Podcasts about Machine learning

This week on Catalyst, Tammy is joined by Beth Howen, President of State, Local Government and Education (SLED) at NTT DATA, who helps government agencies and school systems modernize the technology touching everyday life. Beth traces her path from becoming a state CIO in Indiana at 28 through leadership roles at Capgemini, TELUS International, Atos, and the City of Indianapolis. So how did she do so much at such a young age? Beth credits it to a willingness to work outside rigid processes when they don't serve the outcome. She and Tammy dig into how AI risks compounding government's legacy-system problems if agencies automate broken workflows instead of modernizing first, and the promise of AI-powered care reaching rural and underserved communities.Please note that the views expressed may not necessarily be those of NTT DATALinks: Beth Howen Learn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

As AI applications become more complex, the infrastructure powering them needs to evolve. Corey Sanders, SVP of Product at CoreWeave, joins Chris to discuss why AI requires a fundamentally different approach than traditional cloud computing. They explore AI-native infrastructure, training and inference workloads, the rise of agentic development, optimizing GPU performance, AI research workflows, and why the future of software will be built around AI-first experiences rather than websites and apps.Featuring:Corey Sanders – LinkedIn Chris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:CoreWeaveSponsors:Framer: The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalaiUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

Quantum materials discovery shows how quantum computing can create real value in industry by working alongside AI, advanced computing, and experiments to better understand materials, improve decision-making, and accelerate innovation at scale, ultimately helping deliver practical, measurable progress for the energy transition.This week, Dave, Esmee, and Rob are joined by co-host and quantum expert Phalgun Lolur, together with Jonathan Owens, Senior Scientist in Computational Materials Physics at GE Vernova to explore how quantum computing could reshape materials discovery and why that matters for the future of energy.  TLDR00:00 – Introduction01:50 – Hang out: The wet-bulb thermometer03:20 – Dig in: Technology Convergence and the Link to Quantum11:30 – Conversation with Jonathan Owens44:26 – Exciting to see how the quantum landscape matures and the magic wand for magnetismGuestJonathan Owens: https://www.linkedin.com/in/jonathan-r-owens-phd/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/Co-host Phalgun Lolur:  https://www.linkedin.com/in/phalgun-lolur/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Small business fraud is evolving — and fraudsters are using increasingly sophisticated tactics, from synthetic identities to AI-generated documents and digital deception. In this episode of Market Pulse, Equifax's David Adams talks with Jill Molitor, Director of Fraud and Credit Administration at Stearns Bank, about how lenders can balance faster decisioning with stronger fraud prevention.In this episode:How are fraudsters using AI to target small businesses?Fraudsters are increasingly using AI and digital tools to create synthetic identities, falsified documents and more sophisticated fraud schemes. These tactics can make fraudulent businesses appear legitimate, requiring lenders to look beyond traditional verification methods, according to Stearns Bank. What is synthetic identity fraud in business lending?Stearns Bank describes synthetic identity fraud as a “Frankenstein” identity — where fraudsters combine fabricated or manipulated information to create a person or business profile that appears real. These identities may establish credit history before eventually defaulting, leaving lenders with limited recourse. Can AI solve fraud detection challenges?Equifax and Stearns Bank discuss how AI and machine learning can help identify suspicious patterns, unusual activity and potential risks. However, technology alone is not enough. Human expertise remains critical for evaluating context, reducing false positives and making informed decisions. 

Is AI Already Biased? Who's Really Programming Your Future? | The Karel Cast Yesterday Is AI Already Biased? Who's Really Programming Your Future? | The Karel Cast Can artificial intelligence ever be truly neutral, or is it already reflecting the beliefs, priorities, and agendas of the people and governments building it? AI is growing at an astonishing pace, but someone is teaching it how to think. Tech companies, programmers, regulators, and now governments are all helping shape the future of artificial intelligence. As AI becomes more deeply connected to politics, business, and society, one question becomes impossible to ignore: Can we trust AI to be objective? Today, Karel explores how AI is trained, why bias may be unavoidable, and whether artificial intelligence is becoming the world's most powerful influence—or simply another reflection of human flaws. Also on today's show:

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to separate artificial intelligence speed from actual business value and what we value from humans in an age of AI. You will discover why productivity charts hide critical context that changes everything. You will learn how to spot the difference between quick output and solid results. You will master a simple framework for letting machines handle data while you keep full control over every choice. You will walk away with practical steps to scale your daily workload without sacrificing your unique perspective. 00:00 – Introduction 02:15 – The misleading productivity chart 05:40 – Decoding the midterm results 09:10 – When tests measure the wrong skills 13:25 – The seven ways to use AI properly 18:50 – Why humans must keep the steering wheel 23:40 – Practical tools for smarter workflows 28:15 – Fixing the education gap 32:00 – Call to action Press play to uncover how you can turn artificial intelligence into a reliable partner that amplifies your best work. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-ai-in-academia-workforce.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: In this week’s In Ear Insights, let’s talk about AI productivity and results-oriented mindsets. We talk a lot about AI productivity gains, and a lot of people are rightfully asking, “Where’s the beef?” Going back to the 1980s Wendy’s commercial. I want to show you a chart. Katie, I want to get your reaction to this chart on some AI productivity gains and whether you would consider this a success or not. So let me bring this chart up here. This is from Brown University. We have individual workers, we have their original productivity scores in the gray, their AI-enhanced scores where they’re using an AI tool and how they increased. And the green numbers represent the percent change. Now, without any other context, at a first glance, what do you make of this? Is this an AI success story? Katie Robbert: Not necessarily. Christopher S. Penn: Okay, tell me why. Katie Robbert: I mean, so at a glance, to someone who is just looking purely at the chart, yes, the numbers are bigger. You have a bunch of green in the middle. So the percent change is positive. But as someone who is skeptical, I say, where did you start? What was the baseline? What are the roles? I have more questions than answers. I can’t look at this and go, wow, yes. Okay. Because to me there’s so much missing context. Who are these people? Is it self-report? What is the period of time that there? Is it one task? Is it multiple tasks? Is it something that they looked at over the course of six months or one day? I don’t know. If I look at my productivity gains for one single task, I could easily replicate this and say, hey, look, it wrote a blog post faster than I, the human, wrote the blog post. So therefore productivity gains. But what I don’t know is the blog post any good? How much editing does it have to go through? Is it something that’s actually ever going to see the light of day? And that’s one blog post. That doesn’t mean that every single post is created that efficiently. AI can create things really quickly. It doesn’t mean they’re any good. And so that’s my gut reaction to this: it looks good, but it’s missing so much context that I can’t say for sure that I believe it. Christopher S. Penn: Okay, I can tell you for sure these are actual scores. They are actual gains or losses. If your employee number S22 is there, you got it. Your performance went down. Katie Robbert: Yeah, yikes. Christopher S. Penn: Yeah, you got to go. But, and these are real outcomes that matter. Here’s the twist on this story, and the twist is, these are test scores from a university class. The midterm. The professor said, something’s up. The orange scores of the midterm scores. So in the final, he prohibited it. He made the test in person. No assistance, no devices. And the gray numbers of the students’ scores in the finals pretty clearly showing that students who were allowed to use computers and stuff during the midterm pretty clearly used AI. And this story has been floating around the social media sphere. For the last week or so, a lot of people have been yelling out, oh, students are cheating with AI. This is terrible. It’s the end of education. And my take on it was, well, I think there’s a bit more nuance to that. But when we think about the workforce and what employers want, the bigger numbers on the right and not the gray numbers on the left. Now, with this new context, what do you think? Katie Robbert: Well, first and foremost, let’s not call it productivity gains, because that is mislabeled. Second, I’m with you, Chris. The notion of an open book test is not new. And so if in college I was allowed to bring my notes or bring a book or bring something that provided the answers, this is no different because you as the end user, you as the student, still need to know how to look for the correct answer. Because AI hallucinates a lot. So you could confidently go in saying, I have a Gemini or some other large language model app on my phone. I can just look up all the answers. Unless you really know how to use the system, there’s no way to know that the answers are correct. And so I feel like it is nuanced. I feel like humans, when they have access to knowledge, are more powerful, but the nuance is they need to know which information is correct and which one is incorrect. So, I agree. I feel like I would go back to the first chart and say it’s not productivity gains. That is 100% misleading. That is not at all what this is. Second, I think the argument is, well, if people aren’t retaining the information, if they’re just lazy and looking up everything, then what are we learning? Well, you’re learning critical thinking and how to research things. That in and of itself is a whole skill set. Ask the academics. There’s a place for it. Christopher S. Penn: Yep. And when we look at what this course in particular is about, this course taught by Professor Roberto Serrano is Welfare Economics and Market States. But this is from the syllabus. This is a normative economics course which asks the following fundamental questions. Are markets good or bad for the economy? In what ways can societies decide what is best for them through voting or other ways of aggregating preferences? Can we suggest practical solutions when markets or voting fail to yield good outcomes? Are there current political economic institutions good for society? Are they or not? In what ways? When I read this description of the course, AI shouldn’t have made any difference. Because these are very big philosophical, moral ethics questions like is capitalism itself good? Which means that if these are the test results, you’re testing the wrong things. Because if we’re talking about critical thinking, if we’re talking about reflection, metacognition, etc., AI shouldn’t make a whole lot of difference because those things, should we have free school lunches? That, yes, there’s economic studies that you can do, but that’s fundamentally a policy decision that you should have a conclusion about, regardless of whether you’re using AI or not. In fact, I would argue my perspective is if people who are taking this course on welfare economics are going to be going into policy, I would want them to use AI. I would want them to gather research. I would want them to have it push back and forth. Now, whether or not they were actually doing that, I don’t know. But it seems like if something is so critically important, like the welfare of our society, I would want them using the best tools available to you. Katie Robbert: So it’s interesting, it strikes me. I don’t disagree with you. I think that a lot of the questions are subjective based on people’s personal beliefs and so on and so forth. My sense then is if the question was should schools offer free lunch? Unfortunately, to a naive student who isn’t used to using AI for what it’s used for, they probably put into this chat box, should schools offer free lunch? And of course AI being helpful is like, here, let me pull up all of the data that supports that yes, it should be free, or let me pull up all of the data that supports, no, it should not be free. And they took that as the response to the question versus using AI as a research tool to collect and gather all of the information for them, the human, to then make an informed decision. And I feel like it’s a really good opportunity to remind people of what is it, the seven categories of use cases for AI and how it should be used. Like, don’t use AI to make a decision. You’re the human, you make the decision. Use AI to gather your information. Summarize. I’m not going to remember all seven off the top of my head. Yeah, I was like, I got summarize, I got rewriting. That’s all I have for abstraction. Christopher S. Penn: Take data out of data classification. Organize your data summarization. Take your big data and make it small. Rewriting. Take your data from one form to another. Synthesis. Take a small data and make it big. Question answering. Ask questions of your data and generation. Make new data from your data. Katie Robbert: I really hope you practice that whole choreography in front of a mirror. Christopher S. Penn: Well, I do that in my talks. Katie Robbert: I know, but I think that. And so thank you for that. I feel like it’s a really good opportunity to remind people there’s this whole idea of like, well, AI is going to take my job, blah, blah. You, the human, still need to have those critical thinking skills. I feel like I’m beyond a broken record at this point. I don’t even know what the next phase of broken. Christopher S. Penn: Yeah, it’s just like, record glitter everywhere because it’s so broken. Katie Robbert: That’s a thing. The test example is a really good example of misuse of AI. Like we’re making a bunch of assumptions. We don’t know how students actually use these tools. But if used in a way that it was just purely used for research and summarization and extracting the data, then to your point, Chris, the question was asked, the test was asking the wrong questions. Because how are you going to grade based on subjective questions? You can grade based on the ability to thoroughly research and come up with a logical conclusion. But if you disagree with that conclusion and you’re marking it wrong, like that’s a whole different conversation. Christopher S. Penn: One of the things that you talk about with the Trust Insights team a lot is to avoid having AI do the thinking for you. You talk about this with our marketing reports and things like that. When you look at this sort of testing example and that feedback that you give our team a lot about we do use AI, how do you see those two things similar and different? Katie Robbert: I don’t have a problem with people using AI. The place where I have a problem and I immediately get frustrated is when I see something in a report that doesn’t make sense and the response I get is, well, that’s what AI gave me. And my first thought is, well, where are you in this? Where’s your thinking? Where’s your brain? I want to know your insights, Chris. I want to know your insights. Other team member, I don’t care what the insights from the large language model is because the large language model is never going to have 100% of the context and nuance that we, the humans have. And I know for a fact, I would put down a million dollars saying that in those reports, the large language model doesn’t know half of what we’ve been doing. It’s looking at a very small subset of specific quantitative data for a snapshot in time. It does not have the whole story. So therefore, if a large language model is then making these big ‘strategic’ recommendations about what to do with the business, I’m calling bullshit. Christopher S. Penn: Yep. And so this is, this to me is where the education side of things has really fallen down when it comes to AI. Is it binary, oh, yes, you should use it, or no, you shouldn’t use it? And it’s academic dishonesty if you’re using it’s a tool. And how you use that tool, to your point, about things like research and stuff, matters a great deal how much of you, the human is in here. Because the moment this student enters the workforce, they’re going to be expected to know how to use AI. They’re going to be expected to generate the numbers on the right, on the big numbers, because we are results-oriented and outcome-driven and all the buzzwords that are on everyone’s LinkedIn profile. But that’s in a lot of ways that’s true. That’s what we hire for. We hire for those big numbers. We don’t hire. We don’t necessarily. And ethics is a whole separate discussion. But putting aside ethics, that’s what leaders want. That’s what managers want. Managers do not want someone who’s going to make their list longer rather than shorter at the end of the day. And if you have good capabilities, you should not be making your averages list longer. Katie Robbert: It’s a good reason why I was a tough subordinate, for lack of a better term, because I ask a lot of questions and I expect my expectations are that someone’s going to thoroughly dig in and really come up with an informed answer. And my managers at the time were not doing that. Maybe it’s my expectations. I have a really hard time with the lightweight. Oh, I just looked at one study. So therefore it’s fine. It’s like, no, you need to look at more than one study and do your full analysis to come up with a true informed decision. Emphasis on informed, making decisions. What is it? Decisions without data is distraction. Christopher S. Penn: Data without decisions is distraction. Katie Robbert: Data without decisions. But I also feel like decisions without data is dangerous. Christopher S. Penn: Yeah, absolutely. So here’s two examples. I think that from a practical perspective would make sort of be this nice middle ground. Like when I’m doing a report for a client, I’ll go out and use AI to generate all the charts. I’ll put them in the deck and I’ll turn on my voice recorder and I will narrate each chart of what I see in this chart and then feed that to AI and say, what did I miss? Or what didn’t I see? And usually it doesn’t come up with anything. It will ask me questions. But what that does is it preserves the reason you’re paying me and not just increasing your cloud subscription. That’s one useful use case. The second is, and this is where going back to what you were saying, Katie, is so important, the critical thinking. Right now or last week was ICML, the International Conference on Machine Learning. It was in Seoul, South Korea. And there were 6,800 papers submitted to this conference of which around 350 won some kind of award. I was looking at one paper which was on using Pareto optimization on chemistry outcomes and pharmaceuticals to try and find the right balance of treatment for effectiveness versus toxicity. And when I read this paper, that’s a really cool idea. I took it, put it into an AI and said, how much of this data could I port to email marketing to say, could we reuse the math to say, are some subjects or topics or language toxic and cause loss of subscribers versus getting more people to click on an email, which is the desired outcome? And it gave me a whole long list of things that I’m still working on. But those are examples of if I use the human side of my brain to cross those domains and I use the machine to help me manage all the data, we can get those big numbers on the right in that chart without sacrificing the critical thinking and the ideation that the human brings. Katie Robbert: I’m going to say something that I say a lot. New tech doesn’t solve old problems. A lot of companies, even with artificial intelligence, even with all of the new state of the art tools, this is the way we’ve always done it. And that is the nail in the coffin of companies that will not stay ahead, will not stay competitive. Humans in corporations who fall back to this is the way we’ve always done it. Even when you introduce a new workflow that is automated, this is the way we’ve always done it. That workflow is going to get stale real fast. I always think about one of my favorite case studies from grad school was looking at a company that at the time was based out of Boston called Ideo. Ideo. And their whole mission was to understand human behavior. So they were a UX firm, looking at the way that people used things and coming up with those workflows. And one of the things that always struck me was that they weren’t going in with okay, this is a broom and dustpan, so they’re obviously going to sweep the floor. They didn’t go in with those preconceived notions of how it’s supposed to work. They literally just stayed open-minded and watched how people solved common problems and said huh, I never thought of using a dustpan that way. That’s really interesting. What else can it do? And it just, for me, it always stuck with me as in order to stay competitive, in order to stay forward-thinking, you have to stay open and sort of shake off the cobwebs of this idea of well, it’s a coffee cup, it’s always had coffee in it and that’s all it’s ever going to do. It has to be, oh, this is a coffee cup. Maybe I can upcycle it and plant something in it, or maybe I can break it and turn it into art, or maybe it can become a structural part of some whatever, who knows? I don’t even know. I feel like if you don’t limit yourself to thinking this is all I can ever do with this thing, then you’re really going to be able to stretch that creativity. But that critical thinking. So back to the initial example of the students taking the test. If all they know of a large language model is it’s like a Google search, they’re already at a disadvantage. Christopher S. Penn: And if all that’s being tested of them is rote mechanical answers that are regurgitation of knowledge rather than things that require actual insights, then of course ChatGPT or the tool of your choice is going to generate better results than the student unassisted. But you’re not testing the skills that the modern workforce needs. You are testing the skills that the 1930s needed, right? You need to be an obedient factory worker to come in and make widgets. We have robots for that now. We do not need humans for that. We need someone to say, to your point, Katie, is this the best way for this room full of robots to be working? Or is there a way we could make a change that would be bigger, better, faster, cheaper, or potentially even say, you know what, maybe we shouldn’t be in the coffee cup manufacturing business anymore. Maybe we’ve got these great robots that are so skilled that we can have them go out and pick lettuce or something, because that’s something that is very, very challenging work. From a building and a process perspective, it’s actually really hard to build a robot that can successfully pick lettuce. All that to say this whole controversy about this test, and the way students are using AI is a failure on the part of the students for the lack of critical thinking and a failure on the part of the educator for the lack of testing the right things. Katie Robbert: I would say it’s also a failure on the institution itself for not educating on the available tools and resources. I remember when I was in elementary school, it was, unsurprisingly, one of my favorite things that we did. There was a whole class on how to use the card catalog at the library. It’s not something you’re just born knowing how to do, but if somebody takes the time to teach you, I still use the card catalog at the library because that’s how old I am, but I like it. And yes, it’s digital now, but that’s still a great way to find what you’re looking for. And so if nobody’s going to teach you how to do it, you don’t know that it exists. If you’re someone who’s curious enough to find out on your own, that’s great. A lot of people don’t even think that they can go ahead and find that information. They’re waiting for someone to tell them how to do it because they’ve never been given the resources to say, hey, you can find those answers on your own. You can teach yourself. Some people just, that’s not just how their brain functions. It’s not a weakness or a bad thing. It just is what it is. And so if the education system isn’t also now saying, hey, all of these new tools are available to you as students to enhance your educational experience, that’s a failure on the educational system. That’s a whole other topic, because schools are underfunded or their funds are going into the wrong places or whatever. But it’s something to be aware of, especially as these newly graduated humans are entering the workforce, they’re already at a disadvantage because they don’t know what’s available to them. Christopher S. Penn: Yeah. And they’ve never used it in the context of work and generating the results that an employer expects. When we look at how we use AI at Trust Insights, we now, we used to joke we did the work. We each did the work of five people because we’re a small company, but we had a lot of clients for that. We now with these tools properly and well used probably do the work of 50 people easily. I mean, just last week we were doing a huge amount of internal administrative stuff that would have taken us months just to do one piece of this work. And, we were doing 18, 19 pieces. Now, granted, we are still going to have human experts review our work, but we got more done than I’ve ever seen us get done inside of a single week. Katie Robbert: I would agree with that. I mean, this is the whole. I’ve talked about it on live events. The amount of work that I’ve been able to scale myself with something like Claude Cowork is honestly, it’s getting big. That’s an understatement. Christopher S. Penn: I don’t know. Katie Robbert: I don’t have a better word for it, but. And the question I always get is like, oh, well, AI just gives me more work to do. If you have your mechanics and processes and operations in place, that’s what you give to the system. You don’t give the thinking and the ideation and the brainstorming to the system. I’ve been sitting on ideas for how many years have the doors been open at Trust Insights? Christopher S. Penn: 8. Katie Robbert: I’ve been sitting on things that I want to do. Ideas. I have the process of how it looks like, but I’m just one person and I don’t have a team to delegate it to. So now that’s how we’re scaling things. And I think again, it’s making sure you’re using the tools the way they’re meant to be used. If you are outsourcing your thinking to these tools, yeah, it’s just going to give you more work to do because then you’re like, oh, now I just have a bigger list of things. No, give the list of things that you’ve already thought of to the system. Let the system do it. You continue to create and ideate. Christopher S. Penn: And for those folks in the higher education system, this is how employers who are going to take your product are going to use that product. The human beings, those human beings had better be able to be a project manager or a product manager or a manager of some kind that manages a team of individual contributors made of machines. Because we’re paying for, we want to pay for the critical thinking. We want to pay for the genuinely good new ideas. We do not need to pay for someone that just regurgitates things. A machine can do that perfectly fine. We do not need to pay for somebody that can type. Again, a machine can do that perfectly fine. We need people who think. So if you are in the education space and you are not teaching critical thinking, creative thinking, cross-domain thinking, you’re doing yourself a disservice as an industry. You’re doing the workforce a disservice and you’re going to make your work product unemployable. Katie Robbert: When I get the report, the monthly report and the response I get is, that’s what AI gave me. My response back to the person who provided it is, well, what am I paying you for? And it’s a really cold and harsh comment, but it’s real true. It’s true. Perhaps my delivery is not that direct all the time, but sometimes it is. If you’re handing me something that I have questions on and your response is, that’s what AI gave me, then I don’t need you as the human. I can do this myself and get crappy insights from a large language model. I don’t need someone to push a button for me. Christopher S. Penn: Right, exactly. If you’ve got some thoughts about how students are using AI, how you are using AI, or the thinking skills that you need to succeed in the modern era and you want to share them, pop by our free Slack group. Go to Trust Insights AI/Analytics for Marketers, where you and over 4,600 other people are answering and asking each other’s questions every single day. Well, I got that backwards. Clearly not AI generated today. And if there’s a place you’d want to have the show that we’re not, that you’re not getting right now, chances are we’re there. Go to Trust Insights ASGI Podcast. You can find us at all the places fine podcasts are served. Thanks for tuning in and we’ll talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or Data Scientist to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What Livestream webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. 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.

In this episode, I sit down with Scott Duncan, a chemical engineer who built an agentic manufacturing troubleshooting assistant – without any coding background. We dive into the real-world challenges of process manufacturing and how AI is transforming the way plant issues are solved. Scott shares his journey from hands-on troubleshooting to leveraging large language models, knowledge graphs, and real-time data for smarter, faster problem-solving. We also debate the future of domain expertise in an AI-driven world and what it means for education and workforce development. Join me as we explore the potential – and the limitations – of AI in reshaping the future of industrial operations.

Read the article here: https://journals.sagepub.com/doi/full/10.1177/30494826251413753

Marion Azoulai est Staff Data Scientist chez Astronomer, l'éditeur d'Astro, la plateforme managée d'Airflow qui est aussi l'un des principaux contributeurs de la solution open source.Créé en 2018, Astronomer a levé plus de 370 millions de dollars et accompagne des centaines d'organisations dans le monde, parmi lesquelles la Société Générale, Booking.com, Autodesk ou encore WeWork.Marion nous raconte comment ils ont lancé l'équipe Data & IA et comment ils ont construit leur stack autour d'Airflow.On aborde :

This week on Catalyst, Tammy is joined by Emma Boyd, a recent Chapman University graduate stepping into her first job as a practice analyst at a San Francisco law firm just as her generation navigates entering the workforce in the age of AI. Emma and Tammy discuss how she's learning to use AI as a tool for condensing and outlining her work, without letting it take over the writing and critical thinking she loves. They also dig into her capstone project at Fabletics, where she researched the legal and ethical questions around AI-generated models in fashion marketing, uncovering a constant cycle of new technology outpacing legal standards almost as soon as they catch up. Emma also has some advice for senior leaders: don't underestimate Gen Z, who are more gritty and driven than older generations often assume.Please note that the views expressed may not necessarily be those of NTT DATALinks: Emma Boyd - LinkedInLearn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Not all work happens in writing. Teams that work with photos, videos, and audio need AI that works for them too. This is why, with Dropbox, you can search within multimedia content for key moments and important information—not just text. In this episode, we talk with Appu Shaji and Hicham Badri, two Dropbox machine learning engineers who are part of the team that makes all of this possible. They explain how multimodal search works—from understanding the context of the initial query, to identifying objects and actions in complex scenes—and how they ensure those models work fast, even at Dropbox-scale. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

In this episode of The Voice of Retail podcast, host Michael LeBlanc sits down with one of retail technology's most accomplished leaders: Julie Averill, former Chief Information Officer of Lululemon and REI, and author of the new book Chief Impact Officer: Real Transformation Comes from Human, Not Just Artificial Intelligence. Joining the conversation is Menachem Salinas, Co-Founder and Chief Revenue Officer of Nimble, the expert AI web search platform making live web data enterprise-grade. A self-described "serial retail technologist," Julie's career spans the defining chapters of modern retail technology. She spent a decade at Nordstrom leading pioneering omnichannel initiatives, drove a technology transformation as CIO of REI, and then took a leap to a then-$2 billion company called Lululemon — where, over eight years as CIO, she helped power the brand's growth to $10 billion. Today, she runs her own advisory business focused on enterprise AI adoption, and her new book makes the case that real transformation comes from people, not just technology. Drawing on years of parsing signal from noise in the CIO chair, Julie delivers a masterclass in AI reality-checking. Her verdict on where retail stands in the AI journey? "Maybe the first inning." She argues the technology itself is now the easy part — the real reasons so many AI pilots fail are data quality, governance, workflow readiness and organizational capability. Julie shares the exact questions she asks to separate a slick demo from a solution built to survive contact with a real omnichannel operation: Whose data does this run on? What workflow actually changes, and for whom? Who owns the errors? What does this cost at scale? And she tells a cautionary tale of a board-driven AI pitch that promised everything — and why "yes, we can do everything" is the surest sign of an immature vendor. Julie also maps the future of the CIO role itself: from technology gatekeeper to curious, strategic business enabler. With AI tools now arriving through the browser, control is gone — the new job is curation, education and helping executives tell what's real. The businesses that win, she argues, will be the ones that learn to evolve continuously and dream about what was previously impossible. Menachem Salinas adds the vendor-side perspective, explaining how Nimble delivers real-time competitive pricing, digital shelf monitoring and out-of-stock intelligence across thousands of retail sites — and why he believes agentic commerce will transform e-commerce within twelve months. His candid ShopTalk Barcelona assessment: even the world's biggest brands rate no better than five out of ten on AI data readiness. Julie's book Chief Impact Officer is available now everywhere books are sold. Learn more at julieaverill.com and nimbleway.com. Michael LeBlanc is the president and founder of M.E. LeBlanc & Company Inc, a senior retail advisor, keynote speaker and now, media entrepreneur. He has been on the front lines of retail industry change for his entire career. Michael has delivered keynotes, hosted fire-side discussions and participated worldwide in thought leadership panels. He brings 25+ years of brand/retail/marketing & eCommerce leadership experience with Levi's, Black & Decker, Hudson's Bay, CanWest Media, Pandora Jewellery, The Shopping Channel and Retail Council of Canada to his advisory, speaking and media practice.Michael produces and hosts a network of leading retail trade podcasts, including the award-winning No.1 independent retail industry podcast in America, Remarkable Retail with his partner, Dallas-based best-selling author Steve Dennis; Canada's top retail industry podcast The Voice of Retail and Canada's top food industry and one of the top Canadian-produced management independent podcasts in the country, The Food Professor with Dr. Sylvain Charlebois from Dalhousie University in Halifax.Rethink Retail has recognized Michael as one of the top global retail experts for the fifth year in a row, the National Retail Federation has designated Michael as on their Top Retail Voices for 2025 and 2026. Thinkers 360 has named him on of the Top 50 global thought leaders in retail. If you are a BBQ fan, you can tune into Michael's cooking show, Last Request BBQ, on YouTube, Instagram, X and yes, TikTok.Michael is available for keynote presentations helping retailers, brands and retail industry insiders explaining the current state and future of the retail industry in North America and around the world.

What does it take to move AI agents from demos to reliable production systems? In this episode, Hamza Tahir explores how MLOps principles are shaping the future of generative AI, covering workflows, agent harnesses, fleets, and the infrastructure needed to build durable, scalable systems.  The conversation dives into open source tools, production challenges, and how ZenML's new project, Kitaru, helps developers build resilient, replayable, and observable agent systems.Featuring:Hamza Tahir – LinkedInDaniel Whitenack – Website, GitHub, XLinks:ZenMLKitaruMachine Learning Tools Landscape v2 (+84 new tools)Sponsors:Framer: The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalaiUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

Life sciences are at a turning point, where scientific innovation, regulatory pressure, and patient expectations collide with unprecedented advances in data, AI, and digital platforms. IT is no longer a supporting function but a critical driver of how therapies are discovered, developed, scaled, and delivered safely and at speed.This week, Dave and Rob wrap up our State of Life Sciences mini-series with Thorsten Rall, Global Industry Lead for Life Sciences at Capgemini and together, they connect the dots across the series, exploring how AI, data and innovation are accelerating drug discovery, transforming med tech, modernising manufacturing and improving patient outcomes, all built on a strong digital foundation. TLDR00:27 – Introduction and conclusion of the Life Sciences mini-series 02:16 – Key insights and lessons from the previous episodes on the Life Sciences landscape 21:18 – Building resilient, efficient and future-ready operations 34:35 – Creating integrated, patient-centric healthcare experiences 43:15 – Why the Digital Core is the foundation for transformation and innovation 53:21 – Final reflections: the future of Life Sciences and the key takeaways 54:53 – Weekend BBQs, Thorsten's daughter's theatre performance, and the role of R&D HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/with co-host Thorsten Rall: https://www.linkedin.com/in/thorsten-alexander-rall-b232185/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

In this episode, Professor Paola Cinnella - Professor of Fluid Mechanics at Sorbonne University and Director of the Sorbonne Cluster for Artificial Intelligence (SCAI) - joins Neil to discuss her path from classical fluid mechanics and high-order numerical methods into uncertainty quantification, Bayesian methods, data-driven turbulence modeling and AI for Science.Paola has built a career at the intersection of CFD, compressible and turbulent flows, dense gas dynamics, uncertainty quantification, robust optimization and machine learning. We discuss academic careers, dense gases, RANS uncertainty, AirfRANS, surrogate modeling, scientific publishing, education in the age of AI, and the idea of the "centaur scientist".Key topicsFluid mechanics, CFD and high-order schemesDense gases, real-gas effects and expansion shockwavesUncertainty quantification and Bayesian methodsRANS turbulence-model uncertaintyAirfRANS and CFD datasets for machine learningTurbulence modeling vs surrogate modelingScientific publishing and ML-for-CFD standardsSCAI and AI for ScienceEducation, ChatGPT and centaur scientistsPapersQuantification of model uncertainty in RANS simulations: A review - Heng Xiao, Paola Cinnellahttps://doi.org/10.1016/j.paerosci.2018.10.001Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression - Martin Schmelzer, Richard P. Dwight, Paola Cinnellahttps://doi.org/10.1007/s10494-019-00089-xBayesian estimates of parameter variability in the k-epsilon turbulence model - W.N. Edeling, P. Cinnella, R.P. Dwight, H. Bijlhttps://doi.org/10.1016/j.jcp.2013.10.027AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutionshttps://arxiv.org/abs/2212.07564Data-driven turbulence modeling - Paola Cinnellahttps://arxiv.org/abs/2404.09074Direct numerical simulations of supersonic turbulent channel flows of dense gases - Luca Sciacovelli, Paola Cinnella, Xavier Gloerfelthttps://doi.org/10.1017/jfm.2017.237LinksPaola Cinnella named Director of SCAIhttps://scai.sorbonne-universite.fr/news/paola-cinnella-new-directorSCAIhttps://scai.sorbonne-universite.fr/Paola Cinnella - HAL publicationshttps://cv.hal.science/paola-cinnellaPaola Cinnella - Google Scholarhttps://scholar.google.com/citations?hl=fr&user=wBRA0JAAAAAJERCOFTAC SIG 54 - Machine Learning for Fluid Dynamicshttps://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/master-of-science-internships/Chapters00:00 Podcast intro00:39 Introducing Prof. Paola Cinnella03:28 Conversation begins03:56 How Paola found fluid mechanics07:09 Moving from Italy to France08:37 High-order schemes and compressible flows09:30 Building an academic career12:06 Dense gases and uncertainty quantification15:16 Expansion shockwaves and real-gas effects19:17 Returning to Paris and academic mobility24:52 Academia, passion and persistence27:51 Bayesian methods and turbulence uncertainty30:47 Learning statistics across disciplines33:07 LearnFluidS, AirfRANS and CFD datasets36:33 Skepticism and physics in ML turbulence modeling40:41 Could ML lead to a universal turbulence model?42:59 Turbulence models, surrogate models and RANS45:03 Why LES alone cannot solve optimization47:15 Multi-fidelity modeling49:08 What Computers & Fluids looks for in ML-for-CFD papers54:05 CFD metrics vs machine-learning metrics57:13 Overselling, publication pressure and quality62:22 SCAI and AI for Science66:07 Cross-disciplinary AI for Science69:26 Education in the AI era72:44 Critical thinking and AI outputs78:15 AI as a companion, not a replacement81:42 AlphaFold and the future of discovery83:43 Training centaur scientists85:11 Closing thoughts

Stephen Alvarez & the Ancient Art Archive: Inside the World of Rock Art, Cave Paintings & National Geographic Photography

We just returned from three inspiring days at AI in the Alps, and in this episode, we dive into the most exciting breakthroughs and honest challenges facing industrial AI today. I share my firsthand impressions of revolutionary tools like TiRex-2, and we unpack what agentic AI really means for engineering, from breaking down data silos to transforming anomaly detection. Alongside Stefan Suwelack from Renumics, we discuss why skepticism has given way to optimism, how data infrastructure is finally catching up, and what the future holds for AI-driven organizations. The conversation is loaded with real-world examples, candid reflections, and a look at the evolving balance between sovereignty, open source, and big tech. Whether you're an engineer, decision-maker, or just curious about the future of AI in industry, this episode offers practical insights and a glimpse into what's next.

In this episode from the archives, Tammy is joined by Nimesh Mehta. Nimesh is the Chief Information and Strategy Officer at National Life Group and is passionate about creating human experiences with AI — music to our ears! Tammy and Nimesh discuss how to implement digital transformations with empathy and purpose. Nimesh also shares his thoughts on why psychological safety is so important at organizations working at the forefront of technological change, because disruption can't happen in the presence of fear.Please note that the views expressed may not necessarily be those of NTT DATA.Links:The Trusted Advisor Learn more about Launch by NTT DATAThis podcast is presented by Catalyst, the opinions and views expressed are those of the host and participants and do not necessarily reflect National Life Group's views on the topics discussed. This podcast is for informational purposes only and is not to be construed as an offer to sell or the solicitation of an offer to buy a security in any state where such offer or solicitation would be illegal. Any unauthorized use is prohibited. National Life Group is a trade name of National Life Insurance Company (NLIC), Montpelier, VT and its affiliates. This podcast was recorded on 2/21/2025. TC7784363(0325)3See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

By owning nearly its entire supply chain and leveraging AI raw-material forecasting, Danish endurance sportswear brand Fusion managed to sell an impressive 101% of the product it produced last year. The company achieves this near-perfect sell-through rate with a nightly ecommerce inventory sync that directly dictates what its sewing factory produces the very next day.   For more on Fusion and show notes, click here. Subscribe and watch Shopify Masters on YouTube!Sign up for your FREE Shopify Trial here.

How has AI image generation evolved from blurry outputs to powerful visual intelligence models? Dustin Podell, Co-Founder and Researcher at Black Forest Labs, explains the progression from diffusion to flow matching, how modern image models work, and how they're being used for image editing and practical visual workflows. The conversation also explores the FLUX family of models, running image generation locally, and where visual AI is headed next. Featuring:Dustin Podell – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Black Forest LabsDeveloper DashboardResearch PageFLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent SpaceLaying the Foundations for Visual IntelligenceSponsors:Midwest AI Summit: Join AI practitioners on October 15 in Indianapolis for practical sessions, hands-on discussions, and real-world AI solutions. Use code PracticalAI20 to save 20% on your registration. https://midwestaisummit.com/#ticketsPrediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalaiUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

Life sciences are at a critical inflection point, where scientific innovation, regulatory demands, and patient expectations converge with advances in data and artificial intelligence, positioning IT as a central driver of faster and more effective drug discovery and clinical development.This week, Dave and Rob continue with part 3 off the Life Sciences mini-series with Predrag Angelovski, VP, CTO at Healthcare Informatics at Philips to exploring how MedTech products are more and more becoming connected platforms, combining hardware, software and services.TLDR00:21 – Introduction with co-host Thorsten Rall01:00 – Hang out: Esmee joins and Rob is lost at a train station03:00 – Dig in: Life Sciences mini-series, Part 304:57 – Conversation with Predrag Angelovski50:56 – Travelling to Europe, agents vs. agentic, and the age of intelligence GuestPredrag Angelovski: https://www.linkedin.com/in/predrag-angelovski/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Send us Fan MailWhat does AI literacy actually look like for pathologists, researchers, and future clinicians? And how do you teach it in a way that is practical, not abstract?In this episode, I talk with Candice Chu, DVM, PhD about something I think a lot of people in digital pathology and computational pathology are feeling right now: AI is moving fast, but education is still catching up.Candice is a clinical pathologist, veterinarian, and educator building AI-focused teaching and research at Texas A&M. We worked together before on digital pathology and image analysis projects, so this conversation felt especially grounded. We talk about her AI literacy curriculum framework for veterinary education, why she decided to build it, and what it takes to teach AI in a way that is useful, ethical, and realistic.This episode is about understanding what AI tools are good for, where they can waste your time, and why hands-on experience matters. Candice explains why she sees AI as a set of tools, not a belief system. Try them. Learn them. Keep what improves your workflow. Drop what does not.We also talk about the difference between putting educational content online and building formal institutional teaching. That matters because social media can move quickly, but curriculum changes, research, and professional organizations shape longer-term adoption. Candice shares how her course started as a low-stakes elective, then grew into a more structured framework that combines education with publishable research.A big part of this conversation is the curriculum itself. We go through what students actually learn: AI fundamentals without heavy math, machine learning and image analysis, large language models, prompt engineering, chatbot building, ethics, literature research, and final projects where students evaluate real tools and workflows. I liked that the course does not stop at theory. It asks students to use tools, question them, and explain where they help and where they do not.We also get into something that matters far beyond veterinary medicine: professional responsibility. If AI is involved in a workflow, the clinician is still responsible. That includes fabricated citations, bad outputs, weak prompts, and the temptation to trust tools too quickly. Candice makes a strong case that AI education needs ethics, legal context, and interdisciplinary teaching built in from the start.If you are trying to think more clearly about AI in pathology, education, workflow design, or professional training, this episode gives you a concrete example of what responsible AI literacy can look like.Episode Highlights00:00 – Why AI tools are just tools, and why trying them matters even if you later decide not to keep using them00:33 – Who Candice Chu is and why her work on AI literacy in veterinary medicine is worth paying attention to02:33 – Why going back to Texas A&M changed the scale of Candice's AI research and teaching07:53 – How the AI course was designed as a low-stakes elective first, and why that helped student engagement11:16 – Where veterinary AI education stands now, and what professional organizations like ACVP are doing13:08 – Why AI adoption in veterinary medicine is still slow, and what skepticism usually sounds like in practice15:19 – Real examples of how Candice uses LLMs and computer vision in pathology, medical records, and research19:58 – What is actually inside the 15-week AI literacy curriculum, from fundamentals to final projects24:16 – Why ethics and legal responsibility are not optional in AI education31:35 – Why no-code tools and vibe coding are entering the curriculum already38:50 – The AI tools Candice is testing in her own workflow, including Claude, Codex, and PerplexityResources mentionedCandice Chu's AI literacy curriculum framework paper in Frontiers in Veterinary ScienceCandice's earlier work on ChatGPT in veterinary medicineTexas A&M and the institutional setting where Candice is building AI research and teachingMr. Don Riddick and the AVMA AI working group, mentioned in the ethics and legal contextClaude, Codex, and Perplexity as AI tools Candice is actively testingDigital Pathology 101, mentioned in the conversation as a teaching resourceCandice's online educational work on Instagram.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailThis week on Sidecar Sync, Amith Nagarajan and Mallory Mejias break down a whirlwind week in AI, from Anthropic's rapid-fire Claude releases to OpenAI's tightly controlled GPT 5.6 rollout. They unpack the surprising performance of mid-tier models like Sonnet 5, the implications of government intervention in frontier AI, and what it means when access to the most powerful tools is suddenly restricted. The conversation then shifts to a new study reshaping the AI jobs narrative, revealing that companies investing deeply in AI are actually growing headcount—while others fall behind. From practical model selection strategies to big-picture workforce implications, this episode connects the dots between cutting-edge tech, policy, and the future of work. 

Welcome to MS Research Briefs, a new ECTRIMS podcast series delivering an expert guided tour of important new studies in multiple sclerosis research. In each episode, leading MS experts will take a small number of recently published studies and go beyond the headline findings – exploring what the research shows and how it may influence clinical practice and future discovery. In this inaugural episode, Prof. Alan Thompson and Prof. Olga Ciccarelli discuss two studies exploring how advanced MRI biomarkers and machine learning may transform the diagnosis and prognosis of multiple sclerosis. Featured Publications Paramagnetic Rim Lesions and Development of Clinical MS in Radiologically Isolated Syndrome.JAMA Neurology. 2026;83(3):250–258. Machine learning-based combination of the central vein sign, cortical lesions and paramagnetic rim lesions: a web-based tool for the diagnosis of multiple sclerosis.Brain Communications. 2026;8(2):fcag079. Discussed in this episode Can paramagnetic rim lesions identify individuals with radiologically isolated syndrome (RIS) who are most likely to develop clinical MS? Why do paramagnetic rim lesions appear to be stronger prognostic biomarkers while the central vein sign remains a powerful diagnostic biomarker? Can machine learning models combining MRI biomarkers outperform traditional dissemination-in-space criteria? What role might advanced MRI biomarkers play in future diagnostic criteria and treatment decisions? How close are we to AI-supported diagnosis and automated biomarker detection in routine clinical practice? As advanced MRI biomarkers move from research tools towards clinically meaningful decision-making, this episode explores one of the most important developments in contemporary MS research.

A.G.G. Liu is a writer and online educator based in New Jersey. Liu has reached millions of online learners on YouTube—both as the lead script writer for the AI and futurism channel Rational Animations, and through their own educational channel, Signore Galilei. Liu co-authored the book 30-Second Space Travel with Dr. Charles Liu and Dr. Karen Masters and co-hosts the podcast The LIUniverse with Dr. Charles Liu. Covering the basics, its history, and the science behind it, The Handy Artificial Intelligence Answer Book by A.G.G. Liu and Aishwary Pawar, Ph.D. (Visible Ink Press / June 30, 2026) is the perfect starting point to understanding the emerging AI revolution, what is currently possible, what might be possible in the future—and what's just hype. Covering the past, present and future of AI, this informative book answers over 1,300 of the most important, intriguing, and urgent questions about AI.

Qdrant Roundtable episode: The Current State of Agentic RetrievalJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to Qdrant for the collaboration!// AbstractAI agents are only as good as the information they can find, retrieve, and remember. In this community roundtable with the Qdrant team, we explored the latest advances in agentic memory, vector search, retrieval systems, and production AI architectures.As AI agents move beyond simple chatbots into systems that can reason across large amounts of information, retrieval is becoming one of the most important layers in the AI stack. The discussion covered the real-world challenges of building agents that remember what matters, forget what doesn't, and consistently retrieve the right context at the right time.If you're building AI agents, RAG systems, or production AI applications, this conversation offers practical insights into where retrieval is headed and what it takes to build reliable, scalable agentic systems.// BioEwa SzyszkaEwa is a Developer Relations professional based in San Francisco with a background in Computer Science and Hardware Engineering, passionate about bridging the gap between technology and the developer community. She holds a BSc in Computer Science and an MSc in Electronics, bringing a strong blend of deep technical foundations and communication skills to her work.Dylan CouzonDylan is based in New York City, and he helps developers build better AI applications. He is passionate about AI, programming, open source, and robotics, and enjoys sharing what he's building and learning along the way.Neil KanungoNeil is an experienced professional with expertise in data science, developer relations, and product growth. Currently serving as the Head of Developer Relations at Qdrant, Neil previously held the position of VP of Product Led Growth & Developer Relations at KX, where significant increases in product registration and user activation were achieved. At TIBCO, Neil managed a team focused on enhancing the adoption of TIBCO Spotfire through various initiatives, including tutorial videos and live webinars. With a strong technical background, Neil has developed innovative solutions in analytics, machine learning, and data visualization across multiple roles, including Engineering Data Analyst and Asset Integrity Engineer at Enterprise Products. Neil holds a Bachelor of Science in Radiation Physics from The University of Texas at Austin, a Master of Science in Mechanical Engineering from Texas Tech University, and is pursuing a Master in Applied Data Science from the University of Michigan.Evgeniya SukhodolskayaDeveloper Relations at Qdrant with 8 years of IT experience across software engineering, machine learning, and technical management, and 4 years in Developer Relations. Holds a Master's in Machine Learning, Data Analytics, and Data Engineering. Passionate about NLP, data-centric AI, and the role of vector search in advancing AI technologies.Andrei CristeaAndrei is a Berlin-based Developer Relations Engineer at Qdrant, a prominent open-source vector database. With a Master's degree in Artificial Intelligence from TU Munich, his expertise bridges AI, data infrastructure, and knowledge engineering.Hosted by Demetrios// Related LinksWebsite: https://qdrant.tech/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]

Relying entirely on point-of-sale merchant channels can leave fintech platforms exposed to low consumer visibility and channel dependencies.Dylan Conroy sits down with Shachar Scott, Chief Marketing Officer at Sunbit, to map out the operational framework used to transition legacy merchant infrastructure into an unfragmented consumer brand footprint.Moving Past Transaction Layers: How to structure a direct consumer brand that transcends merchant checkout counters to capture top-of-wallet placement.Accelerated Corporate Modernization: The behind-the-scenes mechanics of executing an enterprise rebrand within an intense 60-day operational window.Predictive Risk Engineering: Why deploying machine-learning loops to analyze full financial histories delivers personalized options within 0.00003 seconds.Radical Timelines Flattening: Leveraging automated orchestration platforms to condense campaign delivery loops from weeks to hours.Shachar Scott is a veteran technology marketing strategist and the Chief Marketing Officer at Sunbit, directing global brand narrative, machine-learning data integrations, and omni-channel client scale for an enterprise holding over 5.5 million consumers.Connect with Shachar Scott on LinkedIn: https://www.linkedin.com/in/shacharscott/Explore the Sunbit Personal Finance Roadmap: https://sunbit.com/Optimize your automated performance media channels with Strike Social: https://strikesocial.com/guaranteed-paid-social-media-ads-outcomes/Follow host Dylan Conroy on LinkedIn: https://www.linkedin.com/in/dylanconroy/

In this episode, I sit down with Levente Zolyomi, a leading PhD researcher at NXAI, to unpack the next evolution in time series AI: TiRex-2. We explore how TiRex-2 builds on its predecessor by handling complex multivariate data and streaming scenarios, opening new frontiers for industrial forecasting. Levente shares the story behind TiRex-2's architecture, its breakthrough capabilities, and what sets it apart from transformer-based models like Kronos. I ask the questions you're thinking—about zero-shot forecasting, synthetic data, and real-world benchmarks—so you can understand what matters most for your business. If you're navigating industrial AI or just curious about the future of time series models, this conversation is packed with the insights you need.

How is artificial intelligence reshaping the way we train and assess the next generation of family physicians? In this episode, Dr John Hayes and Dr Karim Hanna explore the promises and pitfalls of AI in residency assessment, from automating milestone tracking and grading robust essay exams to getting started with your first automated workflow. They also discuss how when AI takes on more of the data analysis and synthesis tasks, faculty will need to increase their coaching skills. On the other side, our guests offer an honest look at the risks of over-reliance on technology, cautioning educators to guard against "un-skilling" learners who may already lean too heavily on AI for clinical reasoning. Learn how to incorporate AI as a powerful supplemental tool while preserving the human connection that lies at the heart of family medicine. Hosted by Omari A. Hodge, MD, FAAFP and Jay-Sheree Allen Akambase, MDCopyright © Society of Teachers of Family Medicine, 2026Resources:Artificial Intelligence and Machine Learning for Primary Care Curriculum (AiMPC) CourseEthical Use of AI in the Family Medicine Clinic - STFM WebinarAn Opportunity to Thrive - AI in Family Medicine - STFM PodcastAI Deep Dive Summer Series Episode 1: Integrating AI into Family Medicine Curriculum Design - STFM PodcastLearning after Training: The Master Adaptive Learner - STFM PodcastSTFM Competency-based Medical Education ToolkitSTFM Collaborative List - Artificial Intelligence in Medical Education CollaborativeJohn Hayes DOPrior to his current roles, Dr Hayes served as a Family Medicine Clerkship Director, where he led a major curriculum modernization effort that enhanced the clerkship experience for medical students. Dr Hayes is now pioneering the integration of Artificial Intelligence in medical assessments, specifically in Objective Structured Clinical Exams (OSCEs). Additionally, he is developing a summative clinical reasoning examination designed to move away from traditional multiple-choice testing and into essay prompts with AI assisted grading. He is also exploring the use of ambient AI to enhance residency education and improve communication skills for both residents and faculty, aiming to foster a collaborative learning environment. He definitely used AI to help generate this blurb.Karim Hanna, MDDr Karim Hanna is an Associate Professor at the University of South Florida Morsani College of Medicine and the founding Program Director for the USF TGH Family Medicine Residency. His work integrates primary care education and AI advancements. Dr Hanna's interests in health tech spurred his writing of a blog - MedEd+AI - with a focus on AI's role in medical education and clinical implications. His scholarly work includes research on LLMs for clinical applications and improving education through AI.

This week on Catalyst, Tammy is joined by Shelly Swanback, a transformation executive who spent her career helping global organizations navigate digital disruption and large-scale cultural change. Before retiring, Shelly co-founded and helped build Accenture Digital, then went on to lead Western Union and TTEC through major transformations, and she recently published a chapter in The AI Edge: How to Thrive Within Civilization's Next Big Disruption. Shelly and Tammy discuss what leaders need to be communicating right now, and why in this moment of disruption, messaging matters as much as the vision itself. They also dig into why so-called soft skills, curiosity and emotional intelligence are, in fact, hard business requirements at a time when employees are anxious about what AI means for their jobs and their futures. As Shelly puts it, leadership isn't about having all the answers, it's about building teams that can thrive within the questions. Please note that the views expressed may not necessarily be those of NTT DATALinks: Shelly Swanback The AI Edge: How to Thrive Within Civilization's Next Big DisruptionLearn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of the PRS Global Open Keynotes Podcast, the team from Great Ormond Street Hospital in London discuss the use of machine learning in assessing the volumetric changes that occur following different surgical procedures to treat sagittal craniosynostosis. This episode discusses the following PRS Global Open article: "The Use of Machine Learning to Enable Objective Assessment of Surgical Outcomes in Sagittal Craniosynostosis" by Saskia I.H.M. Brulleman, Luke Smith, Alexander J. Rickart, Lara S. van de Lande, Noor ul Owase Jeelani, David J. Dunaway, Silvia Schievano, Simone Foti and Juling Ong. Read it for free on PRSGlobalOpen.com:https://journals.lww.com/prsgo/fulltext/2026/04000/the_use_of_machine_learning_to_enable_objective.9.aspx Dr. Juling Ong is a craniofacial surgeon at Great Ormond Street Hospital in London. Saskia Brulleman is medical student at Erasmus University in Rotterdam the Netherlands. Simone Foti is a postdoctoral researcher at Imperial College London Your host, Dr. Damian Marucci, is a board-certified plastic surgeon and Associate Professor of Plastic Surgery at the University of Sydney in Australia. #PRSGlobalOpen; #KeynotesPodcast; #PlasticSurgery; Plastic and Reconstructive Surgery- Global Open The views expressed by hosts and guests are their own and do not necessarily reflect the official policies or positions of ASPS.

When AI is at its best, the conversations can feel uncanny—almost magical in their accuracy, relevance, and speed. For that you can thank the AI agents that work together behind the scenes to search, reason, and sift through all your content to get you what you need to do your job. We talk with Jongmin Baek and Marta Mendez, two Dropbox machine learning engineers, about building conversational AI that's helpful, useful, and grounded in your team's shared context, so you can spend more time on the work that really matters. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

In this episode of Product Rising's AI Safety, Ethics & Responsibility series, host Shannon Peavey sits down with Kelly Anderson, founder and CEO of DyeConverter, an AI-powered platform helping food and beverage companies replace synthetic dyes with natural alternatives.Kelly's path to entrepreneurship is anything but typical. A chef-technologist and the first chef in the world certified by MIT in AI and Machine Learning, she has spent more than a decade leading innovation, R&D, and product development initiatives for brands including Nestlé, Disney, Impossible Foods, Panera Bread, Starbucks, and US Foods.As regulators, consumers, and food manufacturers grapple with the future of food ingredients, Kelly saw an opportunity to apply AI to one of the industry's most complex challenges: reformulating products that rely on synthetic colors. If you've ever wondered how artificial intelligence can move beyond content generation and help solve real-world scientific and operational challenges, this episode is for you.00:00 Introduction to Kelly Anderson and DyeConverter02:08 What is a "chef-technologist"?03:01 How culinary training shaped Kelly's approach to AI and innovation04:08 From executive chef to food innovation leader05:28 Discovering a broken food system and finding an AI opportunity08:10 Why replacing synthetic food dyes is harder than it sounds10:11 Health, regulatory, and manufacturing risks of natural color alternatives12:41 How DyeConverter works14:20 The role of the human in the loop17:55 Flavor transfer, consumer expectations, and reformulation challenges19:12 Which food dyes are being phased out?20:40 AI-powered color matching and the Color Studio22:42 Data sharing, trade secrets, and AI collaboration in food25:01 Why the food industry needs food people building food technology26:28 Beyond food dyes: the future of ingredient reformulation28:05 Ultra-processed foods, consumer demand, and healthier products30:22 Why business incentives matter more than good intentions31:34 Food regulations around the world and lessons from Europe34:07 What every industry should know about AI adoption35:40 Consumer rights, transparency, and AI disclosure36:21 How food companies are using AI today38:14 AI, sustainability, agriculture, and the future of farming39:32 Supply chain transparency and food traceability40:38 Predictions for food, health, and AI over the next decade42:18 Will healthier food become more affordable?43:38 Resources for following innovation in food and CPG44:18 Kelly's favorite cuisine and why she loves classic French cooking45:15 Closing remarks

Host Eric Lanke interviews Barun Acharya, system engineering manager for electrification at Parker Hannifin, about energy efficiency gains from electrified hydraulics and insights tied to NFPA's white paper and a CONEXPO technology conference. Acharya describes how emissions rules, noise limits, and ESG mandates are driving electrification, and outlines key challenges including unclear duty cycles (addressed via energy mapping), power-density mismatch between hydraulic and electric actuators, complex system integration (CAN, software, EMC, operator feel), and revealed hydraulic noise once diesel is removed.   He explains that traditional inefficiency is largely the engine–pump coupling, while smart hybrid architectures use batteries to buffer transients and run engines near optimal BSFC. He details a hybrid backhoe case with engine downsizing and indoor electric operation, enabled by sensors, data, and machine-learning use for duty-cycle classification, predictive maintenance, and operator coaching. Subscribe to the Fluid Power Forum today to never miss an episode. The podcast is available on all of your favorite podcast platforms, including YouTube, Apple Podcasts, Spotify, and iHeart Radio.   Connect with our host, Eric Lanke, at elanke@nfpa.com.   Connect with our guest, Barun Acharya, at bacharya@parker.com.   Learn more about the company at www.parker.com.   Find and share more interesting fluid power technologies and unique applications using #onlyfluidpowercan and follow podcast and other fluid power industry-related updates at @TheNFPA.   #FluidPowerForum #EnergyEfficiency #HybridArchitecture #Electrification

Hard optimization problems often look impossible through worst-case analysis, but real-world problems can contain structure that helps algorithms work faster. Ellen Vitercik, Ph.D., of Stanford University explains how machine learning can improve algorithm design for NP-hard optimization problems while preserving the formal guarantees that make solvers useful. She discusses beyond worst-case analysis, problem-specific heuristics, and the gap between tools that perform well in practice and methods that prove optimality. Vitercik also describes research on LLM reasoning using data structure tasks, where answers can be checked programmatically and failures reveal when models rely on pattern matching rather than true generalization. Her work helps clarify how AI may support stronger algorithms, more useful benchmarks, and more reliable reasoning systems. Series: "Data Science Channel" [Science] [Show ID: 41179]

While Elsevier's most recent Clinician of the Future Report shows increasing adoption of artificial intelligence tools among physicians and nurses, and optimism that they will improve quality of care in the future, a majority raised concerns about trust and reliability. To increase the level of trust, 60% said transparent citations of evidence-based and peer-reviewed research will be key. How to provide that transparency is our focus today as Raise the Line host Lindsey Smith welcomes Elsevier colleagues Rhett Alden and Raman Kaur to guide us through the complexities involved, including the concept of traceability and what role it plays in how AI tools such as Elsevier's ClinicalKey AI are built and deployed.  “Traceability changes the confidence that a clinician has in an AI tool so that they aren't trusting the AI, they're trusting the underlying evidence they're consuming from the AI-assisted platform,” says Raman, who brings years of experience as a primary care practitioner to her work.  It's also important, Rhett adds, to provide additional information, pulled from both the clinician's query and the patient's medical record, to inform clinical thinking. “ClinicalKey AI can be more than a response engine by establishing a larger context to provide a more precise answer for that individual patient.” In this thought-provoking discussion, these experts also provide insights on: Mitigating bias in AI results; Using AI responsibly with sustainability in mind; What type of clinician will benefit most from AI Mentioned in this episode: ClinicalKey AI Clinician of the Future Report If you like this podcast, please share it on your social channels. You can also subscribe to the series and check out all of our episodes at www.osmosis.org/podcast

How do we build trust in AI agents before the AI hailstorm arrives? Emil Lassen from the Artificial Intelligence Underwriting Company (AIUC) joins the show to discuss how the enterprise flywheel of standards, certification, audit, and insurance is being applied to AI agents. They explore the AIUC-1 framework, the challenges of securing agentic AI systems, and why red teaming (based on standards) may be key to accelerating enterprise AI adoption.Featuring:Emil Lassen – LinkedIn Daniel Whitenack – Website, GitHub, XLinks: Artificial Intelligence Underwriting CompanySponsors:Framer: The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalaiPrediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalaiUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

Resilience is the recognition that in today's highly interconnected and unpredictable world, disruption cannot always be anticipated or prevented and therefore requires a shift from traditional risk avoidance toward designing systems that can absorb shocks, adapt in real time, and recover quickly, ultimately emerging stronger and turning uncertainty into a source of advantage.This week, Dave, Esmee, and Rob are joined by Benjamin Trump, President Society for Risk Analysis and Cedrick Moriggi, Chief Resilience Officer and co-found the CCRO network under the United Nations Office for Disaster Risk Reduction, to explore what resilience means in a world shaped by systemic risk, fragile supply chains, climate shocks, cyber threats and human decision-making. TLDR00:30 – Introduction01:29 – Hang out: Heatwave weather and the perfect pub temperature03:18 – Dig in: What is resilience, and how do you deal with it?10:35 – Conversation with Benjamin Trump and Cedrick Moriggi48:32 – Ben is a writer and Cedrick teaches children GuestBenjamin Trump: https://www.linkedin.com/in/benjamin-trump-ba062523/Cedrick Moriggi: https://www.linkedin.com/in/cedrickmoriggi/HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

In this episode, Professor Ricardo Vinuesa - Associate Chair for Research and Associate Professor of Aerospace Engineering at the University of Michigan - explores with Neil one of the biggest questions in modern fluid mechanics: can AI help us move beyond faster CFD and toward genuine autonomous scientific discovery? Drawing on his work at the intersection of turbulence, machine learning, explainable AI, reduced-order modeling, and flow control, Neil and Prof. Vineusa discusses the promise and limits of foundation models for fluids, why the right latent representations may matter more than simply scaling data, and how agentic AI systems could uncover physical mechanisms that humans might otherwise miss.Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations — Abhijeet Vishwasrao et al.https://arxiv.org/abs/2604.09584The episode's most direct follow-up: multi-agent LLMs and latent foundation models autonomously explore flow physics in a tandem-cylinder setup.Enhancing computational fluid dynamics with machine learning — Ricardo Vinuesa, Steven L. Bruntonhttps://doi.org/10.1038/s43588-022-00264-7A concise roadmap for useful ML in CFD, from faster simulations and turbulence modelling to reduced-order models.Identifying regions of importance in wall-bounded turbulence through explainable deep learning — Andrés Cremades et al.https://doi.org/10.1038/s41467-024-47954-6Uses explainable AI to identify flow structures that matter for prediction and control, not just visually striking turbulence features.β-Variational autoencoders and transformers for reduced-order modelling of fluid flows — Alberto Solera-Rico et al.https://doi.org/10.1038/s41467-024-45578-4Shows how disentangled latent spaces, autoencoders, and transformers can support interpretable reduced-order models of nonlinear flows.Improving turbulence control through explainable deep learning — Miguel Beneitez et al.https://arxiv.org/abs/2504.02354Links explainable AI with deep reinforcement learning to target turbulence-sustaining mechanisms, with relevance for flow control, drag reduction, and energy efficiency.LinksVinuesaLabhttps://www.vinuesalab.com/Ricardo Vinuesa — University of Michigan Aerospace Engineeringhttps://aero.engin.umich.edu/people/ricardo-vinuesa/AI and ML for Fluid Dynamics course — Ricardo Vinuesa & Sergio Hoyashttps://www.flowthermolab.com/courses/ai-ml-for-fluids/VinuesaLab YouTube channelhttps://www.youtube.com/@VinuesaLabAI for Fluid Mechanics, Sustainability & XAI — Ricardo Vinuesahttps://www.youtube.com/watch?v=TOfwf4ffPnURicardo Vinuesa — Modelling and controlling turbulent flows through deep learninghttps://www.youtube.com/watch?v=0AOY_agZ8WMChapters00:00 Podcast Intro03:20 The Evolution of Foundation Models in Fluid Dynamics10:22 Understanding Explainable AI in Fluid Mechanics15:34 Challenges in Data Fidelity for Foundation Models20:29 Machine Learning vs. Reduced Order Modeling24:22 The Shift in Focus: Turbulence Modeling to Surrogate Models29:48 Exploring Agentic Systems for Scientific Discovery37:21 Exploring Latent Representations in Fluid Dynamics40:40 The Role of AI in Autonomous Discovery41:57 Bridging Fluid Mechanics and Computer Science45:28 Data-Driven vs Physics-Driven Models51:34 The Role of Academia in AI and Fluid Mechanics56:27 Optimization and Control in Machine Learning01:00:28 Future of AI in Fluid Dynamics: Beyond ChatGPT

MeidasTouch host Ben Meiselas reports on breaking news from the day. Zbiotics: Head to https://zbiotics.com/MEIDAS to get 15% off your first order when you use MEIDAS at checkout. Netsuite: Download the CFO's guide to Al and Machine Learning at https://Netsuite.com/meidas Smart Credit: Go to https://SmartCredit.com/meidas and start your 7-day trial for just $1. Subscribe to Meidas+ at https://meidasplus.com Get Meidas Merch: https://store.meidastouch.com Remember to subscribe to ALL the MeidasTouch Network Podcasts: MeidasTouch: https://www.meidastouch.com/tag/meidastouch-podcast Legal AF: https://www.meidastouch.com/tag/legal-af MissTrial: https://meidasnews.com/tag/miss-trial The PoliticsGirl Podcast: https://www.meidastouch.com/tag/the-politicsgirl-podcast Cult Conversations: The Influence Continuum with Dr. Steve Hassan: https://www.meidastouch.com/tag/the-influence-continuum-with-dr-steven-hassan The Weekend Show: https://www.meidastouch.com/tag/the-weekend-show The Ken Harbaugh Show: https://meidasnews.com/tag/the-ken-harbaugh-show Majority 54: https://www.meidastouch.com/tag/majority-54 On Democracy with FP Wellman: https://www.meidastouch.com/tag/on-democracy-with-fpwellman Uncovered: https://www.meidastouch.com/tag/maga-uncovered Learn more about your ad choices. Visit megaphone.fm/adchoices

This week on Catalyst, Tammy is joined by Cal Thompson, designer, product advisor, artist and mindfulness teacher with over 15 years of experience in human-centered design. Cal most recently served as VP of Product Design and Research at Headspace, where they led both a design team and a scientific research team through the company's expansion from a meditation app into a full mental health platform. Cal explains how growing up gay in the south impacted how they see design and makes a strong business case for incorporating accessibility into all products and designs. They also explore why the design toolkit is more critical in the AI era than ever before, and why vibe coding without domain expertise risks producing what Cal calls "feature slop." They also dig into the attention economy and how mindfulness is quietly becoming a radical act in a world engineered to capture your focus.Please note that the views expressed may not necessarily be those of NTT DATALinks: Cal Thompson Headspace Future London Academy Center for Humane TechnologyGreenway Institute Learn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of The Dairy Podcast Show, Dr. David Cook, CEO of BoviSync, explains how dairy operations can turn herd data into practical management decisions. Drawing from expertise in dairy nutrition, engineering, and machine learning, Dr. Cook discusses automation, predictive insights, artificial intelligence, and protocol compliance. He also explores how data systems can improve dairy efficiency, employee consistency, and herd management. Listen now on all major platforms!“The moment that records can provide insight and automate small decisions, data collection costs finally create measurable operational value for dairy management systems.”Meet the guest: Dr. David Cook is the CEO and Managing Partner of BoviSync and holds degrees in agronomy, agricultural engineering, and dairy nutrition from the University of Wisconsin Madison. His work focuses on dairy data systems, machine learning, herd management automation, and decision support tools that improve dairy production efficiency and protocol consistency. Learn more from Dr. David Cook on The Dairy Podcast Show, available on all major platforms.Liked this one? Don't stop now — Here's what we think you'll love!What you'll learn:(00:00) Highlight(01:11) Introduction(02:15) Data insights(08:05) Breeding automation(11:22) Protocol consistency(17:05) Artificial intelligence(21:35) Automated reporting(24:19) Final QuestionsThe Dairy Podcast Show is trusted and supported by innovative companies like:- BoviSync* Priority IAC* Evonik* Afimilk* Adisseo* Agri-Comfort* CowManager- Chemlock Nutrition- Natural Biologics- Protekta- AHV- Agrarian Solutions- DietForge- dsm-firmenich

There's an assistant that will work for you 24 hours a day — never sleeps, never complains, helps you build a business, write your emails, and teach you anything — for about $20 a month. So why are most people getting garbage answers out of it? It's not because AI is dumb. It's because they're talking to it wrong. In this episode I break down "prompt engineering" in plain English — no math, no coding, no degree. Just one simple recipe you can remember on four fingers: R - C - T - A (Role, Context, Task, Ask). Master this and you'll talk to AI better than people who've been doing it for years. By the end you'll have a copy-and-paste prompt you can use TODAY to learn literally anything.

Co-founders Dr. Salman Avestimehr and Dr. Aiden He join the podcast to discuss their new "agentic" company, Teamily AI. They dive into how their platform is disrupting the social landscape by weaving multi-agent AI into group chats, enabling groups, friends, and families to interact with virtual friends, essentially creating a collaborative environment where AI acts as a participant that anticipates needs and remembers the full context of a conversation.This conversation explores the core value proposition of an AI-first social platform—not just making an individual superhuman, but enabling a collective of human and AI agents to do "fascinating things together." The founders detail their technology, which is built on deep expertise in distributed machine learning and multi-agent systems, and their long-term vision to IPO and evolve the very nature of social networks by bridging the gap between human and artificial intelligence.In the news segment, Charlie Fink and Rony Abovitz unpack the week's biggest AI stories: Ben Affleck selling his stealth AI film company, Interpositive, to Netflix; Anthropic's Claude briefly dethroning OpenAI's ChatGPT in the app store; and a deep dive into Jack Dorsey's company Block cutting 4,000 employees. The hosts also discuss the social fallout of AI acceleration, particularly the counter-movement seeking tactile, real-world connection and the economic risk of displacing white-collar data analysts.Key Moments00:03:00 – App Store War: Discussing Anthropic's Claude topping the app charts and why the US Department of Defense will use the best AI system regardless of corporate objection.00:04:00 – Hollywood's AI Play: Netflix acquiring Ben Affleck's AI company, Interpositive, which uses unedited film dailies to train an AI for editing and optimization.00:05:00 – The Mediocrity Threat: Rony Abovitz's take on the risk of AI creating a "very, very long tail of Okay" content, leading to a cultural sameness.00:07:00 – Counter-Culture: Exploring the growing emotional need for "something real" and a massive movement away from purely digital experiences.00:09:00 – The White-Collar Risk: The hosts argue that the white-collar data analyst is the worker "most easy to replace" by AI, contrasting with the high value of blue-collar workers.00:11:00 – The "Oh Wow" Moment: Charlie Fink describes his first experience with Teamily AI, noting the immediate power of real-time, multi-person and multi-agent prompting.00:13:00 – The Science Behind Teamily: Dr. Aiden He, PhD in Machine Learning, explains how Teamily is built upon his previous research in distributed learning and multi-agent systems.00:26:00 – Global Memory: Aiden details Teamily's unique "cross domain, long horizon memory," which allows the AI to combine human-human chat context with human-AI memory for a more natural interaction.The biggest takeaway is the conceptual shift from using AI as a solo productivity tool to using it as a collaborative team member. The path to the next phase of social networking hinges on building platforms where AI is not isolated but is a natural, evolving part of a human community.This episode of The AI XR Podcast is brought to you by Zappar, the folks behind Mattercraft, a leading visual development environment for building immersive 3D web experiences for mobile headsets and desktop. Start building smarter at mattercraft.io. Listen and subscribe to the AI XR Podcast wherever you get your podcasts! Watch the full thing on YouTube https://youtu.be/s78WZJSfGeo. Hosted on Acast. See acast.com/privacy for more information.

It's been one year since the U.S. Centers for Disease Control and Prevention, in an unprecedented move, dismissed all the members of its Advisory Committee on Immunization Practices (ACIP), kicking off what would turn out to be a very concerning and busy year for infectious disease specialists.  We're going to recap this turbulent period – which includes a resurgence of measles, an unusually rough flu season, the emergence of a new COVID strain and outbreaks of hantavirus and Ebola – with Dr. William Schaffner, one of the country's most frequently quoted medical experts on infectious disease, vaccination, and public health. As a member of ACIP for decades, Dr. Schaffner brings unique insight into the dismantling of the committee and the distrust of vaccines that lies at the root of the changes. As he explains to Raise the Line host Lindsey Smith, while many vaccine critics are beyond reach, there are those he describes as vaccine hesitant that may be persuadable if the right approach is taken. “Beyond providing facts, we have to listen to them and respond to their concerns and make them feel comfortable. Information is fundamental, but behavior change only comes with a change in attitude.” Tune in for a wealth of wisdom and context that includes observations on: What's complicating containment of the Ebola outbreak; Challenges in public health communication in the current social media environment; What grade health authorities should get on their response to the hantavirus outbreak. Mentioned in this episode:Vanderbilt University School of Medicine If you like this podcast, please share it on your social channels. You can also subscribe to the series and check out all of our episodes at www.osmosis.org/podcast

This week on Catalyst, Tammy is joined by Carolyn Lee, President of The Manufacturing Institute, the workforce development and education affiliate of the National Association of Manufacturers. Carolyn grew up in a manufacturing family on Long Island and spent years on Capitol Hill before taking the helm of the MI in 2017. Tammy and Carolyn dig into the widening gap between AI adoption at the executive level and awareness on the shop floor, and why closing it is the defining challenge for American manufacturing right now. They also unpack the fear factor driving resistance to change and Carolyn announces the forthcoming AI for Manufacturing 101 curriculum to help manufacturers who are at risk of falling behind. Please note that the views expressed may not necessarily be those of NTT DATALinks:Carolyn LeeThe Manufacturing Institute - AI Skills Training Learn more about Launch by NTT DATASee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Episode 304 reunites The Analysts — Remarkable Retail's celebrated panel of Forrester's Sucharita Kodali, Guggenheim's Simeon Siegel, and GlobalData's Neil Saunders — to take stock of retail coming out of earnings season. Steve Dennis and Michael LeBlanc open on the paradox of 2026: results are largely strong, sentiment is dismal. Simeon argues the link between the two is "tenuous at best" — people talk one way and spend another. Neil has the data: roughly 60% of shoppers who expect the economy to worsen still spent more than a year ago, propped up by spring tax refunds that won't repeat. Then the K-shaped economy. Higher-income households drive most of the real volume growth; middle-income shoppers prop up value growth mainly because prices are higher. Sucharita revisits "peak ambiguity" and the "vibe session," noting record sales barely outrun stubborn inflation. The panel unpacks the standouts — Ross's 17% comp, Victoria's Secret up 15% — and debates GLP-1's role in surging apparel and beauty: wardrobe replacement, new confidence, trading up to statement pieces. On turnarounds, Simeon lands the episode's sharpest thesis: brands "ubiquitize" and peak around $3–4 billion in the US. Lululemon got too big, over-distributed, and over-earning — so the bad sales have to "walk out the door" before the brand can re-elevate, the same lens that frames Nike's long reset. He and Sucharita draw the Gap parallel ahead of Simeon's on-stage interview with Mickey Drexler, noting Old Navy now dwarfs Gap itself. Neil makes the case for Macy's under Tony Spring — basics fixed first, satisfaction and visitation improving — while Steve stays skeptical of the pace. Next, the DTC reckoning. Simeon reframes his old "DTC is not all it's cracked up to be" call as "anti-anti-wholesale": outside high-margin luxury, nearly every brand needs a healthy wholesale business — and stores remain the best channel because "the customer is your employee." Sucharita pushes back on the AI narrative, reminding everyone it's far more than generative hype, as the panel digs into why scaled players — Amazon, Walmart, Costco, off-price — keep compounding through retail media, marketplaces, and flywheel economics. It closes on the wealth effect, trillion-dollar market caps, and whether a market correction could rattle high-end spending — then rapid-fire hot takes: brands to watch (Cozey, Ross Stores, Goyard) and what's on each analyst's radar, from inflation and surging oil prices to a quiet "middle of the doughnut" news lull and an election year's hunt for stability. Join us at the CommerceNext Growth Show in New York June 23rd and 24th with this exclusive discount code for 10% off general admission tickets and FREE retail tickets: Your code is "REMARKABLE" . See you in the Big Apple! About UsSteve Dennis is a strategic advisor and keynote speaker focused on growth and innovation, who has also been named one of the world's top retail influencers. He is the bestselling author of two books: Leaders Leap: Transforming Your Company at the Speed of Disruption and Remarkable Retail: How To Win & Keep Customers in the Age of Disruption. Steve regularly shares his insights in his role as a Forbes senior retail contributor and on social media.Michael LeBlanc is a senior retail advisor, keynote speaker and media entrepreneur. Michael has delivered keynotes, hosted fire-side discussions hosted senior retail executive on-stage in 1:1 interviews worldwide. Michael produces and hosts a network of leading retail trade podcasts, including The Remarkable Retail Podcast, The Voice of Retail The Food Professor, The FEED powered by Loblaw and the Global eCommerce Leaders podcast. He has been recognized by the NRF as a global Top Retail Voice for 2025 and 2025 and continues to be a ReThink Retail Top Retail Expert for the fifth year in a row.

Send us Fan MailAI, will it bring about the apocalypse, or just change the world?AI is making headlines daily. It is changing how we create online content, make web pages, buy products, receive medical care, invest in the stock market, etc. Will this tech bring about the end of the world if left unchecked? Will it just make early adopters wealthy? Forrest and Patrick dive into this cutting-edge topic, discuss how they are using it, how they have tested it, and their predictions about the future.GarvinAcademy.com Join PrepperNet.Net - https://www.preppernet.netPrepperNet is an organization of like-minded individuals who believe in personal responsibility, individual freedoms and preparing for disasters of all origins.PrepperNet Support the showPlease give us 5 Stars! www.preppingacademy.com Daily deals for preppers, survivalists, off-gridders, homesteaders  https://prepperfinds.com www.preppernet.com

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, creator of the Knowledge Graph Insights Podcast, for their second conversation together. The two cover a wide range of interconnected topics, starting with a correction Larry makes about the true origin of the term "artificial intelligence," tracing it back to the 1956 Dartmouth Conference and its distinction from Norbert Wiener's cybernetics. From there, the conversation moves through the history and structure of knowledge graphs, ontologies, RDF (Resource Description Framework), and the W3C standards process, touching on concepts like the T-box, A-box, and C-box, as well as the 25th anniversary of the Semantic Web paper. Stewart and Larry also dig into the limitations of large language models — particularly around reasoning, confabulation, and what Larry describes as "cognitive surrender" — and why symbolic AI and knowledge engineering may hold answers that the neural network world hasn't fully embraced. The episode also ventures into consciousness, panpsychism, Michael Pollan's ideas, and Stewart's own hands-on experience vibe coding a personal chatbot to replace functionality he feels he's lost with recent changes to Claude. Larry's podcast can be found at kgi.fm.Timestamps00:00 - Stewart introduces Larry Swanson; Larry corrects the record on AI's origin, distinguishing it from Norbert Wiener's cybernetics at the 1956 Dartmouth conference.05:00 - Larry discusses interviewing semantic web paper coauthors on its 25th anniversary; RDF's hidden ubiquity compared to SIM cards powering everything invisibly.10:00 - Knowledge graphs explained through t-box terms, a-box assertions, and Dave McComb's c-box; IKEA's three-layer knowledge graph as a practical example.15:00 - Stewart connects metadata complexity to AI needs; faceted search explained as c-box attributes driving product filtering experiences.20:00 - RDF 1.2 reification standards discussed; W3C's rigorous recommendation process powering governments and enterprises worldwide through collaborative standards.25:00 - Cyc project examined as influential "successful failure"; Pat Hayes bringing description logic into semantic web; LLMs lacking true reasoning capability.30:00 - Epistemological fault lines between human and computer intelligence; cognitive surrender paper reveals no intelligence threshold protects against AI manipulation.35:00 - Stewart's Claude regression problem drives chatbot vibe coding quest; small language models and domain-specific approaches explored as alternatives.40:00 - Consciousness discussion through Michael Pollan's panpsychism lens; language versus cognition disconnect revealing LLMs as pure token-stitching without genuine thought.45:00 - Context graphs as purpose-built knowledge graphs for AI; Stewart's planning agents versus coding agents architecture and ground truth verification problem.50:00 - Docs-as-code versus code-as-docs paradigm shift; knowledge graphs as universal verifiers against validated facts; RDF 1.2 enabling provenance and degrees of certainty.55:00 - Jessica Talisman's Knowledge Graph Academy recommended for onboarding; kgi.fm podcast shared; knowledge representation community needs better abstraction for wider adoption.Key Insights1. The term "artificial intelligence" was not a marketing gimmick but was coined deliberately at the 1956 Dartmouth Conference to distinguish the work of John McCarthy from Norbert Wiener's cybernetics. The two camps represented genuinely different approaches, and the AI label was a form of intentional intellectual branding rather than empty promotion.2. The semantic web, often called the most successful failure in technology history, has quietly embedded itself everywhere despite never achieving its original vision. Technologies like RDF power metadata standards inside every Adobe product and form the invisible backbone of government systems, enterprise data infrastructure, and cultural heritage organizations worldwide.3. Knowledge graphs are best understood as an ontology combined with all the instances that populate it. The distinction between things and strings, popularized by Google in 2012, captures the core idea that knowledge representation is about concepts as distinct from the labels we give them.4. The t-box, a-box, and c-box framework offers a practical model for understanding knowledge architecture. The t-box holds terminology and concepts, the a-box holds assertions about specific instances, and the c-box manages the attributes, taxonomies, and controlled vocabularies that sit between them and enable things like faceted search.5. Large language models produce fluent, convincing output but lack genuine reasoning, epistemological grounding, or judgment. Research on cognitive surrender shows that even people who understand how LLMs work are still susceptible to being misled by their fluency, meaning intelligence and awareness offer no reliable protection against being deceived.6. The gap between language and cognition matters deeply when evaluating AI. Evidence from people with aphasia shows that thinking can occur without language, which suggests LLMs, being purely language-based systems, are missing a fundamental layer of cognition that cannot be recovered through more tokens or better training.7. Knowledge graphs and RDF-based representation are well suited to the problem of verification and grounding in AI systems. Rather than relying on vectorized embeddings of language, a knowledge graph can store validated, provenance-tracked facts with degrees of certainty, making it a natural foundation for building trustworthy AI applications.

The MeidasTouch Podcast is back for a packed Memorial Day episode as the brothers break down Donald Trump's latest self-inflicted chaos on the world stage, including how he continues to torpedo his own Iran negotiations by suddenly demanding Middle East allies join the Abraham Accords, reportedly leaving leaders in stunned silence on a tense call. The episode also covers Trump's bizarre Memorial Day attacks on former presidents Barack Obama and Biden, major Democratic campaign speeches drawing sharp contrasts ahead of the midterms, and the growing backlash from prominent MAGA influencers now openly turning on Trump as his approval ratings crater, especially among young voters. Ben, Brett and Jordy also discuss Trump's latest bizarre ballroom court filing that reads like it was written by Trump himself, new details surrounding the shooting near the White House and Trump's immediate effort to politicize it for his ballroom project, Russia's escalating attacks on Kyiv, and all the latest breaking news. Subscribe to Meidas+ at https://meidasplus.com Get Meidas Merch: https://store.meidastouch.com Deals from our sponsors!  Smalls: Head to https://Smalls.com/meidas and use promo code MEIDAS at checkout for 60% off your first order PLUS free shipping! IQBar: Get 20% OFF all IQBAR products. Text TRUTH to 64000. NetSuite: Download the CFO's guide to Al and Machine Learning at https://Netsuite.com/meidas Willie's Remedy: Order now at https://drinkwillies.com and use code MEIDAS for 20% off of your first order + free shipping on orders over $95 Remember to subscribe to ALL the MeidasTouch Network Podcasts: MeidasTouch: https://www.meidastouch.com/tag/meidastouch-podcast Legal AF: https://www.meidastouch.com/tag/legal-af MissTrial: https://meidasnews.com/tag/miss-trial The PoliticsGirl Podcast: https://www.meidastouch.com/tag/the-politicsgirl-podcast Cult Conversations: The Influence Continuum with Dr. Steve Hassan: https://www.meidastouch.com/tag/the-influence-continuum-with-dr-steven-hassan The Weekend Show: https://www.meidastouch.com/tag/the-weekend-show Burn the Boats: https://www.meidastouch.com/tag/burn-the-boats Majority 54: https://www.meidastouch.com/tag/majority-54 On Democracy with FP Wellman: https://www.meidastouch.com/tag/on-democracy-with-fpwellman Uncovered: https://www.meidastouch.com/tag/maga-uncovered Learn more about your ad choices. Visit megaphone.fm/adchoices