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Cem Karsan sits down with Ben Hunt, founder of Epsilon Theory, to explore how narratives shape markets, politics, and decision making itself. Drawing on decades of experience across academia, hedge funds, and applied AI, Ben explains why stories, not data, increasingly drive outcomes in modern markets. The conversation spans unstructured data, inference, common knowledge, and the mechanics of narrative momentum. Together, they examine consumer expectations, inflation silence, geopolitical signaling, and the slow shift away from US dominance. What emerges is a framework for understanding markets as reflexive systems, where perception often matters more than reality.-----50 YEARS OF TREND FOLLOWING BOOK AND BEHIND-THE-SCENES VIDEO FOR ACCREDITED INVESTORS - CLICK HERE-----Follow Niels on Twitter, LinkedIn, YouTube or via the TTU website.IT's TRUE ? – most CIO's read 50+ books each year – get your FREE copy of the Ultimate Guide to the Best Investment Books ever written here.And you can get a free copy of my latest book “Ten Reasons to Add Trend Following to Your Portfolio” here.Learn more about the Trend Barometer here.Send your questions to info@toptradersunplugged.comAnd please share this episode with a like-minded friend and leave an honest Rating & Review on iTunes or Spotify so more people can discover the podcast.Follow Cem on Twitter.Episode TimeStamps: 00:00 - Introduction to U Got Options and the trading floor setting02:18 - Ben Hunt's background and Epsilon Theory origins04:11 - Markets as the ultimate multiplayer game06:15 - Inference, unstructured data, and narrative analysis08:18 - Why sentiment and word counts miss the real signal11:16 - Mapping meaning and truthy stories15:00 - LLMs as operating systems, not oracles18:01 - Giving money back and when models stop working21:16 - Applying narrative tools beyond markets24:10 - Consumer weakness versus bullish expectations30:43 - Inflation, recession, and why markets do not care33:29 - Dormant stories and volatility discovery34:26 -
The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch
AGENDA: 03:36 Brex Acquisition by Capital One for $5.15BN 10:54 Does Brex's Acquisition Help or Hurt Ramp? 16:28 TikTok Deal Completed: Who Won & Who Lost: Analysis 19:30 Anthropic Inference Costs Higher Than Expected 37:50 Open Evidence Raises at $12BN from Thrive and DST 53:56 Wealthront IPO Disaster: Is $1.5BN IPO Too Small? 01:07:27 Salesforce Wins $5BN Army Contract: The Last Laugh for SaaS
In this episode of Data Driven, Frank and Andy dive into the future of market intelligence with Dr. Jill Axline, co-founder and CEO of Mavera—a company building synthetic populations that simulate real human behaviour, cognition, and emotion. Forget Personas. We're talking real-time, AI-driven behavioural modeling that's more predictive than your horoscope and considerably more data-backed.Dr. Axline shares how Mavera's swarm of AI models situates these synthetic humans within real-world business contexts to forecast decisions, measure emotional resonance, and even test marketing messages before they go live. From governance and model drift to the surprising uses in financial services, political campaigns, and speechwriting—this is one of the most forward-looking conversations we've had yet.If you've ever wanted a deeper understanding of how AI can augment decision-making—or just want to hear Frank admit asset managers love ice cream—this one's for you.LinksLearn more about Mavera:https://mavera.ioConnect with Jill Axline on LinkedIn:https://linkedin.com/in/jillaxlineMorningstar:https://www.morningstar.comTime Stamps00:00 - Introduction & AI Swarms Explained03:30 - Forget Personas: Contextual AI Models07:00 - Evidence vs Inference & AI Governance10:20 - Simulation Scenarios & Model Drift14:30 - Synthetic Audiences in Action18:00 - Evidence Feedback Loops & Small Data Challenges22:00 - Industry Applications & Use Cases27:00 - Analyzing Speeches & Emotional Resonance30:45 - Sentiment, Social Listening, and Real-Time News Reactions34:00 - Adversarial Models & Strategic Pushback38:00 - The Cartoon Bank Portal That Failed Spectacularly41:00 - From Skeptic to CEO: Jill's Journey45:00 - Data Privacy, Compliance & Synthetic Ethics48:00 - Reflections on Empathy, Engineers, and Selling Without SellingSupport the ShowIf you enjoy Data Driven, leave us a review on Apple Podcasts or your favourite pod platform. It helps more people find the show—and fuels Frank's Monster Energy habit.
Sebastian Raschka joins the MAD Podcast for a deep, educational tour of what actually changed in LLMs in 2025 — and what matters heading into 2026.We start with the big architecture question: are transformers still the winning design, and what should we make of world models, small “recursive” reasoning models and text diffusion approaches? Then we get into the real story of the last 12 months: post-training and reasoning. Sebastian breaks down RLVR (reinforcement learning with verifiable rewards) and GRPO, why they pair so well, what makes them cheaper to scale than classic RLHF, and how they “unlock” reasoning already latent in base models.We also cover why “benchmaxxing” is warping evaluation, why Sebastian increasingly trusts real usage over benchmark scores, and why inference-time scaling and tool use may be the underappreciated drivers of progress. Finally, we zoom out: where moats live now (hint: private data), why more large companies may train models in-house, and why continual learning is still so hard.If you want the 2025–2026 LLM landscape explained like a masterclass — this is it.Sources:The State Of LLMs 2025: Progress, Problems, and Predictions - https://x.com/rasbt/status/2006015301717028989?s=20The Big LLM Architecture Comparison - https://magazine.sebastianraschka.com/p/the-big-llm-architecture-comparisonSebastian RaschkaWebsite - https://sebastianraschka.comBlog - https://magazine.sebastianraschka.comLinkedIn - https://www.linkedin.com/in/sebastianraschka/X/Twitter - https://x.com/rasbtFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)Blog - https://mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) - Intro (01:05) - Are the days of Transformers numbered?(14:05) - World models: what they are and why people care(06:01) - Small “recursive” reasoning models (ARC, iterative refinement)(09:45) - What is a diffusion model (for text)?(13:24) - Are we seeing real architecture breakthroughs — or just polishing?(14:04) - MoE + “efficiency tweaks” that actually move the needle(17:26) - “Pre-training isn't dead… it's just boring”(18:03) - 2025's headline shift: RLVR + GRPO (post-training for reasoning)(20:58) - Why RLHF is expensive (reward model + value model)(21:43) - Why GRPO makes RLVR cheaper and more scalable(24:54) - Process Reward Models (PRMs): why grading the steps is hard(28:20) - Can RLVR expand beyond math & coding?(30:27) - Why RL feels “finicky” at scale(32:34) - The practical “tips & tricks” that make GRPO more stable(35:29) - The meta-lesson of 2025: progress = lots of small improvements(38:41) - “Benchmaxxing”: why benchmarks are getting less trustworthy(43:10) - The other big lever: inference-time scaling(47:36) - Tool use: reducing hallucinations by calling external tools(49:57) - The “private data edge” + in-house model training(55:14) - Continual learning: why it's hard (and why it's not 2026)(59:28) - How Sebastian works: reading, coding, learning “from scratch”(01:04:55) - LLM burnout + how he uses models (without replacing himself)
What are practical strategies to avoid overload and exhaustion in today's digital world? What norms can organizations create for tool usage, and how can finding offline activities that provide a mental contrast to digital work?Paul Leonardi is the Duca Family Professor of Technology Management at UC Santa Barbara, a consultant and speaker on digital transformation and the future of work, and an author of several works. His latest book is called Digital Exhaustion: Simple Rules for Reclaiming Your Life.Greg and Paul discuss the complementary nature of his two most recent books: the first focuses on harnessing digital tools, and the second on mitigating the overwhelm they can cause. They also explore teaching technology management, including the importance of understanding technology's impact on people and organizational processes. Paul explains the 30% rule, emphasizing the need to understand digital tools well enough to use them effectively. They also explore the concept of digital exhaustion, the subject of his most recent book, its symptoms, and how to manage it, both at work and in daily life. *unSILOed Podcast is produced by University FM.*Episode Quotes:How can we reduce exhaustion?41:29: One easy way of reducing our exhaustion is to match the sort of complexity of the task that we are trying to do with the affordances or the capabilities of the technology. And I say match, not over exceed, because we also have the problem where, like me, I am sure you have been in many, many meetings that should have just been an email, that there is not the need. And so what we have done in that situation is we have overstimulated people, right, in a setting with, you know, 15 other folks, and we have taken an hour out of their day and maybe the travel time to get there. And that has created other avenues for exhaustion when, if we had just perceived this information via email, we could not have had the meeting. So you do not want to overmatch, you just want to like match to the complexity of the task. And that is the key to reducing our exhaustion.It's not just distraction that exhausts us18:28: I think we have failed to look at how it is not just being distracted that is a problem, but it is the act of switching itself across all of these different inputs really is a significant source of our exhaustion.Inference is a big driver of exhaustion32:45: Inference is really a big driver of exhaustion. And I would say the place that it most shows up, although not exclusively, is in our social media lives. Because, of course, people are curating their lives in terms of what they post, whether that is LinkedIn or TikTok or Instagram, that does not really matter. And we are constantly not only making inferences of them, but what I find is that we are also very often making inferences about ourselves because we see a past record of all the things that we wrote and all of the things that we posted. And then we are also making inferences of what we think other people think about us based on all the things that we post.Show Links:Recommended Resources:Human MultitaskingTask SwitchingFatigueUnsiloed Podcast Episode 612: Rebecca HindsGuest Profile:Faculty Profile at UC Santa BarbaraPaulLeonardi.comWikipedia ProfileLinkedIn ProfileGuest Work:Amazon Author PageDigital Exhaustion: Simple Rules for Reclaiming Your LifeThe Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AIExpertise, Communication, and OrganizingMateriality and Organizing: Social Interaction in a Technological WorldCar Crashes without Cars: Lessons About Simulation Technology and Organizational Change from Automotive DesignGoogle Scholar Page Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
AI Chat: ChatGPT & AI News, Artificial Intelligence, OpenAI, Machine Learning
In this episode, we discuss Microsoft's new Maya 200 AI inference chip, highlighting its capabilities, its importance for efficient AI model deployment, and how it signifies a major shift towards custom silicon in the AI industry. We also touch upon its potential impact on cost savings and Microsoft's strategy to become a leading player in the AI hardware space.Chapters00:00 Microsoft's Maya 200 AI Chip00:29 AI Box.ai Tools02:03 Power and Performance04:54 Inference vs. Training08:21 Efficiency and Competition14:06 Internal Deployment and Future
In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Roni Burd, a data and AI executive with extensive experience at Amazon and Microsoft, for a deep dive into the evolving landscape of data management and artificial intelligence in enterprise environments. Their conversation explores the longstanding challenges organizations face with knowledge management and data architecture, from the traditional bronze-silver-gold data processing pipeline to how AI agents are revolutionizing how people interact with organizational data without needing SQL or Python expertise. Burd shares insights on the economics of AI implementation at scale, the debate between one-size-fits-all models versus specialized fine-tuned solutions, and the technical constraints that prevent companies like Apple from upgrading services like Siri to modern LLM capabilities, while discussing the future of inference optimization and the hundreds-of-millions-of-dollars cost barrier that makes architectural experimentation in AI uniquely expensive compared to other industries.Timestamps00:00 Introduction to Data and AI Challenges03:08 The Evolution of Data Management05:54 Understanding Data Quality and Metadata08:57 The Role of AI in Data Cleaning11:50 Knowledge Management in Large Organizations14:55 The Future of AI and LLMs17:59 Economics of AI Implementation29:14 The Importance of LLMs for Major Tech Companies32:00 Open Source: Opportunities and Challenges35:19 The Future of AI Inference and Hardware43:24 Optimizing Inference: The Next Frontier49:23 The Commercial Viability of AI ModelsKey Insights1. Data Architecture Evolution: The industry has evolved through bronze-silver-gold data layers, where bronze is raw data, silver is cleaned/processed data, and gold is business-ready datasets. However, this creates bottlenecks as stakeholders lose access to original data during the cleaning process, making metadata and data cataloging increasingly critical for organizations.2. AI Democratizing Data Access: LLMs are breaking down technical barriers by allowing business users to query data in plain English without needing SQL, Python, or dashboarding skills. This represents a fundamental shift from requiring intermediaries to direct stakeholder access, though the full implications remain speculative.3. Economics Drive AI Architecture Decisions: Token costs and latency requirements are major factors determining AI implementation. Companies like Meta likely need their own models because paying per-token for billions of social media interactions would be economically unfeasible, driving the need for self-hosted solutions.4. One Model Won't Rule Them All: Despite initial hopes for universal models, the reality points toward specialized models for different use cases. This is driven by economics (smaller models for simple tasks), performance requirements (millisecond response times), and industry-specific needs (medical, military terminology).5. Inference is the Commercial Battleground: The majority of commercial AI value lies in inference rather than training. Current GPUs, while specialized for graphics and matrix operations, may still be too general for optimal inference performance, creating opportunities for even more specialized hardware.6. Open Source vs Open Weights Distinction: True open source in AI means access to architecture for debugging and modification, while "open weights" enables fine-tuning and customization. This distinction is crucial for enterprise adoption, as open weights provide the flexibility companies need without starting from scratch.7. Architecture Innovation Faces Expensive Testing Loops: Unlike database optimization where query plans can be easily modified, testing new AI architectures requires expensive retraining cycles costing hundreds of millions of dollars. This creates a potential innovation bottleneck, similar to aerospace industries where testing new designs is prohibitively expensive.
Inferact is a new AI infrastructure company founded by the creators and core maintainers of vLLM. Its mission is to build a universal, open-source inference layer that makes large AI models faster, cheaper, and more reliable to run across any hardware, model architecture, or deployment environment. Together, they broke down how modern AI models are actually run in production, why “inference” has quietly become one of the hardest problems in AI infrastructure, and how the open-source project vLLM emerged to solve it. The conversation also looked at why the vLLM team started Inferact and their vision for a universal inference layer that can run any model, on any chip, efficiently.Follow Matt Bornstein on X: https://twitter.com/BornsteinMattFollow Simon Mo on X: https://twitter.com/simon_mo_Follow Woosuk Kwon on X: https://twitter.com/woosuk_kFollow vLLM on X: https://twitter.com/vllm_project Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show 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.
Is the AI SOC a reality, or just vendor hype? In this episode, Antoinette Stevens (Principal Security Engineer at Ramp) joins Ashish to dissect the true state of AI in detection engineering.Antoinette shares her experience building detection program from scratch, explaining why she doesn't trust AI to close alerts due to hallucinations and faulty logic . We explore the "engineering-led" approach to detection, moving beyond simple hunting to building rigorous testing suites for detection-as-code .We discuss the shrinking entry-level job market for security roles , why software engineering skills are becoming non-negotiable , and the critical importance of treating AI as a "force multiplier, not your brain".Guest Socials - Antoinette's LinkedinPodcast Twitter - @CloudSecPod If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-Cloud Security Podcast- Youtube- Cloud Security Newsletter If you are interested in AI Security, you can check out our sister podcast - AI Security PodcastQuestions asked:(00:00) Introduction(02:25) Who is Antoinette Stevens?(04:10) What is an "Engineering-Led" Approach to Detection? (06:00) Moving from Hunting to Automated Testing Suites (09:30) Build vs. Buy: Is AI Making it Easier to Build Your Own Tools? (11:30) Using AI for Documentation & Playbook Updates (14:30) Why Software Engineers Still Need to Learn Detection Domain Knowledge (17:50) The Problem with AI SOC: Why ChatGPT Lies During Triage (23:30) Defining AI Concepts: Memory, Evals, and Inference (26:30) Multi-Agent Architectures: Using Specialized "Persona" Agents (28:40) Advice for Building a Detection Program in 2025 (Back to Basics) (33:00) Measuring Success: Noise Reduction vs. False Positive Rates (36:30) Building an Alerting Data Lake for Metrics (40:00) The Disappearing Entry-Level Security Job & Career Advice (44:20) Why Junior Roles are Becoming "Personality Hires" (48:20) Fun Questions: Wine Certification, Side Quests, and Georgian Food
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.
My links:My Ko-fi: https://ko-fi.com/rhetoricrevolutionSend me a voice message!: https://podcasters.spotify.com/pod/show/liam-connerlyTikTok: https://www.tiktok.com/@mrconnerly?is_from_webapp=1&sender_device=pcEmail: rhetoricrevolution@gmail.comInstagram: https://www.instagram.com/connerlyliam/Podcast | Latin in Layman's - A Rhetoric Revolution https://open.spotify.com/show/0EjiYFx1K4lwfykjf5jApM?si=b871da6367d74d92YouTube: https://www.youtube.com/@MrConnerly
Inference is now the biggest challenge in enterprise AI. In this episode of Eye on AI, Craig Smith speaks with Nick Pandher, VP of Product at Cirrascale, about why AI is shifting from model training to inference at scale. As AI moves into production, enterprises are prioritizing performance, latency, reliability, and cost efficiency over raw compute. The conversation covers the rise of inference-first infrastructure, the limits of hyperscalers, the emergence of neoclouds, and how agentic AI is driving always-on inference workloads. Nick also explains how inference-optimized hardware and serverless AI platforms are shaping the future of enterprise AI deployment. If you are deploying AI in production, this episode explains why inference is the real frontier. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) Preview (00:50) Introduction to Cirrascale and AI inference (03:04) What makes Cirrascale a neocloud (04:42) Why AI shifted from training to inference (06:58) Private inference and enterprise security needs (08:13) Hyperscalers vs neoclouds for AI workloads (10:22) Performance metrics that matter in inference (13:29) Hardware choices and inference accelerators (20:04) Real enterprise AI use cases and automation (23:59) Hybrid AI, regulated industries, and compliance (26:43) Proof of value before AI pilots (31:18) White-glove AI infrastructure vs self-serve cloud (33:32) Qualcomm partnership and inference-first AI (41:52) Edge-to-cloud inference and agentic workflows (49:20) Why AI pilots fail and how enterprises succeed
Discover how Cerebras is challenging NVIDIA with a fundamentally different approach to AI hardware and large-scale inference.In this episode of Startup Project, Nataraj sits down with Andrew Feldman, co-founder and CEO of Cerebras Systems, to discuss how the company built a wafer-scale AI chip from first principles. Andrew shares the origin story of Cerebras, why they chose to rethink chip architecture entirely, and how system-level design decisions unlock new performance for modern AI workloads.The conversation explores:Why inference is becoming the dominant cost and performance bottleneck in AIHow Cerebras' wafer-scale architecture overcomes GPU memory and communication limitsWhat it takes to compete with incumbents like NVIDIA and AMD as a new chip companyThe tradeoffs between training and inference at scaleCerebras' product strategy across systems, cloud offerings, and enterprise deploymentsThis episode is a deep dive into AI infrastructure, semiconductor architecture, and system-level design, and is especially relevant for builders, engineers, and leaders thinking about the future of AI compute.
Plus: China sanctions U.S. defense companies and executives including Northrop Grumman, Boeing and Palmer Luckey over Taiwan arms sale. And Google will let users change their Gmail address. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices
Today virtually all AI compute takes place in centralized data centers, driving the demand for massive power infrastructure. But as workloads shift from training to inference, and AI applications become more latency-sensitive (autonomous vehicles, anyone?), there‘s another pathway: migrating a portion of inference from centralized computing to the edge. Instead of a gigawatt-scale data center in a remote location, we might see a fleet of smaller data centers clustered around an urban core. Some inference might even shift to our devices. So how likely is a shift like this, and what would need to happen for it to substantially reshape AI power? In this episode, Shayle talks to Dr. Ben Lee, a professor of electrical engineering and computer science at the University of Pennsylvania, as well as a visiting researcher at Google. Shayle and Ben cover topics like: The three main categories of compute: hyperscale, edge, and on-device Why training is unlikely to move from hyperscale The low latency demands of new applications like autonomous vehicles How generative AI is training us to tolerate longer latencies Why distributed inference doesn‘t face the same technical challenges as distributed training Why consumer devices may limit model capability Resources: ACM SIGMETRICS Performance Evaluation Review: A Case Study of Environmental Footprints for Generative AI Inference: Cloud versus Edge Internet of Things and Cyber-Physical Systems: Edge AI: A survey Credits: Hosted by Shayle Kann. Produced and edited by Daniel Woldorff. Original music and engineering by Sean Marquand. Stephen Lacey is our executive editor. Catalyst is brought to you by EnergyHub. EnergyHub helps utilities build next-generation virtual power plants that unlock reliable flexibility at every level of the grid. See how EnergyHub helps unlock the power of flexibility at scale, and deliver more value through cross-DER dispatch with their leading Edge DERMS platform, by visiting energyhub.com. Catalyst is brought to you by Bloom Energy. AI data centers can't wait years for grid power—and with Bloom Energy's fuel cells, they don't have to. Bloom Energy delivers affordable, always-on, ultra-reliable onsite power, built for chipmakers, hyperscalers, and data center leaders looking to power their operations at AI speed. Learn more by visiting BloomEnergy.com. Catalyst is supported by Third Way. Third Way's new PACE study surveyed over 200 clean energy professionals to pinpoint the non-cost barriers delaying clean energy deployment today and offers practical solutions to help get projects over the finish line. Read Third Way's full report, and learn more about their PACE initiative, at www.thirdway.org/pace.
Today's clip is from episode 147 of the podcast, with Martin Ingram.Alex and Martin discuss the intricacies of variational inference, particularly focusing on the ADVI method and its challenges. They explore the evolution of approximate inference methods, the significance of mean field variational inference, and the innovative linear response technique for covariance estimation. The discussion also delves into the trade-offs between stochastic and deterministic optimization techniques, providing insights into their implications for Bayesian statistics.Get the full discussion here.Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support. Why is AI so powerful in the cloud but still so limited inside everyday devices, and what would it take to run intelligent systems locally without draining battery or sacrificing privacy? In this episode of Eye on AI, host Craig Smith speaks with Steve Brightfield, Chief Marketing Officer at BrainChip, about neuromorphic computing and why brain inspired architectures may be the key to the future of edge AI. We explore how neuromorphic systems differ from traditional GPU based AI, why event driven and spiking neural networks are dramatically more power efficient, and how on device inference enables faster response times, lower costs, and stronger data privacy. Steve explains why brute force computation works in data centers but breaks down at the edge, and how edge AI is reshaping wearables, sensors, robotics, hearing aids, and autonomous systems. You will also hear real world examples of neuromorphic AI in action, from smart glasses and medical monitoring to radar, defense, and space applications. The conversation covers how developers can transition from conventional models to neuromorphic architectures, what role heterogeneous computing plays alongside CPUs and GPUs, and why the next wave of AI adoption will happen quietly inside the devices we use every day. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI
Chris and Rob are buried in snow again and John is the only one who has done anything in the past week. That doesn't stop them from looking at some snow shoveling alternatives (that may or may not work), discussing the Christmas spirit and remembering "Battle of the Network Stars". They round out the episode by trying to see if they remember the 2010's per Buzzfeed. Emjoy!
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:DADVI is a new approach to variational inference that aims to improve speed and accuracy.DADVI allows for faster Bayesian inference without sacrificing model flexibility.Linear response can help recover covariance estimates from mean estimates.DADVI performs well in mixed models and hierarchical structures.Normalizing flows present an interesting avenue for enhancing variational inference.DADVI can handle large datasets effectively, improving predictive performance.Future enhancements for DADVI may include GPU support and linear response integration.Chapters:13:17 Understanding DADVI: A New Approach21:54 Mean Field Variational Inference Explained26:38 Linear Response and Covariance Estimation31:21 Deterministic vs Stochastic Optimization in DADVI35:00 Understanding DADVI and Its Optimization Landscape37:59 Theoretical Insights and Practical Applications of DADVI42:12 Comparative Performance of DADVI in Real Applications45:03 Challenges and Effectiveness of DADVI in Various Models48:51 Exploring Future Directions for Variational Inference53:04 Final Thoughts and Advice for PractitionersThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël...
Jonathan Bryce, the new CNCF executive director, argues that inference—not model training—will define the next decade of computing. Speaking at KubeCon North America 2025, he emphasized that while the industry obsesses over massive LLM training runs, the real opportunity lies in efficiently serving these models at scale. Cloud-native infrastructure, he says, is uniquely suited to this shift because inference requires real-time deployment, security, scaling, and observability—strengths of the CNCF ecosystem. Bryce believes Kubernetes is already central to modern inference stacks, with projects like Ray, KServe, and emerging GPU-oriented tooling enabling teams to deploy and operationalize models. To bring consistency to this fast-moving space, the CNCF launched a Kubernetes AI Conformance Program, ensuring environments support GPU workloads and Dynamic Resource Allocation. With AI agents poised to multiply inference demand by executing parallel, multi-step tasks, efficiency becomes essential. Bryce predicts that smaller, task-specific models and cloud-native routing optimizations will drive major performance gains. Ultimately, he sees CNCF technologies forming the foundation for what he calls “the biggest workload mankind will ever have.” Learn more from The New Stack about inference: Confronting AI's Next Big Challenge: Inference Compute Deep Infra Is Building an AI Inference Cloud for Developers Join our community of newsletter subscribers to stay on top of the news and at the top of your game. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Sid Sheth is the CEO and co-founder of d-Matrix, the AI chip company making inference efficient and scalable for datacenters. Backed by Microsoft and with $160M raised, Sid shares why rethinking infrastructure is critical to AI's future and how a decade in semiconductors prepared him for this moment.In this conversation, we discuss:Why Sid believes AI inference is the biggest computing opportunity of our lifetime and how it will drive the next productivity boomThe real reason smaller, more efficient models are unlocking the era of inference and what that means for AI adoption at scaleWhy cost, time, and energy are the core constraints of inference, and how D-Matrix is building for performance without compromiseHow the rise of reasoning models and agentic AI shifts demand from generic tasks to abstract problem-solvingThe workforce challenge no one talks about: why talent shortages, not tech limitations, may slow down the AI revolutionHow Sid's background in semiconductors prepared him to recognize the platform shift toward AI and take the leap into building D-MatrixResources:Subscribe to the AI & The Future of Work NewsletterConnect with Sid on LinkedInAI fun fact articleOn How Mastering Skills To Stay Relevant In the Age of AI
This Week in Machine Learning & Artificial Intelligence (AI) Podcast
In this episode, Zain Asgar, co-founder and CEO of Gimlet Labs, joins us to discuss the heterogeneous AI inference across diverse hardware. Zain argues that the current industry standard of running all AI workloads on high-end GPUs is unsustainable for agents, which consume significantly more tokens than traditional LLM applications. We explore Gimlet's approach to heterogeneous inference, which involves disaggregating workloads across a mix of hardware—from H100s to older GPUs and CPUs—to optimize unit economics without sacrificing performance. We dive into their "three-layer cake" architecture: workload disaggregation, a compilation layer that maps models to specific hardware targets, and a novel system that uses LLMs to autonomously rewrite and optimize compute kernels. Finally, we discuss the complexities of networking in heterogeneous environments, the trade-offs between numerical precision and application accuracy, and the future of hardware-aware scheduling. The complete show notes for this episode can be found at https://twimlai.com/go/757.
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Ethan's research involves using lasers to compress matter to extreme conditions to study astrophysical phenomena.Bayesian inference is a key tool in analyzing complex data from high energy density experiments.The future of high energy density physics lies in developing new diagnostic technologies and increasing experimental scale.High energy density physics can provide insights into planetary science and astrophysics.Emerging technologies in diagnostics are set to revolutionize the field.Ethan's dream project involves exploring picno nuclear fusion.Chapters:14:31 Understanding High Energy Density Physics and Plasma Spectroscopy21:24 Challenges in Data Analysis and Experimentation36:11 The Role of Bayesian Inference in High Energy Density Physics47:17 Transitioning to Advanced Sampling Techniques51:35 Best Practices in Model Development55:30 Evaluating Model Performance01:02:10 The Role of High Energy Density Physics01:11:15 Innovations in Diagnostic Technologies01:22:51 Future Directions in Experimental Physics01:26:08 Advice for Aspiring ScientistsThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady,
In this episode, we sit down with Solution Architect Robert Alvarez to discuss the technology behind Pure Key-Value Accelerator (KVA) and its role in accelerating AI inference. Pure KVA is a protocol-agnostic, key-value caching solution that, when combined with FlashBlade data storage, dramatically improves GPU efficiency and consistency in AI environments. Robert—whose background includes time as a Santa Clara University professor, NASA Solution Architect, and work at CERN—explains how this innovation is essential for serving an entire fleet of AI workloads, including modern agentic or chatbot interfaces. Robert dives into the massive growth of the AI Inference market, driven by the need for near real-time processing and low-latency AI applications. This trend makes the need for a solution like Pure KVA critical. He details how KVA removes the bottleneck of GPU memory and shares compelling benchmark results: up to twenty times faster inference with NFS and six times faster with S3, all over standard Ethernet. These performance gains are key to helping enterprises scale more efficiently and reduce overall GPU costs. Beyond the technical deep dive, the episode explores the origin of the KVA idea, the unique Pure IP that enables it, and future integrations like Dynamo and the partnership with Comet for LLM observability. In the popular “Hot Takes” segment, Robert offers his perspective on blind spots IT leaders might have in managing AI data and shares advice for his younger self on the future of the data management space. To learn more about Pure KVA, visit purestorage.com/launch. Check out the new Pure Storage digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 02:21 Background on Our Guest 06:57 Stat of the Episode on AI Inferencing Spend 09:10 Why AI Inference is Difficult at Scale 11:00 How KV Cache Acceleration Works 14:50 Key Partnerships Using KVA 20:28 Hot Takes Segment
In this episode of Skin Anarchy, Dr. Ekta Yadav sits down with Estella Benz, founder and CEO of Inference Beauty, to explore how data intelligence, ingredient transparency, and AI personalization are reshaping the future of skincare. What began as Estella's frustration with confusing ingredient lists and overwhelming product choices evolved into a powerful technology platform designed to decode beauty — not through trends, but through truth.Estella shares how Inference Beauty translates complex INCI labels into language consumers can understand, offering not just definitions but context: why an ingredient is used, what it does, and how it behaves on real skin. In an era dominated by fear-based “clean beauty” rhetoric, her mission is to bring clarity back to the consumer. “People don't need scare tactics,” she says. “They need understanding.”Through structured data, AI, and environmental analytics, Inference Beauty creates intelligent, personalized product recommendations based on climate, sensitivities, ethical preferences, and biological needs — shifting beauty away from one-size-fits-all marketing into a world built “for each.”As e-commerce grows, Estella envisions dynamic digital experiences where product pages adapt to each user, bridging the long-standing gap between what brands believe they're communicating and what consumers actually understand. Yet even as she champions AI, she emphasizes the irreplaceable role of human expertise — especially for active, medical-grade, or prescriptive products.Tune in to hear how Estella Benz is building the next generation of beauty intelligence — where transparency, personalization, and smart data come together to redefine how we discover and trust skincare.Learn more about Inference Beauty on their website and social media!CHAPTERS:0:27 – Introduction & Guest Welcome1:27 – Estella's Background & Early Inspiration2:31 – The Birth of Inference Beauty4:06 – Market Misconceptions & The Female Consumer Gap5:45 – How Personalization Transforms Shopping6:56 – Ingredient Data, Ethics & Environmental Impact10:05 – Transparency, Clean Beauty & Consumer Education16:36 – The Role of AI in Personalized Beauty23:06 – The Future of Tech, Data & Human Touch in BeautyPlease fill out this survey to give us feedback on the show!Don't forget to subscribe to Skin Anarchy on Apple Podcasts, Spotify, or your preferred platform.Reach out to us through email with any questions.Sign up for our newsletter!Shop all our episodes and products mentioned through our ShopMy Shelf!*This is a paid collaboration Hosted on Acast. See acast.com/privacy for more information.
The Information's Finance Editor Ken Brown talks with TITV Host Akash Pasricha about the debut of his new finance newsletter and whether the current AI boom is an impending bubble. We also talk with General Catalyst's Pranav Singhvi and KV Mohan about their novel Customer Value Fund (CVF) that finances growth without equity or traditional debt, and AWS Director of Technology Shaown Nandi discusses how companies can achieve profit margins with AI as inference costs are expected to drop 90%. Lastly, we get into the current state of the creator economy, the shift to interest-based algorithms, and the importance of a sustainable business outside of social with Creator Ventures' Partner and successful creator, Caspar Lee.Articles discussed on this episode:https://www.theinformation.com/articles/soon-call-end-ai-boomTITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to: - The Information on YouTube: https://www.youtube.com/@theinformation- The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda
In this episode of Gradient Dissent, Lukas Biewald talks with Tuhin Srivastava, CEO and founder of Baseten, one of the fastest-growing companies in the AI inference ecosystem. Tuhin shares the real story behind Baseten's rise and how the market finally aligned with the infrastructure they'd spent years building.They get into the core challenges of modern inference, including why dedicated deployments matter, how runtime and infrastructure bottlenecks stack up, and what makes serving large models fundamentally different from smaller ones.Tuhin also explains how vLLM, TensorRT-LLM, and SGLang differ in practice, what it takes to tune workloads for new chips like the B200, and why reliability becomes harder as systems scale. The conversation dives into company-building, from killing product lines to avoiding premature scaling while navigating a market that shifts every few weeks.Connect with us here: Tuhin Srivastva: https://www.linkedin.com/in/tuhin-srivastava/ Lukas Biewald: https://www.linkedin.com/in/lbiewald/Weights & Biases: https://www.linkedin.com/company/wandb/
Juicy citrus meets pepper snap. Orange Block Berry hits fast, bright, and happy, with a blood-orange flavor that sticks around while your cheeks and forehead warm up like sunrise. It's solventless, clean, and way tastier than it has any right to be.Form, look, smellLive rosin batter processed with ice water, glossy light-gold batter with a soft, micro-whipped texture that scoops clean. Nose opens with sweet orange zest and berry candy, backed by black-pepper spice. On inhale you get blood-orange and Earl Grey vibes, on exhale a peppery tickle that can spark a cough. TerpenesLimonene, the citrus driver, mood lift, bright focus. Beta-caryophyllene, peppery grounding, body ease. Myrcene, smooths edges and settles the body. Humulene, light uplift, keeps things crisp. Trace α-pinene possible from the profile and flavor, [Inference].CannabinoidsThe jar listed total THC around 622 mg per gram, think low-to-mid 60 percent potency for this batch, CBD negligible. ExperienceOnset, fast. Head high blooms behind the eyes, wraps the temples, lifts mood without jitter. Feels sativa-leaning hybrid, caffeinated clarity with a calm undercurrent. Minimal cottonmouth, moderate pepper-spice cough from the caryophyllene. Function, great daytime concentrate for focus, light pain relief, and creative flow, with mild munchies potential. How we ran itPer the session, this shined in an e-rig at controlled temps for flavor first, potency second, which kept the citrus terps intact. Pair it withEarl Grey or citrus-forward tea, the bergamot plays nice with the blood-orange terp profile. Light snacks over sugar bombs to avoid chasing munchies, cold water helps if hunger spikes. Bottom lineIf your lane is bright, flavorful solventless that actually wakes you up, this batch of Orange Block Berry from Out of State is a ringer. Clean lift, real citrus, pepper pop, and legs long enough to carry you through a work block without frying you. Save on Bomb Erigs with Code: CSPSave on Dr Dabber with Code: Cannabisschool10Save on Storz & Bickel with Code : CannabisschoolSave on Santa Cruz Shredder with Code: CSP10Score 100 on your test
Marina Moore, a security researcher and the co-chair of the security and compliance TAG of CNCF, shares her concerns about the security vulnerabilities of containers. She explains where the issues originate, providing solutions and discussing alternative routes to using micro-VMs rather than containers. Additionally, she highlights the risks associated with AI inference. Read a transcript of this interview: https://bit.ly/4qUCcyi Subscribe to the Software Architects' Newsletter for your monthly guide to the essential news and experience from industry peers on emerging patterns and technologies: https://www.infoq.com/software-architects-newsletter Upcoming Events: QCon San Francisco 2025 (November 17-21, 2025) Get practical inspiration and best practices on emerging software trends directly from senior software developers at early adopter companies. https://qconsf.com/ QCon AI New York 2025 (December 16-17, 2025) https://ai.qconferences.com/ QCon London 2026 (March 16-19, 2026) https://qconlondon.com/ The InfoQ Podcasts: Weekly inspiration to drive innovation and build great teams from senior software leaders. Listen to all our podcasts and read interview transcripts: - The InfoQ Podcast https://www.infoq.com/podcasts/ - Engineering Culture Podcast by InfoQ https://www.infoq.com/podcasts/#engineering_culture - Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: - Mastodon: https://techhub.social/@infoq - X: https://x.com/InfoQ?from=@ - LinkedIn: https://www.linkedin.com/company/infoq/ - Facebook: https://www.facebook.com/InfoQdotcom# - Instagram: https://www.instagram.com/infoqdotcom/?hl=en - Youtube: https://www.youtube.com/infoq - Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Learn and share the changes and innovations in professional software development. - Join a community of experts. - Increase your visibility. - Grow your career. https://www.infoq.com/write-for-infoq
This was a terrible call!
The episode features Baseten CEO and cofounder Tuhin, who shares Baseten's journey from a small team in the pre-GenAI era to scaling rapidly and raising $150M in Series D funding. The discussion delves into building robust inference infrastructure for AI applications, navigating market shifts, and developing tools that prioritize speed, developer experience, and customer feedback loops.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links: • Baseten • Tuhin's Linkedin
In this episode of Phoenix Cast, hosts John, Rich, and Kyle are joined by special guest Capt Chris Clark - the Marine Corps Artificial Intelligence Lead in the Marine Corps Deputy CLinks:-(USMC Fellowships) https://www.marines.mil/News/Messages/Messages-Display/Article/4315247/fy26-artificial-intelligence-fellowship-programs/-https://www.marines.mil/News/Messages/Messages-Display/Article/4325857/update-to-maradmin-46025-fy26-artificial-intelligence-fellowship-programs/ -(Private Sector Solutions) https://www.anduril.com/article/anduril-s-eagleeye-puts-mommandant for Information Service Data Office to discuss Marine Corps AI. Have a listen, and let us know what you think!We'd love to hear your thoughts! Tweet us at our new handle, @ThePhoenixCast, and don't forget to join our LinkedIn Group to connect with fellow Phoenix Casters. If you enjoyed the episode, help us out by leaving one of those coveted 5-star reviews on Apple Podcasts. Thanks for listening!ission-command-and-ai-directly-into-the-warfighter-s-helmet/ -(Inference at the Edge) https://research.ibm.com/blog/northpole-ibm-ai-chip Kurzgesagt video on AI Slop AI Slop Is Destroying The Internet
Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas
Science has an incredibly impressive track record of uncovering nonintuitive ideas about the universe that turn out to be surprisingly accurate. It can be tempting to think of scientific discoveries as being carefully constructed atop a rock-solid foundation. In reality, scientific progress is tentative and fallible. Scientists propose models, assign them probabilities, and run tests to see whether they succeed or fail. In cosmologist Andrew Jaffe's new book, The Random Universe, he illustrates how models and probability help us make sense of the cosmos.Blog post with transcript: https://www.preposterousuniverse.com/podcast/2025/11/10/335-andrew-jaffe-on-models-probability-and-the-universe/Support Mindscape on Patreon.Andrew Jaffe received his Ph.D. in physics from the University of Chicago. He is currently a professor of astrophysics and cosmology and Director of the Imperial Centre for Inference and Cosmology at Imperial College, London. His research lies at the intersection of theoretical and observational cosmology, including the Planck Surveyor, Euclid, LISA, and Simons Observatory collaborations.Web siteImperial web pageGoogle Scholar publicationsAmazon author pageSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Australia needs control over its intelligence layer, not just its data. We explore SCX's sovereign AI cloud, Project Magpie's cultural reasoning, and why inference economics and time-to-market beat hype-driven buildouts.• sovereign AI as control and context, not just security• SCX's inference cloud and partnership with SambaNova• Project Magpie fine-tuning the reasoning layer for Australia• training vs inference split to optimize cost and speed• tokens per kilowatt as the core unit economics• open source vs closed models in enterprise adoption• retrofitting existing data centers with pre-assembled racks• moving pilots to production through cost, control, and confidence• regional strategy across Southeast Asia and exportable tokens• agents shifting work to domain teams, doing more not just cutting costs• candid MBA debate on value, narrative, and people skills• playful Spark Tank on pickleball and rapid-fire personal insightsWhat if a nation's most critical asset isn't oil, power, or spectrum—but intelligence? We sit down with Southern Cross AI (SCX) founder David Keane, co-founder and CSO Akash Agrawal, and SambaNova's Chief Product and Strategy Officer Abhi Ingle to unpack how a sovereign AI cloud can protect context, culture, and control while still competing on cost and speed. From Australia's national needs to regional demand across Southeast Asia, we chart a pragmatic route from vision to working systems.David explains why SCX is built around inference as a service and how Project Magpie fine-tunes the reasoning layer so models “think like an Australian,” reflecting local law, language, and norms. Abhi breaks down training vs inference in plain English, clarifying why pretraining might live on massive GPU clusters while high-throughput, energy-efficient inference thrives on SambaNova's ASIC-based systems. Akash digs into enterprise realities—data sovereignty, runaway costs, and integration roadblocks—and makes the case for open source models you can fork, fine-tune, and operate within your perimeter.We get practical about tokens per kilowatt as the new ROI, pre-assembled racks that drop into existing data centers, and managed services that cut time-to-market from years to months. We explore why most buyers don't care which chip is under the hood—they care about latency, reliability, and price—and how that shifts competition from hardware logos to delivered outcomes. Go to SCX.ai to experience the future of sovereign AI.Remember, in order to win the “$1,000 token credit" you'll have to explain what a magpie is in the comments, and the team at SCX will judge the winner!David Keane - https://www.linkedin.com/in/dakeane/David serves as the Founder & CEO of SouthernCrossAI (SCX.ai), an Inference-as-a-Service platform dedicated to establishing sovereign, scalable, and cost-efficient AI infrastructure tailored for Australian requirements.Akash Agarwal - https://www.linkedin.com/in/aagarwal/Currently, Akash serves as the Chief Strategy Officer and Co-Founder of SouthernCrossAI (SCX.ai).Abhi Ingle - https://www.linkedin.com/in/ingle-abhi/Abhi Ingle - Currently, Abhi serves as the Chief Product & Strategy Officer (CPSO) at SWebsite: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Sandeep Parikh: https://www.instagram.com/sandeepparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com
In this special bonus episode of the Every Little Model podcast, we explore the Ladder of Inference—a mental model that reveals how we move from observable data to conclusions and actions, often in the blink of an eye. Developed by Chris Argyris and popularized in Peter Senge's The Fifth Discipline, this framework helps us understand how we jump to conclusions, misread situations, and sometimes make decisions we later regret.
Tensormesh uses an expanded form of KV Caching to make inference loads as much as ten times more efficient. Plus, Palantir said on Thursday it had struck a partnership with Lumen Technologies that will see the telecommunications company using the data management company's AI software to build capabilities to support enterprise AI services. Learn more about your ad choices. Visit podcastchoices.com/adchoices
Edge of the Web - An SEO Podcast for Today's Digital Marketer
Erin welcomes Carolyn Shelby, the Principal SEO at Yoast and a renowned authority in technical and enterprise SEO. Carolyn brings decades of hands-on experience from her pioneering days in digital marketing, working with brands like Disney's ESPN, Tribune Publishing, and major nonprofits. The conversation kicks off with a surprising twist—Carolyn's unique title as Queen of the micronation Ladonia—before diving into her role at Yoast and their latest innovation: the LLMs.txt file generator. Carolyn explains how this new file helps websites communicate their most valuable content directly to large language models like ChatGPT and Google's AI, streamlining the way future search agents discover and answer questions with information from your site. We explore what inspired Yoast's push to roll out LLMs.txt to over 13 million sites, what website owners should include in their files, potential industry pushback, the adoption challenge with search giants, and how this moment could change the way websites optimize for AI-driven search results. Key Segments: [00:01:46] Introducing Carolyn Shelby, Senior SEO at Yoast [00:03:09] Queen of the Micronation Ladonia? [00:07:56] What is the LLMS.txt file? [00:08:59] LLMS.txt is a Treasure Map [00:14:38] A New File, along with Robots.txt and Sitemap.xml [00:15:41] What inspired Yoast to create this LLM text file? [00:17:12] EDGE of the Web Sponsor: PreWriter.AI [00:18:22] LLMS.txt proposed by Jeremy Howard (Sept, 2024) [00:22:37] Standard Uniformity and Acceptance? [00:24:43] Housekeeping [00:29:37] LLM Markdown Effort Questioned: Exploitation? [00:31:41] LLMs Lack Memory at Inference [00:34:07] EDGE of The Web Sponsor: Inlinks (WAIKAY) [00:36:09] Pushback on the LLMS.txt file Thanks to Our Sponsors! PreWriter.AI: https://edgeofthewebradio.com/prewriter Inlinks WAIKAY https://edgeofthewebradio.com/waikay Follow Our Guest Twitter: @cshel LinkedIn: https://www.linkedin.com/in/cshel/ Resources Learn about Ladonia (DONATE!): https://www.ladonia.org/about/ Carolyn's Posts on LLMS.txt: https://www.cshel.com/ai-seo/how-llms-interpret-content-structuring-for-ai-search-unedited-version/ https://searchengineland.com/llms-txt-isnt-robots-txt-its-a-treasure-map-for-ai-456586
My most recent guest, Gerry O'Sullivan, talked with me about her process, The Journey of Inference. As she puts it succinctly: “Our Journey of Inference interprets the world of observable data according to our unique perspective or paradigm.”It's clear from Gerry's process and our conversation that our inferences can get us into trouble, precisely because we each carry a unique perspective or paradigm.Dictionary definitions of infer are, if not quite unique, not fully consistent.For example, one says infer means to conclude through reasoning. Another than infer means to guess or use reasoning. And yet another statesInfer can mean “to derive by reasoning; conclude or judge from premises or evidence.”It's that guessing, those premises, that can wreak havoc. Do you have comments or suggestions about a topic or guest? An idea or question about conflict management or conflict resolution? Let me know at jb@dovetailresolutions.com! And you can learn more about me and my work as a mediator and a Certified CINERGY® Conflict Coach at www.dovetailresolutions.com and https://www.linkedin.com/in/janebeddall/.Enjoy the show for free on your favorite podcast app or on the podcast website: https://craftingsolutionstoconflict.com/
This Week in Machine Learning & Artificial Intelligence (AI) Podcast
In this episode, we're joined by Kunle Olukotun, professor of electrical engineering and computer science at Stanford University and co-founder and chief technologist at Sambanova Systems, to discuss reconfigurable dataflow architectures for AI inference. Kunle explains the core idea of building computers that are dynamically configured to match the dataflow graph of an AI model, moving beyond the traditional instruction-fetch paradigm of CPUs and GPUs. We explore how this architecture is well-suited for LLM inference, reducing memory bandwidth bottlenecks and improving performance. Kunle reviews how this system also enables efficient multi-model serving and agentic workflows through its large, tiered memory and fast model-switching capabilities. Finally, we discuss his research into future dynamic reconfigurable architectures, and the use of AI agents to build compilers for new hardware. The complete show notes for this episode can be found at https://twimlai.com/go/751.
The panel explores how M-series Macs—with huge unified memory and efficient silicon—are gaining traction for AI inference and on-device privacy, citing MacStadium use cases and enterprise angles like Copilot adoption. Chuck Joiner, David Ginsburg, Marty Jencius, Brian Flanigan-Arthurs, Eric Bolden, Guy Serle, Web Bixby, Jeff Gamet, Jim Rea, and Mark Fuccio contrast training vs. inference, discuss small language models, and corporate data policies. The session wraps up with the alleged FCC leak of iPhone 16e schematics, and Xiaomi's unabashed Apple cloning—plus a quick note on viral AI fakes. This edition of MacVoices is brought to you by the MacVoices Dispatch, our weekly newsletter that keeps you up-to-date on any and all MacVoices-related information. Subscribe today and don't miss a thing. Show Notes: Chapters: [0:30] AI workloads on Macs and unified memory advantages [1:36] Training vs. inference explained; why memory matters [3:49] M3/M4 bandwidth, neural accelerators, and privacy [5:35] Avoiding the “NVIDIA tax” with custom silicon [7:13] Power efficiency and enterprise adoption angles [9:45] User education and Copilot in corporate settings [12:42] Small language models for classrooms and offline use| [16:34] Alleged FCC leak of iPhone 16e schematics [22:47] Xiaomi's cloning culture and SEO gaming [25:29] Viral AI “security footage” hoaxes and media literacy Links: MacStadium: Macs increasingly being adopted for enterprise AI workloads https://appleworld.today/2025/09/macstadium-macs-increasingly-being-adopted-for-enterprise-ai-workloads/ College football keeps picking iPad over Surface as fourth conference joins team Apple https://9to5mac.com/2025/09/25/college-football-keeps-picking-ipad-over-surface-as-fourth-conference-joins-team-apple/ Xiaomi's latest Apple clones include 'Hyper Island' and 'Pad Mini' tablet https://9to5google.com/2025/09/26/xiaomis-latest-apple-clones-include-hyper-island-and-pad-mini-tablet-gallery AI Video of Sam Altman Stealing GPUs https://www.instagram.com/ai.innovationshub/reel/DPPdo3VDxmI/ FCC mistakenly leaks confidential iPhone 16e schematics https://appleinsider.com/articles/25/09/29/fcc-mistakenly-leaks-confidential-iphone-16e-schematics?utm_source=rss Guests: Web Bixby has been in the insurance business for 40 years and has been an Apple user for longer than that.You can catch up with him on Facebook, Twitter, and LinkedIn, but prefers Bluesky. Eric Bolden is into macOS, plants, sci-fi, food, and is a rural internet supporter. You can connect with him on Twitter, by email at embolden@mac.com, on Mastodon at @eabolden@techhub.social, on his blog, Trending At Work, and as co-host on The Vision ProFiles podcast. Brian Flanigan-Arthurs is an educator with a passion for providing results-driven, innovative learning strategies for all students, but particularly those who are at-risk. He is also a tech enthusiast who has a particular affinity for Apple since he first used the Apple IIGS as a student. You can contact Brian on twitter as @brian8944. He also recently opened a Mastodon account at @brian8944@mastodon.cloud. Mark Fuccio is actively involved in high tech startup companies, both as a principle at piqsure.com, or as a marketing advisor through his consulting practice Tactics Sells High Tech, Inc. Mark was a proud investor in Microsoft from the mid-1990's selling in mid 2000, and hopes one day that MSFT will be again an attractive investment. You can contact Mark through Twitter, LinkedIn, or on Mastodon. Jeff Gamet is a technology blogger, podcaster, author, and public speaker. Previously, he was The Mac Observer's Managing Editor, and the TextExpander Evangelist for Smile. He has presented at Macworld Expo, RSA Conference, several WordCamp events, along with many other conferences. You can find him on several podcasts such as The Mac Show, The Big Show, MacVoices, Mac OS Ken, This Week in iOS, and more. Jeff is easy to find on social media as @jgamet on Twitter and Instagram, jeffgamet on LinkedIn., @jgamet@mastodon.social on Mastodon, and on his YouTube Channel at YouTube.com/jgamet. David Ginsburg is the host of the weekly podcast In Touch With iOS where he discusses all things iOS, iPhone, iPad, Apple TV, Apple Watch, and related technologies. He is an IT professional supporting Mac, iOS and Windows users. Visit his YouTube channel at https://youtube.com/daveg65 and find and follow him on Twitter @daveg65 and on Mastodon at @daveg65@mastodon.cloud. Dr. Marty Jencius has been an Associate Professor of Counseling at Kent State University since 2000. He has over 120 publications in books, chapters, journal articles, and others, along with 200 podcasts related to counseling, counselor education, and faculty life. His technology interest led him to develop the counseling profession ‘firsts,' including listservs, a web-based peer-reviewed journal, The Journal of Technology in Counseling, teaching and conferencing in virtual worlds as the founder of Counselor Education in Second Life, and podcast founder/producer of CounselorAudioSource.net and ThePodTalk.net. Currently, he produces a podcast about counseling and life questions, the Circular Firing Squad, and digital video interviews with legacies capturing the history of the counseling field. This is also co-host of The Vision ProFiles podcast. Generally, Marty is chasing the newest tech trends, which explains his interest in A.I. for teaching, research, and productivity. Marty is an active presenter and past president of the NorthEast Ohio Apple Corp (NEOAC). Jim Rea built his own computer from scratch in 1975, started programming in 1977, and has been an independent Mac developer continuously since 1984. He is the founder of ProVUE Development, and the author of Panorama X, ProVUE's ultra fast RAM based database software for the macOS platform. He's been a speaker at MacTech, MacWorld Expo and other industry conferences. Follow Jim at provue.com and via @provuejim@techhub.social on Mastodon. Guy Serle, best known for being one of the co-hosts of the MyMac Podcast, sincerely apologizes for anything he has done or caused to have happened while in possession of dangerous podcasting equipment. He should know better but being a blonde from Florida means he's probably incapable of understanding the damage he has wrought. Guy is also the author of the novel, The Maltese Cube. You can follow his exploits on Twitter, catch him on Mac to the Future on Facebook, at @Macparrot@mastodon.social, and find everything at VertShark.com. Support: Become a MacVoices Patron on Patreon http://patreon.com/macvoices Enjoy this episode? Make a one-time donation with PayPal Connect: Web: http://macvoices.com Twitter: http://www.twitter.com/chuckjoiner http://www.twitter.com/macvoices Mastodon: https://mastodon.cloud/@chuckjoiner Facebook: http://www.facebook.com/chuck.joiner MacVoices Page on Facebook: http://www.facebook.com/macvoices/ MacVoices Group on Facebook: http://www.facebook.com/groups/macvoice LinkedIn: https://www.linkedin.com/in/chuckjoiner/ Instagram: https://www.instagram.com/chuckjoiner/ Subscribe: Audio in iTunes Video in iTunes Subscribe manually via iTunes or any podcatcher: Audio: http://www.macvoices.com/rss/macvoicesrss Video: http://www.macvoices.com/rss/macvoicesvideorss
Everyone's talking about the AI datacenter boom right now. Billion dollar deals here, hundred billion dollar deals there. Well, why do data centers matter? It turns out, AI inference (actually calling the AI and running it) is the hidden bottleneck slowing down every AI application you use (and new stuff yet to be released). In this episode, Kwasi Ankomah from SambaNova Systems explains why running AI models efficiently matters more than you think, how their revolutionary chip architecture delivers 700+ tokens per second, and why AI agents are about to make this problem 10x worse.
Do you ever feel like you're lost in a digital funhouse, bombarded by conflicting headlines, biased sources, and endless rabbit holes? In an age of information overload, the most crucial skill isn't coding or a new language—it's learning how to think. This episode kicks off our journey by redefining critical thinking not as a negative act of criticism, but as a constructive, powerful toolkit for building a reliable understanding of the world. We strip away the jargon and explore the fundamental actions and mindsets that empower a clear and disciplined mind. Join us as we make the urgent case for why this timeless skill has become the essential survival guide for the 21st century. In this episode, you'll learn: What critical thinking reallyis: Moving beyond cynicism to a constructive process of Analysis, Evaluation, and Inference. The Three Pillars of a Thinking Mind:Discover why Logic, Intellectual Humility, and Skepticism are the bedrock of rational thought. The Three Tsunamis of the Modern Age:Understand why the rise of misinformation, generative AI, and global complexity makes critical thinking more essential now than ever before. To unlock full access to all our episodes, consider becoming a premium subscriber on Apple Podcasts or Patreon. And don't forget to visit englishpluspodcast.com for even more content, including articles, in-depth studies, and our brand-new audio series and courses now available in our Patreon Shop!
Is chronic pain really “in the body”… or in the brain's predictions about the body?Today on the Crackin' Backs Podcast, we sit down with Dr. Jorge Esteves, PhD, DO—an osteopath, educator, and researcher whose work reframes low back pain, sciatica, and other MSK issues through the lens of predictive processing, active inference, and interoception. Dr. Esteves explains why pain is more than a physical signal: it's shaped by mood, memory, context, and environment—and how the right mix of smart touch, simple movement, precise language, and meaning can rewrite faulty predictions and dial down threat in the nervous system.We explore what he calls “smart touch”—the affective, well-timed, well-paced contact that improves therapeutic alliance, entrains breath and rhythm, and helps the brain feel safe enough to update its story about the spine. We also unpack fresh imaging work suggesting hands-on care can influence connectivity in pain and interoceptive hubs, including the insula—right where body-signal meaning is made. You'll leave with a 5-minute daily recalibration (breath cue + one gentle movement + one self-touch drill) to keep predictions aligned with reality—especially during a flare.What You'll LearnPain ≠ damage: Why back pain often persists due to over-protective predictions and how to nudge them toward safety.Smart touch, real change: How affective touch, pacing, and breath cues shift interoceptive processing and calm threat.Therapeutic alliance matters: The first 10 minutes that build trust—and the phrases clinicians should avoid because they raise threat.Brains on hands-on care: New imaging insights on how manual therapy may modulate brain connectivity in chronic low back pain.Learn More / Contact Dr. EstevesOfficial site: Prof Jorge EstevesGoogle Scholar (Atlântica University, Portugal): Google ScholarResearchGate: https://www.researchgate.net/profile/Jorge-Esteves-3 ResearchGate(En)active Inference paper (open-access): FrontiersEmail (from CV): osteojorge@gmail.com Pro OsteoLinkedIn: https://www.linkedin.com/in/dr-jorge-esteves-27371522/ Pro OsteoTwitter/X: https://twitter.com/JEsteves_osteo Pro OsteoWe are two sports chiropractors, seeking knowledge from some of the best resources in the world of health. From our perspective, health is more than just “Crackin Backs” but a deep dive into physical, mental, and nutritional well-being philosophies. Join us as we talk to some of the greatest minds and discover some of the most incredible gems you can use to maintain a higher level of health. Crackin Backs Podcast
Simon and Jillian keep you up to date with all the latest releases and capabilities!
When your client jumps to conclusions, the Ladder of Inference can help. In this episode, Elena demonstrates how to use the Ladder to surface assumptions, expand thinking, and move toward more grounded decision-making.Notable moments: Keep learning: Subscribe: Ladder of Inference Skill Session in the Coach Learning Library The First 10 Minutes Receive weekly wisdom and tools from Elena delivered to your inboxWatch the Bright Morning Podcast on YouTube and subscribe to our channelBecome a Bright Morning Member Follow Elena on Instagram and LinkedInFollow Bright Morning on LinkedIn and InstagramSupport the show:Become a Friend of the Podcast Rate and review usReflection questions: What kinds of judgments or generalizations do you commonly hear in your coaching conversations?How might the Ladder of Inference help your clients think more clearly or equitably?What do you need to feel confident using this tool in your own practice?Podcast Transcript and Use:Bright Morning Consulting owns the copyright to all content and transcripts of The Bright Morning Podcast, with all rights reserved. You may not distribute or commercially exploit the content without our express written permission.We welcome you to download and share the podcast with others for personal use; please acknowledge The Bright Morning Podcast as the source of the material.Episode Transcript
Fill out this short listener survey to help us improve the show: https://forms.gle/bbcRiPTRwKoG2tJx8 Tri Dao, Chief Scientist at Together AI and Princeton professor who created Flash Attention and Mamba, discusses how inference optimization has driven costs down 100x since ChatGPT's launch through memory optimization, sparsity advances, and hardware-software co-design. He predicts the AI hardware landscape will shift from Nvidia's current 90% dominance to a more diversified ecosystem within 2-3 years, as specialized chips emerge for distinct workload categories: low-latency agentic systems, high-throughput batch processing, and interactive chatbots. Dao shares his surprise at AI models becoming genuinely useful for expert-level work, making him 1.5x more productive at GPU kernel optimization through tools like Claude Code and O1. The conversation explores whether current transformer architectures can reach expert-level AI performance or if approaches like mixture of experts and state space models are necessary to achieve AGI at reasonable costs. Looking ahead, Dao sees another 10x cost reduction coming from continued hardware specialization, improved kernels, and architectural advances like ultra-sparse models, while emphasizing that the biggest challenge remains generating expert-level training data for domains lacking extensive internet coverage. (0:00) Intro(1:58) Nvidia's Dominance and Competitors(4:01) Challenges in Chip Design(6:26) Innovations in AI Hardware(9:21) The Role of AI in Chip Optimization(11:38) Future of AI and Hardware Abstractions(16:46) Inference Optimization Techniques(33:10) Specialization in AI Inference(35:18) Deep Work Preferences and Low Latency Workloads(38:19) Fleet Level Optimization and Batch Inference(39:34) Evolving AI Workloads and Open Source Tooling(41:15) Future of AI: Agentic Workloads and Real-Time Video Generation(44:35) Architectural Innovations and AI Expert Level(50:10) Robotics and Multi-Resolution Processing(52:26) Balancing Academia and Industry in AI Research(57:37) Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint
Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified, focused on AI and complex systems. – My companies: Reverse the age of my skin using the same cream at https://qr.diamandis.com/oneskinpod Apply to Dave's and my new fund:https://qr.diamandis.com/linkventureslanding –- Connect with Peter: X Instagram Connect with Dave: X LinkedIn Connect with Alex Website LinkedIn X Email Listen to MOONSHOTS: Apple YouTube – *Recorded on September 2nd, 2025 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. -------- Chapters 02:50 - The Importance of Positive News in Tech 05:49 - Education and the Future of Learning 09:02 - AI Wars: Colossus II and Hardware Scaling 12:02 - Training vs. Inference in AI Models 18:02 - Elon Musk's XAI and Recruitment Strategies 20:47 - The Rise of NanoBanana and AI in Media 26:38 - Google's AI-Powered Live Translation 29:03 - The Future of Language and Cultural Diversity 48:07 - AI Disruption in Language Learning 51:56 - The Future of SaaS Companies 57:28 - NVIDIA's Market Position and AI Chips 59:51 - China's AI Chip Landscape 01:03:13 - India's AI Infrastructure Revolution 01:11:11 - The Concept of AI Governance 01:15:16 - Economic Implications of AI Investment 01:19:54 - AI in Healthcare Innovations 01:36:32 - The Future of Urban Planning with AI 01:40:39 - Electricity Costs and AI's Impact Learn more about your ad choices. Visit megaphone.fm/adchoices
Live from the 2025 Nonference, Mike and Tim (In the same room) are joined in studio by Journey Church Pastors Suzie P. Lind and Sam Barnhart. What does it mean to truly deconstruct faith, and how can that journey lead to healing? In this heartfelt and thought-provoking conversation, the hosts tackle the complexities of "deconstruction," exploring disillusionment, doubt, discipleship, and ultimately, the pursuit of Jesus amidst cultural challenges. From addressing church hurt and systemic issues to reexamining theologies and navigating the intersection of faith and politics, this episode unpacks the role of the church in society and the personal journeys that shape our understanding of Christianity. Through themes of justice, cruciformity, and reimagining what it means to follow Jesus, the discussion dives deep into how cultural realities and historical practices influence our faith. The panel shares stories of heartbreak and hope, challenging the idea that questioning or rethinking faith is a departure from Jesus—instead, it's often a move toward deeper authenticity. Whether you're wrestling with theological questions, processing church trauma, or striving to navigate cultural issues as a follower of Jesus, this episode offers a space for reflection and community. Feel free to share your thoughts, send in your questions, or engage with us on Facebook and Instagram. Let's continue pursuing a faith marked by humility, curiosity, and justice together. CHAPTERS: 00:00 - Welcome to the Nonference 02:12 - The Tennessee Buzz 04:35 - Deconstruction: A Second Innocence 07:11 - The Six D's of Deconstruction 14:46 - Why People Are Disillusioned 18:18 - Did the Church Move or Did the Curtain Open 23:16 - Deconstruction as Repentance 28:32 - Discipleship in Deconstruction 29:41 - Understanding Deconversion 32:44 - Redefinition in Faith 34:58 - Navigating Doubt 38:50 - Biblical Foundations of Deconstruction 41:00 - Purpose of Inference 42:26 - Q&A: Insights from Stafford 49:49 - National Park Moments 51:09 - Experiencing Death and Grief 56:32 - Neuroscience of Belief 56:41 - Josh McDowell and the Talking Snake 1:02:40 - Embracing the Power of Weakness 1:03:12 - Thank You 1:04:08 - Credits As always, we encourage and would love discussion as we pursue. Feel free to email in questions to hello@voxpodcast.com, and to engage the conversation on Facebook and Instagram. We're on YouTube (if you're into that kinda thing): VOXOLOGY TV. Our Merch Store! ETSY Learn more about the Voxology Podcast Subscribe on iTunes or Spotify Support the Voxology Podcast on Patreon The Voxology Spotify channel can be found here: Voxology Radio Follow us on Instagram: @voxologypodcast and "like" us on Facebook Follow Mike on Twitter: www.twitter.com/mikeerre Music in this episode by Timothy John Stafford Instagram & Twitter: @GoneTimothy
Many students graduate high school today without having read a book cover to cover. Many students struggle to learn to read at all. How did this happen? Listen as educator and author Doug Lemov talks with EconTalk's Russ Roberts about the failed fads in reading education, the mistaken emphasis on vocabulary as a skill, and the importance of background knowledge for thinking and reading comprehension. Lemov and Roberts also discuss their love of difficult-to-read authors, the power of reading in groups, the value of keeping a reading journal, and how even basketball can be more enjoyable when we have the right terminology.
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