Podcasts about Computer vision

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. 

Maria Koskinopoulou is an Assistant Professor in Robotics and Computer Vision at Heriot-Watt University. She co-leads the ARM²Lab – Autonomous Robotic Manipulation & Multi-Agent Systems Lab at Heriot-Watt and the National Robotarium, alongside Ignacio Carlucho. Her research interests include robotic manipulation, perception, robot vision, medical robotics, human-robot interaction, and machine learning. She is involved in major UKRI and EU-funded research projects advancing robotic manipulation, surgical and underwater robotics, autonomous assembly, and waste sorting.   Check out the bonus episodes from the European Robotics Forum: https://www.patreon.com/posts/robot-talk-at-157276631  Join the Robot Talk community on Patreon: https://www.patreon.com/ClaireAsher

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

This week I'm joined by John Larkin, Head of Engineering at Rebar. Rebar are driving AI and Computer Vision into Construction Software to help innovate and build faster with tighter collaboration. We unpacked the impact they are having in the construction industry, the enormity of the engineering challenge and some of how they are building.Intro into John and his background.Intro into the problem space of constructionIntroducing AI and CV into ConstructionTechnical challenges they face at Rebar and whyTypes of problems they're solving and the people they are looking for to help them solve them.If you're keen to share your story, please reach out to us!Guest: https://www.linkedin.com/in/johnlarkin1/Powered by Artifeks!https://www.linkedin.com/company/artifeksrecruitmenthttps://www.artifeks.co.ukhttps://www.linkedin.com/in/agilerecruiterLinkedIn: https://www.linkedin.com/company/enginearsioTwitter: https://x.com/EnginearsioAll Podcast Platforms: https://smartlink.ausha.co/enginearsHosted on Ausha. See ausha.co/privacy-policy for more information.

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!

In this episode, Priya Ranjan Mohanty speaks with Krunal Kantale, Co-founder & MD of Alligator Automations - a packaging automation company based in Pune that has grown from 2 people to 500+ employees since its founding in 2008.Krunal and his co-founder Srinivas Choudhary started their journey winning 43 national and international robotics awards while still in college. They quit their jobs during the 2008 recession - against their families' wishes - and landed their first order of Rs 1.5 lakhs. Today, Alligator Automations serves industries from FMCG to fertilizers to chemicals, handling everything from primary packaging to palletizing, warehousing, and dispatch. He discusses the challenge of selling automation in a country where labor is cheap, the role of AI and computer vision in modern manufacturing, and how customization wins projects over standard machines.Chapters:00:00 - Introduction00:31 - About Alligator Automations01:00 - Krunal's Background & Experience04:14 - Starting During the 2008 Recession05:27 - First Order: Rs 1.5 Lakhs from Mumbai08:20 - What Alligator Automations Does09:47 - Is Packaging Automation a Big Market?12:30 - Use Cases: FMCG, Fertilizer, Chemicals15:15 - Pitching Automation When Labor is Cheap19:30 - Customization vs. Standard Machines21:00 - IoT, AI & Computer Vision in Manufacturing23:30 - Global Expansion: Middle East, Africa, Americas27:46 - Advice for Entrepreneurs29:05 - Closing Remarks---About ELI Podcast:ELI (Entrepreneur's Live Interviews) brings you inspiring stories from India's startup ecosystem. Real founders, real journeys, real insights.Website: https://eli-podcast.com

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

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

PrimePoint just raised $10M to solve the one problem AI still can't crack in construction: drawings.Lubomir Bourdev built the first computer vision system at Facebook. Sold a neural net startup to Apple. Hamid was employee five at Trello. Now they're betting that drawings are the key to unlocking AI's full potential in construction.Tune in to find out about:✅ Why LLMs fundamentally can't handle technical drawings — it's an architecture issue, not a capability gap✅ How PrimePoint's Knowledge Graph connects drawings, specs, RFIs, submittals, and schedules✅ How AI does the first pass on constructability reviews, RFIs, and submittals — and why humans still make the call✅ Why early users are actually spending more time understanding their projects, not lessWatch the exclusive episode on Bricks & Bytes YouTube Channel now. Link in the comments below. #aec #construction #constructiontech #bricksandbytes #bricksbytes #ai #vcOur Sponsors:BreadCrumb- 50,000+ projects globally. All running safer, faster, with Breadcrumb. - breadcrumb.coAphex is the multiplayer planning platform where construction teams plan together, stay aligned, and deliver projects faster – check out aphex.coArchdesk - “The #1 Construction Management Software for Growing Companies - Manage your projects from Tender to Handover” check archdesk.com

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!

What does “AI at the edge” really mean in 2026, and why does it matter now more than ever before? In this episode, we're joined by Brandon Shibley, Edge AI Solutions Engineering Lead at Qualcomm's Edge Impulse, to discuss the current state and future of Edge AI in 2026. We discuss Gen AI, Small Models, and Cascades of Models, along with real-world constraints like latency, power, and privacy. We also dive into the role of MLOps, evolving hardware, and how developers can start building practical edge AI systems today.Featuring:Brandon Shibley – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Read our Ultimate Guide to Edge AIDownload your copy of O'Reilly's AI at the Edge Check out the Edge Impulse blogSign-up for an expert led trial of Edge ImpulseUpcoming Events: Register for upcoming webinars here!

In this episode we dive into scheduling software Oracle Primavera P6 and Oracle Primavera Cloud.The ChallengeNearly every major construction project in the world has touched Primavera P6 at some point. It is the godfather of scheduling software. But Oracle has been quietly building something entirely different -- Oracle Primavera Cloud -- and it is not just P6 with a web browser. That distinction matters, and most schedulers don't fully understand it yet. We sat down with Chad Wattendorf, Director of Product Management for P6 and OPC at Oracle, and Garrett Harley, Director of Product Marketing for Oracle Primavera Cloud and P6, to get straight answers on what's changing, what's staying, and what every planner needs to know right now.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

What happens when an AI hater starts building with AI agents? In this episode, we talk with software engineer Steve Klabnik, known for his work on the Rust programming language, about his journey from criticizing AI to experimenting with it firsthand. We explore Steve's programming language Rue, largely built with the help of AI tools like Claude, and discuss what this means for software engineering and the future of coding in an AI-driven world.Featuring:Steve Klabnik – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:The Rust Programming LanguageRustRueDaniel's RSA Meeting link for March 23, 2026Daniel's RSA Meeting link for March 24-25, 2026Upcoming Events: Register for upcoming webinars here!

AI is reshaping global power, from chip manufacturing and computing power to AI governance and US-China relations.  In this episode, Ben Buchanan, Assistant Professor at The Johns Hopkins University and former White House Special Advisor for AI, explores how AI policy, geopolitics, and international cooperation intersect with AI  innovation and AI safety. We discuss the strategic importance of computing power, the future of AI governance, and what it will take for democracies to lead responsibly in the age of AI.Featuring:Ben Buchanan – LinkedIn Chris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:The AI Grand BargainUpcoming Events: Register for upcoming webinars here!

On this episode, Bryan and AIVRE CEO Jake Lew break down the latest on UAD 3.6—what it means for your reports and how to stay ahead of the changes. They also take AIVRE's software for a spin to demonstrate how AI helps appraisers handle repetitive tasks, streamline workflows, and free up time to focus on what really matters.  

In this episode we dive into Progress ReportsThe ChallengeYou know the moment. That message lands. Your project manager needs a status update on the project and they need it right now. Not in two weeks when it's supposed to happen. Right now.You have an 11,000 activity schedule. A critical path that keeps shifting. A structural steel delay throwing everything off. And zero minutes to spare.Usually this means three hours buried in P6. Copying data to Excel. Building out reports. Attaching baselines, unattaching baselines, running analysis. All just to deliver a simple email.On this episode of Beyond Deadlines, I sat down with Greg Lawton, CEO of Nodes and Links, to test whether a purpose built AI tool could draft that same status email in 60 seconds. Not a general chatbot. A multi agent AI system designed from the ground up to answer scheduling's hardest questions.What we found changed my perspective on what's possible.Check out Nodes & Links here and mention Micah Piippo and this podcast.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

As AI accelerates innovation and adoption, leaders are facing rising cognitive load, shifting systems, and new emotional realities inside their organizations. In this episode, Deloitte's Chief Innovation Officer Deborah Golden joins us to explore how AI is reshaping leadership, why vulnerability and empathy are critical in this moment, and how anti-fragility, not just resilience, will define the future of work.Featuring:Deborah Golden – LinkedIn Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:DeloitteSponsor: Framer - The website builder that turns your dot com from a formality into a tool for growth. Check it out at framer.com/PRACTICALAIUpcoming Events: Register for upcoming webinars here!

AI is moving fast from research to real-world deployment, and when things go wrong, the consequences are no longer hypothetical. In this episode, Sean McGregor, co-founder of the AI Verification & Evaluation Research Institute and also the founder of the AI Incident Database, joins Chris and Dan to discuss AI safety, verification, evaluation, and auditing. They explore why benchmarks often fall short, what red-teaming at DEF CON reveals about machine learning risks, and how organizations can better assess and manage AI systems in practice.Featuring:Sean McGregor– LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:AI Verification & Evaluation Research InstituteAI Incident Database38th convening of IAAIBenchRiskState of Global AI Incident ReportingUpcoming Events: Register for upcoming webinars here!

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.Timestamps00:00 Introduction to Knowledge Graphs and Ontologies01:09 The Importance of Ontologies in AI04:14 Philosophy's Role in Knowledge Management10:20 Debating the Relevance of RDF15:41 The Distinction Between Knowledge Management and Knowledge Engineering21:07 The Human Element in AI and Knowledge Architecture25:07 Startups vs. Enterprises: The Knowledge Gap29:57 Deterministic vs. Probabilistic AI32:18 The Marketing of AI: A Historical Perspective33:57 The Role of Knowledge Architecture in AI39:00 Understanding RDF and Its Importance44:47 The Intersection of AI and Human Intelligence50:50 Future Visions: AI, Ontologies, and Human BehaviorKey Insights1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora Lassila published a landmark article in Scientific American proposing the semantic web. While Berners-Lee had already connected documents across the web through HTML and HTTP, the semantic web aimed to connect all the data—essentially turning the internet into a giant database. This vision led to the development of RDF (Resource Description Framework), which emerged from DARPA research and provides the technical foundation for building knowledge graphs and ontologies. The origin story involved solving simple but important problems, like disambiguating whether "Cook" referred to a verb, noun, or a person's name at an academic conference.4. Symbolic AI and Neural Networks Represent Complementary Approaches Like Fast and Slow Thinking. Drawing on Kahneman's "thinking fast and slow" framework, LLMs represent the "fast brain"—learning monsters that can process enormous amounts of information and recognize patterns through natural language interfaces. Symbolic AI and knowledge graphs represent the "slow brain"—capturing actual knowledge and facts that can counter hallucinations and provide deterministic, explainable reasoning. This complementarity is driving the re-emergence of neuro-symbolic AI, which combines both approaches. The fundamental distinction is that symbolic AI systems are deterministic and can be fully explained, while LLMs are probabilistic and stochastic, making them unsuitable for applications requiring absolute reliability, such as industrial robotics or pharmaceutical research.5. Knowledge Architecture Remains Underappreciated Despite Powering Major Enterprises. While machine learning engineers currently receive most of the attention and budget, knowledge graphs actually power systems at Netflix (the economic graph), Amazon (the product graph), LinkedIn, Meta, and most major enterprises. The technology has been described as "the most astoundingly successful failure in the history of technology"—the semantic web vision seemed to fail, yet more than half of web pages now contain RDF-formatted semantic markup through schema.org, and every major enterprise uses knowledge graph technology in the background. Knowledge architects remain underappreciated partly because the work is cognitively difficult, requires talking to people (which engineers often avoid), and most advanced practitioners have PhDs in computer science, logic, or philosophy.6. RDF's Simple Subject-Predicate-Object Structure Enables Meaning and Data Linking. Unlike relational databases that store data in tables with rows and columns, RDF uses the simplest linguistic structure: subject-predicate-object (like "Larry knows Stuart"). Each element has a unique URI identifier, which permits precise meaning and enables linked data across systems. This graph structure makes it much easier to connect data after the fact compared to navigating tabular structures in relational databases. On top of RDF sits an entire stack of technologies including schema languages, query languages, ontological languages, and constraints languages—everything needed to turn data into actionable knowledge. The goal is inferring or articulating knowledge from RDF-structured data.7. The Future Requires Decoupled Modular Architectures Combining Multiple AI Approaches. The vision for the future involves separation of concerns through microservices-like architectures where different systems handle what they do best. LLMs excel at discovering possibilities and generating lists, while knowledge graphs excel at articulating human-vetted, deterministic versions of that information that systems can reliably use. Every one of Swanson's 300 podcast interviews over ten years ultimately concludes that regardless of technology, success comes down to human beings, their behavior, and the cultural changes needed to implement systems. The assumption that we can simply eliminate people from processes misses that huma...

In dieser Folge ist Andreas Klinger, Gründer und General Partner von PROTOTYPE, zu Gast. Andreas hat tiefgreifende Erfahrungen aus der US-Tech-Szene (u.a. AngelList, Product Hunt, OnDeck) und fokussiert sich heute auf Investments in Europas DeepTech-Sektor. Er spricht über die Herausforderungen des europäischen Startup-Ökosystems, die Notwendigkeit einer paneuropäischen Firmenstruktur (EU Inc.), die spannendsten Technologien im Bereich Robotics und Manufacturing und warum jetzt der beste Zeitpunkt ist, ein Robotics-Startup zu gründen. Andreas gibt zudem Einblicke in seinen Investmentansatz, die größten Probleme Europas und warum er politisches Engagement für essenziell hält, um das europäische Tech-Ökosystem langfristig konkurrenzfähig zu machen. Was du aus der Folge mitnimmst: Europas Herausforderungen im Startup-Bereich: Warum fragmentierte Märkte, fehlende Standards und mangelnde Kapitalstrukturen das Wachstum behindern. EU Inc. als Lösung: Andreas erklärt, wie eine paneuropäische Firmenstruktur das Gründen und Investieren in Europa revolutionieren könnte. Warum DeepTech Europas Stärke ist: Mit einem Fokus auf Robotics, Manufacturing und Frontier Tech hat Europa die Möglichkeit, eine globale Führungsrolle einzunehmen. Tech-Trends der Zukunft: Von autonomen Traktoren bis zu kleinen Roboterzellen für Produktion – Andreas zeigt, wie Fortschritte in Computer Vision, Reasoning und Hardware die Industrie verändern. Warum 2026 der ideale Zeitpunkt für Robotics-Startups ist: Durch technologische Durchbrüche in AI und Manufacturing ist jetzt die perfekte Zeit, um in Robotics einzusteigen. Das Potenzial von Hardware-Startups: Trotz höherer Anfangskosten bieten Hardware-Startups langfristig oft mehr Wettbewerbsvorteile und größere Marktchancen. Andreas' Appell an Gründer: Fokussiere dich auf innovative und unkonventionelle Ideen, die durch technologische Fortschritte möglich geworden sind. ALLES ZU UNICORN BAKERY: https://stan.store/fabiantausch   Mehr zu Andreas: LinkedIn: https://de.linkedin.com/in/andreasklinger  Website: https://www.prototypecap.com/  Join our Founder Tactics Newsletter: 2x die Woche bekommst du die Taktiken der besten Gründer der Welt direkt ins Postfach: https://www.tactics.unicornbakery.de/  Kapitel: (00:00:00) Einstieg: Europas Rolle in einer globalen Tech-Welt (00:02:37) Die Herausforderungen des europäischen Startup-Ökosystems (00:04:49) Warum paneuropäische Standards fehlen und wie EU Inc. das ändern soll (00:09:19) EU Inc.: Wie eine einheitliche europäische Firmenstruktur Innovation fördern könnte (00:13:00) Vergleich Europa vs. USA: Was macht die USA besser? (00:17:27) Politisches Engagement: Warum Andreas sich für EU Inc. einsetzt (00:20:59) PROTOTYPE: Fokus auf DeepTech, Robotics und Manufacturing (00:26:28) Warum 2026 der beste Zeitpunkt ist, ein Robotics-Startup zu gründen (00:32:12) Wie PROTOTYPE Hardware-Startups unterstützt und finanziert (00:37:16) Sunrise, Voltrack und Sensmoor: Beispiele für spannende DeepTech-Startups (00:44:17) Breakthroughs in Robotics: Von Computer Vision bis zu autonomen Maschinen (00:51:29) Die größten Unterschiede zwischen Software- und Hardware-Startups (00:56:48) Warum Europas Fragmentierung das größte Hindernis bleibt (01:00:00) Abschluss: Chancen für Europäische Startups und Andreas' Appell an Gründer

AI agents are moving from demos to real workplaces, but what actually happens when they run a company? In this episode, journalist Evan Ratliff, host of Shell Game, joins Chris to discuss his immersive journalism experiment building a real startup staffed almost entirely by AI agents. They explore how AI agents behave as coworkers, how humans react when interacting with them, and where ethical and workplace boundaries begin to break down.Featuring:Evan Ratliff  – LinkedIn, XChris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:Shell GameUpcoming Events: Register for upcoming webinars here!

As AI increasingly shapes geopolitics, elections, and civic life, its impact on democracy is becoming impossible to ignore. In this episode, Daniel and Chris are joined by security expert Bruce Schneier to explore how AI and technology are transforming democracy, governance, and citizenship. Drawing from his book Rewiring Democracy, they explore real examples of AI in elections, legislation, courts, and public AI models, the risks of concentrated power, and how these tools can both strengthen and strain democratic systems worldwide.Featuring:Bruce Schneier – XChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks: Schneier on SecuritySponsors:Framer - The website builder that turns your dot com from a formality into a tool for growth. Check it out at framer.com/PRACTICALAIZapier - The AI orchestration platform that puts AI to work across your company. Check it out at zapier.com/practicalUpcoming Events: Register for upcoming webinars here!

If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects.  In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge.  So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below.  Thank you and enjoy the episode!Links For The Occult Rejects and The Spiritual Gangsters https://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Cash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejects

If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects.  In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge.  So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below.  Thank you and enjoy the episode!Links For The Occult Rejects and The Spiritual Gangsters https://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Cash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejects

As generative AI moves into production, traditional guardrails and input/output filters can prove too slow, too expensive, and/or too limited. In this episode, Alizishaan Khatri of Wrynx joins Daniel and Chris to explore a fundamentally different approach to AI safety and interpretability. They unpack the limits of today's black-box defenses, the role of interpretability, and how model-native, runtime signals can enable safer AI systems. Featuring:Alizishaan Khatri – LinkedInChris 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 Kelvin Lwin for their second conversation exploring the fascinating intersection of AI and Buddhist cosmology. Lwin brings his unique perspective as both a technologist with deep Silicon Valley experience and a serious meditation practitioner who's spent decades studying Buddhist philosophy. Together, they examine how AI development fits into ancient spiritual prophecies, discuss the dangerous allure of LLMs as potentially "asura weapons" that can mislead users, and explore verification methods for enlightenment claims in our modern digital age. The conversation ranges from technical discussions about the need for better AI compilers and world models to profound questions about humanity's role in what Lwin sees as an inevitable technological crucible that will determine our collective spiritual evolution. For more information about Kelvin's work on attention training and AI, visit his website at alin.ai. You can also join Kelvin for live meditation sessions twice daily on Clubhouse at clubhouse.com/house/neowise.Timestamps00:00 Exploring AI and Spirituality05:56 The Quest for Enlightenment Verification11:58 AI's Impact on Spirituality and Reality17:51 The 500-Year Prophecy of Buddhism23:36 The Future of AI and Business Innovation32:15 Exploring Language and Communication34:54 Programming Languages and Human Interaction36:23 AI and the Crucible of Change39:20 World Models and Physical AI41:27 The Role of Ontologies in AI44:25 The Asura and Deva: A Battle for Supremacy48:15 The Future of Humanity and AI51:08 Persuasion and the Power of LLMs55:29 Navigating the New Age of TechnologyKey Insights1. The Rarity of Polymath AI-Spirituality Perspectives: Kelvin argues that very few people are approaching AI through spiritual frameworks because it requires being a polymath with deep knowledge across multiple domains. Most people specialize in one field, and combining AI expertise with Buddhist cosmology requires significant time, resources, and academic background that few possess.2. Traditional Enlightenment Verification vs. Modern Claims: There are established methods for verifying enlightenment claims in Buddhist traditions, including adherence to the five precepts and overcoming hell rebirth through karmic resolution. Many modern Western practitioners claiming enlightenment fail these traditional tests, often changing the criteria when they can't meet the original requirements.3. The 500-Year Buddhist Prophecy and Current Timing: We are approximately 60 years into a prophesied 500-year period where enlightenment becomes possible again. This "startup phase of Buddhism revival" coincides with technological developments like the internet and AI, which are seen as integral to this spiritual renaissance rather than obstacles to it.4. LLMs as UI Solution, Not Reasoning Engine: While LLMs have solved the user interface problem of capturing human intent, they fundamentally cannot reason or make decisions due to their token-based architecture. The technology works well enough to create illusion of capability, leading people down an asymptotic path away from true solutions.5. The Need for New Programming Paradigms: Current AI development caters too much to human cognitive limitations through familiar programming structures. True advancement requires moving beyond human-readable code toward agent-generated languages that prioritize efficiency over human comprehension, similar to how compilers already translate high-level code.6. AI as Asura Weapon in Spiritual Warfare: From Buddhist cosmological perspective, AI represents an asura (demon-realm) tool that appears helpful but is fundamentally wasteful and disruptive to human consciousness. Humanity exists as the battleground between divine and demonic forces, with AI serving as a weapon that both sides employ in this cosmic conflict.7. 2029 as Critical Convergence Point: Multiple technological and spiritual trends point toward 2029 as when various systems will reach breaking points, forcing humanity to either transcend current limitations or be consumed by them. This timing aligns with both technological development curves and spiritual prophecies about transformation periods.

#395 - Sponsor Spotlight - RedblockThis episode is sponsored by Redblock. Visit redblock.ai/idac to learn more.Jeff and Jim come to you live from the Gartner IAM Summit in Grapevine, Texas, for a special Sponsor Spotlight with Redblock. They sit down with CEO Indus Khaitan to discuss how Redblock uses AI and computer vision to solve the "last mile" problem in identity management: disconnected applications.Indus explains how Redblock acts as an "agentic" layer, using screen recordings to learn administrative tasks for apps that lack APIs. The conversation covers the origin of the company name, the urgency of securing the "long tail" of applications, and how they build trust and guardrails around AI execution. They also discuss the "DoorDash" analogy for identity fulfillment and wrap up with a fun chat about Indus's passion for flying planes.Connect with Indus: https://www.linkedin.com/in/khaitan/Learn more: redblock.ai/idacConnect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at [idacpodcast.com](http://idacpodcast.com)Timestamps00:00 Introduction from Gartner IAM Summit00:46 Guest Introduction: Indus Khaitan of Redblock01:40 Indus's Journey into Identity02:41 The Origin of the Name "Redblock"04:20 The Underserved Market: Services vs. Software07:34 The Urgency of Securing Disconnected Apps09:19 Why Traditional IGA and PAM Aren't Enough11:35 The DoorDash Analogy: Where Redblock Fits14:30 What Makes Redblock Unique? (Agentic Process Automation)16:15 Trusting AI with Security Tasks18:50 Onboarding Apps via Video Recording21:23 Deployment: Running Air-Gapped on Customer Cloud22:17 Handling UI Changes and "Full Self-Driving" Analogy25:40 Integration with SailPoint and Governance Tools27:13 Speed of Integration: Days vs. Years32:00 How the "Headless Browser" Works33:35 Limitations: Web Apps vs. Thick Clients36:58 Redblock's 2025 Milestones and Future Outlook39:48 Call to Action: Solving Disconnected Apps40:27 Impressions of the Gartner IAM Summit44:26 Are We in an AI Bubble?46:46 Indus's Hobby: Flying PlanesKeywordsIDAC, Identity at the Center, Jeff Steadman, Jim McDonald, Redblock, Indus Khaitan, AI, Artificial Intelligence, IAM, Identity and Access Management, Disconnected Apps, Agentic AI, Computer Vision, Gartner IAM Summit, RPA, IGA, Cybersecurity

The dreaded missing indicator—you've trained on it, created systems for it, and yet trays still reach the OR without them. What if technology could catch that mistake before the tray ever leaves your department? On this episode of Beyond Clean, Gregory Warino, Director of Central Sterile Processing at Mercy Health, shares how his team used AI to tackle missing CIs across three facilities. From getting skeptical staff on board to watching the technology actually catch missing indicators before trays leave assembly, Gregory breaks down the real story behind implementing AI in SPD—from what it cost, how long it took, and whether the tech lived up to the hype. If missing indicators are a pain point in your department or you're curious about what AI means for your SPD's workflows, this episode has the answers you need! After finishing this podcast episode, earn your 1 CE credit immediately by passing the short quiz linked here: https://www.flexiquiz.com/SC/N/episode31-02 Visit our CE Credit Hub at https://www.beyondcleanmedia.com/ce-credit-hub to access this quiz and over 350 other free CE credits. #BeyondClean #SterileProcessing #Podcast #Season31 #UnderPressure #Indicator #CIs #Workflows #AI #Technology

In this start-of-year FC episode, Chris and Daniel break down what really mattered in AI in 2025, and what to expect in 2026. They explore the rise of AI agents, the practical reality of multimodal AI, and how reasoning models are reshaping workflows. The conversation dives into infrastructure and energy constraints, the continued value of predictive models, and why orchestration (not just better models) is becoming the defining skill for AI teams. The episode wraps with grounded 2026 predictions on where AI systems, tooling, and builders are headed next.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XSponsor: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!

Fei-Fei Li and Justin Johnson are pioneers in AI. While the world has only recently witnessed a surge in consumer AI, they have long been laying the groundwork for the innovations transforming industries today.With the recent launch of Marble, the first product from their company World Labs, we are revisiting this conversation to explore the ideas that started it all. World Labs is focused on spatial intelligence, building Large World Models that can perceive, generate, and interact with the 3D world. Marble brings that vision to life, allowing anyone, from individual creators to major platforms, to generate 3D scenes directly from text or image prompts and turn complex 3D creation into a simple, creative process.In this episode, a16z general partner Martin Casado talks with Fei-Fei and Justin about the journey from early AI winters to the rise of deep learning and multimodal AI. From foundational breakthroughs like ImageNet to the cutting-edge realm of spatial intelligence, they discuss the evolution of the field and what is next for innovation at World Labs. Timecode:0:00 – The Next Decade of AI2:45 – Origins: Backgrounds of the Founders6:50 – The Rise of Deep Learning & ImageNet8:00 – Algorithmic Unlocks: Compute, Data, and Supervised Learning12:00 – From Predictive to Generative AI16:20 – The Journey to Spatial Intelligence18:35 – Defining Spatial Intelligence21:15 – 3D Data, Computer Vision, and Breakthroughs23:15 – Reconstruction vs. Generation in Computer Vision24:45 – Spatial Intelligence vs. Language Models29:00 – Applications: Virtual, Augmented, and Physical Worlds39:55 – Building World Labs: Team and Vision41:55 – The North Star: Measuring Success in Spatial Intelligence Resources:Learn more about World Labs: https://www.worldlabs.aiLearn more about Marble: https://Marble.WorldLabs.aiFind Fei-Fei on Twitter: https://x.com/drfeifeiFind Justin on Twitter: https://x.com/jcjohnssFind Martin on Twitter: https://x.com/martin_casado Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.