9-day challenge to learn and build AI agents, focusing on fundamentals, architecture, how they interact with the environment, daily tasks, collaborative discussions, and hands-on coding exercises. By the end, you'll have a solid foundation and a working AI agent prototype.

Google Build with AI Virtual Mixer as part of virtual sessions.

Our forecasts are in flux as a result of uncertainty around the transmission of the trade war shock and the potential for more policy surprises. That said, the data flow this week aligned with our forecast of continued resilience in April activity and May surveys, alongside a rebound in May sentiment readings from depressed levels. Our forecasts are in flux as a result of uncertainty around the transmission of the trade war shock and the potential for more policy surprises. That said, the data flow this week aligned with our forecast of continued resilience in April activity and May surveys, alongside a rebound in May sentiment readings from depressed levels. 

Speakers:

Bruce Kasman

Joseph Lupton

This podcast was recorded on 23 May 2025.

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

Takeaways Many data professionals struggle with knowing where to start with new datasets. Asking the right questions is crucial for effective data analysis. The AI tool summarizes data and generates exploratory questions. Real-world data analysis is often messier than textbook examples. The tool is designed to help users gain analytical momentum. It can be beneficial for new hires, students, and experienced analysts alike. The tool is not a replacement for deep thinking but a partner for better thinking. Users have reported increased confidence when using the tool for interviews. Future developments include adding visualization support and custom question tones. Better questions lead to better insights and career growth. Medium Blog: https://medium.com/data-science-collective/how-i-built-an-ai-that-thinks-like-a-data-analyst-69fba12ad08d Free Blog: https://mukundansankar.substack.com/p/how-i-built-an-ai-that-thinks-like

🎙️ Future of Data and AI Podcast: Episode 06 with Robin Sutara What do Apache, Excel, Microsoft, and Databricks have in common? Robin Sutara! From being a technician for Apache helicopters to leading global data strategy at Microsoft and now Databricks, Robin Sutara’s journey is anything but ordinary. In this episode, she shares how enterprises are adopting AI in practical, secure, and responsible ways—without getting lost in the hype. We dive into how Databricks is evolving beyond the Lakehouse to power the next wave of enterprise AI—supporting custom models, Retrieval-Augmented Generation (RAG), and compound AI systems that balance innovation with governance, transparency, and risk management. Robin also breaks down the real challenges to AI adoption—not technical, but cultural. She explains why companies must invest in change management, empower non-technical teams, and embrace diverse perspectives to make AI truly work at scale. Her take on job evolution, bias in AI, and the human side of automation is both refreshing and deeply relevant. A sharp, insightful conversation for anyone building or scaling AI inside the enterprise—especially in regulated industries where trust and explainability matter as much as innovation.

Last week I posted about the gulf in the data industry between people in favor and opposed AI. The data industry has an atrocious track record of success over the decades. Shall we keep repeating the same mistakes with AI?

I think AI denialism is holding back and AI has the potential to help us correct a lot of the sins of the past, namely quick delivery of value. Whether we get there is another question...

Thanks to dbt, GoodData, and Ellie.ai for sponsoring this podcast.

dbt: https://www.getdbt.com GoodData: https://www.gooddata.com Ellie.ai: https://www.ellie.ai

A pragmatic view on how to approach CloudOps automation in modern organizations. Whether you're just getting started or aiming to scale automation across your company, this session will equip you with actionable insights and a clear roadmap tailored to your organization's risk tolerance and maturity. In addition, it will offer a way to prepare to an AI-Enabled future.

AI agents have enterprises in a chock-hold. From drafting your emails and scheduling your calendar to chatbots and omni-channel contact centre solutions with API integrations a lot is changing in white collar jobs. But, alongside the rise of trad wives we have the Stepford Wives, so I am 3D printing a robot to make my bed, iron and empty the dishwasher. How will embodied AI reshape what it means to be human, and how do we stay ahead of the curve.

Summary In this episode of the Data Engineering Podcast, host Tobias Macy welcomes back Shinji Kim to discuss the evolving role of semantic layers in the era of AI. As they explore the challenges of managing vast data ecosystems and providing context to data users, they delve into the significance of semantic layers for AI applications. They dive into the nuances of semantic modeling, the impact of AI on data accessibility, and the importance of business logic in semantic models. Shinji shares her insights on how SelectStar is helping teams navigate these complexities, and together they cover the future of semantic modeling as a native construct in data systems. Join them for an in-depth conversation on the evolving landscape of data engineering and its intersection with AI.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Shinji Kim about the role of semantic layers in the era of AIInterview IntroductionHow did you get involved in the area of data management?Semantic modeling gained a lot of attention ~4-5 years ago in the context of the "modern data stack". What is your motivation for revisiting that topic today?There are several overlapping concepts – "semantic layer," "metrics layer," "headless BI." How do you define these terms, and what are the key distinctions and overlaps?Do you see these concepts converging, or do they serve distinct long-term purposes?Data warehousing and business intelligence have been around for decades now. What new value does semantic modeling beyond practices like star schemas, OLAP cubes, etc.?What benefits does a semantic model provide when integrating your data platform into AI use cases?How is it different between using AI as an interface to your analytical use cases vs. powering customer facing AI applications with your data?Putting in the effort to create and maintain a set of semantic models is non-zero. What role can LLMs play in helping to propose and construct those models?For teams who have already invested in building this capability, what additional context and metadata is necessary to provide guidance to LLMs when working with their models?What's the most effective way to create a semantic layer without turning it into a massive project? There are several technologies available for building and serving these models. What are the selection criteria that you recommend for teams who are starting down this path?What are the most interesting, innovative, or unexpected ways that you have seen semantic models used?What are the most interesting, unexpected, or challenging lessons that you have learned while working with semantic modeling?When is semantic modeling the wrong choice?What do you predict for the future of semantic modeling?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links SelectStarSun MicrosystemsMarkov Chain Monte CarloSemantic ModelingSemantic LayerMetrics LayerHeadless BICubePodcast EpisodeAtScaleStar SchemaData VaultOLAP CubeRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeKNN == K-Nearest NeighbersHNSW == Hierarchical Navigable Small Worlddbt Metrics LayerSoda DataLookMLHexPowerBITableauSemantic View (Snowflake)Databricks GenieSnowflake Cortex AnalystMalloyThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Inscreva-se na Segunda Edição do BEES : Arte de Transformar dados em Experiência;Veja as vagas do BEES; Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Paulo Vasconcellos Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Ryan Russon is an ML Engineer. He stopped by my house for a practical and grounded chat about ML and AI. Enjoy!

Join dbt Labs May 28 for the dbt Launch Showcase to hear from executives and product leaders about the latest features landing in dbt. See firsthand how features will empower data practitioners and organizations in the age of AI. Thanks to dat Labs for sponsoring this episode.

Send us a text Buckle up—automation is shifting into high gear! In Part 1 of our Automation mini-series, host Al Martin VP WW Technical Sales sits down with IBM insiders Sarah McAndrew (VP WW Automation, Technical Sales) and Vikram Murali (VP App Mod & IT Automation, Development) to decode the now and next of enterprise automation. 🔑 What’s inside 00:53 Meet Sarah McAndrew VP WW Automation, Technical Sales01:29 Meet Vikram Murali VP App Mod & IT Automation, Development03:25 A crisp, boardroom-ready definition of automation09:21 Why observability is automation’s super-power11:45 The latest breakthroughs propelling innovation faster than ever22:29 How Aspera rockets data transfer into warp speed23:43 The big “Why IBM?”—and why it matters for your bottom lineExpect practical tips you can deploy tomorrow, myth-busting insights, and bold predictions that will have your Dev & Ops teams buzzing. Hit play, share with a tech-curious friend, and let’s #MakeDataSimple—together.

🔗 Connect: Linkedin Sarah McAndrew | LinkedIn Vikram Murali  🌐 Explore IBM Automation: ibm.com/automation

MakingDataSimple #IBMAutomation #AI #Observability #TechPodcast #FutureOfWork

Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

In this episode, we uncover how Caenorhabditis elegans males pick the right mate — by literally feeling for it! Researchers discovered that body stiffness, controlled by special furrow collagens, acts as a key mechanical cue for contact-mediated mate recognition.

We discuss:

How males detect species, sex, and reproductive stage through touch Why body stiffness and surface signals must work together for successful mating Experiments using ruptured worms, chemical treatments, and even 3D-printed bionic worms to test mechanical cues Why mating is not just about scent or sight — it’s about how a partner feels

📖 Based on the research article: “Body stiffness is a mechanical property that facilitates contact-mediated mate recognition in Caenorhabditis elegans” Jen-Wei Weng, Heenam Park, Claire Valotteau, Nathalie Pujol, Paul W. Sternberg & Chun-Hao Chen. Published in Current Biology (2023). 🔗 https://doi.org/10.1016/j.cub.2023.07.020

🎧 Subscribe to the WoRM Podcast for more quirky stories at the crossroads of mechanics, behaviour, and evolution!

This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.

📩 More info: 🔗 ⁠www.veerenchauhan.com⁠ 📧 [email protected]

In this episode, we welcome you to the 2025 Women in Data Science Charlottesville event hosted at the University of Virginia School of Data Science. WiDS Charlottesville seeks to increase the participation of women in data science and feature outstanding women doing outstanding work.

Leading the conversation is Lisa Bowers, a former executive with Genentech/Roche and current director of UVA’s Enterprise Studio. She is joined by our keynote speaker Lexi Reese, CEO and Co-Founder of Lanai Software and UVA alumna, who brings experience spanning tech giants like Google and Gusto. Drawing from their wealth of knowledge at the intersection of innovation and enterprise, Reese and Bowers share their unique perspectives on how data science is shaping the future of work and innovation.

From empowering the next generation of data scientists to the real-world impact of AI, this fireside chat dives deep into what it means to build meaningful, transformative careers in data science. 

Discover the key practices for accountability and openness in AI deployments.

See how leading organizations are applying fair AI principles in the real world.

Learn how to build AI systems that reflect ethical values and diverse perspectives.

Get practical steps for integrating fairness into your organization’s AI workflows.

Stay informed on evolving standards and how to align with ethical AI guidelines.

Explore the types, causes, and real-world consequences of bias in AI models.