alex “Sandy” Pentland, a Professor at MIT, a Stanford University Fellow, and one of the most cited computational scientists in the world, dives into the misunderstood issues and opportunities around artificial intelligence, including alignment, human centricity, how different nations are handling the new tech, and the application you can put to work in your business straight away that he calls "a little bit of genius".Mentioned in this episode:Get 10% OFF Taelor gift cards right now using promo code PODCASTGIFT at Taelor.styleTaelorGet 10% OFF Taelor gift cards right now using promo code PODCASTGIFT at Taelor.styleTaelor

On November 19th, 2025, National College of Ireland in (NCI) collaboration with Citi proudly announced the official kick-off of the Citi upStart programme for the 2025/26 academic year. The initiative, designed to foster innovation and entrepreneurship among postgraduate students, saw Citi organisers, mentors, NCI students, academics, and new partners gather for the launch event. Activate mentorship This year's programme features 165 NCI postgraduate students who took part in a series of rigorous in-house idea-development workshops facilitated by NCI academic staff. This intensive process saw 60 students progress to team formation, advancing the most promising proposals which were then presented via elevator pitches at the event. Addressing participants and mentors, Dr Prag Sharma, Director, Future of Finance Think tank, former Global Head of AI CoE at Citi expressed his admiration for the nascent ideas, and provided crucial advice on AI's role: "AI is a tool for you to use, alongside the other tools you have acquired through college and your working life. AI augments our skills; so, become experts in using it to accelerate your capabilities." Following the pitches, a "speed dating" session allowed mentors from various Citi departments to connect with student teams, exploring project proposals and identifying alignment with their skills and insights. Dr Anu Sahni, Programme Director for the MSc in AI for Business, Data Analytics, and Knowledge Transfer Champion at National College of Ireland underscored the transformative power of mentorship: "Having the guidance and support of an experienced mentor can provide a mentee with a broad range of personal and professional benefits, including gaining practical advice and encouragement, as well being exposed to new ideas, and new ways of thinking, and now having another big organisation, Mphasis onboard to support this initiative, we will definitely see a remarkable amount of value added to an already innovative collaboration." New supports This year's cohort has already benefited from additional supports, including valuable insights into innovative solution development from Georgina Lupu Florian and Adrian Florian of Wolfpack Digital. Pritesh Tiwari, CEO of Data Science Wizards (itself a spin-out company from NCI MSc in Data Science), provided guidance on idea building and validation, while Swapnil Parashar, Director of Software Engineering at Oracle Cloud, shared industry perspectives on innovation. New partnership A?significant development for this year's programme is the new strategic partnership withMphasis, a global AI-led, platform-driven technology solutions provider. Mphasis will support participating student teams through project guidance and will sponsor awards and prizes for the winners at the upcoming Dragons' Den event. Rohit Jayachandran, Head of Banking & Financial Services at Mphasis, said: "Our long-standing partnership with Citi has opened the door to impactful collaborations, such as Dragons' Den. At Mphasis, we see immense potential in the next generation of technologists, and working with Citi upStart allows us to nurture that potential and fuel innovation for the future. Additionally, Mphasis' philosophy, "AI Without Intelligence Is Artificial", aligns perfectly with the programme's focus on intelligent application of technology." The ten participating teams, comprised of master's students in Cloud Computing, Data Analytics, AI, AI for Business, Fintech, or Cybersecurity, are developing a diverse range of impactful ideas. These include "Finpals," an AI-driven solution for automating credit risk analysis; "Lendloop," a peer-to-peer lending platform; "Medinova AI" and "Medtrix," both focused on enhancing healthcare access and patient support; "Phantom," an all-in-one Irish tourism app; and "Venture Forge," which aims to innovate within the Carbon Credits Market using blockchain technology. You can read more about the teams and their projects here on the NCI we...

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview here:https://gotopia.tech/episodes/399Matt Housley - Co-Author of "Fundamentals of Data Engineering", Keynote Speaker & PodcasterJoe Reis - Co-Author of "Fundamentals of Data Engineering", Keynote Speaker, Professor & PodcasterRESOURCESMatthttps://www.linkedin.com/in/housleymatthewJoehttps://www.linkedin.com/in/josephreishttps://github.com/JoeReishttps://joereis.substack.comLinkhttps://mathstodon.xyz/@tao/114915604830689046DESCRIPTIONJoe Reis and Matt Housley, co-authors of "Fundamentals of Data Engineering," discuss the evolution of their field three years after their book's publication. They explore how the rise of AI tools has transformed data engineering practices, the ongoing importance of foundational knowledge, and the challenges facing junior engineers in an AI-dominated landscape. The conversation covers the balance between leveraging AI assistance and maintaining core expertise, the resurgence of classical techniques, and why fundamental principles remain more relevant than ever.RECOMMENDED BOOKSJoe Reis & Matt Housley • Fundamentals of Data Engineering • https://amzn.to/4n85049Karen Hao • Empire of AI • https://amzn.to/46qeL6BKeach Hagey • The Optimist • https://amzn.to/4nlcS20Parmy Olson • Supremacy • https://amzn.to/3IpHdgIPeter Norvig & Stuart Russel • Artificial Intelligence • https://amzn.to/420ZgR8David Foster • Generative Deep Learning • https://amzn.to/48ZgP4xSol Rashidi • Your AI Survival Guide • https://amzn.to/3UFYnKCHow Hacks HappenHacks, scams, cyber crimes, and other shenanigans explored and explained. Presented...Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Topics covered in this episode: Deprecations via warnings docs PyAtlas: interactive map of the top 10,000 Python packages on PyPI. Buckaroo Extras Joke Watch on YouTube About the show Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Deprecations via warnings Deprecations via warnings don't work for Python libraries Seth Larson How to encourage developers to fix Python warnings for deprecated features Ines Panker Michael #2: docs A collaborative note taking, wiki and documentation platform that scales. Built with Django and React. Made for self hosting Docs is the result of a joint effort led by the French

For years, building interactive widgets in Python notebooks meant wrestling with toolchains, platform quirks, and a mountain of JavaScript machinery. Most developers took one look and backed away slowly. Trevor Manz decided that barrier did not need to exist. His idea was simple: give Python users just enough JavaScript to unlock the web's interactivity, without dragging along the rest of the web ecosystem. That idea became anywidget, and it is quickly becoming the quiet connective tissue of modern interactive computing. Today we dig into how it works, why it has taken off, and how it might change the way we explore data. Episode sponsors Seer: AI Debugging, Code TALKPYTHON PyCharm, code STRONGER PYTHON Talk Python Courses Links from the show Trevor on GitHub: github.com anywidget GitHub: github.com Trevor's SciPy 2024 Talk: www.youtube.com Marimo GitHub: github.com Myst (Markdown docs): mystmd.org Altair: altair-viz.github.io DuckDB: duckdb.org Mosaic: uwdata.github.io ipywidgets: ipywidgets.readthedocs.io Tension between Web and Data Sci Graphic: blobs.talkpython.fm Quak: github.com Walk through building a widget: anywidget.dev Widget Gallery: anywidget.dev Video: How do I anywidget?: www.youtube.com PyCharm + PSF Fundraiser: pycharm-psf-2025 code STRONGER PYTHON Watch this episode on YouTube: youtube.com Episode #530 deep-dive: talkpython.fm/530 Episode transcripts: talkpython.fm Theme Song: Developer Rap

What if everything we think we know about AI understanding is wrong? Is compression the key to intelligence? Or is there something more—a leap from memorization to true abstraction? In this fascinating conversation, we sit down with **Professor Yi Ma**—world-renowned expert in deep learning, IEEE/ACM Fellow, and author of the groundbreaking new book *Learning Deep Representations of Data Distributions*. Professor Ma challenges our assumptions about what large language models actually do, reveals why 3D reconstruction isn't the same as understanding, and presents a unified mathematical theory of intelligence built on just two principles: **parsimony** and **self-consistency**.**SPONSOR MESSAGES START**—Prolific - Quality data. From real people. For faster breakthroughs.https://www.prolific.com/?utm_source=mlst—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyHiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst—**END**Key Insights:**LLMs Don't Understand—They Memorize**Language models process text (*already* compressed human knowledge) using the same mechanism we use to learn from raw data. **The Illusion of 3D Vision**Sora and NeRFs etc that can reconstruct 3D scenes still fail miserably at basic spatial reasoning**"All Roads Lead to Rome"**Why adding noise is *necessary* for discovering structure.**Why Gradient Descent Actually Works**Natural optimization landscapes are surprisingly smooth—a "blessing of dimensionality" **Transformers from First Principles**Transformer architectures can be mathematically derived from compression principles—INTERACTIVE AI TRANSCRIPT PLAYER w/REFS (ReScript):https://app.rescript.info/public/share/Z-dMPiUhXaeMEcdeU6Bz84GOVsvdcfxU_8Ptu6CTKMQAbout Professor Yi MaYi Ma is the inaugural director of the School of Computing and Data Science at Hong Kong University and a visiting professor at UC Berkeley. https://people.eecs.berkeley.edu/~yima/https://scholar.google.com/citations?user=XqLiBQMAAAAJ&hl=en https://x.com/YiMaTweets **Slides from this conversation:**https://www.dropbox.com/scl/fi/sbhbyievw7idup8j06mlr/slides.pdf?rlkey=7ptovemezo8bj8tkhfi393fh9&dl=0**Related Talks by Professor Ma:**- Pursuing the Nature of Intelligence (ICLR): https://www.youtube.com/watch?v=LT-F0xSNSjo- Earlier talk at Berkeley: https://www.youtube.com/watch?v=TihaCUjyRLMTIMESTAMPS:00:00:00 Introduction00:02:08 The First Principles Book & Research Vision00:05:21 Two Pillars: Parsimony & Consistency00:09:50 Evolution vs. Learning: The Compression Mechanism00:14:36 LLMs: Memorization Masquerading as Understanding00:19:55 The Leap to Abstraction: Empirical vs. Scientific00:27:30 Platonism, Deduction & The ARC Challenge00:35:57 Specialization & The Cybernetic Legacy00:41:23 Deriving Maximum Rate Reduction00:48:21 The Illusion of 3D Understanding: Sora & NeRF00:54:26 All Roads Lead to Rome: The Role of Noise00:59:56 All Roads Lead to Rome: The Role of Noise01:00:14 Benign Non-Convexity: Why Optimization Works01:06:35 Double Descent & The Myth of Overfitting01:14:26 Self-Consistency: Closed-Loop Learning01:21:03 Deriving Transformers from First Principles01:30:11 Verification & The Kevin Murphy Question01:34:11 CRATE vs. ViT: White-Box AI & ConclusionREFERENCES:Book:[00:03:04] Learning Deep Representations of Data Distributionshttps://ma-lab-berkeley.github.io/deep-representation-learning-book/[00:18:38] A Brief History of Intelligencehttps://www.amazon.co.uk/BRIEF-HISTORY-INTELLIGEN-HB-Evolution/dp/0008560099[00:38:14] Cyberneticshttps://mitpress.mit.edu/9780262730099/cybernetics/Book (Yi Ma):[00:03:14] 3-D Vision bookhttps://link.springer.com/book/10.1007/978-0-387-21779-6 refs on ReScript link/YT

Exploring innovation where education meets entrepreneurship. About Durga Suresh-Menon Durga Suresh-Menon, Ph.D., is Head of School at New England Innovation Academy. An energizing, dynamic and growth-minded educator with a record of inclusive leadership and passionate storytelling, Dr. Suresh-Menon joins NEIA with over two decades of collaborative higher-education experience, academic program development and a unique understanding of what makes students successful. She has a rich background in higher education, leadership, curriculum development, and academic excellence. Before joining NEIA, she served as Dean of the School of Computing and Data Science and Dean of Graduate Education at Wentworth Institute of Technology, as well as an Associate Professor, where she led efforts to implement progressive learning strategies and interdisciplinary curriculum that promoted innovation and global awareness. She is recognized for her work fostering a culture of growth, development and innovation, ensuring that a STEAM curriculum remains aligned with the ever-evolving technological landscape and industry demands. Fluent in multiple languages, Dr. Suresh-Menon loves to connect with tech-minded students and parents from all backgrounds, and brings a global perspective and collaborative spirit to NEIA's academic community. Instagram: https://www.instagram.com/hello.neia/ Twitter: https://x.com/helloneia Facebook: https://www.facebook.com/HelloNEIA/ LinkedIn: https://www.linkedin.com/in/durga-suresh-menon/ About John Camp (he goes by Camp) Camp has been teaching in independent schools for over 25 years. His experience includes English and writing classes as well as interdisciplinary courses such as “The Art and Physics of Time Travel.” At St. Mark's School, which bestowed him with The Trustees Chair and the Kidder Faculty Prize, Camp served as the Director of Experiential Learning and Associate Director of The Center of Innovation in Teaching and Learning. A pair of his pedagogical mantras include “I aim to teach what cannot be Googled” and “I expect you to work hard, so I work hard.” He has a B.A. English/Creative Writing from Middlebury College and M.A.L.S. from Dartmouth College. Instagram: https://www.instagram.com/hello.neia/ Facebook: https://www.facebook.com/HelloNEIA/ LinkedIn: https://www.linkedin.com/in/campsm/ Resources https://neiacademy.org/ https://www.linkedin.com/company/new-england-innovation-academy/  John Mikton on Social Media LinkedIn: https://www.linkedin.com/in/jmikton/ Twitter: https://twitter.com/jmikton Web: beyonddigital.org Dan Taylor on social media: LinkedIn: https://www.linkedin.com/in/appsevents  Twitter: https://twitter.com/appdkt  Web: www.appsevents.com Listen on: iTunes / Podbean / Stitcher / Spotify / YouTube Would you like to have a free 1 month trial of the new Google Workspace Plus (formerly G Suite Enterprise for Education)? Just fill out this form and we'll get you set up bit.ly/GSEFE-Trial

While the transition from academia to industry can be brutal for data scientists, academics don't show up in industry empty-handed. They bring powerful transferable skills that many industry-trained data scientists never develop.In this Value Boost episode, Dr. Sayli Javadekar joins Dr. Genevieve Hayes to flip the script on their previous conversation, exploring the valuable skills that academic-trained data scientists bring to industry and how any data scientist can develop these same strengths.You'll learn:The most valuable skills academics bring to industry [01:30]Why the experimental mindset matters so much [03:43]The hidden benefit of extended research projects [04:54]How mentorship can work both ways for mutual benefit [07:06]Guest BioDr Sayli Javadekar is a data scientist at Thoughtworks, with experience at the World Bank and UNAIDS. Before this, she was an Assistant Professor at the University of Bath and holds a PhD in Econometrics from the University of Geneva.LinksConnect with Sayli on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Topics covered in this episode: PEP 798: Unpacking in Comprehensions Pandas 3.0.0rc0 typos A couple testing topics Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: PEP 798: Unpacking in Comprehensions After careful deliberation, the Python Steering Council is pleased to accept PEP 798 – Unpacking in Comprehensions. Examples [*it for it in its] # list with the concatenation of iterables in 'its' {*it for it in its} # set with the union of iterables in 'its' {**d for d in dicts} # dict with the combination of dicts in 'dicts' (*it for it in its) # generator of the concatenation of iterables in 'its' Also: The Steering Council is happy to unanimously accept “PEP 810, Explicit lazy imports” Brian #2: Pandas 3.0.0rc0 Pandas 3.0.0 will be released soon, and we're on Release candidate 0 Here's What's new in Pands 3.0.0 Dedicated string data type by default Inferred by default for string data (instead of object dtype) The str dtype can only hold strings (or missing values), in contrast to object dtype. (setitem with non string fails) The missing value sentinel is always NaN (np.nan) and follows the same missing value semantics as the other default dtypes. Copy-on-Write The result of any indexing operation (subsetting a DataFrame or Series in any way, i.e. including accessing a DataFrame column as a Series) or any method returning a new DataFrame or Series, always behaves as if it were a copy in terms of user API. As a consequence, if you want to modify an object (DataFrame or Series), the only way to do this is to directly modify that object itself. pd.col syntax can now be used in DataFrame.assign() and DataFrame.loc() You can now do this: df.assign(c = pd.col('a') + pd.col('b')) New Deprecation Policy Plus more - Michael #3: typos You've heard about codespell … what about typos? VSCode extension and OpenVSX extension. From Sky Kasko: Like codespell, typos checks for known misspellings instead of only allowing words from a dictionary. But typos has some extra features I really appreciate, like finding spelling mistakes inside snake_case or camelCase words. For example, if you have the line: *connecton_string = "sqlite:///my.db"* codespell won't find the misspelling, but typos will. It gave me the output: *error: `connecton` should be `connection`, `connector` ╭▸ ./main.py:1:1 │1 │ connecton_string = "sqlite:///my.db" ╰╴━━━━━━━━━* But the main advantage for me is that typos has an LSP that supports editor integrations like a VS Code extension. As far as I can tell, codespell doesn't support editor integration. (Note that the popular Code Spell Checker VS Code extension is an unrelated project that uses a traditional dictionary approach.) For more on the differences between codespell and typos, here's a comparison table I found in the typos repo: https://github.com/crate-ci/typos/blob/master/docs/comparison.md By the way, though it's not mentioned in the installation instructions, typos is published on PyPI and can be installed with uv tool install typos, for example. That said, I don't bother installing it, I just use the VS Code extension and run it as a pre-commit hook. (By the way, I'm using prek instead of pre-commit now; thanks for the tip on episode #448!) It looks like typos also publishes a GitHub action, though I haven't used it. Brian #4: A couple testing topics slowlify suggested by Brian Skinn Simulate slow, overloaded, or resource-constrained machines to reproduce CI failures and hunt flaky tests. Requires Linux with cgroups v2 Why your mock breaks later Ned Badthelder Ned's taught us before to “Mock where the object is used, not where it's defined.” To be more explicit, but probably more confusing to mock-newbies, “don't mock things that get imported, mock the object in the file it got imported to.” See? That's probably worse. Anyway, read Ned's post. If my project myproduct has user.py that uses the system builtin open() and we want to patch it: DONT DO THIS: @patch("builtins.open") This patches open() for the whole system DO THIS: @patch("myproduct.user.open") This patches open() for just the user.py file, which is what we want Apparently this issue is common and is mucking up using coverage.py Extras Brian: The Rise and Rise of FastAPI - mini documentary “Building on Lean” chapter of LeanTDD is out The next chapter I'm working on is “Finding Waste in TDD” Notes to delete before end of show: I'm not on track for an end of year completion of the first pass, so pushing goal to 1/31/26 As requested by a reader, I'm releasing both the full-so-far versions and most-recent-chapter Michael: My Vanishing Gradient's episode is out Django 6 is out Joke: tabloid - A minimal programming language inspired by clickbait headlines

In this episode, we look at the actuarial principles that make models safer: parallel modeling, small data with provenance, and real-time human supervision. To help us, long-time insurtech and startup advisor David Sandberg, FSA, MAAA, CERA, joins us to share more about his actuarial expertise in data management and AI. We also challenge the hype around AI by reframing it as a prediction machine and putting human judgment at the beginning, middle, and end. By the end, you might think about “human-in-the-loop” in a whole new way.• Actuarial valuation debates and why parallel models win• AI's real value: enhance and accelerate the growth of human capital• Transparency, accountability, and enforceable standards• Prediction versus decision and learning from actual-to-expected• Small data as interpretable, traceable fuel for insight• Drift, regime shifts, and limits of regression and LLMs• Mapping decisions, setting risk appetite, and enterprise risk management (ERM) for AI• Where humans belong: the beginning, middle, and end of the system• Agentic AI complexity versus validated end-to-end systems• Training judgment with tools that force critique and citationCultural references:Foundation, AppleTVThe Feeling of Power, Isaac AsimovPlayer Piano, Kurt VonnegutFor more information, see Actuarial and data science: Bridging the gap.What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

This podcast is brought to you by Outcomes Rocket, your exclusive healthcare marketing agency. Learn how to accelerate your growth by going to⁠ outcomesrocket.com Thoughtful, problem-first innovation drives real clinical impact in healthcare. In this episode, Saul Marquez and Ed Gaudet from Censinet host Dr. Jason Hill, Innovation Officer at Ochsner Health, and David Leingang, Director of Innovation Data Science at Ochsner Health, to discuss how their team uses machine learning, workflow redesign, and data-driven insights to reduce physician message burden and improve patient routing. They share how analyzing 2.4 million inbox messages revealed that 4% were tied to weight-loss drugs, prompting the creation of a new weight-management digital medicine program instead of an AI tool. They explain how reorganizing message flows, adding e-visits, and using ML to uncover hidden system strain has improved efficiency, while predictive deterioration models saved lives but had to be retrained as outcomes changed. The conversation closes with an exploration of value-based care, problem-solving in AI, and the AHEAD Network's role in advancing healthcare innovation. Tune in and learn how practical AI, smarter workflows, and cross-industry collaboration are reshaping modern healthcare! Resources Connect with and follow Jason Hill on LinkedIn. Follow and connect with David Leingang on LinkedIn. Follow Ochsner Health on LinkedIn and explore their website!

What do you do when your day job feels empty — but you still need to show up, provide, and stay honest?In this episode of Shtark Tank, I sit down with my cousin and friend Yoni Schwartz — Head of Data Science at Exponential and Head Producer of Living L'chaim. Yoni shares how he went from a corporate role that felt like “a complete lack of purpose” to leading 10 shows that have inspired and helped countless people. He is also the host of Spirit of the Song podcast, make sure to check it out!We talk about meaning, ambition, family, and the real-life tradeoffs of building something big on top of a demanding day job.In this conversation we cover:What it feels like when work is steady… but meaninglessHow Yoni first joined Living L'chaim and how the role grew over timeThe ethics of balancing a primary job with major side projectsStartup life vs. corporate life — and what actually changed for himHow he manages two intense roles without a rigid systemThe idea of intentionality as a survival tool for busy peopleSetting boundaries after COVID blurred everythingEarly morning learning as a realistic anchor for fathers with young kidsThe impact Living L'chaim aims for — inspiration, mental health, and financial clarityCultivating a relationship with your RebbiKey takeaway:You don't need a perfect system to juggle a lot — but you do need honesty, priorities, and intentional choices you can live with.If this episode resonated, please take a moment to follow the show and leave a 5-star rating. It helps more Bnei Torah in the workforce find these conversations.Guest: Yoni SchwartzHost: Yaakov Wolff

Dr. Satyajit Wattamwar is a Data Science & Digital Expertise Leader with over 17 years of professional experience in enabling digital transformation across manufacturing, R&D, and innovation processes. He currently leads the development of in-house cloud AI platforms and data science technologies for cross-country, cross-functional business initiatives. His expertise spans predictive modeling, IoT analytics, advanced process control, and manufacturing analytics.On The Menu:Data scientist empowerment through AI-powered digital assistantsScaling digital solutions across 180+ global factoriesIndustry 4.0 ROI measurement strategies and KPIsDigital twin technology revolutionizing manufacturing operationsQuantum computing's potential impact on process optimizationExplainable AI methodologies for industrial safety applicationsSupply chain optimization using predictive data science

Michael Crow is the president of Arizona State University, which U.S. News & World Report has called the most innovative school in the country for 11 years running. He tells Steve about why higher education needs to change, and how A.S.U. is leading the way. Plus: Steve has an announcement about the podcast. SOURCES:Michael Crow, president of Arizona State University. RESOURCES:The Fifth Wave: The Evolution of American Higher Education, by Michael Crow (2020)."College Admissions Shocker!," by Frank Bruni (New York Times, 2016).New American University.Dreamscape Learn.University Innovation Alliance.FYI.AI. EXTRAS:"Chemistry, Evolved," by People I (Mostly) Admire (2025)."America's Math Curriculum Doesn't Add Up," by People I (Mostly) Admire (2021).Data Science 4 Everyone. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

From studying Business Data Science to landing a role in investment banking at Centerview Partners, this is my honest story of how I discovered my path, the mistakes I made, and what I wish every student knew before starting their career. In this video, I share how I transitioned from college to corporate life — the lessons I learned outside the classroom, why real-world experience matters, and how small opportunities can lead to big growth. Whether you're a university student, career changer, or just curious about finance and personal growth, this episode will give you insight, motivation, and practical steps to help you find your direction.

AI is becoming ubiquitous in our lives. It shapes how we work, play, interact, create, and even manage our health—and this is only the beginning. To understand where we are and where we might go, we first need to understand how we got here. By tracing the evolving nature of machine intelligence, we can appreciate how today's AI differs from its past and how it is likely to evolve. With that in mind, we can begin to ask the big questions: When should we trust AI over human judgment? How should we govern its development? How will it change what it means to be human? And what roles will humans play in the future of work?To help us through this journey, I'm delighted to welcome back to TRIUM Connects Professor Vasant Dhar, the Robert A. Miller Professor at NYU's Stern School of Business and Professor of Data Science at NYU. Vasant is one of the world's leading thinkers on the impact of AI on society. He was present at the birth of AI and has been involved in every step of its evolution—both as an entrepreneur and as a scholar. He also hosts the acclaimed podcast Brave New World, which explores how machines are transforming humanity in the post-COVID era.In this episode, we discuss his newest book, Thinking With Machines: The Brave New World of AI. It's a remarkable hybrid: part autobiography, tracing how his professional life has intertwined with the development of AI; part user's guide, offering a lucid framework for deciding when to trust machines over human control; and part deep dive into the philosophical and policy implications of creating an alien intelligence.It was a real pleasure to welcome Vasant back onto the show. I learned a lot during our conversation, and I hope you will enjoy it as much as I did.CitationsDawid A, LeCun Y. Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence. arXiv. June 5, 2023.Dennett DC. Intentional systems. J Philos. 1971;68(4):87-106.Dhar V. Thinking With Machines: The Brave New World of AI. Galloway S, foreword. Hoboken, NJ: Wiley; 2025.Frank, R. H., & Cook, P. J. The winner-take-all society: Why the few at the top get so much more than the rest of us. Penguin Books; 1995.Ganguli D, Askell A, Henighan T, et al. Alignment faking in large language models. arXiv. December 20, 2024.Harari YN. Nexus: A Brief History of Information Networks from the Stone Age to AI. New York, NY: Random House; 2024.Kauffmann J, Dippel J, Ruff L, et al. Explainable AI reveals Clever Hans effects in unsupervised learning models. Nat Mach Intell. 2025;7:1–10.Pearl J, Mackenzie D. The Book of Why: The New Science of Cause and Effect. New York, NY: Basic Books; 2018.Pfungst O. Clever Hans (The Horse of Mr. Von Osten): A Contribution to Experimental Animal and Human Psychology. Rahn H, trans. New York: Henry Holt; 1911.Popper KR. The Logic of Scientific Discovery. London, UK: Hutchinson; 1959Suleyman M, Bhaskar M. The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma. New York, NY: Crown; 2023.Yudkowsky E, Soares N. If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All. New York, NY: Little, Brown and Company; 2025. Hosted on Acast. See acast.com/privacy for more information.

Chris Morgan, VP of Data Science at Lincoln Financial Group, joins me to unpack what a real data culture looks like inside a complex, highly regulated business that has policies on the books for decades. We talk about how to turn Gen AI buzz into real value, why governance and quality suddenly matter to everyone, and how to tackle data technical debt without stalling delivery.Chris shares concrete ways he finds champions in the business, balances centralized and federated models, and keeps stakeholders excited about the future while he quietly fixes the messy data foundation underneath it all.Key takeawaysData culture is less about dashboards and more about curiosity, repeatable processes, and raising the analytical watermark across the company, not just in the data team.The teams that will win with Gen AI are the ones that can safely connect proprietary data to these models, which demands strong governance, clear definitions, and shared standards.A blended model works best for scaling data work, where a central function sets guardrails and standards while domain teams stay close to the business and own local decisions.Paying down technical debt works when it is framed in business terms, tied to revenue and risk, and treated as a regular slice of capacity instead of a one time side project.Education is now part of the job for data leaders, from internal road shows on Gen AI to simple stories that explain why foundational data work matters before you can ship shiny tools.Timestamped highlights00:04 Setting the stage Chris explains his role at Lincoln Financial and how data science supports life and annuity products that can live for decades.03:33 The Cobb salad story A simple grocery store analogy that makes data standards and shared definitions instantly clear to non technical stakeholders.06:06 Finding the right champions Why Chris prefers curious partners who will invest time with the data team over senior leaders who just want results without changing behavior.08:33 Governance as Gen AI fuel How regulatory pressure and the need to trust what goes into models are pushing data governance and quality into the spotlight.11:11 A practical way to attack data technical debt How Chris decides what to fix first, and why he tries to reserve a steady slice of team time for cleanup so progress is visible and sustainable.17:44 Managing Gen AI expectations From road shows to constant communication, Chris shares how he keeps enthusiasm high while also being honest about the timeline and effort.One line that sums it up“These generative models are going to become a commodity and what will separate companies is who can take the most advantage of their proprietary data.”Practical playbookStart small with data culture by picking one engaged business partner, one problem, and one outcome you can measure clearly.Reserve a consistent portion of team capacity for technical debt, even if it is only a small percentage at first, and make the tradeoffs visible.Use stories, analogies, and simple rules of the road so stakeholders can understand how data systems work without becoming experts in the tech.Call to actionIf this conversation helped you think differently about data culture and Gen AI inside your company, follow the show and leave a rating so more engineering and data leaders can find it. To keep the discussion going, connect with me on LinkedIn and share how your team is tackling data culture and technical debt right now.

Can AI and human creativity truly coexist—or are we watching the beginning of the end for original artistry? In this episode of Today in Tech, host Keith Shaw dives deep into the future of visual content with Allesandra Sala, Shutterstock's Head of AI and Data Science. Together, they explore how generative AI is transforming the creative industry — from image perfection and stock photography disruption to copyright chaos, ethical dilemmas, and artistic identity. Discover: Why Shutterstock chose to embrace, not resist, generative AI How AI-generated content is both exciting and dangerously generic The ongoing legal battle over AI authorship and content ownership How artists can stay relevant (and possibly even thrive) with AI What ethical guardrails and transparency measures are needed now Whether a backlash to “too perfect” imagery is already underway Follow TECH(talk) for the latest tech news and discussion!

A lot of people building software today never took the traditional CS path. They arrived through curiosity, a job that needed automating, or a late-night itch to make something work. This week, David Kopec joins me to talk about rebuilding computer science for exactly those folks, the ones who learned to program first and are now ready to understand the deeper ideas that power the tools they use every day. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON NordStellar Talk Python Courses Links from the show David Kopec: davekopec.com Classic Computer Science Book: amazon.com Computer Science from Scratch Book: computersciencefromscratch.com Computer Science from Scratch at NoStartch (CSFS30 for 30% off): nostarch.com Watch this episode on YouTube: youtube.com Episode #529 deep-dive: talkpython.fm/529 Episode transcripts: talkpython.fm Theme Song: Developer Rap

This episode examines a new CNA model that can help government officials optimally deploy unmanned systems, and how it overlaps with our existing tools. Guest Biographies Arpita Verma is an expert in optimization, modeling, and simulation in CNA's Data Science for Production Program. John Crissman is an expert in artificial intelligence (AI), machine learning (ML) and natural language processing in CNA's Center for Data Management Analytics. Rebekah Yang is an expert in artificial intelligence and machine learning (AI/ML) for FAA NextGen and a specialist in data visualization and modeling in CNA's Center for Data Management Analytics. Further Reading UAS Cooperative Airspace Traffic Simulation (UCATS) First Responder Awareness Monitoring during Emergencies (FRAME) 

Kenneth Hochhauser is Partner and Head of Data and Analytics at RTL. His background includes roles as a retail executive at Macy's and GNC and as a small business and economic development officer for the City of New York. He has advised both tenants and landlords on site selection, trade area analysis, and retail strategy, including introducing Chipotle to the New York metro market and representing Duxiana nationally. His past assignments span major projects such as Brookfield Place, Trump Place, and Columbia University's Manhattanville and Morningside campuses.(02:39) - Ken's Journey(04:59) - Retail Market Trends(06:05) - Retail vs. Office Innovation(07:53) - Shopping Trends and Retail Insights(08:31) - Retail Challenges in Manhattan(10:05) - Retail's Historical Context and Future(12:14) - Tenant Preferences(17:33) - Experiential Retail & Unique Locations(20:56) - Non-Traditional Retail (23:21) - Feature: Blueprint - The Future of Real Estate - Register for 2026: The Premier Event for Industry Executives, Real Estate & Construction Tech Startups and VC's, at The Venetian, Las Vegas on September 22nd-24th, 2026. As a friend of Tangent, you can save $300 on your All-Access pass(28:11) - Retail Tech & Data Utilization(34:29) - Location Indicators & Retail Expansion(38:29) - Collaboration Superpower: an economist(40:08) - US Gov. Shutdown Impact

Join us for a special farewell episode of the Alter Everything Podcast as we celebrate the impactful journey of host Megan Bowers. In this episode, Megan reflects on her career in data analytics, her experiences at Alteryx, and the evolution of the podcast. Discover insights on building a personal brand, the importance of networking in the data industry, and the future of data science and AI. Hear memorable stories from past episodes, expert interviews, and practical advice for data professionals. Panelists: Michael Cusic, Sr. Learning Experience Designer @ Alteryx - @mikecusic, LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Alteryx Community Blog ContentEpisode 194: AI and Data PipelinesEpisode 134: Building Trust in AI with FiddlerEpisode 140: Using Alteryx to Understand Climate ChangeAlteryx ACE program Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music.

Activities outside of data science can strengthen the very skills data scientists need for their careers in surprising ways. From improving stakeholder communication to learning how to work with resistance rather than against it, hobbies and interests often teach lessons that directly translate to professional effectiveness.In this Value Boost episode, Colin Priest joins Dr. Genevieve Hayes to explore how unexpected hobbies and activities can make you a more effective data scientist and enhance your career.You'll discover:How dancing skills translate into better stakeholder presentations [02:02]What swimming teaches about working with resistance [06:30]Why coaching swimmers improves communication with non-technical colleagues [08:10]The simple activity anyone can try to expand their data science thinking [11:03]Guest BioColin Priest is an actuary, data scientist and educator who has held several CEO and general management roles where he has championed data-driven initiatives. He now lectures at UNSW, where he specialises in adapting education for the age of AI.LinksConnect with Colin on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

We have been sold a story of complexity. Michael Kennedy (Talk Python) argues we can escape this by relentlessly focusing on the problem at hand, reducing costs by orders of magnitude in software, data, and AI.In this episode, Michael joins Hugo to dig into the practical side of running Python systems at scale. They connect these ideas to the data science workflow, exploring which software engineering practices allow AI teams to ship faster and with more confidence. They also detail how to deploy systems without unnecessary complexity and how Agentic AI is fundamentally reshaping development workflows.We talk through:- Escaping complexity hell to reduce costs and gain autonomy- The specific software practices, like the "Docker Barrier", that matter most for data scientists- How to replace complex cloud services with a simple, robust $30/month stack- The shift from writing code to "systems thinking" in the age of Agentic AI- How to manage the people-pleasing psychology of AI agents to prevent broken code- Why struggle is still essential for learning, even when AI can do the work for youLINKSTalk Python In Production, the Book! (https://talkpython.fm/books/python-in-production)Just Enough Python for Data Scientists Course (https://training.talkpython.fm/courses/just-enough-python-for-data-scientists)Agentic AI Programming for Python Course (https://training.talkpython.fm/courses/agentic-ai-programming-for-python)Talk Python To Me (https://talkpython.fm/) and a recent episode with Hugo as guest: Building Data Science with Foundation LLM Models (https://talkpython.fm/episodes/show/526/building-data-science-with-foundation-llm-models)Python Bytes podcast (https://pythonbytes.fm/)Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)Watch the podcast video on YouTube (https://youtube.com/live/jfSRxxO3aRo?feature=share)Join the final cohort of our Building AI Applications course starting Jan 12, 2026 (35% off for listeners) (https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav): https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com

Thanks to Prosus Group for collaborating on the Agents in Production Virtual Conference 2025.Abstract //The discussion centers on highly technical yet practical themes, such as the use of advanced post-training techniques like Direct Preference Optimization (DPO) and Parameter-Efficient Fine-Tuning (PEFT) to ensure LLMs maintain stability while specializing for e-commerce domains. We compare the implementation challenges of Computer-Using Agents in automating legacy enterprise systems versus the stability issues faced by conversational agents when inputs become unpredictable in production. We will analyze the role of cloud infrastructure in supporting the continuous, iterative training loops required by Reinforcement Learning-based agents for e-commerce!Bio // Paul van der Boor (Panel Host) //Paul van der Boor is a Senior Director of Data Science at Prosus and a member of its internal AI group.Arushi Jain (Panelist) // Arushi is a Senior Applied Scientist at Microsoft, working on LLM post-training for Computer-Using Agent (CUA) through Reinforcement Learning. She previously completed Microsoft's competitive 2-year AI Rotational Program (MAIDAP), building and shipping AI-powered features across four product teams.She holds a Master's in Machine Learning from the University of Michigan, Ann Arbor, and a Dual Degree in Economics from IIT Kanpur. At Michigan, she led the NLG efforts for the Alexa Prize Team, securing a $250K research grant to develop a personalized, active-listening socialbot. Her research spans collaborations with Rutgers School of Information, Virginia Tech's Economics Department, and UCLA's Center for Digital Behavior.Beyond her technical work, Arushi is a passionate advocate for gender equity in AI. She leads the Women in Data Science (WiDS) Cambridge community, scaling participation in her ML workshops from 25 women in 2020 to 100+ in 2025—empowering women and non-binary technologists through education and mentorship.Swati Bhatia //Passionate about building and investing in cutting-edge technology to drive positive impact.Currently shaping the future of AI/ML at Google Cloud.10+ years of global experience across the U.S, EMEA, and India in product, strategy & venture capital (Google, Uber, BCG, Morpheus Ventures).Audi Liu //I'm passionate about making AI more useful and safe.Why? Because AI will be ubiquitous in every workflow, powering our lives just like how electricity revolutionized our society - It's pivotal we get it right.At Inworld AI, we believe all future software will be powered by voice. As a Sr Product Manager at Inworld, I'm focused on building a real-time voice API that empowers developers to create engaging, human-like experiences. Inworld offers state-of-the-art voice AI at a radically accessible price - No. 1 on Hugging Face and Artificial Analysis, instant voice cloning, rich multilingual support, real-time streaming, and emotion plus non-verbal control, all for just $5 per million characters.Isabella Piratininga //Experienced Product Leader with over 10 years in the tech industry, shaping impactful solutions across micro-mobility, e-commerce, and leading organizations in the new economy, such as OLX, iFood, and now Nubank. I began my journey as a Product Owner during the early days of modern product management, contributing to pivotal moments like scaling startups, mergers of major tech companies, and driving innovation in digital banking.My passion lies in solving complex challenges through user-centered product strategies. I believe in creating products that serve as a bridge between user needs and business goals, fostering value and driving growth. At Nubank, I focus on redefining financial experiences and empowering users with accessible and innovative solutions.

Topics covered in this episode: Advent of Code starts today Django 6 is coming Advanced, Overlooked Python Typing codespell Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Advent of Code starts today A few changes, like 12 days this year, which honestly, I'm grateful for. See also: elf: Advent of Code CLI helper for Python Michael #2: Django 6 is coming Expected December 2025 Django 6.0 supports Python 3.12, 3.13, and 3.14 Built-in support for the Content Security Policy (CSP) standard is now available, making it easier to protect web applications against content injection attacks such as cross-site scripting (XSS). The Django Template Language now supports template partials, making it easier to encapsulate and reuse small named fragments within a template file. Django now includes a built-in Tasks framework for running code outside the HTTP request–response cycle. This enables offloading work, such as sending emails or processing data, to background workers. Email handling in Django now uses Python's modern email API, introduced in Python 3.6. This API, centered around the email.message.EmailMessage class Brian #3: Advanced, Overlooked Python Typing get_args, TypeGuard, TypeIs, and more goodies Michael #4: codespell Learned from this PR for the Talk Python book. Fix common misspellings in text files. It's designed primarily for checking misspelled words in source code (backslash escapes are skipped), but it can be used with other files as well. It does not check for word membership in a complete dictionary, but instead looks for a set of common misspellings. Therefore it should catch errors like "adn", but it will not catch "adnasdfasdf". It shouldn't generate false-positives when you use a niche term it doesn't know about. Extras Brian: Is mkdocs maintained? Hatch 1.16 Michael: Follow up on tach from Gerben Dekker: tach has been unmaintained for a bit but is not anymore. It was the main product from Gauge which is a Y combinator startup that pivoted to something unrelated and abandoned tach. However, https://github.com/DetachHead forked it but now got access to the main repo and has committed to maintaining it. ruff analyze graph is fully independent of tach - we actually started to look into alternatives for tach when it became unmaintained and then found ruff analyze graph. For our use case, with just a bit of manipulation on top of ruff analyze graph we replaced our use of deptry (which was slower - and I try to be careful depending on one-man projects). A Review of Michael Kennedy's book, “Talk Python in Production” - Thanks Doug Joke: NoaaS

Skills, tasks, jobs, activities. These terms get used interchangeably across HR and talent acquisition, but they mean fundamentally different things. Skills are attributes of people. Tasks are components of work. Jobs are bundles of activities.  Having clarity here matters more now than ever. As AI begins reshaping how work gets done, organisations need a precise understanding of their workforce at the task level. Without clear taxonomies, it becomes impossible to understand how to effectively implement AI for automation and augmentation. So how should companies be preparing to take the most advantage of the inevitable shifts AI will bring? My guest this week is Ben Zweig, CEO of Revelio Labs and author of the new book Job Architecture. In our conversation, he explains how to build effective taxonomies cheaply and scalably with LLMs and why this foundation is critical for navigating change. Ben also teaches Data Science and The Future of Work at NYU Stern and talks through an invaluable framework for assessing the likelihood of AI-driven job displacement. In the interview, we discuss: Why grouping people is the core of any HR analysis. What we get wrong about skills, jobs, tasks, and activities Why skills aren't the right unit of observation to analyse jobs AI automates tasks and activities, not jobs and skills. The vital importance of taxonomies Using LLMs to build taxonomies cost-effectively at scale. What are the advantages of doing this properly? The three forces that help measure the potential for AI-driven job displacement What does the future look like Follow this podcast on Apple Podcasts. Follow this podcast on Spotify.

In this episode, I'm talking with Vincent Warmerdam about treating LLMs as just another API in your Python app, with clear boundaries, small focused endpoints, and good monitoring. We'll dig into patterns for wrapping these calls, caching and inspecting responses, and deciding where an LLM API actually earns its keep in your architecture. Episode sponsors Seer: AI Debugging, Code TALKPYTHON NordStellar Talk Python Courses Links from the show Vincent on X: @fishnets88 Vincent on Mastodon: @koaning LLM Building Blocks for Python Co-urse: training.talkpython.fm Top Talk Python Episodes of 2024: talkpython.fm LLM Usage - Datasette: llm.datasette.io DiskCache - Disk Backed Cache (Documentation): grantjenks.com smartfunc - Turn docstrings into LLM-functions: github.com Ollama: ollama.com LM Studio - Local AI: lmstudio.ai marimo - A Next-Generation Python Notebook: marimo.io Pydantic: pydantic.dev Instructor - Complex Schemas & Validation (Python): python.useinstructor.com Diving into PydanticAI with marimo: youtube.com Cline - AI Coding Agent: cline.bot OpenRouter - The Unified Interface For LLMs: openrouter.ai Leafcloud: leaf.cloud OpenAI looks for its "Google Chrome" moment with new Atlas web browser: arstechnica.com Watch this episode on YouTube: youtube.com Episode #528 deep-dive: talkpython.fm/528 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Send Bidemi a Text Message!In this episode, host Bidemi Ologunde spoke with data scientist and AI/machine learning (ML) enthusiast Daria Dubovskaia in a wide-ranging conversation about cybersecurity, data analytics, and building robust ML systems in the real world. Daria shares her journey from studying rocket propulsion in Russia to moving to the United States, completing a Master's degree in Data Science at CUNY, and working at a healthcare startup in Tampa, Florida. Along the way, she talks about cleaning messy data, deploying production models in the cloud, protecting sensitive information, and communicating complex insights to non technical stakeholders. This episode is full of practical lessons for anyone interested in data-driven decision-making, career pivots into tech, and the growing intersection of machine learning and cybersecurity.Support the show

Eric Bradlow and Adi Wyner examine surprising NBA and NFL performance patterns while highlighting the innovative sports analytics research conducted by students—including advancements in expected-goals modeling, rugby decision analytics, and tennis serve evaluation—showcasing how data science and AI are reshaping modern sports analysis. Hosted on Acast. See acast.com/privacy for more information.

When most data scientists think about using LLMs and generative AI, the first thing that springs to mind is writing code faster. While that's certainly useful, if it's the only application you're exploring, you're missing some of the most powerful opportunities to enhance your effectiveness as a data scientist.In this episode, Colin Priest joins Dr. Genevieve Hayes to explore advanced LLM applications that go far beyond code generation, including techniques for processing unstructured data, improving stakeholder communication, and identifying blind spots in your analysis.You'll learn:How to use LLMs to extract structured insights from messy unstructured data [02:50]The role-playing technique that helps you practice difficult stakeholder conversations [14:12]Why using multiple LLMs helps reduce AI hallucinations [20:38]A step-by-step approach for integrating LLMs into your workflow safely [25:52]Guest BioColin Priest is an actuary, data scientist and educator who has held several CEO and general management roles where he has championed data-driven initiatives. He now lectures at UNSW, where he specialises in adapting education for the age of AI.LinksConnect with Colin on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Kevin Shtofman is the Global Head of Alliances and Corporate Development at Cherre, a real estate data platform powering over $3.3 trillion in AUM. With 20+ years of experience across real estate, finance, and consulting, Kevin leads global initiatives to integrate and contextualize data from systems, third parties, and JV partners, helping investors, operators, and asset managers make smarter decisions. At Cherre, he also oversees strategic partnerships, global expansion, and the innovation roadmap. Prior to joining Cherre, Kevin held leadership roles across the industry, including Chief Operating Officer at NavigatorCRE, and Global Real Estate Technology Strategy Lead at Deloitte, where he advised clients on emerging technologies like AI, automation, and blockchain. A recognized voice in real estate innovation, Kevin brings two decades of experience bridging data, operations, and technology across global real estate markets. Outside of work, Kevin is a golf enthusiast, occasional Ironman, and proud father of three daughters.(02:05) - Kevin's Background(05:19) - Challenges in Real Estate Data Management(06:52) - Cherre's Approach to Data Integration(13:48) - Evolution of Cherre's Platform(21:41) - Client Success Stories(24:58) - Future of Real Estate and AI(25:23) - Feature: Blueprint - The Future of Real Estate - Register for 2026: The Premier Event for Industry Executives, Real Estate & Construction Tech Startups and VC's, at The Venetian, Las Vegas on September 22nd-24th, 2026. As a friend of Tangent, you can save $300 on your All-Access pass(29:58) - Introducing Cherre AI Agent Marketplace(33:58) - AI Use Cases(40:06) - The Future of Real Estate Data(42:29) - Affordable Housing and Investment(47:37) - Collaboration Superpower: William Levitt (Wiki) & Larry Brown (Wiki)

Topics covered in this episode: PEP 814 – Add frozendict built-in type From Material for MkDocs to Zensical Tach Some Python Speedups in 3.15 and 3.16 Extras Joke About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #0: Black Friday is on at Talk Python What's on offer: An AI course mini bundle (22% off) 20% off our entire library via the Everything Bundle (what's that? ;) ) The new Talk Python in Production book (25% off) Brian: This is peer pressure in action 20% off The Complete pytest Course bundle (use code BLACKFRIDAY) through November or use save50 for 50% off, your choice. Python Testing with pytest, 2nd edition, eBook (50% off with code save50) also through November I would have picked 20%, but it's a PragProg wide thing Michael #1: PEP 814 – Add frozendict built-in type by Victor Stinner & Donghee Na A new public immutable type frozendict is added to the builtins module. We expect frozendict to be safe by design, as it prevents any unintended modifications. This addition benefits not only CPython's standard library, but also third-party maintainers who can take advantage of a reliable, immutable dictionary type. To add to existing frozen types in Python. Brian #2: From Material for MkDocs to Zensical Suggested by John Hagen A lot of people, me included, use Material for MkDocs as our MkDocs theme for both personal and professional projects, and in-house docs. This plugin for MkDocs is now in maintenance mode The development team is switching to working on Zensical, a static site generator to overcome some technical limitations with MkDocs. There's a series of posts about the transition and reasoning Transforming Material for MkDocs Zensical – A modern static site generator built by the creators of Material for MkDocs Material for MkDocs Insiders – Now free for everyone Goodbye, GitHub Discussions Material for MkDocs still around, but in maintenance mode all insider features now available to everyone Zensical is / will be compatible with Material for Mkdocs, can natively read mkdocs.yml, to assist with the transition Open Source, MIT license funded by an offering for professional users: Zensical Spark Michael #3: Tach Keep the streak: pip deps with uv + tach From Gerben Decker We needed some more control over linting our dependency structure, both internal and external. We use tach (which you covered before IIRC), but also some home built linting rules for our specific structure. These are extremely easy to build using an underused feature of ruff: "uv run ruff analyze graph --python python_exe_path .". Example from an app I'm working on (shhhhh not yet announced!) Brian #4: Some Python Speedups in 3.15 and 3.16 A Plan for 5-10%* Faster Free-Threaded JIT by Python 3.16 5% faster by 3.15 and 10% faster by 3.16 Decompression is up to 30% faster in CPython 3.15 Extras Brian: LeanTDD book issue tracker Michael: No. 4 for dependencies: Inverted dep trees from Bob Belderbos Joke: git pull inception

Ever wonder what it takes to level up your career in data science? Senior Data Scientist Darya Petrashka joins Ned and Kyler to share her personal journey from management and linguistics into data science, the real difference between a junior and a senior role, and helps us get under the “data science umbrella” to see... Read more »

Topics covered in this episode: Possibility of a new website for Django aiosqlitepool deptry browsr Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Possibility of a new website for Django Current Django site: djangoproject.com Adam Hill's in progress redesign idea: django-homepage.adamghill.com Commentary in the Want to work on a homepage site redesign? discussion Michael #2: aiosqlitepool