Podcasts about Data science

Today, we're talking about building real AI products with foundation models. Not toy demos, not vibes. We'll get into the boring dashboards that save launches, evals that change your mind, and the shift from analyst to AI app builder. Our guide is Hugo Bowne-Anderson, educator, podcaster, and data scientist, who's been in the trenches from scalable Python to LLM apps. If you care about shipping LLM features without burning the house down, stick around. Episode sponsors Posit NordStellar Talk Python Courses Links from the show Hugo Bowne-Anderson: x.com Vanishing Gradients Podcast: vanishinggradients.fireside.fm Fundamentals of Dask: High Performance Data Science Course: training.talkpython.fm Building LLM Applications for Data Scientists and Software Engineers: maven.com marimo: a next-generation Python notebook: marimo.io DevDocs (Offline aggregated docs): devdocs.io Elgato Stream Deck: elgato.com Sentry's Seer: talkpython.fm The End of Programming as We Know It: oreilly.com LorikeetCX AI Concierge: lorikeetcx.ai Text to SQL & AI Query Generator: text2sql.ai Inverse relationship enthusiasm for AI and traditional projects: oreilly.com Watch this episode on YouTube: youtube.com Episode #526 deep-dive: talkpython.fm/526 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Dr. Julia Stoyanovich is Institute Associate Professor of Computer Science and Engineering, Associate Professor of Data Science, Director of the Center for Responsible AI, and member of the Visualization and Data Analytics Research Center at New York University. She is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) and a Senior member of the Association of Computing Machinery (ACM). Julia's goal is to make “Responsible AI” synonymous with “AI”. She works towards this goal by engaging in academic research, education and technology policy, and by speaking about the benefits and harms of AI to practitioners and members of the public. Julia's research interests include AI ethics and legal compliance, and data management and AI systems. Julia is engaged in technology policy and regulation in the US and internationally, having served on the New York City Automated Decision Systems Task Force, by mayoral appointment, among other roles. She received her M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics & Statistics from the University of Massachusetts at Amherst.Links:https://engineering.nyu.edu/faculty/julia-stoyanovich https://airesponsibly.net/nyaiexchange_2025/ Hosted on Acast. See acast.com/privacy for more information.

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Become a Patron! https://www.python.org/downloads/release/python-3140 Lufi

Many leaders are trapped between chasing ambitious, ill-defined AI projects and the paralysis of not knowing where to start. Dr. Randall Olson argues that the real opportunity isn't in moonshots, but in the "trillions of dollars of business value" available right now. As co-founder of Wyrd Studios, he bridges the gap between data science, AI engineering, and executive strategy to deliver a practical framework for execution. In this episode, Randy and Hugo lay out how to find and solve what might be considered "boring but valuable" problems, like an EdTech company automating 20% of its support tickets with a simple retrieval bot instead of a complex AI tutor. They discuss how to move incrementally along the "agentic spectrum" and why treating AI evaluation with the same rigor as software engineering is non-negotiable for building a disciplined, high-impact AI strategy. They talk through: How a non-technical leader can prototype a complex insurance claim classifier using just photos and a ChatGPT subscription. The agentic spectrum: Why you should start by automating meeting summaries before attempting to build fully autonomous agents. The practical first step for any executive: Building a personal knowledge base with meeting transcripts and strategy docs to get tailored AI advice. Why treating AI evaluation with the same rigor as unit testing is essential for shipping reliable products. The organizational shift required to unlock long-term AI gains, even if it means a short-term productivity dip. LINKS Randy on LinkedIn (https://www.zenml.io/llmops-database) Wyrd Studios (https://thewyrdstudios.com/) Stop Building AI Agents (https://www.decodingai.com/p/stop-building-ai-agents) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/-YQjKH3wRvc)

Entreprendre, être parent, être soi : est-ce compatible ?Que vous soyez RH, recruteur, manager ou entrepreneur, je décrypte le sujet pour vous.Dans cette capsule RH de 5 minutes, je vous partage mes 3 apprentissages suite à mon échange passionnant avec Caroline Chavier, CEO de The Allyance, et fondatrice de la communauté Paris Women in Machine Learning and Data Science.

Zu Gast in dieser Folge des BVL Podcasts, die live auf der Bühne der BVL Supply Chain CX in Berlin aufgenommen wurde, sind Prof. Dr. Andreas Kaplan, Präsident der Kühne Logistics University und Nils Haupt, Leiter der Unternehmenskommunikation bei Hapag-Lloyd. Gemeinsam mit unserem Host Boris Felgendreher diskutieren sie das Zusammenspiel von Logistik und Marketing. Dabei geht es unter anderem um folgende Themen: Image der Logistikbranche: - Logistik gilt in Deutschland noch als altmodisch und wenig attraktiv. - In Ländern wie Vietnam wird sie als Zukunftsbranche wahrgenommen. - Fehlende Studierende in Logistik – andere Fachrichtungen (BWL, Finanzen, IT) sind populärer. - Imagefilme und Werbung bedienen oft veraltete Klischees (Schiffe, Container). Attraktivität & Ausbildung: - Große Unternehmen (DHL, Lufthansa, Hapag-Lloyd) bleiben attraktive Arbeitgeber. - KLU (Kühne Logistics University) versucht, Logistik als Hightech-Fach zu positionieren. - Neue Studiengänge mit Fokus auf Data Science, KI und Business Analytics. Technologie & Digitalisierung: - Diskussion über Automatisierung, Robotik, künstliche Intelligenz (KI). - Wachsende Bedeutung von IT in der Logistik (z. B. 8 % IT-Mitarbeiter bei Hapag-Lloyd). Bildung & interdisziplinäre Ansätze: - KLU bildet breit auf – BWL, Nachhaltigkeit, Data Science. - Ziel: Manager mit logistischem Verständnis in Vorständen. - Logistiker brauchen auch Marketing- und Kommunikationskompetenz. Ausbildung & Praxisbezug: - Hapag-Lloyd als größter Ausbilder der maritimen Branche in Deutschland. - Enge Zusammenarbeit mit Universitäten (z. B. CEO unterrichtet an der KLU). - Praktiker sollen mehr in Schulen und Unis gehen, um Logistik greifbarer zu machen. Internationalisierung: - Hoher Anteil internationaler Mitarbeitender in Hamburg. - Englisch als Unternehmenssprache bei Hapag-Lloyd. - Deutsche Studierende sollten Auslandserfahrung sammeln. - Deutschland bleibt attraktiver Studien- und Logistikstandort. Neue Berufsbilder & Nachhaltigkeit: - Neue Abteilungen z. B. zu Menschenrechten und Lieferkettengesetz. - Nachhaltigkeit, alternative Kraftstoffe und soziale Verantwortung als wachsende Felder. - Studierende stark interessiert an Nachhaltigkeit – Unternehmen teils weniger investitionsbereit. KI & Marketing: - Wandel durch KI im Marketing: Automatisierung, Content-Generierung, SEO-Veränderungen. - KI kann Kosten sparen, ersetzt aber noch nicht menschliche Kreativität. - Authentizität bleibt entscheidend für Markenkommunikation. Influencer & neue Medien: - Influencer-Marketing (Beispiel: YouTuber Tomatolix auf Containerschiff) als wirksames Mittel, junge Zielgruppen zu erreichen. - Bedeutungsverlust klassischer Medien → Fokus auf Owned Media und Social Media. Zusammenarbeit mit Agenturen: - Rolle der Agenturen verändert sich: KI übernimmt Teile der Ideenfindung. - Agenturen müssen sich anpassen und KI produktiv nutzen, um relevant zu bleiben. Zukunft & Wünsche: - Andreas: Logistik soll als Hightech- und Zukunftsbranche anerkannt werden. - Nils: Konsumenten sollen sich der logistischen Prozesse hinter Produkten bewusster werden. Hilfreiche Links: Kühne Logistics University: https://www.klu.org/ Hapag-Lloyd: https://www.hapag-lloyd.com/de/home.html Prof. Dr. Andreas Kaplan auf LinkedIn: https://www.linkedin.com/in/andreaskaplan/ Nils Haupt auf LinkedIn: https://www.linkedin.com/in/nils-haupt-16a8045/ Boris Felgendreher auf LinkedIn: https://www.linkedin.com/in/borisfelgendreher/ BVL: https://www.bvl.de/

In this episode of Climate Positive, Guy Van Syckle and Gil Jenkins sit down with Caroline Spears, Executive Director of Climate Cabinet, a nonprofit dedicated to supporting clean energy and climate policy leaders at state and local levels. These often-forgotten races are sometimes decided by a couple hundred votes and can also decide the fate of billions of dollars of decarbonization investment. Caroline explains how Climate Cabinet strategically identifies target candidates through data science and political expertise, aiming to elect climate champions with the highest potential ability to shape positive change. Through real-world examples, she demonstrates the organization's effectiveness in close political races and the tangible difference their support can make.LinksClimate Cabinet Website Sign up for a monthly donation to help Climate Cabinet find and elect the highest ROI clean energy champions in state and local elections across the U.S. Caroline Spears on LinkedInEpisode recorded on October 2, 2025   Email your feedback to Chad, Gil, Hilary, and Guy at climatepositive@hasi.com.

Every data scientist is running their own business - it's just that most of those businesses are solo operations with one client: their employer. Unfortunately, most data scientists don't realise this and too many fall into the trap of believing their employer will magically take care of their career development, putting them on the right projects and ensuring they get proper training. The reality is that while bosses usually mean well, they have their own careers to worry about.In this episode, Danny Ruspandini joins Dr. Genevieve Hayes to explore how applying a solo business mindset to your data science career can help you take control of your professional destiny, increase your value within organisations, and create opportunities that others miss.You'll learn:How to become the go-to person for specific problems within your organisation [07:11]The "secondary sale" technique that gets your projects approved even when you're not in the room [14:49]Why focusing on one shiny object at a time accelerates your career faster than juggling multiple priorities [19:06]How to find your signature service that makes you indispensable to your employer [23:00]Guest BioDanny Ruspandini is a brand strategist, business coach and director of Impact Labs Australia. He is also the creator of One Shiny Object, a program for helping solo creatives package what they do into sellable, fixed-price services.LinksConnect with Danny on LinkedInDownload the One Shiny Object frameworkConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Building a UI in Python usually means choosing between "quick and limited" or "powerful and painful." What if you could write modern, component-based web apps in pure Python and still keep full control? NiceGUI, pronounced "Nice Guy" sits on FastAPI with a Vue/Quasar front end, gives you real components, live updates over websockets, and it's running in production at Zauberzeug, a German robotic company. On this episode, I'm talking with NiceGUI's creators, Rodja Trappe and Falko Schindler, about how it works, where it shines, and what's coming next. With version 3.0 releasing around the same time this episode comes out, we spend the end of the episode celebrating the 3.0 release. Episode sponsors Posit Agntcy Talk Python Courses Links from the show Rodja Trappe: github.com Falko Schindler: github.com NiceGUI 3.0.0 release: github.com Full LLM/Agentic AI docs instructions for NiceGUI: github.com Zauberzeug: zauberzeug.com NiceGUI: nicegui.io NiceGUI GitHub Repository: github.com NiceGUI Authentication Examples: github.com NiceGUI v3.0.0rc1 Release: github.com Valkey: valkey.io Caddy Web Server: caddyserver.com JustPy: justpy.io Tailwind CSS: tailwindcss.com Quasar ECharts v5 Demo: quasar-echarts-v5.netlify.app AG Grid: ag-grid.com Quasar Framework: quasar.dev NiceGUI Interactive Image Documentation: nicegui.io NiceGUI 3D Scene Documentation: nicegui.io Watch this episode on YouTube: youtube.com Episode #525 deep-dive: talkpython.fm/525 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: Cyclopts: A CLI library * The future of Python web services looks GIL-free* * Free-threaded GC* * Polite lazy imports for Python package maintainers* 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: Cyclopts: A CLI library A CLI library that fixes 13 annoying issues in Typer Much of Cyclopts was inspired by the excellent Typer library. Despite its popularity, Typer has some traits that I (and others) find less than ideal. Part of this stems from Typer's age, with its first release in late 2019, soon after Python 3.8's release. Because of this, most of its API was initially designed around assigning proxy default values to function parameters. This made the decorated command functions difficult to use outside of Typer. With the introduction of Annotated in python3.9, type-hints were able to be directly annotated, allowing for the removal of these proxy defaults. The 13: Argument vs Option Positional or Keyword Arguments Choices Default Command Docstring Parsing Decorator Parentheses Optional Lists Keyword Multiple Values Flag Negation Help Defaults Validation Union/Optional Support Adding a Version Flag Documentation Brian #2: The future of Python web services looks GIL-free Giovanni Barillari “Python 3.14 was released at the beginning of the month. This release was particularly interesting to me because of the improvements on the "free-threaded" variant of the interpreter. Specifically, the two major changes when compared to the free-threaded variant of Python 3.13 are: Free-threaded support now reached phase II, meaning it's no longer considered experimental The implementation is now completed, meaning that the workarounds introduced in Python 3.13 to make code sound without the GIL are now gone, and the free-threaded implementation now uses the adaptive interpreter as the GIL enabled variant. These facts, plus additional optimizations make the performance penalty now way better, moving from a 35% penalty to a 5-10% difference.” Lots of benchmark data, both ASGI and WSGI Lots of great thoughts in the “Final Thoughts” section, including “On asynchronous protocols like ASGI, despite the fact the concurrency model doesn't change that much – we shift from one event loop per process, to one event loop per thread – just the fact we no longer need to scale memory allocations just to use more CPU is a massive improvement. ” “… for everybody out there coding a web application in Python: simplifying the concurrency paradigms and the deployment process of such applications is a good thing.” “… to me the future of Python web services looks GIL-free.” Michael #3: Free-threaded GC The free-threaded build of Python uses a different garbage collector implementation than the default GIL-enabled build. The Default GC: In the standard CPython build, every object that supports garbage collection (like lists or dictionaries) is part of a per-interpreter, doubly-linked list. The list pointers are contained in a PyGC_Head structure. The Free-Threaded GC: Takes a different approach. It scraps the PyGC_Head structure and the linked list entirely. Instead, it allocates these objects from a special memory heap managed by the "mimalloc" library. This allows the GC to find and iterate over all collectible objects using mimalloc's data structures, without needing to link them together manually. The free-threaded GC does NOT support "generations” By marking all objects reachable from these known roots, we can identify a large set of objects that are definitely alive and exclude them from the more expensive cycle-finding part of the GC process. Overall speedup of the free-threaded GC collection is between 2 and 12 times faster than the 3.13 version. Brian #4: Polite lazy imports for Python package maintainers Will McGugan commented on a LI post by Bob Belderbos regarding lazy importing “I'm excited about this PEP. I wrote a lazy loading mechanism for Textual's widgets. Without it, the entire widget library would be imported even if you needed just one widget. Having this as a core language feature would make me very happy.” https://github.com/Textualize/textual/blob/main/src/textual/widgets/__init__.py Well, I was excited about Will's example for how to, essentially, allow users of your package to import only the part they need, when they need it. So I wrote up my thoughts and an explainer for how this works. Special thanks to Trey Hunner's Every dunder method in Python, which I referenced to understand the difference between __getattr__() and __getattribute__(). Extras Brian: Started writing a book on Test Driven Development. Should have an announcement in a week or so. I want to give folks access while I'm writing it, so I'll be opening it up for early access as soon as I have 2-3 chapters ready to review. Sign up for the pythontest newsletter if you'd like to be informed right away when it's ready. Or stay tuned here. Michael: New course!!! Agentic AI Programming for Python I'll be on Vanishing Gradients as a guest talking book + ai for data scientists OpenAI launches ChatGPT Atlas https://github.com/jamesabel/ismain by James Abel Pets in PyCharm Joke: You're absolutely right

Mike Sall is the founder and CEO of Heron Finance, an SEC-registered investment advisor, making investing in the world's leading private market funds more accessible. Earlier in his career, Mike led product analytics at Coinbase, served as Head of Data Science at Medium, and worked in analytics and product roles at Adobe and Deloitte. He earned his Bachelor of Science in Economics from the Wharton School at the University of Pennsylvania.

Rohan Kodialam, cofounder and CEO of Sphinx, is building AI agents that treat data as its own language—one most models and humans still fail to understand. In this episode, he unpacks why data science has lagged behind software engineering, how AI can finally close the gap between business questions and answers, and what happens when small teams gain the analytical power of a thousand person quant desk.What You'll Learn• How AI models that actually see data can unlock insights traditional transformers miss• Why enterprises must rethink dashboards and embrace real time ad hoc analysis• Where AI truly saves the most time across the data lifecycle and why modeling is not the hardest part• How decoupling statistics from business context gives teams freedom to focus on strategy and creativity• Why success in data science now means reclaiming human creativity while automating repetitive workTimestamped Highlights[01:44] Why data is fundamentally different from text and code and why most AI models struggle with it[06:39] The cultural problem with ad hoc being a dirty word in enterprises and why that mindset is changing[11:09] Where AI tools actually fit into the data science workflow[17:09] How to measure success when using an AI data scientist[21:04] What happens when a small team gains the data firepower of a hedge fund quant operation[24:37] Why bad data science is worse than none and why quality matters more than hypeA Thought That Stuck With Us“We are cutting the time to completion by 20x, 40x, even 50x and that remaining human review is not a bottleneck. It is the feature that keeps AI accountable.”Worth FollowingConnect with Rohan Kodialam on X (@KodialamRo) or LinkedIn and learn more about Sphinx AI and how they are transforming enterprise data science.If This ResonatedShare this with someone in the data world who is tired of waiting weeks for insights that should take minutes. Follow The Tech Trek for more conversations about how people and technology create lasting impact.

Alberto Mayol, sociólogo y analista; y Cristóbal Huneeus, director de Data Science de Unholster, participaron en Estado Nacional, donde analizaron los resultados de los sondeos presidenciales. "Por primera vez, desde 1946, la mayoría de las encuestas le dan 50% más a los tres candidatos de derecha", explicaron.

In this episode of Real Talk with Anant Veeravalli, the discussion revolves around the evolving data landscape and the necessity for strategic partnerships to achieve holistic measurement. The team unpacks the importance of ethical data sourcing, privacy compliance, and the utilization of clean room environments like Snowflake and Databricks to bridge data gaps. Enabling secure and scalable data connectivity and facilitating real-time data sharing is key for brands to derive meaningful intelligence, including predictive modeling and AI-driven insights. This episode is essential listening for anyone focused on governance, security, and future-proofing data systems.Thanks for listening! Follow us on Twitter and Instagram or find us on Facebook.

In this talk, Sebastian, a bioinformatics researcher and software engineer, shares his inspiring journey from wet lab biotechnology to computational bioinformatics. Hosted by Data Talks Club, this session explores how data science, AI, and open-source tools are transforming modern biological research — from DNA sequencing to metagenomics and protein structure prediction.You'll learn about: - The difference between wet lab and dry lab workflows in biotechnology - How bioinformatics enables faster insights through data-driven modeling - The MCW2 Graph Project and its role in studying wastewater microbiomes - Using co-abundance networks and the CC Lasso algorithm to map microbial interactions - How AlphaFold revolutionized protein structure prediction - Building scientific knowledge graphs to integrate biological metadata - Open-source tools like VueGen and VueCore for automating reports and visualizations - The growing impact of AI and large language models (LLMs) in research and documentation - Key differences between R (BioConductor) and Python ecosystems for bioinformaticsThis talk is ideal for data scientists, bioinformaticians, biotech researchers, and AI enthusiasts who want to understand how data science, AI, and biology intersect. Whether you work in genomics, computational biology, or scientific software, you'll gain insights into real-world tools and workflows shaping the future of bioinformatics.Links:- MicW2Graph: https://zenodo.org/records/12507444- VueGen: https://github.com/Multiomics-Analytics-Group/vuegen- Awesome-Bioinformatics: https://github.com/danielecook/Awesome-BioinformaticsTIMECODES00:00 Sebastian's Journey into Bioinformatics06:02 From Wet Lab to Computational Biology08:23 Wet Lab vs Dry Lab Explained12:35 Bioinformatics as Data Science for Biology15:30 How DNA Sequencing Works19:29 MCW2 Graph and Wastewater Microbiomes23:10 Building Microbial Networks with CC Lasso26:54 Protein–Ligand Simulation Basics29:58 Predicting Protein Folding in 3D33:30 AlphaFold Revolution in Protein Prediction36:45 Inside the MCW2 Knowledge Graph39:54 VueGen: Automating Scientific Reports43:56 VueCore: Visualizing OMIX Data47:50 Using AI and LLMs in Bioinformatics50:25 R vs Python in Bioinformatics Tools53:17 Closing Thoughts from EcuadorConnect with SebastianTwitter - https://twitter.com/sayalaruanoLinkedin - https://linkedin.com/in/sayalaruano Github - https://github.com/sayalaruanoWebsite - https://sayalaruano.github.io/Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn - https://www.linkedin.com/company/datatalks-club/Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

Sean Bruich, Senior Vice President of AI, Engineering and Data Science at Amgen, joins Barbara Kahn and Dr. Americus Reed, II to discuss how AI's rapid evolution is transforming industries—not by replacing humans, but by creating new opportunities that blend human expertise with advanced technology to drive innovation and efficiency. Hosted on Acast. See acast.com/privacy for more information.

Sean Bruich, Senior Vice President of AI, Engineering and Data Science at Amgen, joins Barbara Kahn and Dr. Americus Reed, II to discuss how AI's rapid evolution is transforming industries—not by replacing humans, but by creating new opportunities that blend human expertise with advanced technology to drive innovation and efficiency. Hosted on Acast. See acast.com/privacy for more information.

INTERVIEWS AREN'T EVALUATING THE RIGHT SKILLS: INSIGHTS FROM ANALYSING 1,311 INTERVIEW LOOPS   What is actually happening at interviews?   It's the most critical part of any assessment process yet it is also the one which we know least about, not least because we've never had the means to really study them at scale. That is why this latest research from Harvard Business Review is so significant - across 1300+ organisations, 50+_ organisations and tens of thousands of interviews, researchers using BrightHire proprietary data track what is actually happening in this crucial step of the process.   The insight reveals challenges to which we will in recruitment have an urgency in handling.   (a) the gap between what JDs communicate to candidates are the critical qualifications for positions vs. what's actually covered in interviews   (b) the effectiveness of interviews in the evaluation of skills   (c) the extent to which interviews are evaluating for AI skills     We're doing a Brainfood Live special with BrightHire CEO Ben Sesser and William Leeds, Head of Data Science - we're going through this research with the Harvard Business Review!   We are on Thursday 23rd October, 4pm BST / 11am ET - follow the channel here (recommended) and save your spot for this demo by clicking on the green button.   Ep334 is sponsored by our friends BrightHire   BrightHire, the AI copilot for exceptional hiring used by hundreds of game-changing companies like Canva, Vercel, Multiverse, up to the Fortune 500.

Join us for a masterclass in building data-driven marketing cultures. Moe Kiss, Director of Data Science in Marketing at Canva, shares how she bridges analytics and storytelling to make data approachable across teams. She breaks down the shift from attribution to experimentation, why storytelling is a must-have skill for every data professional, and how to balance intuition with evidence. Discover how Moe's redefining the balance between technology, trust, and empathy in data leadership.Key Moments:Data as “Part of the Meal” (09:09): Using a cooking analogy, Moe describes how data should be integrated into decision-making, not as a garnish or afterthought, but as part of the meal itself. She highlights Canva's culture of balancing intuition with data, ensuring both creative instinct and analytics inform every decision.Storytelling as a Data Superpower (10:57): Moe breaks down her “insight headline” method, turning flat dashboards into stories that drive action. She argues that data storytelling isn't about aesthetics but understanding, and that trust comes from how clearly insights connect to business impact.Technical Fluency in the Age of AI (18:16): While some leaders claim programming is obsolete, Moe insists it's more important than ever. Understanding what's under the hood helps data professionals vet AI outputs, maintain data quality, and confidently guide stakeholders through rapid technological change.Buy vs. Build: The Cost–Benefit Equation (20:11): Moe breaks down how Canva evaluates when to build internal tools versus buy off-the-shelf solutions. She stresses that the best answer depends on scalability, cost, and time to market — and that implementation costs are often underestimated.AI's New Role in Marketing (24:48): Moe shares how AI is reshaping marketing teams and their relationship with data. She highlights how generative AI tools can democratize access to insights, but warns that without trusted, high-quality data foundations, AI can just as easily amplify mistakes.Key Quotes:"Data is about creativity…It's not about it being pretty, it's about being understood.” - Moe Kiss“ We are leveraging AI tools more, and so understanding what's under the hood, I would say, is more essential than ever.” - Moe Kiss“[Know] the business appetite. You need to know how to get the answer with the right level of certainty or uncertainty…at pace with the business.” - Moe KissMentionsAnalytics Power Hour PodcastWhy Are Semantic Layers Suddenly Sexy?The Agentic Semantic Layer and OSI: A New Standard for AIThoughtSpot Joins Forces with Snowflake and Industry Leaders to Spearhead Open Semantic Interchange, Ushering in a New Era of Data and AI InteroperabilityGuest Bio Moe is a Director of Data Science in Marketing at Canva. Prior to that, she was in senior product and marketing analytics roles at THE ICONIC and in an attribution agency, Datalicious. Moe is passionate about growing and developing data scientists, driving industry leading measurement techniques and tooling and expanding the business impact of her team.She is a passionate and active member of the analytics community, co-hosting a bi-weekly podcast, the Analytics Power Hour. She served as the president of the Analytics Association of New South Wales for 7 years and is an ongoing committee member where she helps run Data Analytics Wednesday, a monthly meet up, and Sydney MeasureCamp, yearly unconference. She won the Digital Analytics Association USA's top new practitioner award in 2018. In 2024 was nominated in the Women Leading Tech Awards in the Data Science category and was awarded Snowflake's Data Hero of the Year award. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Brandon Weber, Co-founder & CEO of Nava Benefits, joined us on The Modern People Leader.We talked about why benefits have become the second-largest company expense — and how HR can “moneyball” their healthcare spend, cut down on benefits-related admin work, and deliver better employee outcomes through the emerging “alt marketplace.”---- Nava Links:

Join us on the Alter Everything Podcast as we sit down with Olga Beregovaya, Vice President of AI at Smartling, to explore the evolving landscape of translation technology and AI model strategy. In this episode, Olga shares her 25+ years of experience in language technology, discusses the shift from rule-based to transformer models, and explains the importance of purpose-built AI models for translation.Panelists: Olga Beregovaya, VP of AI @ Smartling - LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: SmartlingDeepLGoogle Vertex AIIBM WatsonxAWS BedrockAzure OpenAIScale AITELUS International 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.

Here's something that data science courses don't prepare you for: even your most brilliant analysis can fail if you can't navigate the human side of your organisation. And office politics becomes especially tricky when you're running experiments. You're essentially asking people to place bets on their ideas - and then potentially delivering the news that their bet didn't "win".In this Value Boost episode, Miguel Curiel joins Dr. Genevieve Hayes to share practical strategies for handling the political challenges that come with experimentation and data science work, so you can drive real change without creating enemies.You'll learn:Why running experiments is politically riskier than regular analysis [01:50]The mindset shift that turns experiment "failures" into wins [03:56]How to overcome the "it worked for Netflix" objection [05:07]The simple strategy for reducing political friction around data work [08:24]Guest BioMiguel Curiel is the Product Analytics Manager at Bloomberg, where he works at the intersection of technology, data and human behaviour. He has a background in neuroscience and psychology and is currently writing a book on product analytics.LinksConnect with Miguel on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the stark reality of the future of work presented at the Marketing AI Conference, MAICON 2025. You’ll learn which roles artificial intelligence will consume fastest and why average employees face the highest risk of replacement. You’ll master the critical thinking and contextual skills you must develop now to transform yourself into an indispensable expert. You’ll understand how expanding your intellectual curiosity outside your specific job will unlock creative problem solving essential for survival. You’ll discover the massive global AI blind spot that US companies ignore and how this shifting landscape affects your career trajectory. Watch now to prepare your career for the age of accelerated automation! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-maicon-2025-generative-ai-for-marketers.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn – 00:00 In this week’s In Ear Insights, we are at the Marketing AI Conference, Macon 2025 in Cleveland with 1,500 of our best friends. This morning, the CEO of SmartRx, formerly the Marketing AI Institute, Paul Ritzer, was talking about the future of work. Now, before I go down a long rabbit hole, Dave, what was your immediate impressions, takeaways from Paul’s talk? Katie Robbert – 00:23 Paul always brings this really interesting perspective because he’s very much a futurist, much like yourself, but he’s a futurist in a different way. Whereas you’re on the future of the technology, he’s focused on the future of the business and the people. And so his perspective was really, “AI is going to take your job.” If we had to underscore it, that was the bottom line: AI is going to take your job. However, how can you be smarter about it? How can you work with it instead of working against it? Obviously, he didn’t have time to get into every single individual solution. Katie Robbert – 01:01 The goal of his keynote talk was to get us all thinking, “Oh, so if AI is going to take my job, how do I work with AI versus just continuing to fight against it so that I’m never going to get ahead?” I thought that was a really interesting way to introduce the conference as a whole, where every individual session is going to get into their soldiers. Christopher S. Penn – 01:24 The chart that really surprised me was one of those, “Oh, he actually said the quiet part out loud.” He showed the SaaS business chart: SaaS software is $500 billion of economic value. Of course, AI companies are going, “Yeah, we want that money. We want to take all that money.” But then he brought up the labor chart, which is $12 trillion of money, and says, “This is what the AI companies really want. They want to take all $12 trillion and keep it for themselves and fire everybody,” which is the quiet part out loud. Even if they take 20% of that, that’s still, obviously, what is it, $2 trillion, give or take? When we think about what that means for human beings, that’s basically saying, “I want 20% of the workforce to be unemployed.” Katie Robbert – 02:15 And he wasn’t shy about saying that. Unfortunately, that is the message that a lot of the larger companies are promoting right now. So the question then becomes, what does that mean for that 20%? They have to pivot. They have to learn new skills, or—the big thing, and you and I have talked about this quite a bit this year—is you really have to tap into that critical thinking. That was one of the messages that Paul was sharing in the keynote: go to school, get your liberal art degree, and focus on critical thinking. AI is going to do the rest of it. Katie Robbert – 02:46 So when we look at the roles that are up for grabs, a lot of it was in management, a lot of it was in customer service, a lot of it was in analytics—things that already have a lot of automation around them. So why not naturally let agentic AI take over, and then you don’t need human intervention at all? So then, where does that leave the human? Katie Robbert – 03:08 We’re the ones who have to think what’s next. One of the things that Paul did share was that the screenwriter for all of the Scorsese films was saying that ChatGPT gave me better ideas. We don’t know what those exact prompts looked like. We don’t know how much context was given. We don’t know how much background information. But if that was sue and I, his name was Paul. Paul Schrader. Yes, I forgot it for a second. If Paul Schrader can look at Paul Schrader’s work, then he’s the expert. That’s the thing that I think needed to also be underscored: Paul Schrader is the expert in Paul Schrader. Paul Schrader is the expert in screenwriting those particular genre films. Nobody else can do that. Katie Robbert – 03:52 So Paul Schrader is the only one who could have created the contextual information for those large language models. He still has value, and he’s the one who’s going to take the ideas given by the large language models and turn them into something. The large language model might give him an idea, but he needs to be the one to flush it out, start to finish, because he’s the one who understands nuance. He’s the one who understands, “If I give this to a Leonardo DiCaprio, what is he gonna do with the role? How is he gonna think about it?” Because then you’re starting to get into all of the different complexities where no one individual ever truly works alone. You have a lot of other humans. Katie Robbert – 04:29 I think that’s the part that we haven’t quite gotten to, is sure, generative AI can give you a lot of information, give you a lot of ideas, and do a lot of the work. But when you start incorporating more humans into a team, the nuance—it’s very discreet. It’s very hard for an AI to pick up. You still need humans to do those pieces. Christopher S. Penn – 04:49 When you take a look, though, at something like the Tilly Norwood thing from a couple weeks ago, even there, it’s saying, “Let’s take fewer humans in there,” where you have this completely machine generated actor avatar, I guess. It was very clearly made to replace a human there because they’re saying, “This is great. They don’t have to pay union wages. The actor never calls in sick. The actor never takes a vacation. The actor’s not going to be partying at a club unless someone makes it do that.” When we look at that big chart of, “Here’s all the jobs that are up for grabs,” the $12 trillion of economic value, when you look at that, how at risk do you think your average person is? Katie Robbert – 05:39 The key word in there is average. An average person is at risk. Because if an average person isn’t thinking about things creatively, or if they’re just saying, “Oh, this is what I have to do today, let me just do it. Let me just do the bare minimum, get through it.” Yes, that person is at risk. But someone who looks at a problem or a task that’s in front of them and thinks, “What are the five different ways that I could approach this? Let me sit down for a second, really plan it out. What am I not thinking of? What have I not asked? What’s the information I don’t have in front of me? Let me go find that”—that person is less at risk because they are able to think beyond what’s right in front of them. Katie Robbert – 06:17 I think that is going to be harder to replace. So, for example, I do operations, I’m a CEO. I set the vision. You could theoretically give that to an AI to do. I could create CEO Katie GPT. And GPT Katie could set the vision, based on everything I know: “This is the direction that your company should go in.” What that generative AI doesn’t know is what I know—what we’ve tried, what we haven’t tried. I could give it all that information and it could still say, “Okay, it sounds like you’ve tried this.” But then it doesn’t necessarily know conversations that I’ve had with you offline about certain things. Could I give it all that information? Sure. But then now I’m introducing another person into the conversation. And as predictable as humans are, we’re unpredictable. Katie Robbert – 07:13 So you might say, “Katie would absolutely say this to something.” And I’m going to look at it and go, “I would absolutely not say that.” We’ve actually run into that with our account manager where she’s like, “Well, this is how I thought you would respond. This is how I thought you would post something on social media.” I’m like, “Absolutely not. That doesn’t sound like me at all.” She’s like, “But that’s what the GPT gave me that is supposed to sound like you.” I’m like, “Well, it’s wrong because I’m allowed to change my mind. I’m a human.” And GPTs or large language models don’t have that luxury of just changing its mind and just kind of winging it, if that makes sense. Christopher S. Penn – 07:44 It does. What percentage, based on your experience in managing people, what percentage of people are that exceptional person versus the average or the below average? Katie Robbert – 07:55 A small percentage, unfortunately, because it comes down to two things: consistency and motivation. First, you have to be consistent and do your thing well all the time. In order to be consistent, you have to be motivated. So it’s not enough to just show up, check the boxes, and then go about your day, because anybody can do that; AI can do that. You have to be motivated to want to learn more, to want to do more. So the people who are demonstrating a hunger for reaching—what do they call it?—punching above their weight, reaching beyond what they have, those are the people who are going to be less vulnerable because they’re willing to learn, they’re willing to adapt, they’re willing to be agile. Christopher S. Penn – 08:37 For a while now we’ve been saying that either you’re going to manage the machines or the machines are going to manage you. And now of course we are at the point the machine is just going to manage the machines and you are replaced. Given so few people have that intrinsic motivation, is that teachable or is that something that someone has to have—that inner desire to want to better, regardless of training? Katie Robbert – 09:08 “Teachable” I think is the wrong word. It’s more something that you have to tap into with someone. This is something that you’ve talked about before: what motivates people—money, security, blah, blah, whatever, all those different things. You can say, “I’m going to motivate you by dangling money in front of you,” or, “I’m going to motivate you by dangling time off in front of you.” I’m not teaching you anything. I’m just tapping into who you are as a person by understanding your motives, what motivates you, what gets you excited. I feel fairly confident in saying that your motivations, Chris, are to be the smartest person in the room or to have the most knowledge about your given industry so that you can be considered an expert. Katie Robbert – 09:58 That’s something that you’re going to continue to strive for. That’s what motivates you, in addition to financial security, in addition to securing a good home life for your family. That’s what motivates you. So as I, the other human in the company, think about it, I’m like, “What is going to motivate Chris to get his stuff done?” Okay, can I position it as, “If you do this, you’re going to be the smartest person in the room,” or, “If you do this, you’re going to have financial security?” And you’re like, “Oh, great, those are things I care about. Great, now I’m motivated to do them.” Versus if I say, “If you do this, I’ll get off your back.” That’s not enough motivation because you’re like, “Well, you’re going to be on my back anyway.” Katie Robbert – 10:38 Why bother with this thing when it’s just going to be the next thing the next day? So it’s not a matter of teaching people to be motivated. It’s a matter of, if you’re the person who has to do the motivating, finding what motivates someone. And that’s a very human thing. That’s as old as humans are—finding what people are passionate about, what gets them out of bed in the morning. Christopher S. Penn – 11:05 Which is a complex interplay. If you think about the last five years, we’ve had a lot of discussions about things like quiet quitting, where people show up to work to do the bare minimum, where workers have recognized companies don’t have their back at all. Katie Robbert – 11:19 We have culture and pizza on Fridays. Christopher S. Penn – 11:23 At 5:00 PM when everyone wants to just— Katie Robbert – 11:25 Go home and float in that day. Christopher S. Penn – 11:26 Exactly. Given that, does that accelerate the replacement of those workers? Katie Robbert – 11:37 When we talk about change management, we talk about down to the individual level. You have to be explaining to each and every individual, “What’s in it for me?” If you’re working for a company that’s like, “Well, what’s in it for you is free pizza Fridays and funny hack days and Hawaiian shirt day,” that doesn’t put money in their bank account. That doesn’t put a roof over their head; that doesn’t put food on their table, maybe unless they bring home one of the free pizzas. But that’s once a week. What about the other six days a week? That’s not enough motivation for someone to stay. I’ve been in that position, you’ve been in that position. My first thought is, “Well, maybe stop spending money on free pizza and pay me more.” Katie Robbert – 12:19 That would motivate me, that would make me feel valued. If you said, “You can go buy your own pizza because now you can afford it,” that’s a motivator. But companies aren’t thinking about it that way. They’re looking at employees as just expendable cogs that they can rip and replace. Twenty other people would be happy to do the job that you’re unhappy doing. That’s true, but that’s because companies are setting up people to fail, not to succeed. Christopher S. Penn – 12:46 And now with machinery, you’re saying, “Okay, since there’s a failing cog anyway, why don’t we replace it with an actual cog instead?” So where does this lead for companies? Particularly in capitalist markets where there is no strong social welfare net? Yeah, obviously if you go to France, you can work a 30-hour week and be just fine. But we don’t live in France. France, if you’re hiring, we’re available. Where does it lead? Because I can definitely see one road where this leads to basically where France ended up in 1789, which is the Guillotines. These people trot out the Guillotines because after a certain point, income inequality leads to that stuff. Where does this lead for the market as you see it now? Katie Robbert – 13:39 Unfortunately, nowhere good. We have seen time and time again, as much as we want to see the best in people, we’re seeing the worst in people today, as of this podcast recording—not at Macon. These are some of the best people. But when you step outside of this bubble, you’re seeing the worst in people. They’re motivated by money and money only, money and power. They don’t care about humanity as a whole. They’re like, “I don’t care if you’re poor, get poorer, I’m getting richer.” I feel like, unfortunately, that is the message that is being sent. “If you can make a dollar, go ahead and make a dollar. Don’t worry about what that does to anybody else. Go ahead and be in it for yourself.” Katie Robbert – 14:24 And that’s unfortunately where I see a lot of companies going: we’re just in it to make money. We no longer care about the welfare of our people. I’ve talked on previous shows, on previous podcasts. My husband works for a grocery store that was bought out by Amazon a few years ago, and he’s seeing the effects of that daily. Amazon bought this grocery chain and said basically, “We don’t actually care about the people. We’re going to automate things. We’re going to introduce artificial intelligence.” They’ve gotten rid of HR. He still has to bring home a physical check because there is no one to give him paperwork to do direct deposit. Christopher S. Penn – 15:06 He’s been—ironic given the company. Katie Robbert – 15:08 And he’s been at the company for 25 years. But when they change things over, if he has an assurance question, there’s no one to go to. They probably have chatbots and an email distribution list that goes to somebody in an inbox that never. It’s so sad to see the decline based on where the company started and what the mission originally was of that company to where it is today. His suspicion—and this is not confirmed—his suspicion is that they are gearing up to sell this business, this grocery chain, to another grocery chain for profit and get rid of it. Flipping it, basically. Right now, they’re using it as a distribution center, which is not what it’s meant to be. Katie Robbert – 15:56 And now they’re going to flip it to another grocery store chain because they’ve gotten what they needed from it. Who cares about the people? Who cares about the fact that he as an individual has to work 50 hours a week because there’s nobody else? They’ve flattened the company. They’re like, “No, based on our AI scheduler, there’s plenty of people to cover all of these hours seven days a week.” And he’s like, “Yeah, you have me on there for seven of the seven days.” Because the AI is not thinking about work-life balance. It’s like, “Well, this individual is available at these times, so therefore he must be working here.” And it’s not going to do good things for people in services industries, for people in roles that cannot be automated. Katie Robbert – 16:41 So we talk about customer service—that’s picking up the phone, logging a plate—that can be automated. Walking into a brick and mortar, there are absolutely parts of it that can be automated, specifically the end purchase transaction. But the actual ordering and picking of things and preparing it—sure, you could argue that eventually robots could be doing that, but as of today, that’s all humans. And those humans are being treated so poorly. Christopher S. Penn – 17:08 So where does that end for this particular company or any large enterprise? Katie Robbert – 17:14 They really have—they have to make decisions: do they want to put the money first or the people first? And you already know what the answer to that is. That’s really what it comes down to. When it ends, it doesn’t end. Even if they get sold, they’re always going to put the money first. If they have massive turnover, what do they care? They’re going to find somebody else who’s willing to do that work. Think about all of those people who were just laid off from the white-collar jobs who are like, “Oh crap, I still have a mortgage I have to pay, I still have a family I have to feed. Let me go get one of those jobs that nobody else is now willing to do.” Katie Robbert – 17:51 I feel like that’s the way that the future of work for those people who are left behind is going to turn over. Katie Robbert – 17:59 There’s a lot of people who are happy doing those jobs. I love doing more of what’s considered the blue-collar job—doing things manually, getting their hands in it, versus automating everything. But that’s me personally; that’s what motivates me. That I would imagine is very unappealing to you. Not that for almost. But if cooking’s off the table, there’s a lot of other things that you could do, but would you do them? Katie Robbert – 18:29 So when we talk about what’s going to happen to those people who are cut and left behind, those are the choices they’re going to have to make because there’s not going to be more tech jobs for them to choose from. And if you are someone in your career who has only ever focused on one thing, you’re definitely in big trouble. Christopher S. Penn – 18:47 Yeah, I have a friend who’s a lawyer at a nonprofit, and they’re like, “Yeah, we have no funding anymore, so.” But I can’t pick up and go to England because I can’t practice law there. Katie Robbert – 18:59 Right. I think about people. Forever, social media was it. You focus on social media and you are set. Anybody will hire you because they’re trying to learn how to master social media. Guess where there’s no jobs anymore? Social media. So if all you know is social media and you haven’t diversified your skill set, you’re cooked, you’re done. You’re going to have to start at ground zero entry level. If there’s that. And that’s the thing that’s going to be tough because entry-level jobs—exactly. Christopher S. Penn – 19:34 We saw, what was it, the National Labor Relations Board publish something a couple months ago saying that the unemployment rate for new college graduates is something 60% higher than the rest of the workforce because all the entry-level jobs have been consumed. Katie Robbert – 19:46 Right. I did a talk earlier this year at WPI—that’s Worcester Polytech in Massachusetts—through the Women in Data Science organization. We were answering questions basically like this about the future of work for AI. At a technical college, there are a lot of people who are studying engineering, there are a lot of people who are studying software development. That was one of the first questions: “I’m about to get my engineering degree, I’m about to get my software development degree. What am I supposed to do?” My response to that is, you still need to understand how the thing works. We were talking about this in our AI for Analytics workshop yesterday that we gave here at Macon. In order to do coding in generative AI effectively, you have to understand the software development life cycle. Katie Robbert – 20:39 There is still a need for the expertise. People are asking, “What do I do?” Focus on becoming an expert. Focus on really mastering the thing that you’re passionate about, the thing that you want to learn about. You’ll be the one teaching the AI, setting up the AI, consulting with the people who are setting up the AI. There’ll be plenty of practitioners who can push the buttons and set up agents, but they still need the experts to tell them what it’s supposed to do and what the output’s supposed to be. Christopher S. Penn – 21:06 Do you see—this is kind of a trick question—do you see the machines consuming that expertise? Katie Robbert – 21:15 Oh, sure. But this is where we go back to what we were talking about: the more people, the more group think—which I hate that term—but the more group think you introduce, the more nuanced it is. When you and I sit down, for example, when we actually have five minutes to sit down and talk about the future of our business, where we want to go or what we’re working on today, the amount of information we can iterate on because we know each other so well and almost don’t have to speak in complete sentences and just can sort of pick up what the other person is thinking. Or I can look at something you’re writing and say, “Hey, I had an idea about that.” We can do that as humans because we know each other so well. Katie Robbert – 21:58 I don’t think—and you’re going to tell me this is going to happen—unless we can actually plug or forge into our brains and download all of the things. That’s never going to happen. Even if we build Katie GPT and Chris GPT and have them talk to each other, they’re never going to brainstorm the way you and I brainstorm in real life. Especially if you give me a whiteboard. I’m good. I’m going to get so much done. Christopher S. Penn – 22:25 For people who are in their career right now, what do they do? You can tell somebody, “You need to be a good critical thinker, a creative thinker, a contextual thinker. You need to know where your data lives and things like that.” But the technology is advancing at such a fast rate. I talk about this in the workshops that we do—which, by the way, Trust Insights is offering workshops at your company, if we like one. But one of the things to talk about is, say, with the model’s acceleration in terms of growth, they’re growing faster than any technology ever has. They went from face rolling idiot in 2023 right to above PhD level in everything two years later. Christopher S. Penn – 23:13 So the people who, in their career, are looking at this, going, “It’s like a bad Stephen King movie where you see the thing coming across the horizon.” Katie Robbert – 23:22 There is no such thing as a bad Stephen King movie. Sometimes the book is better, but it’s still good. But yes, maybe *Creepshow*. What do you mean in terms of how do they prepare for the inevitable? Christopher S. Penn – 23:44 Prepare for the inevitable. Because to tell somebody, “Yeah, be a critical thinker, be a contextual thinker, be a creative thinker”—that’s good in the abstract. But then you’re like, “Well, my—yeah, my—and my boss says we’re doing a 10% headcount reduction this week.” Katie Robbert – 24:02 This is my personal way of approaching it: you can’t limit yourself to just go, “Okay, think about it. Okay, I’m thinking.” You actually have to educate yourself on a variety of different things. I am a voracious reader. I read all the time when I’m not working. In the past three weeks, I’ve read four books. And they’re not business books; they are fiction books and on a variety of things. But what that does is it keeps my brain active. It keeps my brain thinking. Then I give myself the space and time. When I walk my dog, I sort of process all of it. I think about it, and then I start thinking about, “What are we doing as our company today?” or, “What’s on the task list?” Katie Robbert – 24:50 Because I’ve expanded my personal horizons beyond what’s right in front of me, I can think about it from the perspective of other people, fictional or otherwise, “How would this person approach it?” or, “What would I do in that scenario?” Even as I’m reading these books, I start to think about myself. I’m like, “What would I do in that scenario? What would I do if I was finding myself on a road trip with a cannibal who, at the end of the road trip, was likely going to consume all of me, including my bones?” It was the last book I read, and it was definitely not what I thought I was signing up for. But you start to put yourself in those scenarios. Katie Robbert – 25:32 That’s what I personally think unlocks the critical thinking, because you’re not just stuck in, “Okay, I have a math problem. I have 1 + 1.” That’s where a lot of people think critical thinking starts and ends. They think, “Well, if I can solve that problem, I’m a critical thinker.” No, there’s only one way to solve that problem. That’s it. I personally would encourage people to expand their horizons, and this comes through having hobbies. You like to say that you work 24/7. That’s not true. You have hobbies, but they’re hobbies that help you be creative. They’re hobbies that help you connect with other people so that you can have those shared experiences, but also learn from people from different cultures, different backgrounds, different experiences. Katie Robbert – 26:18 That’s what’s going to help you be a stronger, fitable thinker, because you’re not just thinking about it from your perspective. Christopher S. Penn – 26:25 Switching gears, what was missing, what’s been missing, and what is absent from this show in the AI space? I have an answer, but I want to hear yours. Katie Robbert – 26:36 Oh, boy. Really putting me on the spot here. I know what is missing. I don’t know. I’m going to think about it, and I am going to get back to you. As we all know, I am not someone who can think on my feet as quickly as you can. So I will take time, I will process it, but I will come back to you. What do you think is missing? Christopher S. Penn – 27:07 One of the things that is a giant blind spot in the AI space right now is it is a very Western-centric view. All the companies say OpenAI and Anthropic and Google and Meta and stuff like that. Yet when you look at the leaderboards online of whose models are topping the charts—Cling Wan, Alibaba, Quinn, Deepseek—these are all Chinese-made models. If you look at the chip sets being used, the government of China itself just issued an edict: “No more Nvidia chips. We are going to use Huawei Ascend 920s now,” which are very good at what they do. And the Chinese models themselves, these companies are just giving them away to the world. Christopher S. Penn – 27:54 They’re not trying to lock you in like a ChatGPT is. The premise for them, for basically the rest of the world that is in America, is, “Hey, you could take American AI where you’re locked in and you’re gonna spend more and more money, or here’s a Chinese model for free and you can build your national infrastructure on the free stuff that we’re gonna give you.” I’ve seen none of that here. That is completely absent from any of the discussions about what other nations are doing with AI. The EU has Mistral and Black Forest Labs, Sub-Saharan Africa has Lilapi AI. Singapore has Sea Lion, Korea has LG, the appliance maker, and their models. Of course, China has a massive footprint in the space. I don’t see that reflected anywhere here. Christopher S. Penn – 28:46 It’s not in the conversations, it’s not in the hallways, it’s not on stage. And to me, that is a really big blind spot if you think—as many people do—that that is your number one competitor on the world stage. Katie Robbert – 28:57 Why do you think? Christopher S. Penn – 29:01 That’s a very complicated question. But it involves racism, it involves a substantial language barrier, it involves economics. When your competitor is giving away everything for free, you’re like, “Well, let’s just pretend they’re not there because we don’t want to draw any attention to them.” And it is also a deep, deep-seated fear. When you look at all of the papers that are being submitted by Google and Facebook and all these other different companies and you look at the last names of the principal investigators and stuff, nine out of 10 times it’s a name that’s coded as an ethnic Chinese name. China produces more PhDs than I think America produces students, just by population dynamics alone. You have this massive competitor, and it almost feels like people just want to put their heads in the sand and say they’re not there. Christopher S. Penn – 30:02 It’s like the boogeyman, they’re not there. And yet if we’re talking about the deployment of AI globally, the folks here should be aware that is a thing that is not just the Sam Alton Show. Katie Robbert – 30:18 I think perhaps then, as we’re talking about the future of work and big companies, small companies, mid-sized companies, this goes sort of back to what I was saying: you need to expand your horizons of thinking. “Well, we’re a domestic company. Why do I need to worry about what China’s doing?” Take a look at your tech stack, and where are those software packages created? Who’s maintaining them? It’s probably not all domestic; it’s probably more of a global firm than you think you are. But we think about it in terms of who do we serve as customers, not what we are using internally. We know people like Paul has talked about operating systems, Ginny Dietrich has talked about operating systems. Katie Robbert – 31:02 That’s really sort of where you have to start thinking more globally in terms of, “What am I actually bringing into my organization?” Not just my customer base, not just the markets that I’m going after, not just my sales team territories, but what is actually powering my company. That’s, I think, to your point—that’s where you can start thinking more globally even if your customer base isn’t global. That might theoretically help you with that critical thinking to start expanding beyond your little homogeneous bubble. Christopher S. Penn – 31:35 Even something like this has been a topic in the news recently. Rare earth minerals, which are not rare, they’re actually very commonplace. There’s just not much of them in any one spot. But China is the only economy on the planet that has figured out how to industrialize them safely. They produce 85% of it on the planet. And that powers your smartphone, that powers your refrigerator, your car and, oh by the way, all of the AI chips. Even things like that affect the future of work and the future of AI because you basically have one place that has a monopoly on this. The same for the Netherlands. The Netherlands is the only country on the planet that produces a certain kind of machine that is used to create these chips for AI. Christopher S. Penn – 32:17 If that company goes away or something, the planet as a whole is like, “Well, I figured they need to come up with an alternative.” So to your point, we have a lot of these choke points in the AI value chain that could be blockers. Again, that’s not something that you hear. I’ve not heard that at any conference. Katie Robbert – 32:38 As we’re thinking about the future of work, which is what we’re talking about on today’s podcast at Macon, 1,500 people in Cleveland. I guarantee they’re going to do it again next year. So if you’re not here this year, definitely sign up for next year. Take a look at the Smarter X and their academy. It’s all good stuff, great people. I think—and this was the question Paul was asking in his keynote—”Where do we go from here?” The— Katie Robbert – 33:05 The atmosphere. Yes. We don’t need—we don’t need to start singing. I do not need. With more feeling. I do get that reference. You’re welcome. But one of the key takeaways is there are more questions than answers. You and I are asking each other questions, but there are more questions than answers. And if we think we have all of the answers, we’re wrong. We have the answers that are sufficient enough for today to keep our business moving forward. But we have to keep asking new questions. That also goes into that critical thinking. You need to be comfortable not knowing. You need to be comfortable asking questions, and you need to be comfortable doing that research and seeking it out and maybe getting it wrong, but then continuing to learn from it. Christopher S. Penn – 33:50 And the future of work, I mean, it really is a very cloudy crystal wall. We have no idea. One of the things that Paul pointed out really well was you have different scaling laws depending on where you are in AI. He could have definitely spent some more time on that, but I understand it was a keynote, not a deep dive. There’s more to that than even that. And they do compound each other, which is what’s creating this ridiculously fast pace of AI evolution. There’s at least one more on the way, which means that the ability for these tools to be superhuman across tasks is going to be here sooner than people think. Paul was saying by 2026, 2027, that’s what we’ll start to see. Robotics, depends on where you are. Christopher S. Penn – 34:41 What’s coming out of Chinese labs for robots is jaw dropping. Katie Robbert – 34:45 I don’t want to know. I don’t want to know. I’ve seen *Ex Machina*, and I don’t want to know. Yeah, no. To your point, I think a lot of people bury their head in the sand because of fear. But in order to, again, it sort of goes back to that critical thinking, you have to be comfortable with the uncomfortable. I’m sort of joking: “I don’t want to know. I’ve seen *Ex Machina*.” But I do want to know. I do need to know. I need to understand. Do I want to be the technologist? No. But I need to play with these tools enough that I feel I understand how they work. Yesterday I was playing in Opal. I’m going to play in N8N. Katie Robbert – 35:24 It’s not my primary function, but it helps me better understand where you’re coming from and the questions that our clients are asking. That, in a very simple way to me, is the future of work: that at least I’m willing to stretch myself and keep exploring and be uncomfortable so that I can say I’m not static. Christopher S. Penn – 35:46 I think one of the things that 3M was very well known for in the day was the 20% rule, where an employee, as part of their job, could have 20% of the time just work on side projects related to the company. That’s how Post-it Notes got invented, I think. I think in the AI forward era that we’re in, companies do need to make that commitment again to the 20% rule. Not necessarily just messing around, but specifically saying you should be spending 20% of your time with AI to figure out how to use it, to figure out how to do some of those tasks yourself, so that instead of being replaced by the machine, you’re the one who’s at least running the machine. Because if you don’t do that, then the person in the next cubicle will. Christopher S. Penn – 36:33 And then the company’s like, “Well, we used to have 10 people, we only need two. And you’re not one of the two who has figured out how to use this thing to do that. So out you go.” Katie Robbert – 36:41 I think that was what Paul was doing in his AI for Productivity workshop yesterday, was giving people the opportunity to come up with those creative ideas. Our friend Andy Crestadino was relaying a story yesterday to us of a very similar vein where someone was saying, “I’ll give you $5,000. Create whatever you want.” And the thing that the person created was so mind-blowing and so useful that he was like, “Look what happens when I just let people do something creative.” But if we bring it sort of back whole circle, what’s the motivation? Why are people doing it in the first place? Katie Robbert – 37:14 It has to be something that they’re passionate about, and that’s going to really be what drives the future of work in terms of being able to sustain while working alongside AI, versus, “This is all I know how to do. This is all I ever want to know how to do.” Yes, AI is going over your job. Christopher S. Penn – 37:33 So I guess wrapping up, we definitely want you thinking creatively, critically, contextually. Know where your data is, know where your ideas come from, broaden your horizons so that you have more ideas, and be able to be one of the people who knows how to call BS on the machines and say, “That’s completely wrong, ChatGPT.” Beyond that, everyone has an obligation to try to replace themselves with the machines before someone else does it to you. Katie Robbert – 38:09 I think again, to plug Macon, which is where we are as we’re recording this episode, this is a great starting point for expanding your horizons because the amount of people that you get to network with are from different companies, different experiences, different walks of life. You can go to the sessions, learn it from their point of view. You can listen to Paul’s keynote. If you think you already know everything about your job, you’re failing. Take the time to learn where other people are coming from. It may not be immediately relevant to you, but it could stick with you. Something may resonate, something might spark a new idea. Katie Robbert – 38:46 I feel like we’re pretty far along in our AI journey, but in sitting in Paul’s keynote, I had two things that stuck out to me: “Oh, that’s a great idea. I want to go do that.” That’s great. I wouldn’t have gotten that otherwise if I didn’t step out of my comfort zone and listen to someone else’s point of view. That’s really how people are going to grow, and that’s that critical thinking—getting those shared experiences and getting that brainstorming and just community. Christopher S. Penn – 39:12 Exactly. If you’ve got some thoughts about how you are approaching the future of work, pop on by our free Slack group. Go to trust insights AI analysts for marketers, where you and over 4,500 other marketers are asking and answering each other’s questions every single day. Wherever you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to Trust Insights AI Ti Podcast, where you can find us all the places fine podcasts are served. Thanks for tuning in. I’ll talk to you on the next one. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

Send Me a Text MessageIn this episode of Cycling Over Sixty, host Tom Butler is already looking ahead to next year's cycling season while sharing his latest racing adventure. With the Tour de Cure Pacific Northwest still six months away in May, Tom explains why he's already planning for this spring event that he hopes will become an annual launch into the summer cycling season for Cycling Over Sixty. Tom also recounts his first cyclocross race experience – a competition that took unexpected turns but left him eager for more racing adventures.  His participation may have been different from what he envisioned, but he came away from the event looking forward to tackling more cyclocross events. This week's guest is Rob MacLeod, PhD, an accomplished cyclist and cycling advocate from Utah. Dr. MacLeod, a professor at the University of Utah, brings a unique perspective to the show. While his work in improving local bike infrastructure could fill an entire episode, Tom dives into Dr. MacLeod's expertise in data science and health analytics. The conversation explores how modern technology allows us to gather and analyze valuable health data, providing deeper insights into fitness and performance. With a lifetime of experience at the forefront of technological advancement, Dr. MacLeod shares his perspective on how data science is revolutionizing the way people can understand their health status. Whether you're interested in planning your next season, trying cyclocross racing, or leveraging technology to enhance your cycling experience, this episode offers inspiration and insights for cyclists looking to make the most of their time on two wheels.LINKSCycling Over Sixty Tour de Cure: tour.diabetes.org/teams/CO60Road Scholar Age Well: roadscholar.org/collections/agewell/Rob MacLeod's Cycling Page: sci.utah.edu/~macleod/bike/Thanks for Joining Me! Consider becoming a member of the Cycling Over Sixty Strava Club! www.strava.com/clubs/CyclingOverSixty Cycling Over Sixty is also on Zwift. Look for our Zwift club! Please send comments, questions and especially content suggestions to me at info@cyclingoversixty.com Follow and comment on Cycling Over Sixty on Instagram: https://www.instagram.com/cyclingoversixty/ Show music is "Come On Out" by Dan Lebowitz. Find him here : lebomusic.com

Trust in modern AI systems hinges on understanding how they learn—and, increasingly, how they can forget. This talk develops a geometric view of trustworthiness that unifies structure-aware optimization, stability analysis, and the emerging science of unlearning. I will begin by revisiting the role of sharpness and flatness in shaping both generalization and sample sensitivity, showing how the geometry of the loss landscape governs what models remember. Building on these insights, I will present recent results on Sharpness-Aware Machine Unlearning, a framework that characterizes when and how learning algorithms can provably erase the influence of specific data points while preserving accuracy on the rest. The discussion connects theoretical guarantees with empirical findings on the role of data distribution and loss geometry in machine unlearning—ultimately suggesting that the shape of the optimization landscape is the shape of trust itself. About the speaker: Rajiv Khanna is an Assistant Professor in the Department of Computer Science. His research interests span various subfields of machine learning including optimization, theory and interpretability.Previously, he held positions of Visiting Faculty Researcher at Google, postdoctoral scholar at Foundations of Data Analystics Institute at University of California, Berkeley and a Research Fellow in the Foundations of Data Science program at the Simons Institute also at UC Berkeley. He graduated with his PhD from UT Austin.

Today's guest is Xiong Liu, Director of Data Science and AI at Novartis. Novartis is among the world's leading pharmaceutical companies, pioneering data and advanced analytics in the pursuit of new medicines and patient outcomes. Xiong joins Emerj Editorial Director Matthew DeMello to examine how generative AI and foundation models are transforming R&D, clinical workflows, and research collaboration across the life sciences. The discussion highlights how domain-specific data strategies, improved data quality, and shared benchmarks are accelerating discovery and operationalizing AI for measurable ROI in biopharma. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

In this episode of the Data Science Salon Podcast, we sit down with Nitin Kumar, Director of Data Science at Marriott International, to discuss how AI agents are transforming customer support. Nitin shares his experience designing enterprise-scale AI and Generative AI solutions across 30 global brands, creating intelligent, proactive, and human-centered customer care systems. He dives into AI-powered pipelines that monitor incoming emails, analyze sentiment and issues, and draft contextual responses for human agents to review. Beyond individual cases, these systems continuously feed real-time trend data, helping teams identify emerging issues before they become widespread. Key Highlights: -AI Agents in Customer Support: Learn how AI agents automate routine processes while empowering human agents to focus on complex interactions. -Human-in-the-Loop Design: Explore strategies for balancing AI efficiency with human oversight and empathy. -Scaling AI Solutions: Insights into LLM integration, prompt engineering, and global deployment of enterprise AI systems.

The idea for an AI Institute at Florida Gulf Coast University dates back to before OpenAI released ChatGPT in the fall of 2022. Founded in Aug. 2023, the FGCU Dendritic Institute is a hub for all things AI and data science, from research to education to community outreach. FGCU recently announced a multi-year initiative focusing on responsible, ethical and practical use of AI to enhance teaching, learning, researching and collaborating, so we sit down with the Dendritic Institute's founding director Dr. Leandro de Castro to get the Institute's origin story and what lies ahead.

Python in 2025 is different. Threads really are about to run in parallel, installs finish before your coffee cools, and containers are the default. In this episode, we count down 38 things to learn this year: free-threaded CPython, uv for packaging, Docker and Compose, Kubernetes with Tilt, DuckDB and Arrow, PyScript at the edge, plus MCP for sane AI workflows. Expect practical wins and migration paths. No buzzword bingo, just what pays off in real apps. Join me along with Peter Wang and Calvin Hendrix-Parker for a fun, fast-moving conversation. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Calvin Hendryx-Parker: github.com/calvinhp Peter on BSky: @wang.social Free-Threaded Wheels: hugovk.github.io Tilt: tilt.dev The Five Demons of Python Packaging That Fuel Our ...: youtube.com Talos Linux: talos.dev Docker: Accelerated Container Application Development: docker.com Scaf - Six Feet Up: sixfeetup.com BeeWare: beeware.org PyScript: pyscript.net Cursor: The best way to code with AI: cursor.com Cline - AI Coding, Open Source and Uncompromised: cline.bot Watch this episode on YouTube: youtube.com Episode #524 deep-dive: talkpython.fm/524 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: * djrest2 -* A small and simple REST library for Django based on class-based views. Github CLI caniscrape - Know before you scrape. Analyze any website's anti-bot protections in seconds. *

Alexander Mrozek, CEO von Oetker Digital, spricht über die digitale Transformation eines Traditionskonzerns. Er teilt, wie sie die Balance zwischen Innovation und Tradition finden, warum Data Science und KI entscheidend sind und wie sie neue digitale Geschäftsmodelle entwickeln. Was du lernst: Wie du digitale Transformation im Konzern steuerst Die Balance zwischen Innovation und Tradition Warum Daten die neue Währung sind Den richtigen Mix aus Build und Buy finden ALLES ZU UNICORN BAKERY: https://stan.store/fabiantausch    Mehr zum Gast Alex Mrozek: LinkedIn: https://www.linkedin.com/in/dr-ajm/  Website: https://www.digitaleoptimisten.de/  Mehr zum Gast-Host Mike Mahlkow: LinkedIn: https://www.linkedin.com/in/mikemahlkow  Website: https://www.mikemahlkow.com/  Join our Founder Tactics Newsletter: 2x die Woche bekommst du die Taktiken der besten Gründer der Welt direkt ins Postfach: https://www.tactics.unicornbakery.de/

Wie verändert künstliche Intelligenz die Energiewelt von morgen? ⚡ Und wie schafft man es, aus einem klassischen Energieunternehmen ein datengetriebenes Tech-Powerhouse zu machen? In dieser Folge sprechen wir mit Sebastian Schwarz, Head of Data & AI - Products and Platforms bei E.ON. Sebastian erzählt, wie sich Data & AI in den letzten zehn Jahren bei E.ON entwickelt haben – von ersten Forschungsprojekten bis hin zu skalierbaren Plattformen. Wir reden über spannende Use Cases, interne AI-Produkte, Teamaufbau und die Herausforderung, Innovation und Sicherheit in Einklang zu bringen.

As part of our ongoing collaboration with Central Square Foundation, we are excited to bring to you the fourth episode of our five part series where we talk about the evolving landscape of AI in Education.The National Education Policy 2020 marks a bold shift in how we think about technology in learning. It envisions a future where students build not just digital literacy, but also computational thinking and AI fluency — and where teachers are empowered with the tools, training, and support to integrate AI into their curriculums meaningfully and responsibly. To understand how this is being implemented, we'll be joined by Gouri Gupta, Sr. Project Director of EdTech who leads CSF's work in EdTech and AI and Professor Balaraman Ravindran, Head, Wadhwani School of Data Science & AI (WSAI), IIT Madras who is one of India's top AI researchers and has helped shape India's AI policy framework and currently advises the Reserve Bank of India on the uses of AI in finance. Hosted and produced by Niharika NandaEdited and mixed by Suresh PawarLinks to the previous episodes of our series with CSF:Episode 1Episode 2Episode 3

Topics covered in this episode: * PyPI+* * uv-ship - a CLI-tool for shipping with uv* * How fast is 3.14?* * air - a new web framework built with FastAPI, Starlette, and Pydantic.* 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: PyPI+ Very nice search and exploration tool for PyPI Minor but annoying bug: content-types ≠ content_types on PyPI+ but they are in Python itself. Minimum Python version seems to be interpreted as max Python version. See dependency graphs and more Examples content-types jinja-partials fastapi-chameleon Brian #2: uv-ship - a CLI-tool for shipping with uv “uv-ship is a lightweight companion to uv that removes the risky parts of cutting a release. It verifies the repo state, bumps your project metadata and optionally refreshes the changelog. It then commits, tags & pushes the result, while giving you the chance to review every step.” Michael #3: How fast is 3.14? by Miguel Grinberg A big focus on threaded vs. non-threaded Python Some times its faster, other times, it's slower Brian #4: air - a new web framework built with FastAPI, Starlette, and Pydantic. An very new project in Alpha stage by Daniel & Audrey Felderoy, the “Two Scoops of Django” people. Air Tags are an interesting thing. Also Why? is amazing “Don't use AIR” “Every release could break your code! If you have to ask why you should use it, it's probably not for you.” “If you want to use Air, you can. But we don't recommend it.” “It'll likely infect you, your family, and your codebase with an evil web framework mind virus, , …” Extras Brian: Python 3.15a1 is available uv python install 3.15 already works Python lazy imports you can use today - one of two blog posts I threatened to write recently Testing against Python 3.14 - the other one Free Threading has some trove classifiers Michael: Blog post about the book: Talk Python in Production book is out! In particular, the extras are interesting. AI Usage TUI Show me your ls Helium Browser is interesting. But also has Python as a big role. GitHub says Languages Python 97.4%

The podcast, “From CIO to AI Visionary: Nishith Sahay on Marvell's Tech Transformation and the Future of AI Governance, features guest Nishit Sahay, CIO at Marvell Technology who discusses his journey to becoming an SVP and CIO, emphasizing the shift in his role from an "apps and data person" to someone focused on making the company more efficient through technology. The host is Khushboo Bajaj, ServiceNow leader, InfoBeans. 

In this episode of Disruption/Interruption, host KJ sits down with Chris Cyrille, founder of SNTIMNT.AI, to discuss how he’s using data science and AI to bring clarity and confidence to crypto investing. Chris shares his personal journey, the challenges of the crypto market, and how his mission-driven approach is changing the way investors and institutions view digital assets. Key Takeaways: Personal Motivation Drives Innovation [3:52]Chris’s journey began with a personal loss, inspiring him to redirect capital toward causes like pediatric cancer and first-generation students. Crypto Volatility and Misinformation [6:25]The biggest challenge in crypto is volatility, fueled by meme coins, speculation, and misinformation, making it intimidating for new investors. Algorithmic Solutions for Peace of Mind [17:51]Sentiment AI’s algorithm “reads the room” to provide actionable signals, helping users invest with greater confidence and less stress. Staying Curious and Creative [26:34]Chris encourages listeners to maintain childlike wonder and curiosity, as unique perspectives drive creative solutions in any industry. Quote of the Show (26:54):“Keep the childlike wonder and to stay curious. I believe that's where the creativity shows itself.” – Chris Cyrille Join our Anti-PR newsletter where we’re keeping a watchful and clever eye on PR trends, PR fails, and interesting news in tech so you don't have to. You're welcome. Want PR that actually matters? Get 30 minutes of expert advice in a fast-paced, zero-nonsense session from Karla Jo Helms, a veteran Crisis PR and Anti-PR Strategist who knows how to tell your story in the best possible light and get the exposure you need to disrupt your industry. Click here to book your call: https://info.jotopr.com/free-anti-pr-eval Ways to connect with Chris Cyrille: LinkedIn: https://www.linkedin.com/in/chris-cyrille/ Company Website: https://sntimnt.ai How to get more Disruption/Interruption: Amazon Music - https://music.amazon.com/podcasts/eccda84d-4d5b-4c52-ba54-7fd8af3cbe87/disruption-interruption Apple Podcast - https://podcasts.apple.com/us/podcast/disruption-interruption/id1581985755 Spotify - https://open.spotify.com/show/6yGSwcSp8J354awJkCmJlDSee omnystudio.com/listener for privacy information.

Episode #177: Join me, Kimberly Lovi, on an insightful journey as we explore the path to financial empowerment and personal growth in this episode of "In Studio." We'll begin by discussing the critical steps needed to expand your business and income. You'll discover the power of setting clear, specific financial goals rather than settling for vague desires. Together, we'll work backwards to assess where you currently stand and map out the steps needed to reach your desired financial future. By breaking down expenses and creating a concrete plan, we'll guide you towards achieving financial security. Next, we'll tackle the transformative shift from a scarcity mindset to one of abundance. It's essential to recognize money as a form of energy that fuels your ambitions and impacts the world around you. We'll talk about the importance of investing in your business and involving others in your vision to drive success. Letting go of limiting beliefs about money is key, and you'll learn how collaboration and financial investment in capable hands can elevate your endeavors. Finally, we'll highlight the significance of investing in personal growth and development. Drawing from my experiences with courses like Amy Porterfield's Digital Course Academy and a Harvard course on data science principles, I'll share how acquiring new skills can propel both your business and personal goals. We'll also emphasize the importance of setting ambitious goals, surrounding yourself with proactive individuals, and avoiding negativity. Plus, for those struggling with mindset barriers, I'll introduce my mindset course to help you gain clarity and overcome obstacles. As we wrap up, we'll reflect on the importance of accountability and invite you to connect and share your progress. Thank you for joining us, and we hope you leave inspired to take action toward your success. Chapters:  (00:00) Empowerment Through Action (09:48) Overcoming Scarcity Mindset for Success" (24:46) Investing in Personal Growth and Development (35:41) Taking Action for Success Follow Kimberly on Instagram and TikTok @kimberlylovi or @iconicnationmedia  WATCH us on YouTube and view our brand new studio! 

When working with data, it can be easy to fall into the trap of believing that your dataset represents nothing more than numbers on a page. However, behind every data point is a human story - people clicking through websites, abandoning shopping carts, or binge-watching Netflix shows. And in our app-driven world, understanding these human behaviours has become absolutely critical - for businesses to flourish and for data scientists to have a meaningful impact in the work they do. This is where product analytics comes in.In this episode, Miguel Curiel joins Dr. Genevieve Hayes to share his practical checklist for maximising business impact through product analytics, drawing from his own experiences analysing how people actually interact with digital products and his upcoming book on the topic.This episode explores:What product analytics actually involves, beyond just measuring clicks and conversions [03:11]Why behavioural science models are crucial for understanding user motivations [07:25]Miguel's seven-step checklist for building impactful product analytics capabilities [15:49]The most valuable skill for data scientists in product analytics [22:27]Guest BioMiguel Curiel is the Product Analytics Manager at Bloomberg, where he works at the intersection of technology, data and human behaviour. He has a background in neuroscience and psychology and is currently writing a book on product analytics.LinksConnect with Miguel on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Python typing got fast enough to feel invisible. Pyrefly is a new, open source type checker and IDE language server from Meta, written in Rust, with a focus on instant feedback and real-world DX. Today, we will dig into what it is, why it exists, and how it plays with the rest of the typing ecosystem. We have Abby Mitchell, Danny Yang, and Kyle Into from Pyrefly here to dive into the project. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Abby Mitchell: linkedin.com Danny Yang: linkedin.com Kyle Into: linkedin.com Pyrefly: pyrefly.org Pyrefly Documentation: pyrefly.org Pyrefly Installation Guide: pyrefly.org Pyrefly IDE Guide: pyrefly.org Pyrefly GitHub Repository: github.com Pyrefly VS Code Extension: marketplace.visualstudio.com Introducing Pyrefly: A New Type Checker and IDE Experience for Python: engineering.fb.com Pyrefly on PyPI: pypi.org InfoQ Coverage: Meta Pyrefly Python Typechecker: infoq.com Pyrefly Discord Invite: discord.gg Python Typing Conformance (GitHub): github.com Typing Conformance Leaderboard (HTML Preview): htmlpreview.github.io Watch this episode on YouTube: youtube.com Episode #523 deep-dive: talkpython.fm/523 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: * Python 3.14* * Free-threaded Python Library Compatibility Checker* * Claude Sonnet 4.5* * Python 3.15 will get Explicit lazy imports* Extras Joke Watch on YouTube About the show Sponsored by DigitalOcean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit 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: Python 3.14 Released on Oct 7 What's new in Python 3.14 Just a few of the changes PEP 750: Template string literals PEP 758: Allow except and except* expressions without brackets Improved error messages Default interactive shell now highlights Python syntax supports auto-completion argparse better support for python -m module has a new suggest_on_error parameter for “maybe you meant …” support python -m calendar now highlights today's date Plus so much more Michael #2: Free-threaded Python Library Compatibility Checker by Donghee Na App checks compatibility of top PyPI libraries with CPython 3.13t and 3.14t, helping developers understand how the Python ecosystem adapts to upcoming Python versions. It's still pretty red, let's get in the game everyone! Michael #3: Claude Sonnet 4.5 Top programming model (even above Opus 4.1) Shows large improvements in reducing concerning behaviors like sycophancy, deception, power-seeking, and the tendency to encourage delusional thinking Anthropic is releasing the Claude Agent SDK, the same infrastructure that powers Claude Code, making it available for developers to build their own agents, along with major upgrades including checkpoints, a VS Code extension, and new context editing features And Claude Sonnet 4.5 is available in PyCharm too. Brian #4: Python 3.15 will get Explicit lazy imports Discussion on discuss.python.org This PEP introduces syntax for lazy imports as an explicit language feature: lazy import json lazy from json import dumps BTW, lazy loading in fixtures is a super easy way to speed up test startup times. Extras Brian: Music video made in Python - from Patrick of the band “Friends in Real Life” source code: https://gitlab.com/low-capacity-music/r9-legends/ Michael: New article: Thanks AI Lots of updates for content-types Dramatically improved search on Python Bytes (example: https://pythonbytes.fm/search?q=wheel use the filter toggle to see top hits) Talk Python in Production is out and for sale Joke: You do estimates?

Ryan Sawchuk is the VP of Product at Crexi, where he leads cross-functional teams to build advanced, data-rich tools aimed at transforming the commercial real estate workflow. With more than 15 years in product leadership, Ryan specializes in crafting AI-enabled, scalable solutions that boost transaction velocity, improve user experience, and integrate seamlessly into CRE operations. Prior to Crexi, Ryan held senior product roles at Indeed, Procore, and LinkedIn, shaping core features and driving growth in high-scale tech environments. He earned his education from Princeton University, which laid the foundation for his data-driven, user-first product philosophy. At Crexi, Ryan's vision is to bridge the gap between real estate professionals and cutting-edge technology, making complex CRE data more accessible, actionable, and efficient. This is episode was recorded live at Blueprint Vegas 2025.

Who's the most clutch quarterback in NFL history — Tom Brady, Patrick Mahomes, Aaron Rodgers, or someone completely unexpected? We'll use Python + Data Science to figure it out.

Episode Description:  Listen as Kim Beer, Senior Vice President of Policy and External Affairs with the National Health Council, and Dermatologist, Dr. Jeffrey Cohen discuss the 2025 Medicare changes in relation to psoriatic disease and what's to come in 2026 with Jason Harris, Vice President of Government Relations and Advocacy at NPF.  Join this discussion about what changes occurred with Medicare in 2025 that impact psoriatic disease care, outcomes to date, what's to come in 2026, and what you should consider when choosing health care plans during open enrollment with Kim Beer, Senior Vice President of Policy and External Affairs with the National Health Council, Dermatologist, Dr. Jeffrey Cohen, Director of the Psoriasis Treatment Program at Yale University School of Medicine, and Jason Harris, Vice President of Government Relations and Advocacy at NPF.  The intent of this episode is to increase knowledge of the 2025 Medicare changes, what's to come, and how such changes impact psoriatic disease from coverage of prescriptions to overall health care. This episode is sponsored by Novartis. Timestamps:   (0:24) Intro to Psoriasis Uncovered and guest welcome Kim Beer, Senior Vice President of Policy   and External Affairs with the National Health Council, and Dermatologist, Dr. Jeffrey Cohen, Director of the Psoriasis Treatment Program at Yale School of Medicine. (2:25) Perspectives on current health care coverage in Medicare.    (5:14) Biggest changes to Medicare in 2025. (6:36) What is the Medicare Prescription Payment Plan and price negotiation for specific medications. (8:22) Challenges associated with the 2025 Medicare changes from a physician's perspective. (13:10) Price negotiation process via CMS  (Centers for Medicare and Medicaid Services) with the first 10 drugs price effective in 2026.   (17:52) Plan ahead and what to anticipate when choosing the right Medicare plan. (20:04) What the National Health Council and other patient advocacy organizations are doing to assess the impact of the CMS changes and identify steps for moving forward. (21:49) Medicare changes for 2026 that affect deductibles for health care services, prescription drug coverage, and vaccinations. (28:38) Potential assistance options for people who have Medicare insurance. (31:32) The role of patients in providing feedback on policy changes. (33:45) Changing from a commercial insurance plan to a Medicare Plan and what to think about when viewing plan options during the open enrollment period. (37:07) Be part of the process – let your voice be heard by sharing your experiences to help effect change. 3 Key Takeaways: ·       There are four key parts to Medicare health insurance (Part A, B, C and D) which underwent changes in 2025 including a payment cap for prescriptions and availability of a 12 month Prescription Payment Plan to opt in for medications. Additional changes are coming in 2026 including enactment of a price negotiated list of 10 medications. ·       The impact of such changes are both positive (better predictability and affordability) yet also reactionary. Such changes and potential impact should be considered when identifying plan coverage for health care and prescriptions during the open enrollment Medicare period of October 15 to December 7th.   ·       Be involved by telling your story about the impact of Medicare changes and find a trusted health care provider who is willing to work with you to identify an effective treatment plan that aligns with your health care needs and coverage. Guest Bios:   Dermatologist Jeffrey Cohen, M.D., MPH, is the Director of the Psoriasis Treatment Program and the Director of Safety with the Department of Dermatology at Yale University School of Medicine where he is also an Associate Professor of Dermatology and Biomedical Informatics and Data Science. Dr. Cohen treats a variety of skin conditions with a special interest in diseases of the immune system such as psoriasis and eczema tailoring treatments for each individual. He is the author of over 150 peer-reviewed articles on psoriasis and other topics in dermatology. Dr. Cohen serves on the Editorial Board of the Journal of the American Academy of Dermatology, is a Senior Editor for NPF's professional journal for health care providers Journal of Psoriasis and Psoriatic Arthritis, is a Councilor of the International Psoriasis Council, and serves on the Medical Board of the National Psoriasis Foundation.  Kimberly (Kim) Beer is Senior Vice President of Policy and External Affairs at the National Health Council (NHC) of which the National Psoriasis Foundation is a member. Kim leads strategic policy initiative and advocacy efforts to improve the lives of individuals with chronic conditions and disabilities. As a member of the NHC's executive leadership team, she helps to ensure access to high-quality, affordable healthcare for all Americans which includes advocating for policy and health care benefits within Medicare. Resources: For more reources and information about Medicare Contact the Patient Navigation Center to learn more about Medicare, find a health care provider, learn about treatments, or programs that may lower costs.   

Across many democracies, far-right movements are gaining momentum — a trend that worries policymakers, researchers, and citizens alike. A common explanation points to material hardship: when people feel left behind economically and socially, they often turn to radical political alternatives. One critical dimension of this hardship is housing — especially the lack of affordable and secure homes. Could building more affordable housing help reduce support for far-right parties?New research provides a nuanced answer. It finds that expanding access to social housing does seem to lower far-right support — but only in areas with low immigration. In communities where immigration is already high, the effect reverses.To unpack why this is happening, and what it means for policymakers, host Prof Alan Renwick speaks with Dr. Gloria Gennaro, Lecturer in Public Policy and Data Science at UCL's Department of Political Science. Dr. Gennaro shares insights from her latest study, exploring how housing policy, economic insecurity, and social dynamics intersect with political behavior.Mentioned in this episode:Immigration, Public Housing and Support for the French National Front by Gloria Gennaro UCL's Department of Political Science and School of Public Policy offers a uniquely stimulating environment for the study of all fields of politics, including international relations, political theory, human rights, public policy-making and administration. The Department is recognised for its world-class research and policy impact, ranking among the top departments in the UK on both the 2021 Research Excellence Framework and the latest Guardian rankings.

See more: thinkfuture.substack.comConnect with Shahzeb: https://shahzebali.com/---Meet the world's youngest certified Python developer—and the founder of an AI startup.In this episode of thinkfuture, host Chris Kalaboukis talks with Shahzeb Ali, a 17-year-old entrepreneur from Pakistan who's already the youngest certified Python developer recognized by the Python Institute and IBM. He's also the author of Data Science for Teens and the founder of DevelMo, an AI startup helping businesses scale with smart, data-driven solutions.Shahzeb's story is as inspiring as it is forward-looking. From struggling with Visual Basic as a kid to mastering Python and building computer vision products, he's proof that the next generation isn't waiting for permission to innovate.We explore:- How Shahzeb discovered Python and became the youngest certified developer- Why Python's simplicity makes it perfect for teens learning to code- The story behind his AI-powered product, CrowdIQ, and its real-world applications- The pros and cons of “vibe coding” and AI-assisted development- Why critical thinking still matters—even in the age of AI- How easy MVPs and AI tools are fueling a wave of young entrepreneurs- Shahzeb's mission to inspire more teens to pursue tech and entrepreneurshipIf you're passionate about AI, programming, or the next generation of innovators, this episode will leave you feeling optimistic about the future of technology.

Stakeholder requirement gathering is often one of the most dreaded parts of data science projects - dry, tedious sessions where conflicting voices talk past each other and senior executives dominate the conversation. Yet without proper requirements, data science projects are doomed to fail due to solving the wrong problems or missing critical business needs.In this Value Boost episode, David Cohen joins Dr. Genevieve Hayes to reveal how gamification can transform stakeholder meetings from painful obligation into collaborative problem-solving sessions that actually produce useful requirements.You'll learn:Why gamification works as a "Trojan horse" for productive business conversations [03:26]How to ensure every voice is heard, not just the loudest or most senior person in the room [06:34]The simple technique that prevents senior executives from dominating and skewing requirements [06:59]The easiest way to add interactive elements to your next stakeholder meeting without complex games [08:20]Guest BioDavid Cohen is a data and AI strategy consultant, with a background in supporting the F500 clients of both Big 4 and boutique consulting firms. He is the founder of Superposition, a consulting firm that builds collaborative workshops focused on data & AI-related use cases.LinksConnect with David on LinkedInSuperposition websiteSuperposition YouTube channelConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Today we're turning tiny tips into big wins. Khuyen Tran, creator of CodeCut.ai, has shipped hundreds of bite-size Python and data science snippets across four years. We dig into open-source tools you can use right now, cleaner workflows, and why notebooks and scripts don't have to be enemies. If you want faster insights with fewer yak-shaves, this one's packed with takeaways you can apply before lunch. Let's get into it. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Khuyen Tran (LinkedIn): linkedin.com Khuyen Tran (GitHub): github.com CodeCut: codecut.ai Production-ready Data Science Book (discount code TalkPython): codecut.ai Why UV Might Be All You Need: codecut.ai How to Structure a Data Science Project for Readability and Transparency: codecut.ai Stop Hard-coding: Use Configuration Files Instead: codecut.ai Simplify Your Python Logging with Loguru: codecut.ai Git for Data Scientists: Learn Git Through Practical Examples: codecut.ai Marimo (A Modern Notebook for Reproducible Data Science): codecut.ai Text Similarity & Fuzzy Matching Guide: codecut.ai Loguru (Python logging made simple): github.com Hydra: hydra.cc Marimo: marimo.io Quarto: quarto.org Show Your Work! Book: austinkleon.com Watch this episode on YouTube: youtube.com Episode #522 deep-dive: talkpython.fm/522 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Jim Tananbaum of Foresite Capital joins Nick to discuss From Data Science to Drug Design: How AI Shifts Discovery, Target Validation, and Portfolio Construction. In this episode we cover: Data Science and Investment Approach Investment Practices and Market Conditions China's Role in Biotech and Regulatory Considerations Impact of AI on Biotech and Healthcare Healthcare Adoption of AI and Preventive Measures Payers and Insurance Companies' Role Lessons from Successful Investments Generating Liquidity in a Sluggish Market Future of GLP-1 Agonists Guest Links: Jim's LinkedIn Jim's X Foresite's LinkedIn Foresite's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.  

English is now an API. Our apps read untrusted text; they follow instructions hidden in plain sight, and sometimes they turn that text into action. If you connect a model to tools or let it read documents from the wild, you have created a brand new attack surface. In this episode, we will make that concrete. We will talk about the attacks teams are seeing in 2025, the defenses that actually work, and how to test those defenses the same way we test code. Our guides are Tori Westerhoff and Roman Lutz from Microsoft. They help lead AI red teaming and build PyRIT, a Python framework the Microsoft AI Red Team uses to pressure test real products. By the end of this hour you will know where the biggest risks live, what you can ship this quarter to reduce them, and how PyRIT can turn security from a one time audit into an everyday engineering practice. Episode sponsors Sentry AI Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Tori Westerhoff: linkedin.com Roman Lutz: linkedin.com PyRIT: aka.ms/pyrit Microsoft AI Red Team page: learn.microsoft.com 2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps: genai.owasp.org AI Red Teaming Agent: learn.microsoft.com 3 takeaways from red teaming 100 generative AI products: microsoft.com MIT report: 95% of generative AI pilots at companies are failing: fortune.com A couple of "Little Bobby AI" cartoons Give me candy: talkpython.fm Tell me a joke: talkpython.fm Watch this episode on YouTube: youtube.com Episode #521 deep-dive: talkpython.fm/521 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy