Podcasts about Computer vision

Send me a messageIf you're buying 120% of the food required to serve 100% of your customers, you don't have a waste problem. You have a forecasting, procurement and margin problem.My guest is Olaf van der Veen, co-founder of Orbisk, working with commercial kitchens to track where food is being lost. His point is blunt: much of that loss happens before anything reaches a plate, through overproduction, overstocking and weak operating procedures. In a low-margin business, that is excess inventory turning directly into lost profit.We examine why food waste is often misdiagnosed as bad behaviour, what changes when kitchens can finally see where losses occur, and why AI forecasts perform better when combined with experienced human judgement. We also look at a hotel that cut one chef to save cost, only to discover the extra waste was expensive enough to pay for two and a half.Listen now to understand where poor planning is quietly eating margin, and what better demand, procurement and operational data can reveal. If disruption hit tomorrow, would you know where your supply chain was most exposed? In 15 minutes my free scorecard helps you assess 27 resilience statements, calculate your score, and turn the result into three priorities and a 30 day action plan. You can download the scorecard free at tomraftery.com/scorecard.Support the showPodcast supportersI'd like to sincerely thank this podcast's generous Subscribers:Alicia FaragKieran OgnevGary LynchAnd remember you too can become a Resilient Supply Chain+ subscriber  - it is really easy and hugely important as it will enable me to continue to create more excellent episodes like this one and give you access to bonus episodes of topical, timely supply chain resilience analysis.

In der neuen Folge des Wandelwerker Podcasts spricht Anna mit Alpay Ilker Toy, Head of Business Developement bei Dataguess GmbH und Experte im Bereich Business Development mit Schwerpunkt auf der Digitalisierung industrieller Prozesse, über den Einsatz von künstlicher Intelligenz und Computer Vision in der Arbeitssicherheit. Alpay erklärt, wie KI-gestützte Kamerasysteme dabei helfen können, Gefahrensituationen wie das zu nahe Aufeinandertreffen von Staplern und Fußgängern oder riskantes Verhalten an Maschinen frühzeitig zu erkennen, bevor daraus ein Unfall entsteht. Er ordnet ein, warum Arbeitssicherheit dadurch von einem reaktiven zu einem präventiven Ansatz übergehen kann, welche technischen Voraussetzungen für solche Systeme nötig sind und warum die Kommunikation mit Mitarbeitenden entscheidend für die Akzeptanz ist. Im Gespräch geht es zudem um Unterschiede in der Offenheit gegenüber solchen Technologien zwischen Deutschland und anderen Ländern sowie um die Rolle von Betriebsräten und Datenschutz.

AI isn't just transforming the way we work, but also the way we write the software that people use for work. In this episode, we talk to two engineering productivity leads at Dropbox: Uma Namasivayam, senior director of software engineering productivity, and Anuradha Agarwal, director of software engineering. Whether it's writing tests, fixing bugs, tackling tech debt, or accelerating migrations, they explain how Dropbox engineers are using agentic AI—including in-house tools like Nova—to build the future of Dropbox, and create more space to do impactful work. ~ ~ ~  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!

Dr. Fei-Fei Li, PhD, is a professor of computer science at Stanford University and a pioneer and expert in artificial intelligence (AI). We discuss how AI can be used safely and effectively to extend human capabilities – not just to search for information but specifically to increase human intelligence and creativity. We also discuss how humans collaborating with AI and robots stand to positively transform human health and one's experience of life. And we cover what makes AI fundamentally different from human cognition, and why your intuition and unique experiences are not replicable by AI or machines. Both AI enthusiasts and skeptics are sure to benefit from the information and tools Dr. Fei-Fei Li shares in this episode. Read the episode show notes at hubermanlab.com. Thank you to our sponsors AG1: https://drinkag1.com/huberman David: https://davidprotein.com/huberman Lingo: https://hellolingo.com/huberman LMNT: https://drinklmnt.com/huberman Wealthfront*: https://wealthfront.com/huberman Timestamps (00:00:00) Fei-Fei Li (00:03:46) Vision & Intelligence; Human Vision & Contribution to AI (00:12:11) Computer Vision & the AI Revolution (00:18:34) Sponsors: Lingo & Wealthfront (00:21:19) Speech, Sound & AI Development (00:23:36) AI & Contextual Learning, Human Intelligence (00:33:43) Current AI Gaps, Emotion & Creativity (00:45:48) Computers Enhancing Humanity; Tool: Personal Agency & Learning about AI (00:53:04) Sponsors: AG1 & LMNT (00:55:37) Public Discourse about AI (00:57:34) AI to Enhance Scientific Discovery & Healthcare; Human Collaboration (01:07:38) Intuition, Motivation & Human States Beyond AI (01:19:18) Sponsor: David (01:20:37) Social & Ethical Considerations for AI (01:27:38) Kids, Development & AI Tools; Tool: Prompt AI Effectively (01:35:04) Next Frontier for Robotics & AI; Human Agency (01:43:52) Human-Centered AI Future (01:50:10) World Labs, Spatial Intelligence (01:54:12) Concerns about AI & Creativity; Movies, Art, Storytelling (01:59:51) Younger Generation & AI, Teachers (02:05:38) Zero-Cost Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Sponsors, Protocols Book, Social Media, Neural Network Newsletter *This experience may not be representative of other Wealthfront clients, and there is no guarantee of future performance or success. Experiences will vary. Andrew Huberman receives cash compensation from Wealthfront Brokerage for paid testimonials in his podcast, creating a conflict of interest. The Cash Account, which is not a deposit account, is offered by Wealthfront Brokerage LLC, member FINRA/SIPC. Wealthfront Brokerage is not a bank. The base APY is 3.30% on cash deposits as of January 30, 2026, is representative, subject to change, and requires no minimum. If eligible for the overall boosted rate of 4.05% offered in connection with this promo, your boosted rate is also subject to change if the base rate decreases during the 3 month promo period. Additional terms and conditions apply, which can be found on Wealthfront.com/Huberman. Funds in the Cash Account are swept to program banks, where it earns the variable APY. Same-day withdrawal or instant payment transfers may be limited by destination institutions, daily transaction caps, and by participating entities such as Wells Fargo, the RTP® Network, and FedNow® Service. New Cash Account deposits are subject to a 2-4 day holding period before becoming available for transfer. Investment advisory services are provided by Wealthfront Advisers LLC, an SEC-registered investment adviser. Securities investments: not bank deposits, bank-guaranteed or FDIC-insured, and may lose value. Disclaimer & Disclosures Learn more about your ad choices. Visit megaphone.fm/adchoices

Host Scott Rettberg speaks with Aarhus University Associate Professor Christian Ulrik Andersen about autonomous AI imaging and his co-authored book, Objects of Interest and Necessity. They examine how fringe user communities, micro-models, and platforms like Civitai, AI Horde, and Hugging Face challenge big-tech dominance in generative AI. The conversation also delves into "minor tech" strategies, alternative infrastructures, and the role of digital art in rethinking our political and cultural relationship to technology.References Andersen, C. U., & Cox, G. (2017). The Metainterface: The Art ofPlatforms Cities and Clouds. MIT Press.https://mitpress.mit.edu/9780262038133/the-metainterface/Andersen,C. U., Milev, N., & Velasco, P. (2024). Objects of Interest and Necessity:A Tour Guide to Autonomous AI Imaging. Digital Aesthetics Research Center /Centre for Advanced Visualization and Interaction (CAVI).https://darc.au.dk/publications/darc-booksDeleuze,G., & Guattari, F. (1986). Kafka: Toward a Minor Literature (D. Polan, Trans.).University of Minnesota Press.https://www.upress.umn.edu/9780816615155/kafka/LAION(Large-scale AI Open Network). (2021). Open-Source Datasets and MachineLearning Infrastructure. LAION.https://laion.ai/Marx,L. U.(2020). The Small File Media Festival. Simon Fraser University Schoolfor the Contemporary Arts.https://smallfile.ca/Pamuk,O. (2012). TheMuseum of Innocence. Vintage Books.https://www.orhanpamuk.net/book.aspx?id=96&lng=engRombach, R., Blattmann, A., Lorenz, D., Esser, P.,& Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE/CVFConference on Computer Vision and Pattern Recognition (CVPR), 10684–10695.https://arxiv.org/abs/2112.10752

AI has moved far beyond chatbots, but what exactly are AI models, agents, agent harnesses, and multi-agent systems, and why do they matter?In this episode, Daniel and Chris break down the terminology behind today's AI landscape, explain the differences between AI features and autonomous agents, and explore why organizations are shifting toward fleets of AI agents powered by multiple models. They also discuss open vs. closed models, enterprise AI architectures, vendor lock-in, and practical ways to begin adopting agentic AI in your own organization.If you feel left behind by this ongoing agentic AI revolution that has rapidly accelerated to warp speed, then this episode is the catch-up primer you've been waiting for!Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XSponsors:Framer: The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalaiMidwest 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/practicalaiResources and Events:Prior Webinars from our partner Prediction GuardMidwest AI Summit 2026

What happens when AI agents driven by a top frontier model escape their secure sandbox? Join Daniel and Chris as they unpack the AI wonk's equivalent of a murder mystery! OpenAI agents went rogue and successfully attacked Hugging Face private infrastructure. Our Dynamic Duo uncover how OpenAI's agents exploited vulnerabilities, moved through networks, and launched a large-scale autonomous attack. They explore what this reveals about agentic AI, cybersecurity, sandboxing, and why organizations need AI systems capable of governing other AI systems. Along the way, Chris and Dan examine the surprising role of open vs. closed models and their link to geopolitics, sovereign AI, and what this incident means for the future of enterprise AI security. Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Hugging Face Security Incident disclosureFull Field Report on the Hugging Face AI Agent IntrusionExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?Keeping your data safe when an AI agent clicks a linkOpen AI GPT-5.6 System CardSponsors:Prediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalaiResources and Events:Prior Webinars from our partner Prediction GuardMidwest AI Summit 2026

What becomes possible when enterprise computer vision no longer depends on expensive GPU infrastructure? In this episode of Tech Talks Daily, I speak with Glenn Jocher, founder and CEO of Ultralytics, about YOLO26, CPU inference, edge AI, open vocabulary vision, deployment economics, and the practical work required to move computer vision from a promising pilot into production. Glenn's route into AI began inside the U.S. intelligence community. He worked with the National Geospatial Intelligence Agency and Defense Intelligence Agency on particle physics applications, attempting to detect and track antineutrinos. Antineutrinos are extraordinarily difficult to detect because they pass through almost everything. Glenn describes them as the perfect spy. While searching for better detection methods, he discovered that computer vision researchers were solving similar problems with images. His original attempt to transfer those techniques into particle physics did not succeed. However, the work introduced him to a field where the technology could create a visible effect on everyday life. That led him toward open source development and eventually the YOLO models for object detection, classification, segmentation, and tracking. Glenn believes computer vision research has historically placed too much attention on small gains in accuracy while overlooking deployment economics. A model can perform impressively inside a laboratory and still remain unsuitable for a factory, warehouse, store, vehicle, drone, or medical environment. Price, latency, power consumption, data privacy, and deployment speed can determine whether the technology is commercially useful. This led Glenn and Ultralytics toward smaller models capable of running close to where images and video are generated. YOLO26 continues that approach with architectural changes designed specifically for CPU inference. Glenn says the model can process camera streams in real time at 30 frames per second and run across Intel CPUs, AMD CPUs, and lower power devices such as Raspberry Pi computers. This matters because specialist GPUs can increase the equipment cost and power requirements of a computer vision project. Running inference on existing CPUs or edge hardware can make deployment economically possible across larger numbers of cameras and locations. The scale already involved is difficult to comprehend. Glenn says Ultralytics models now process approximately three billion inference jobs each day, equivalent to around 30,000 every second. These jobs include images, videos, and collections of images being analyzed to detect, segment, or track objects. He attributes the platform's maturity to thousands of mistakes and bugs corrected through a rapid feedback cycle. New models are released, users report problems and request features, and the team incorporates that information into later versions. We also discuss the respective roles of cloud and edge infrastructure. Glenn sees cloud platforms continuing to provide the computing power required for training, while computer vision inference often belongs at the edge. Local processing can reduce latency, control operating costs, and keep sensitive video or medical information closer to where it was created. The smallest YOLO model is approximately three megabytes, according to Glenn. That allows it to reach mobile phones, vehicles, drones, battery powered devices, and other environments where a large language model would be impractical. Open vocabulary vision provides another development. Traditional object detection models are trained to recognize a fixed collection of objects. If a model learns to detect dogs and the user later wants it to detect cats, retraining can cause it to forget earlier knowledge unless both categories appear in the new training data. Glenn explains how promptable models can identify common everyday objects from text or visual instructions without additional training. A user could request a person wearing a blue shirt and white shoes, for example, and the system could search an image for that description. That flexibility could benefit businesses whose requirements change regularly. It reduces the need to create and label a new data set every time the company wants the model to recognize another common object. The range of current applications is already extensive. Glenn describes YOLO being used across robotics, parking, industrial safety, PPE detection, warehouses, aviation, security, traffic management, food quality, and manufacturing. Some of his favorite examples involve environmental problems. One company uses YOLO with underwater vehicles to identify and recover plastic from the ocean. Other applications detect smoke and fire early enough to support forest fire response. For leaders considering computer vision, Glenn recommends beginning with a defined problem and measurable outcome. A manufacturing company may want to reduce defects, but it still needs labeled examples showing the model what acceptable and defective products look like. He advises testing the idea through a limited pilot, measuring the return, and expanding only when the evidence supports further investment. Computer vision has become easier to deploy, but practical problems involving data, cameras, integration, reliability, and operating conditions still separate a demonstration from a production system. Could CPU inference and open vocabulary models make computer vision practical for processes your organization previously considered too expensive? Listen to the episode and share your thoughts with me. Useful Links   Ultralytics website Ultralytics Platform      

AI makes it easier than ever to find and act on information—especially now that teams can connect to and search across all the apps they use for work. So how do you ensure that only the right people and the right tools can access your team's most sensitive content? In this episode, we talk with Jess Jimenez, the head of security at Dropbox, about what security looks like in the age of AI at Dropbox-scale—from building AI products securely to building trust with the people who use them. Jess talks about the importance of access control lists, defending against the latest AI threats, and how Dropbox Protect helps teams securely share content with both humans and AI so they can collaborate more safely. ~ ~ ~  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!

Send us Fan MailThe rules of metal fabrication are changing faster than ever. Between AI estimators, computer vision for welders, and cobots running production lines, traditional shops are facing a massive crossroads: adapt now or get left behind. In this episode, Adam Heffner (Hero Machine Co. & Maker Table) breaks down what's coming next for fabrication, why plasma cutting is losing the battle to fiber lasers, and how smart shop owners are using AI to scale their output without adding massive overhead. ⏱️ Video Chapters00:00 - The 3-Second Reality Check on Automation 01:30 - Trade Show World Tour: FabTech & Ag Fair Plans 06:40 - Why Hero Machine Co. Paused in 2020 (And the Comeback) 15:20 - Laser vs. Plasma: The Brutal Financial Math 20:20 - Importing Machinery: Scams, Tariffs, & Support Traps 34:30 - What Overseas AI & Robotics Labs are Building Right Now 42:00 - Computer Vision, Weld Puddle Tracking, & Cobots 56:00 - How Adam Trained ChatGPT to Do the Work of 3 Estimators 01:01:30 - SEO Secrets: Why Gemini Beats Chat GPT for Website Traffic 01:05:00 - Building Custom CAD Plugins & In-House AI Data Centers 01:16:00 - What the Modern 3-Man Fabrication Shop Will Look Like

Cheap drones and bounded autonomy are rewriting the economics of warfare and forcing the US military and DefenseTech startups to rethink how we buy, build, and field capability at speed.  Our host, Rajiv Parikh, is joined by Jason Hansberger and John Alora, the director and co-director at the Department of the Air Force Stanford AI Studio, who talk through what they're learning by bridging operators, academics, and founders, and what it will take to compete in GPS-denied, production-constrained reality.• character of war shifting toward scale • why legacy procurement and exquisite logistics break under cheap mass• how the DAF-Standford AI Studio picks problems by first principles and physics• why early funding is not enough without a program of record pathway• government equity stakes, upside and crowding-out risk• edge computing and on-device inference as the default for autonomy• what “autonomy” really means, bounded mission boxes and confidence gates• why data pipelines and fleet instrumentation block modern machine learning• manufacturing capacity limits, plus modular design to solve last-mile logistics• visual navigation for GPS-denied operations using computer visionA $500 drone can change a battlefield faster than a $100M platform can be replaced, and that single fact is forcing a hard rethink across defense technology, autonomy, and procurement. We sit down with Jason Hansberger and Dr. John Alora, the leaders behind the Department of the Air Force Stanford AI Studio, to talk about what the Ukraine war reveals about the “cheap kill” paradigm and why the character of warfare is shifting toward scale, attritable systems, and rapid iteration.We dig into what that shift means for defense startups and investors: why the government may fund more experiments than ever, yet still struggle to provide a clear demand signal, and why SBIR-style wins don't automatically translate into durable outcomes without a program of record. Jason and John walk us through how they start from first principles, define the real operational problem, and then connect academia, operators, and founders to de-risk solutions using defense-unique test infrastructure.From edge AI to manufacturing, the constraints get real fast. We talk about compute at the edge where connectivity is a luxury, the practical meaning of autonomy (bounded mission boxes, confidence thresholds, and comms-degraded behaviors), and the data problems baked into legacy sensors and platforms. We also explore production bottlenecks and why modularity may beat “factory in the field,” plus a capability that feels like science fiction but is quickly becoming standard: GPS-denied navigation using computer vision and visual-based navigation.Jason Hansberger: https://www.linkedin.com/in/jason-hansberger-b1b15374/Jason Hansberger is a leader in technology and strategy currently serving as the Director of the DAF-Stanford AI Studio & Director of Technology Capability Development for the United States Air Force. He has also served as a Commander of the 1st Airlift Squadron. Jason holds a Master of Arts in Regional Studies with a Southeast Asia concentration from the Naval Postgraduate School and got his Bachelor's degree in Economics from the Air Force Academy. He is focused on applying state-of-the-art solutions in AI and autonomy to solve Air Force problems. John Alora:  https://www.linkedin.com/in/johnalora/John Alora is an Air Force pilot and robotics expert currently serving as the Deputy Director for the DAF-Stanford AI Studio and Assistant Dean of Research for the United States Air Force Test Pilot School. He has also served as a B-52 Aircraft Commander and Weapons and Tactics Officer. John holds a Ph.D. in Aeronautics and Astronautics from Stanford University, a Master's degree in Aerospace Engineering from the Massachusetts Institute of Technology (MIT), and got his Bachelor's degree in Electrical Engineering from the United States Air Force Academy. He is focused on developing physics-based machine learning techniques and leveraging AI to maximize human cognition in complex aerospace and defense environments Website: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com

The agentic transformation isn't coming. It has already begun.Companies are deploying thousands — and sometimes tens of thousands — of AI agents. Enterprise software giants are watching their old economic moats erode. Capital is moving, productivity is being redefined, and human labor is being repriced in real time.In this Fully Connected episode, Daniel and Chris explore the new economics of a post-agentic world: the global order that emerges after agents have been woven into every conceivable aspect of business and life. What happens when digital labor becomes abundant, agents manage other agents, and entire organizations operate at a scale no human workforce could match?This isn't another conversation about whether AI will take your job. It's about what happens when the assumptions underneath jobs, companies, software, and productivity stop being true.The post-agentic world is already taking shape.The question is whether you're preparing for it — or becoming part of what it replaces.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Is IBM a Canary in the Tech Coal Mine?Verbalizable Representations Form a Global Workspace in Language ModelsUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

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

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 Everything is Logistics, Blythe talks with Sarit Tamir, CEO and co-founder of Seeteria, about how computer vision is helping warehouse teams find lost time inside their operations.SeeTeria is a software-only company that connects to existing CCTV cameras inside warehouses, distribution centers, fulfillment centers, and 3PL facilities. The platform watches floor activity, detects operational friction, and sends real-time alerts to teams before small problems turn into bigger delays.They cover:Why warehouse teams still miss what happens between system scansHow existing CCTV cameras can become an operational visibility toolWhat dock queues, idle doors, dwell time, staging congestion, and near-misses revealWhy the “messy middle” of warehouse operations adds up quicklyHow real-time alerts help floor teams act fasterHow managers can use shift summaries to find recurring patternsWhy the goal is to support warehouse workers, not replace themThis conversation is part of the CargoRex AI Use Cases in Logistics guide, featuring real examples of how logistics companies are using AI across freight, warehousing, procurement, visibility, and operations.Read the full guide here:https://cargorex.io/research/ai-use-cases-in-logistics/LINKS:SeeTeria:https://seeteria.comCargoRex AI Use Cases in Logistics Guide:https://cargorex.io/research/ai-use-cases-in-logistics/ -----------------------------------------THANK YOU TO OUR SPONSORS!SPI Logistics has been a Day 1 supporter of this podcast which is why we're proud to promote them in every episode. During that time, we've gotten to know the team and their agents to confidently say they are the best home for freight agents in North America for 40 years and counting. Listen to past episodes to hear why.CargoRex is the search engine for the logistics industry—connecting LSPs with the right tools, services, events, and creators to explore, discover, and evolve.Digital Dispatch maximizes and manages your #1 sales tool with a website that establishes trust and builds rock-solid relationships with your leads and customers. 

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

  Join Sundeep Ahluwalia, Chief Product Officer at TDK SensEI, as he explores the future of industrial manufacturing and computer vision. In this webinar, Sundeep introduces edgeRX Vision—a powerful combination of computer vision and edge AI designed to deliver fast, accurate quality control directly on the production line. Currently deployed in TDK manufacturing facilities worldwide, edgeRX Vision can inspect up to 2,000 parts per minute, detecting defects in components as small as 1 mm × 0.5 mm. Discover how edgeRX Vision enables manufacturers to achieve: Enhanced AOI capabilities Higher production throughput Real-time visual feedback Precision driven by AI Reduced human error PRESENTER: Sundeep Ahluwalia Chief Product Officer Presented by TDK SensEI Visit https://advancedmanufacturing.org/webinars for more webinars and an interactive experience with visuals.

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

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.

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!

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

Welcome to this episode of The New Warehouse Podcast, where Kevin speaks with Maanav Iyengar, Co-Founder and CEO of Retina Robotics. Founded by robotics engineers with firsthand exposure to warehouse operations, Retina Robotics makes automation more accessible through computer vision. In this conversation, Iyengar discusses the challenges slowing automation adoption, how Retina Robotics is helping warehouses improve inventory accuracy, and why the company believes computer vision can transform inventory management.Learn more about our sponsor Dexory's Storage Health here. Follow us on LinkedIn and YouTube.Support the show

Join Cihan Fuat Atkin, CEO and Founder of XCINEX, for a forward-looking examination of the structural forces rewriting the business of global entertainment. Boasting over 15 years of operational excellence, Cihan has generated $330M+ in partner revenue and scaled cross-border media-tech companies from raw concept to high-value exit. As Hollywood grapples with deep subscription stagnation, soaring production budgets, and the radical disruption of generative AI, Cihan argues that the real crisis isn't a lack of content—it's a broken distribution model. In this episode—recorded ahead of the highly anticipated July 4th rollout of VENUE+—we unpack how computer vision and automated auditing are replacing legacy tracking to give studios, live event promoters, and individual creators absolute transparency over their at-home audiences.

How do you build AI that actually understands you and the work you do? It all starts with having the right context.  We talk with Dropbox staff product manager Noorain Noorani and principal engineer Sean-Michael Lewis about the art of context engineering and how Dropbox connects to all the tools your team needs for work—so you get AI that works wherever you do.  ~ ~ ~  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!

Yascha Mounk and David Bau delve into the emerging science of AI interpretability and what we can learn from billions of neural signals. David Bau is Assistant Professor at Northeastern University and Director of the National Deep Inference Fabric, researching the emergent internal mechanisms of deep generative networks in both Natural Language Processing and Computer Vision. In this week's conversation, Yascha Mounk and David Bau discuss how AI models actually produce their results and reflect about problems, whether the “thinking” process that models show users reveals their authentic thought processes, and how researchers can decode the internal representations of neural networks to understand what information they contain and use. If you have not yet signed up for our podcast, please do so now by following ⁠this link on your phone⁠. Email: leonora.barclay@persuasion.community Podcast production by Jack Shields and Leonora Barclay. Connect with us! ⁠Spotify⁠ | ⁠Apple⁠ X: ⁠@Yascha_Mounk⁠ & ⁠@JoinPersuasion⁠ YouTube: ⁠Yascha Mounk⁠, ⁠Persuasion⁠ LinkedIn: ⁠Persuasion Community Learn more about your ad choices. Visit megaphone.fm/adchoices

As AI agents become more capable and autonomous, they also introduce new security challenges. In this 'Fully Connected' episode, Dan and Chris unpack Anthropic's Zero Trust for AI Agents security framework and what it means for organizations deploying agentic systems. They examine the key security risks facing agentic systems and discuss how organizations can apply Zero Trust principles to deploy AI agents safely. Along the way, they break down practical security controls and discuss how traditional cybersecurity principles must evolve for the age of AI agents.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks: Zero Trust for AI AgentsOWASP GenAI Project Sponsors:Prediction 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

The pod may have been a little off-schedule over the last month but that's for good reason because I'm trying out a new editorial approach to the show and its taken  more legwork to get to a point where I feel comfortable hitting publish. In May, I scheduled interviews with 8 different companies building AI solutions in logistics. The plan is upload each of those ~30 minute conversations that focus specifically on their product, who it's for and what to expect. Basically an approach of “here's everything I would ask if I was trying to understand and eventually/maybe purchase this software.” We also had some written submissions that I included in a written guide along with companies making moves but I personally didn't interview them for this topic .Because I want CargoRex to be a brand that is successful independent of me being the “voice” of it, I still, and likely will always  want to give my opinion and that home is naturally here. However I think the process needs to be refined where interviews go on one channel and editorial evolves in more narrative/topic based shows that include those interviews where it makes the most sense. I'll still share those interviews here but I think it's important that I drop an episode like this ahead of that to set the tone of how I'm thinking about X topic in logistics. During this new interview process and after learning the real work going into these different AI solutions, I put together a working theory on how the humanoids are already here. How?My theory is most of the public is waiting for the big ~societal crash into AI agents taking over everything~ that's turned into fear mongering. Companies simply over-hired, were run inefficiently, and the free money dried up. Businesses had to grow up, cut costs, and get lean. They blame “AI” but in reality, these companies just had bad processes and failed attempts to adopt AI solutions gave them a chance to blame a boogeyman.When you move past the noise and dig a little deeper you can see logistics is doing what it always does: improving that source to porch journey second by second. These solutions aren't promising the world on a silver platter, but they are committed to creating solutions for specific use cases that requires a human's expertise that is powered by information + insight to be creative with their problem solving. You can listen to the full interviews over on the CargoRex YouTube channel (links below) along with our in-depth Cargorex.io guide with all the companies interviewed, quoted, and featured.I'm really proud to hit publish on this new editorial direction and I hope you'll find value in it. In this episode:How autonomous trucks are filling routes drivers don't want, not replacing driversThe 3-hour report that now takes 15 seconds, and what analysts do with that timeWhy AI is the new boogeyman when bad data and worse processes are the real problemThe trust layer: audit logs, human-in-the-loop phases with defined endpoints, and why demos aren't deploymentsWhat nobody talks about: AI burnout, and what happens when every minute of your day becomes the hard stuffBuild vs. buy: $1.2 million in savings came from solving the right problems, not building everything from scratchToken management as an operational cost, and the Uber cautionary taleWatch this episode on YouTubeFind the full AI Use Cases in Logistics Guide over on the CargoRex website——————————————————Full Interviews available on the new CargoRex YouTube Channel: 1. Sarit Tamir — Founder & CEO, Seeteria "What Happens on Your Floor Between the Scans" Watch on YouTube: https://youtu.be/IiHVk8eOw0wLinkedIn: https://www.linkedin.com/in/sarittamir/ Site: https://seeteria.com2. Michelle McBride — Head of Product, Envoy AI "The Orchestration Layer Brokerages Have Been Missing" Watch: https://youtu.be/YGe5EZLoDYELinkedIn: https://www.linkedin.com/in/michelleposadas/ Site: https://tryenvoy.ai3. Tapan Chaudhari — Founder & CEO, Hey Bubba "Voice AI That Books Freight for Truckers 24/7" Watch: https://youtu.be/XeBVteEJDlwLinkedIn: https://www.linkedin.com/in/ctapan/ Site: https://bubba.ai4. Shawn McCarrick — CEO, Sifted "Why Big Savings Mean You Already Spent the Money" Watch: https://youtu.be/ZH6-40BxstgLinkedIn: https://www.linkedin.com/in/shawn-mccarrick-04719765/ Site: https://sifted.com5. Jett Chitanand — President, EPG Americas "AI That Cuts 13 Minutes Off Every Warehouse Delivery" Watch: https://youtu.be/_Q8aM16gn24LinkedIn: https://www.linkedin.com/in/jett-chitanand/ Site: https://epg.com6. Tom Curee — President, Qued "The One Thing You Actually Control on a Shipment" Watch: https://youtu.be/ymtR9BRvxekLinkedIn: https://www.linkedin.com/in/tomcuree/ Site: https://qued.com7. Tete Xiao — VP of Engineering and AI, Bot Auto "Driverless Trucks Are Already Hauling Freight in Texas" Watch: https://youtu.be/yWXQq_Fa9c0LinkedIn: https://www.linkedin.com/in/tete-xiao-ba2103120/ Site: https://bot.auto8. Nick Boston — VP of Sales, GoodShip "The Report That Took 3 Hours Now Takes 15 Seconds" Watch on YouTube: https://youtu.be/grzIjsDC1rsLinkedIn: https://www.linkedin.com/in/nickboston/ Site: https://goodship.io -----------------------------------------THANK YOU TO OUR SPONSORS!SPI Logistics has been a Day 1 supporter of this podcast which is why we're proud to promote them in every episode. During that time, we've gotten to know the team and their agents to confidently say they are the best home for freight agents in North America for 40 years and counting. Listen to past episodes to hear why.CargoRex is the search engine for the logistics industry—connecting LSPs with the right tools, services, events, and creators to explore, discover, and evolve.Digital Dispatch maximizes and manages your #1 sales tool with a website that establishes trust and builds rock-solid relationships with your leads and customers. 

AI models can win math olympiads… but still struggle to read an analog clock. In this fully connected episode, Dan and Chris break down the latest Stanford AI Index Report and explore what it reveals about the current state of AI. They discuss AI adoption and safety, disappearing junior tech jobs, robotics, AI's “jagged frontier” of intelligence, and the growing race between the U.S. and China. Along the way, they debate whether AI should optimize everything, or if some things are better left human. Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:The 2026 AI Index ReportSponsors:Prediction 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

Modern work can be frustrating and chaotic—if you don't have the right tools. From context engineering to multimodal search, go behind the scenes and hear how Dropbox engineers are building AI that actually understands you, so you can focus on the work that matters most. If you're new to Working Smarter, we've travelled from the F1 track to the bottom of a lake, and heard real stories from chefs, doctors, lawyers, and founders about how AI is helping them do more of what they love about their jobs. But in our third season, we're talking to the people behind the tools—the engineers and product leaders building helpful, time-saving AI features into the Dropbox experience you already know and trust. You'll hear all about their work on agents, inference, security, and, of course, how the people building AI use AI themselves. ~ ~ ~  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!

What happens when AI agents start acting less like chatbots and more like coworkers? In this episode, Dan and Chris sit down with Craig McLuckie, CEO of Stacklok to explore MCP, Kubernetes, ToolHive, enterprise AI, and the emerging infrastructure powering AI-native applications. From identity management to agent orchestration and system architecture, this conversation dives into how organizations may soon manage entire fleets of AI agents working behind the scenes.Featuring:Craig McLuckie – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:StacklokToolhiveSponsors:Prediction 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

Stewart Alsop interviews Nizar, CEO of Pixel Robotics, on the Crazy Wisdom Podcast to explore the intersection of AI, robotics, and perception. The conversation covers a wide range of technical topics including how transformers enable multimodal representation across text, images, and voice, the role of world models in predicting physical interactions, the advantages of diffusion models over traditional LLMs for certain applications, and the challenges of achieving real-time processing for robotics applications. Nizar explains Pixel Robotics' work on creating accurate 3D meshes from smartphone cameras for companies like L'Oréal, moving away from specialized sensors to make the technology more accessible through sophisticated algorithms, and discusses the future of robotics as closing the perception-action loop to enable robots to perform real tasks beyond simple demonstrations. To find out more visit Pixel Robotics' website.Timestamps00:00 Stewart welcomes Nizar, CEO of Pixel Robotics, discussing what a pixel is as the smallest visual unit on screens composed of red green and blue colors05:00 Discussion of perception systems and how logarithmic laws help compress signals in both human and artificial systems, exploring normalization layers and sigmoid functions in deep learning10:00 Exploring how transformers unified different data modalities including text voice and images, creating common representations through methods like contrastive learning15:00 Nizar explains transformers as brute force learning systems with room for improvement through focused attention mechanisms and knowledge graphs rather than processing everything20:00 Conversation about loss functions local minima versus global minima and how mixture of experts uses specialized small models instead of one massive generalist network25:00 Discussion of deterministic versus probabilistic systems and how explicitly defined task graphs often outperform orchestrator-based approaches in AI systems30:00 Exploring world models as predictive physics-based systems that learn environmental flows and transformations, complementing rather than replacing language models35:00 Nizar discusses real-time processing challenges for robotics requiring millisecond responses with small memory footprints using vision transformers for faster experimentation40:00 Pixel's work creating three d meshes from smartphone cameras for companies like L'Oreal, moving away from specialized sensors toward accessible software-based solutions45:00 Explanation of different three d representations including voxels point clouds and meshes, with meshes being optimal for manipulation and rendering in applications50:00 Future direction involves closing perception-action loops in robotics, moving beyond dancing toy robots toward practical multimodal systems that perform real tasks55:00 Pixel's goal is democratizing high-quality three d scanning through smartphones, making mesh creation accessible to unlock applications in gaming cinema and virtual showroomsKey Insights1. Pixel Robotics derives its name from combining perception and action in robotics, where the pixel represents the digital perception component and robotics represents the physical action component. The pixel serves as a metaphor for how robots must quantize and digitize continuous analog information from the real world into discrete units that computer systems can process, similar to how pixels are the fundamental building blocks of images on a screen. This quantization process is essential because numerical systems cannot work with truly continuous data and must convert reality into tractable digital representations that algorithms can manipulate.2. The transformer architecture has created a fundamental unification in how different types of data can be represented and processed across multiple modalities. Before transformers, researchers working on natural language processing, computer vision, and audio analysis used completely different approaches and methodologies. The breakthrough of transformers was establishing a common representational framework that could handle text, images, voice, and other data types using similar underlying mechanisms. This unification is what enabled the development of truly multimodal AI systems and represents one of the most significant advances beyond just the language modeling capabilities that initially gained public attention.3. Current transformer-based systems represent a brute force approach to learning that will likely be superseded or enhanced by more efficient algorithms. Despite claims that we have exhausted internet text data for training, significant improvements continue to emerge every few months through algorithmic innovations rather than simply adding more data. Future developments will likely involve more specialized attention mechanisms that focus on relevant information rather than correlating everything with everything, mixture of experts architectures with small specialized models, and approaches inspired by biological systems such as logarithmic compression laws and event-based processing that humans use naturally.4. Diffusion-based language models represent a promising alternative to standard next-token prediction that could produce more accurate outputs through an iterative refinement process. Unlike traditional language models that predict one token at a time and cannot revise earlier outputs, diffusion models treat text generation like image denoising, starting with a noisy representation and progressively refining the entire output across multiple steps. This holistic approach allows the model to reconsider and improve all parts of the response simultaneously, potentially leading to higher quality results, though it may be slower than current autoregressive methods. This represents an important direction for overcoming fundamental limitations in how language models currently generate text.5. For robotics applications, real-time performance and small model size are critical constraints that differ significantly from the requirements of large language models deployed in data centers. Vision transformers are being used as a testbed for developing efficient real-time algorithms because they require far fewer computational resources to train and test compared to large language models, making them more practical for rapid experimentation. The goal is to achieve millisecond-level response times with minimal memory footprint so that robots can react quickly to dynamic environments and run on affordable hardware that can be embedded in actual robotic systems rather than requiring expensive server infrastructure.6. Practical robotics implementation requires moving beyond specialized sensors to software solutions that work with ubiquitous devices like smartphones for tasks such as three-dimensional reconstruction. Pixel Robotics evolved from building specialized scanning hardware to focusing on algorithms that can generate high-quality mesh representations of environments using only smartphone cameras, making the technology far more accessible and practical for real-world deployment. This approach enables applications ranging from industrial robotic arm control to virtual showrooms, and more importantly, it allows anyone to capture three-dimensional data without expensive equipment, which can also help generate larger training datasets for future AI development.7. The next frontier in AI and robotics is closing the perception-action loop to enable robots to perform real practical tasks rather than remaining as demonstration systems or toys. While significant progress has been made in cognitive capabilities through language models and in robotic mobility through mechanical engineering advances, the critical challenge is integrating perception with action through systems like Vision-Language-Action models. The fundamental starting point for learning this integration is simple perception-action exercises, such as programming a camera mounted on servo motors to track and center a colored object, which demonstrates the basic principle of using sensory input to drive physical response that underlies all more sophisticated robotic behaviors.

Open Source AI is entering a new era, one shaped by self-improving AI Agents, recursive learning systems, and rapidly evolving AI Tools that blur the line between software and autonomous collaborators. In this episode, Daniel and Chris sit down with Nous Research co-founder and CTO Jeffrey Quesnelle to explore Hermes Agent. Along the way, they discuss models vs. harnesses, the changing role of developers, and one of the biggest questions facing the AI Future: what remains uniquely human as AI capabilities continue to accelerate?Featuring:Jeffrey Quesnelle – Website, LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Nous ResearchHermes AgentSponsors: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

This episode was brought to you by Mouser, our go-to source for electronics parts for any hobby or prototype. Click HERE to learn the machine safety best practices that keep modern industrial automation running safely, reliably, and efficiently. Become a founding reader of our newsletter: http://read.thenextbyte.com/ As always, you can find these and other interesting & impactful engineering articles on Wevolver.com.

Mit Nils Graf-Gutsche (Sightwise) Staffel #13 Folge #4 | #Marketing_021 Der Podcast über Marketing, Vertrieb, Entrepreneurship und Startups *** www.sightwise.ai/ www.linkedin.com/in/nils-gutsche/ *** Im Podcast „Marketing From Zero To One“ berichtet Nils Graf-Gutsche, Co-Founder & COO von Sightwise, über die Gründung des Hannoveraner KI-Startups zur automatisierten Qualitätskontrolle in der industriellen Produktion. Im Fokus steht der Einsatz von Computer Vision und insbesondere synthetischen Daten, um Defekte wie Kratzer oder Risse zuverlässig zu erkennen, auch wenn reale Trainingsdaten fehlen. Er gibt Einblicke in die Ausgründung aus der Universität Hannover, die frühe Validierung über Industrieprojekte und Messen sowie den Aufbau erster Kundenbeziehungen. Darüber hinaus geht es erneut um den praktischen Einsatz und Use-Cases von KI in Unternehmen als auch bei Sightwise, Vertriebsstrategien im B2B-Umfeld und die Bedeutung von Vertrauen ggü. B2B-Kunden in der Industrie. *** 1:46 – Hintergrund & Weg in die Gründung (BMW, Computer Vision) 3:17 – Was Sightwise macht (KI-basierte Qualitätskontrolle) 4:18 – Anwendungsfälle in der Produktion (Defekterkennung) 6:19 – Entscheidung für die Gründung 7:23 – Ausgründung aus der Universität 8:37 – Erste Validierung & Marktfeedback 9:47 – Erste Kunden & Industriepartnerschaften 11:02 – Zwei Kundentypen (Plattform vs. Turnkey) 12:14 – Nutzen & ROI der Lösung 13:41 – Synthetische Daten einfach erklärt 15:32 – Datengenerierung statt realer Trainingsdaten 17:12 – Kundenbasis & Wachstum 17:30 – Vertrieb über Messen & Events 20:19 – Leads durch Fachvorträge 21:01 – Tipps für Messeauftritte 23:34 – Hannover Messe 25:02 – Tipps für Fachvorträge 26:56 – Inbound Leads & Sichtbarkeit 27:48 – Typischer Sales-Prozess 29:28 – Robotics-Trends & Zukunftspotenzial 30:53 – KI-Modelle erklärt (Anomalie vs. Objekterkennung) 34:56 – Training & Aufbau der Modelle 37:19 – Individualisierung je Kunde 39:49 – Tipps für Gründer im KI-Bereich 40:25 – Einfluss von KI auf Kunden & Wettbewerb 41:35 – Datenvorteile & Skalierung 42:20 – IT-Infrastruktur & On-Premise 43:51 – Founder-Market-Fit 44:38 – Einsatz von KI im eigenen Unternehmen 46:54 – Einfluss auf Geschäftsmodelle 48:57 – Hiring & Teamaufbau 52:05 – Zukunftstrends (Hardware, Daten, 2D/3D) 53:12 – Startup-Szene in Hannover

In this episode, Lex chats with Evan Malanga — Chief Revenue Officer of Yuma, a subsidiary of Digital Currency Group focused on growing the Bittensor ecosystem. They discuss how Bittensor's $6 billion protocol incentivises AI builders worldwide through token emissions across 128 competing subnets, and why the network has produced real commercial outputs — including a 72 billion parameter model trained on-chain and a coding agent rivalling Claude at a fraction of the cost. Evan explains Yuma's role as the institutional gateway to Bittensor through its validator, accelerator, and asset management products, and they explore why the concentration of AI in OpenAI and Anthropic is a systemic risk, and whether Bittensor's future extends beyond AI into a broader coordination engine for decentralised work. NOTABLE DISCUSSION POINTS: Bittensor has crossed from experimentation into shipping benchmark-competitive work at a fraction of centralized cost. Three recent proof points: Templar (subnet 3) completed the largest decentralized pre-training run of a 72B parameter model using only the network's token incentives. Ridges, an AI agent platform, is hitting 88–90% on software engineering benchmarks, on par with Claude-class agents at ~5x cheaper, built by a 3-to-5-person team under $10M of token emissions. Score (subnet 44) is doing computer vision 200x faster than centralized counterparts. Small distributed teams are producing outputs competitive with frontier labs without raising venture capital or hiring staff. Dynamic TAO restructured emissions from validator-curated to market-curated, making each subnet its own tradeable asset. Previously, dominant validators assigned weights that determined how the 7,200 daily TAO emission flowed across subnets. Under Dynamic TAO, each of the 128 subnets has its own token denominated in TAO, and any holder can buy or sell into specific subnets, pricing them like a market rather than a committee vote. Subnet owners, miners, and validators earn fees in the respective subnet token. Distribution has settled into a power law: the top ten subnets hold ~80% of market cap. This is the move that turned Bittensor from “decentralized AI protocol” into a financial hyperstructure with hundreds of tokenized work markets layered on top. The economics for subnet owners are genuinely unusual — hundreds of millions in annual incentives, fully subsidized labor, no fundraising. A subnet owner gets access to up to ~256 miners globally competing to satisfy their problem statement, with miner compensation paid by protocol emissions rather than the subnet owner. At current TAO prices, annual incentives across the network run into hundreds of millions; at higher prices, this approaches $1B/year up for grabs. No hiring, no benefits, no recruiting, the network runs as a continuous adversarial competition where validators rank miner outputs. This is the mechanical answer to “why would an AI researcher choose Bittensor over Silicon Valley”, and explains why researchers at Meta and Google reportedly mine Bittensor on nights and weekends, with top miners on subnets like Ridges earning ~$30,000/day. TOPICS Yuma, Bittensor, Digital Currency Group, DCG, OpenAI, Anthropic, Foundry, Templar, Ridges, Bitcoin, Meta, Google, BlackRock, JPMorgan, Decentralized AI, Crypto, Blockchain, AI, Tokenomics, Decentralized Science, DeSci, AI Agents, Computer Vision, Proof of Work, Tokenization, Real World Assets, RWA, Machine Economy   ABOUT THE FINTECH BLUEPRINT

In this episode we dive into performance reviews.The ChallengeYou're a senior scheduler running three data center schedules for one client. Your manager calls a quarterly performance review. You're great in P6, you hit your reports, and you assume the conversation will write itself. It won't. In this episode, I play the manager and Greg Lawton plays the senior scheduler to show what most schedulers get wrong in that room and how to turn the review into your next promotion.Continue LearningCheck out our book The Critical Path Career: How to Advance in Construction Planning and SchedulingSubscribe to the Beyond Deadlines Email NewsletterSubscribe to the ⁠⁠⁠⁠Beyond Deadlines⁠⁠⁠⁠ Linkedin Newsletter⁠⁠Check Out Our YouTube Channel⁠⁠.ConnectFollow ⁠⁠⁠Micah⁠⁠⁠, ⁠⁠⁠Greg⁠⁠⁠, and ⁠⁠Beyond Deadlines⁠⁠ on LinkedIn.Beyond DeadlineIt's time to raise your career to new heights with Beyond Deadlines, the ultimate destination for construction planners and schedulers. Our podcast is designed to be your go-to guide whether you're starting out in this dynamic field, transitioning from another sector, or you're a seasoned professional. Through our cutting-edge content, practical advice, and innovative tools, we help you succeed in today's fast-evolving construction planning and scheduling landscape without relying on expensive certifications and traditional educational paths. Join us on Beyond Deadlines, where we empower you to shape the future of construction planning and scheduling, making it more efficient, effective, and accessible than ever before.About MicahMicah, the CEO of Movar US is an Intel and Google alumnus, champions next-gen planning and scheduling at both tech giants. Co-founder of Google's Computer Vision in Construction Team, he's saved projects millions via tech advancements. He writes two construction planning and scheduling newsletters and mentors the next generation of construction planners. He holds a Master of Science in Project Management, Saint Mary's University of Minnesota.About GregGreg, an Astrophysicist turned project guru, managed £100M+ defense programs at BAE Systems (UK) and advised on international strategy. Now CEO at ⁠⁠Nodes and Links⁠⁠, he's revolutionizing projects with pioneering AI Project Controls in Construction. Experience groundbreaking strategies with Greg's expertise.Topics We Coverchange management, communication, construction planning, construction, construction scheduling, creating teams, critical path method, cpm, culture, KPI, microsoft project, milestone tracking, oracle, p6, project planning, planning, planning engineer, pmp, portfolio management, predictability, presenting, primavera p6, project acceleration, project budgeting, project controls, project management, project planning, program management, resource allocation, risk management, schedule acceleration, scheduling, scope management, task sequencing, construction, construction reporting, prefabrication, preconstruction, modular construction, modularization, automation, Power BI, dashboard, metrics, process improvement, reporting, schedule consultancy, planning consultancy, material management

In this episode of the Shift AI Podcast, Steve Mantle, Founder and CEO of Innov8.ag, Raj Khosla, Dean of the College of Agricultural, Human, and Natural Resources at Washington State University, and John Cox, soil scientist and fresh produce industry operator, join host Boaz Ashkenazy for a wide-ranging panel conversation on how AI and emerging technology are transforming agriculture from the ground up.Steve, Raj, and John each bring a distinct lens to the conversation — startup founder, academic dean, and hands-on operator — and together they paint a vivid picture of where precision agriculture has been and where it is going. The discussion opens with the human side of farming: the generational knowledge, seasonal intuition, and field-level pattern recognition that has defined agriculture for centuries.The panel also covers infrastructure realities, edge computing, rural connectivity gaps, ERP systems that still require on-premise servers, and the economic pressures pushing farmers to demand AI that delivers margin today, not in five years. The conversation closes with each guest sharing their two-word vision for the future of AI in agriculture: physical AI, bright and better, and hopeful foresight.This episode is essential listening for anyone who wants to understand how AI is moving beyond the office and into the fields, orchards, and packing houses that feed the world. A huge thanks to Washington State Academy of Sciences for including this event in their Deep Dive into AI in Agriculture and Washington State University's AgAID Institute for organizing this event held at Wenatchee Valley College. This all wouldn't be possible without the support from the funding sponsors the Association for the Advancement of Artificial Intelligence (AAAI) and the USDA ARS.Chapters[00:00] Event Introduction and Background with Jordan Jobe of the AgAid Institute[03:50] Boaz Introduces Himself and the Shift AI Podcast[08:04] Podcast Recording Begins: Welcoming the Panel[08:48] Steve Mantle: From Irrigation Hand Lines to Innovate Ag[09:40] Raj: From a Radio Science Program in India to Precision Agriculture Dean[11:24] John Cox: From Furniture Assembly to Apple Orchards and Kyrgyzstan[13:22] The Human Side of Farming: Intuition, Resilience, and Generational Knowledge[15:10] How GPS Unlocked Precision Agriculture and Field-Level Heterogeneity[16:48] Multi-Generational Farm Knowledge as a Living Large Language Model[18:09] Notebook LM Meets the Farm: The Harvest Replay Concept[21:16] Batteryless Biodegradable Sensors and the Future of Field Diagnostics[24:30] Precision Irrigation Prescription Maps and Dynamic Field Management[26:18] Computer Vision in the Apple Packing House[27:58] AI as a Global Expert: Diagnosing Crop Disease in Kyrgyzstan[30:15] Constraints in Ag AI: Data Stacks, Fragmented Systems, and Cultural Resistance[33:50] Build vs. Buy and the Change Agent Problem in Agriculture[35:50] Edge Computing, On-Premise Servers, and Hybrid Infrastructure on the Farm[39:09] Rural Connectivity: Broadband Gaps and the Starlink Reality[41:54] Economics of Ag AI: Labor Costs, Tightening Margins, and ROI[44:28] Moving from Spreadsheets to Agents: Why Trust Is the Real Barrier[45:50] Future Skills: What the Next Generation of Farmers Needs to Know[48:05] FFA Ag Tech Innovation Day and Hands-On Learning for Students[50:07] Two Words for the Future: Physical AI, Bright and Better, Hopeful Foresight[54:15] How to Connect with Steve, Raj, and JohnConnect with the GuestsSteve MantleLinkedIn: https://www.linkedin.com/in/stevemantle/Raj (Dean, WSU College of Agricultural, Human, and Natural Resources)LinkedIn: https://www.linkedin.com/in/raj-khosla-2566a819/John CoxLinkedIn: https://www.linkedin.com/in/jonathan-cox-soildr/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

In this fully connected episode, Dan and Chris break down one of the biggest questions in AI today: do open vs. closed models still matter? From the rise of physical AI and edge devices to the shifting landscape of open-source models like LLaMA, they explore whether the “model wars” are becoming irrelevant. The conversation then dives into a bigger transformation, the rise of agentic systems, workflows, and AI-driven infrastructure.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

In this episode we dive into the future of construction scheduling.The ChallengeI have been thinking a lot about where this profession is headed. Not in a panicked way. In a curious way. So I sat down with Greg Lawton on the Beyond Deadlines podcast and we tried to do something most people avoid: pick actual years for when the manual parts of scheduling stop being a human job. We worked backwards from a fully autonomous construction future to right now in 2026. What we landed on was less about robots and more about how the next four years quietly reshape what a senior scheduler actually does on a Monday morning.Continue LearningCheck out our book The Critical Path Career: How to Advance in Construction Planning and SchedulingSubscribe to the Beyond Deadlines Email NewsletterSubscribe to the ⁠⁠⁠⁠Beyond Deadlines⁠⁠⁠⁠ Linkedin Newsletter⁠⁠Check Out Our YouTube Channel⁠⁠.ConnectFollow ⁠⁠⁠Micah⁠⁠⁠, ⁠⁠⁠Greg⁠⁠⁠, and ⁠⁠Beyond Deadlines⁠⁠ on LinkedIn.Beyond DeadlineIt's time to raise your career to new heights with Beyond Deadlines, the ultimate destination for construction planners and schedulers. Our podcast is designed to be your go-to guide whether you're starting out in this dynamic field, transitioning from another sector, or you're a seasoned professional. Through our cutting-edge content, practical advice, and innovative tools, we help you succeed in today's fast-evolving construction planning and scheduling landscape without relying on expensive certifications and traditional educational paths. Join us on Beyond Deadlines, where we empower you to shape the future of construction planning and scheduling, making it more efficient, effective, and accessible than ever before.About MicahMicah, the CEO of Movar US is an Intel and Google alumnus, champions next-gen planning and scheduling at both tech giants. Co-founder of Google's Computer Vision in Construction Team, he's saved projects millions via tech advancements. He writes two construction planning and scheduling newsletters and mentors the next generation of construction planners. He holds a Master of Science in Project Management, Saint Mary's University of Minnesota.About GregGreg, an Astrophysicist turned project guru, managed £100M+ defense programs at BAE Systems (UK) and advised on international strategy. Now CEO at ⁠⁠Nodes and Links⁠⁠, he's revolutionizing projects with pioneering AI Project Controls in Construction. Experience groundbreaking strategies with Greg's expertise.Topics We Coverchange management, communication, construction planning, construction, construction scheduling, creating teams, critical path method, cpm, culture, KPI, microsoft project, milestone tracking, oracle, p6, project planning, planning, planning engineer, pmp, portfolio management, predictability, presenting, primavera p6, project acceleration, project budgeting, project controls, project management, project planning, program management, resource allocation, risk management, schedule acceleration, scheduling, scope management, task sequencing, construction, construction reporting, prefabrication, preconstruction, modular construction, modularization, automation, Power BI, dashboard, metrics, process improvement, reporting, schedule consultancy, planning consultancy, material management

Dr Antranig Basman is a mathematician and computer scientist who studied at Cambridge University. After completing a PhD in Computer Vision at the Cambridge University Engineering Department's Machine Intelligence Laboratory he spent five years as Visiting Scholar at the University of Colorado at Boulder. In this podcast we discuss Antranig's many interests and avenues of work, especially around pressing societal challenges which exclude the perspectives of marginalised groups. Now he is based in London and works on a range of community-oriented projects with colleagues across the world. https://ponder.org.uk

In this Fully-Connected episode, Dan and Chris start with Anthropic's Mythos frontier model, parsing what is publicly known about its cybersecurity capabilities and projecting its possible implications from "We've been here before.

Long time listeners know that I'm a huge fan of iNaturalist. Their app literally changed my life by dramatically improving my relationship with, and knowledge of nature.And iNaturalist is much more than just a nature identification app. When you use iNaturalist, yes, you get a helping hand in identifying plants, animals and fungi. But you're also contributing to perhaps the largest community science dataset on Earth, which starts to get to the heart of iNaturalist's mission.After our Jumpstart Nature episode on iNaturalist, I received many questions about how iNaturalist works - just how does it know how to ID so many organisms? How are sensitive species, such as rare plants that are subject to poaching, protected?And with the increased concern about the environmental impact of certain types of AI, how does iNaturalist's AI, called Computer Vision, compare?So who better to answer those questions than Scott Loarie. And if you enjoyed this episode, be sure to check out the Jumpstart Nature Podcast! Episode #5 profiles three creative and inspirational uses of iNaturalist!Be sure to check out the iNaturalist blog and newsletter as well!FULL SHOW NOTESLINKSCalifornia Academy of SciencesiNaturalist, their blog, and their newsletterJumpstart Nature Episode 5 profiles inspiring uses of iNaturalistSupport Us On Patreon!Buy our Merch!Music: Spellbound by Brian Holtz MusicLicense (CC BY 4.0): https://filmmusic.io/standard-licenseArtist site: https://brianholtzmusic.com Discover the Jumpstart Nature Podcast - entertaining and immersive, it's the nature fix we all need.Check past Nature's Archive episodes for amazing guests like Doug Tallamy, Elaine Ingham, and Rae Wynn-Grant, covering topics from bird migration to fungi to frogs and bats!

Episode SummaryWhat do you do when you can't stop the thing you're trying to fix?Three returning guests sit down for one of the most honest conversations about public sector modernization we've had on the show.From the latest on SF's 911 cloud migration, to what it means to modernize Harry Reid International Airport in real time while simultaneously designing the technology architecture for a second commercial airport 20 miles south, to Seguin's workforce transformation from one staff member with a degree and zero certifications in 2018 to 13 degrees and 27 certifications today - join us for this powerful conversation about leadership, community, and what it means to let others carry the message.FeaturingMichelle Geddes CIO San Francisco Department of Emergency ManagementRishma Khimji CITO Clark County Department of Aviation (Harry Reid International Airport) (now CIO Greater Orlando Aviation Authority)Shane McDaniel CIO City of Seguin, TX | TAGITM Past PresidentTimestamps(04:55) - The LinkedIn Banter Origin Story(07:20) - Michelle: SF's 911 Cloud Journey & Hybrid Architecture(09:25) - ESInet, Copper Lines Failing in LA & State Partnership(11:14) - AI for Multilingual 911 Dispatch(12:47) - Rishma: Harry Reid's Second Airport - 20 Miles South(14:31) - Using General Aviation Airports as Innovation Labs(16:40) - Computer Vision at Checkpoints & the 3-Year Rolling Stack(18:23) - Shane: Seguin's Workforce Story - 0 to 27 Certifications(23:48) - The Amazon Warehouse Hire & The Best Buy Delivery Driver(36:12) - TAGITM: The Solution Is in the Room(39:13) - Leadership, Community & Letting Others Carry the MessageListen now: YouTube x Apple x SpotifyWhenever you're ready, there are 3 ways you can connect with TechTables:1.

Autonomous driving is not just a big tech or closed-source game, it's becoming accessible through open innovation and real-world deployment. Dan and Chris sit down with Harald Schäfer, CTO at Comma AI, to explore how OpenPilot is bringing self-driving to everyday vehicles using open source AI. We dive into the intersection of machine learning, robotics, and simulation, including how world models are enabling training at scale and shaping the future of autonomy.Featuring:Harald Schäfer – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Comma

A major shift is underway as enterprises move from lab‑ready computer vision to the far more complex reality of deploying visual intelligence across messy, variable, high‑stakes physical environments. In this episode, Joseph Nelson, Co‑founder and CEO at Roboflow, examines how dependable visual data, models tuned to real operating conditions, and integration with existing production and safety systems determine whether visual AI delivers meaningful value. He highlights the practical moves that matter most: securing consistent visibility into key processes, choosing a first deployment that proves impact, and scaling only once the operational foundations are in place. This episode is sponsored by Roboflow. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner

What would happen if you finally ditched micromanagement and actually let your teams run wild, faster, riskier, and more creative than you'd ever dare on your own?Ben Plomion, COO of Pearl AI, joins Sivana Brewer for a sharp, no-fluff deep dive into the gritty reality of leading in markets where mistakes happen fast and growth is non-negotiable. Drawing from cross-functional battle scars in marketing, ops, and tech, Ben unpacks how he leveraged his CMO chops to become a next-level COO, why most leaders fail at “connecting the dots,” and exactly how he's turning AI into his secret weapon for culture and operational scale.If you're tired of theory and ready for the untold COO playbook that frees you from indecision, protects you from hidden traps, and gives you unfair access to what the best operator-leaders are actually doing, listen now. Stalling means losing team trust, missing radical growth, and getting left behind.Timestamped Highlights[00:03:42] – The shocking “dumpster” pitch that clinched Ben's COO job—would you take this text?[00:05:10] – Connect the dots or die: Why leaders who only skim the surface always lose big[00:07:26] – Zero in-house finance, outsourced chaos—how Ben plugged the leaks before it was too late[00:10:51] – From chief cook to master delegator: The brutal art of giving up “employee benefits” and focusing where it matters[00:14:42] – CEO second-in-command: The secret archetypes and why most COOs get it wrong[00:18:29] – CMO to COO crossover: The superpowers that every operator should steal from marketing[00:21:23] – Ditching values for operating principles—radical new rules for building a creative, AI-savvy team[00:32:19] – “Let them run”: The unorthodox motto that keeps Ben's teams breaking the rules, beating churn, and staying aheadAbout the GuestWith over two decades of experience in marketing, commercial and operational leadership across Artificial Intelligence, Computer Vision, and Blockchain, Ben Plomion is the Chief Operating Officer at Pearl—the leading AI Software-as-a-Service (SaaS) company in dentistry. Prior to Pearl, he served as Chief Marketing Officer at Dibbs, an Amazon-backed tokenization-as-a-service (TaaS) platform. He was previously Chief Growth & Marketing Officer at GumGum, where he played a pivotal role in advancing AI-driven contextual advertising. Earlier in his career, Ben led global digital media efforts at both Magnite and GE Capital. A Forbes contributor and trusted advisor to companies like Deanna.ai, PebblePost, and #Paid, he is also a committed educator in the realms of AI, marketing, and Web3.

In this fully connected episode, Dan and Chris break down the Anthropic Claude Code leak, what went wrong and what it reveals about agentic systems, AI architecture, and AI safety. They also explore how the open source community is responding and why this moment could reshape how AI systems are built and secured.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XUpcoming Events: Register for upcoming webinars here!

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Lucas McKenna, Director of Europe at Point One Navigation, for a wide-ranging conversation about the future of robotics and autonomous systems. They cover topics including the SLAM algorithm and how robots map and position themselves in the world, the role of GPS and sensor fusion in precise localization, swarm robotics and the debate between centralized and decentralized robot intelligence, the differences between urban and rural robotics applications, specialized versus general-purpose robots, the business models around robot ownership and rental, and how autonomous mobility is taking shape differently in Europe versus the United States. They also touch on the cultural implications of robots becoming a fixture in everyday life and what it might mean for human community and connection.Show Notes- Lucas McKenna on LinkedIn: https://www.linkedin.com/in/lucas-mckenna-79269053/- Point One Navigation: https://pointonenav.comTimestamps00:00 - Stewart introduces Luca McKenna from Point One Navigation, diving into robotics and the SLAM algorithm for simultaneous localization and mapping.05:00 - Luca explains swarm robotics, where multiple robots share environmental data, building collective maps that improve positioning accuracy over time.10:00 - Discussion shifts to urban versus rural robot deployment, covering drone delivery limitations, obstacle avoidance challenges, and skyscraper navigation complexity.15:00 - Luca distinguishes specialized versus general-purpose robots, predicting purpose-built machines like seed planters and window washers will dominate near-term deployment.20:00 - Stewart raises unstructured visual data challenges, drawing parallels to AI text processing, while Luca details GPS infrastructure layers enabling precise robot positioning.25:00 - Consumer robot visibility discussed, including Waymo expansion, autonomous delivery robots, and geographic limitations of current self-driving services.30:00 - Robot ownership versus rental models explored, touching on rare earth mineral costs, Chinese supply chains, and economic barriers to personal robot ownership.35:00 - Luca explains state estimation systems using GPS satellites, accelerometers, and gyroscopes working together, contrasting fundamental mathematics against machine learning approaches.40:00 - Sensor fusion parallels between smartphones and autonomous vehicles revealed, explaining how phones mirror car navigation systems at reduced accuracy and cost.45:00 - Conversation concludes examining robots impact on community culture, with Luca advocating autonomous public transit over individualist robotaxis to strengthen human connection.Key Insights1. SLAM is foundational to robot navigation. Simultaneous Localization and Mapping (SLAM) allows robots to map their environment and position themselves within it using computer vision and LiDAR sensors. Unlike humans, who instinctively understand their surroundings, robots require precise algorithmic systems to avoid obstacles and navigate safely.2. GPS and sensor fusion solve the positioning problem. Robots combine absolute sensors like GPS with relative sensors like accelerometers and gyroscopes to maintain accurate positioning. In challenging environments like tunnels or dense cities, these sensors compensate for each other, ensuring continuous and reliable location data.3. Swarm robotics enables collective environmental intelligence. When one robot maps a new area, that data becomes available to all connected robots. This decentralized-yet-centralized model means the entire fleet benefits from each individual robot's experience, continuously improving map quality and navigation precision.4. Specialized robots will dominate before general-purpose ones. Rather than multipurpose humanoid robots, the near-term future favors robots designed for single tasks—delivering food, planting seeds, or drawing lane lines—because the economics and technical bar are far more achievable than building versatile machines.5. Urban, suburban, and rural environments demand different robotic solutions. Open skies in rural areas make GPS-based drones effective, while dense cities require complex sensor stacks. European approaches favor autonomous public transit, while American models lean toward individual robotaxi services.6. Robots will largely be rented as services, not owned. The high cost of hardware, rare earth minerals, and the extensive data required for safe operation makes personal robot ownership impractical for most consumers. Business models will resemble subscription or usage-based services.7. Fundamental mathematics still outperforms machine learning for positioning. Despite AI advances, state estimation systems rely on proven mathematical formulas rather than transformer-based models, which currently underperform classical methods in 3D reconstruction and precise localization tasks.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Lucas McKenna, Director of Europe at Point One Navigation, for a wide-ranging conversation about the future of robotics and autonomous systems. They cover topics including the SLAM algorithm and how robots map and position themselves in the world, the role of GPS and sensor fusion in precise localization, swarm robotics and the debate between centralized and decentralized robot intelligence, the differences between urban and rural robotics applications, specialized versus general-purpose robots, the business models around robot ownership and rental, and how autonomous mobility is taking shape differently in Europe versus the United States. They also touch on the cultural implications of robots becoming a fixture in everyday life and what it might mean for human community and connection.Show Notes- Lucas McKenna on LinkedIn: https://www.linkedin.com/in/lucas-mckenna-79269053/- Point One Navigation: https://pointonenav.comTimestamps00:00 - Stewart introduces Luca McKenna from Point One Navigation, diving into robotics and the SLAM algorithm for simultaneous localization and mapping.05:00 - Luca explains swarm robotics, where multiple robots share environmental data, building collective maps that improve positioning accuracy over time.10:00 - Discussion shifts to urban versus rural robot deployment, covering drone delivery limitations, obstacle avoidance challenges, and skyscraper navigation complexity.15:00 - Luca distinguishes specialized versus general-purpose robots, predicting purpose-built machines like seed planters and window washers will dominate near-term deployment.20:00 - Stewart raises unstructured visual data challenges, drawing parallels to AI text processing, while Luca details GPS infrastructure layers enabling precise robot positioning.25:00 - Consumer robot visibility discussed, including Waymo expansion, autonomous delivery robots, and geographic limitations of current self-driving services.30:00 - Robot ownership versus rental models explored, touching on rare earth mineral costs, Chinese supply chains, and economic barriers to personal robot ownership.35:00 - Luca explains state estimation systems using GPS satellites, accelerometers, and gyroscopes working together, contrasting fundamental mathematics against machine learning approaches.40:00 - Sensor fusion parallels between smartphones and autonomous vehicles revealed, explaining how phones mirror car navigation systems at reduced accuracy and cost.45:00 - Conversation concludes examining robots impact on community culture, with Luca advocating autonomous public transit over individualist robotaxis to strengthen human connection.Key Insights1. SLAM is foundational to robot navigation. Simultaneous Localization and Mapping (SLAM) allows robots to map their environment and position themselves within it using computer vision and LiDAR sensors. Unlike humans, who instinctively understand their surroundings, robots require precise algorithmic systems to avoid obstacles and navigate safely.2. GPS and sensor fusion solve the positioning problem. Robots combine absolute sensors like GPS with relative sensors like accelerometers and gyroscopes to maintain accurate positioning. In challenging environments like tunnels or dense cities, these sensors compensate for each other, ensuring continuous and reliable location data.3. Swarm robotics enables collective environmental intelligence. When one robot maps a new area, that data becomes available to all connected robots. This decentralized-yet-centralized model means the entire fleet benefits from each individual robot's experience, continuously improving map quality and navigation precision.4. Specialized robots will dominate before general-purpose ones. Rather than multipurpose humanoid robots, the near-term future favors robots designed for single tasks—delivering food, planting seeds, or drawing lane lines—because the economics and technical bar are far more achievable than building versatile machines.5. Urban, suburban, and rural environments demand different robotic solutions. Open skies in rural areas make GPS-based drones effective, while dense cities require complex sensor stacks. European approaches favor autonomous public transit, while American models lean toward individual robotaxi services.6. Robots will largely be rented as services, not owned. The high cost of hardware, rare earth minerals, and the extensive data required for safe operation makes personal robot ownership impractical for most consumers. Business models will resemble subscription or usage-based services.7. Fundamental mathematics still outperforms machine learning for positioning. Despite AI advances, state estimation systems rely on proven mathematical formulas rather than transformer-based models, which currently underperform classical methods in 3D reconstruction and precise localization tasks.

AI is rapidly transforming how software is built, shifting economic incentives from open source code and collaboration toward on-demand, personalized development through agentic coding a.k.a. vibe coding. In this episode, Chris speaks with Miklós Koren of Central European University about how AI is reshaping open source and the software industry. They explore the economics of incentives, evolving collaboration patterns, and what this shift means for software development, the future of AI, and its broader impact on the technology sector.Featuring:Miklós Koren – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:Vibe Coding Kills Open SourceThe Directions of Technical ChangeThe Tailwind storyUpcoming Events: Register for upcoming webinars here!