Embedded AI - Intelligence at the Deep Edge

David Such
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“Intelligence at the Deep Edge” is a podcast exploring the fascinating intersection of embedded systems and artificial intelligence. Dive into the world of cutting-edge technology as we discuss how AI is revolutionizing edge devices, enabling smarter sensors, efficient machine learning models, and real-time decision-making at the edge. Discover more on Embedded AI (https://medium.com/embedded-ai) — our companion publication where we detail the ideas, projects, and breakthroughs featured on the podcast. Help support the podcast - https://www.buzzsprout.com/2429696/support

  1. 2d ago

    Large Language Monkeys: Why Noise Yields No Knowledge

    Send us Fan Mail There is an old claim that a truly random source contains all knowledge: give monkeys enough time at typewriters and Shakespeare falls out. In 2024 two Sydney mathematicians did the arithmetic and found the universe ends first. But the idea has a modern tail. We now have language models that can spot meaningful text instantly, so why not let randomness generate and an LLM extract? This episode works through why that fails, and why the failure is precise: in a random stream, the address of any text costs as many bits as the text itself. Along the way: Borges' Library of Babel, a website that actually built it, DeepMind systems that made the generate-and-filter idea work by cheating in exactly the right way, and what your brain does with noise that an LLM cannot. Support the show If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!

    Large Language Monkeys: Why Noise Yields No Knowledge

  2. Aug 23

    Great Minds Think (a Little Too Much) Alike

    Send us Fan Mail Anthropic gave thirty AI agents the same coding task. Eighteen of them named their git branch exactly the same thing. That result opens one of the strangest findings of 2026: when teams of AI agents face the classic "hidden profile" experiment, where the shared evidence points to the wrong answer and the decisive facts are scattered across individuals, they succeed only 17 to 36 percent of the time. Human groups in the original 1985 study? 18 percent. Forty years, a completely different kind of mind, the same failure. In this episode we dig into why. We trace the mathematics of groupthink from information cascades to Condorcet juries, then follow the trail into territory embedded engineers know well: the 1986 Knight and Leveson experiment that shattered the independence assumption in N-version software, Airbus's dissimilar redundancy, and the Lufthansa flight where two frozen sensors outvoted the one telling the truth. Along the way, honeybees show us a working reference design: a two-milligram brain that refuses to repeat a rumor. We close with the fixes: engineered dissenters, forced disclosure protocols, reputation infrastructure for agents, and the case for "keeping the weirdness alive" through a genuinely diverse AI ecosystem, including heterogeneous fleets of small models at the edge. Key sources: Anthropic's "Patterns and problems in emerging multiagent systems," Rohit Krishnan's "LLM councils show groupthink," and Thinking Machines' "The Future Worth Building Is Human." Support the show If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!

    Great Minds Think (a Little Too Much) Alike

  3. Aug 18

    Purpose as a Service

    Send us Fan Mail What happens when work no longer gives our lives structure, identity, or meaning? This episode explores “Purpose as a Service”—the idea that purpose could be intentionally designed and delivered in an age of mass automation. We examine the decline of traditional anchors such as employment, religion, and community; the rise of wellness programs, professional coaching, and AI companions; and what universal basic income trials reveal about the limits of financial security. Can external services genuinely help people build fulfilling lives, or does true purpose depend on personal agency and authorship? Consider this a blueprint for one of humanity’s biggest future challenges: learning how to live well when a job is no longer at the center of life. Support the show If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!

    Purpose as a Service

  4. Jul 24

    The Great Escape or why are LLMs Good at Hacking?

    Send us Fan Mail Last week OpenAI and Hugging Face published a joint post-mortem on an incident that reads like the plot of a heist film. During an internal evaluation designed to measure cyber capability, a set of OpenAI models (GPT-5.6 Sol and an unnamed pre-release sibling, both run with their cyber refusals switched off) were told to solve a benchmark called ExploitGym. They could not reach the answers from inside the sandbox, so they went and got them. So why are Large Language Models so good at hacking? It's the data stupid... Support the show If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!

    The Great Escape or why are LLMs Good at Hacking?

  5. Jun 26

    Who Owns the Hours Inside the Robot's Mind?

    Send us Fan Mail In this episode we argue that the binding constraint on robotic intelligence has moved. It is no longer compute, and it was never the model architecture. It is the supply of human-generated demonstration data, the recorded hours of people driving real robots through real tasks. Unlike text or images, this data cannot be scraped from the internet. It has to be manufactured, one time-aligned teleoperation episode at a time, on instrumented hardware, at considerable cost. Because the performance of vision-language-action models scales directly with the volume and diversity of these recorded hours, a durable advantage is forming for the few organizations wealthy enough to fund collection at scale. We walk through the economics of producing an hour of demonstration data, the skilled and largely invisible human labor inside that hour, and the deployment flywheels that let incumbents compound their lead. What happens when the mind of every robot is distilled from a handful of private data silos? Support the show If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!

    Who Owns the Hours Inside the Robot's Mind?

  6. Jun 15

    Why Humanoid Robots Need Two Clocks

    Send us Fan Mail A useful general-purpose robot has to do two things that fight each other. It has to think slowly enough to understand "put away the groceries," and it has to move fast enough to keep a grip on the milk carton without crushing it. The part that understands is large and slow. The part that moves has to be small and fast. You cannot run both on the same clock. This episode looks at the design now shipping on real robots: Vision-Language-Action models that simply run two clocks at once. A slow brain that thinks a handful of times a second, a fast brain that moves two hundred times a second, and a single note of intent passed between them. We walk through how Figure's Helix splits a 7-billion-parameter planner from an 80-million-parameter controller, why "action chunking" keeps the motion smooth, and how a March 2026 project squeezed the whole pipeline onto a 40-watt module with no cloud connection at all. This two-speed design is the same answer evolution reached, with the cortex deciding the goal and the cerebellum handling the reflexes. When biology and engineering independently land on the same structure, it is probably telling us something fundamental about what it takes to be intelligent inside a moving body.  Support the show If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!

    Why Humanoid Robots Need Two Clocks

  7. May 31

    Who is Liable for Onboard AI?

    Send us Fan Mail As foundation models move from the cloud into physical robots, a fundamental question emerges: who is accountable when an AI-controlled machine makes a decision that causes harm? In this episode, we examine the growing collision between embodied AI, functional safety, and emerging regulation. We explore how new frameworks such as the EU AI Act and the Machinery Regulation are reshaping expectations for developers, manufacturers, and deployers of intelligent robots. From humanoid robots and autonomous mobile manipulators to AI-enabled industrial machinery, the challenge is no longer simply making robots smarter. It is making them governable. We investigate a proposed architectural solution that is gaining traction across industry and academia: the hardware-isolated safety supervisor. By separating non-deterministic AI reasoning from deterministic safety-critical control systems, this approach aims to create clear lines of accountability while preserving the benefits of onboard intelligence. Along the way, we examine NVIDIA’s Cosmos Reason 2 model, the EmbodiedGovBench governance framework, emerging standards efforts, and the practical realities of deploying foundation models on embedded platforms. We also ask whether traditional functional safety concepts such as SIL and ASIL can adequately address the unique challenges posed by robots whose actions are selected by large vision-language models. The broader question is one that every roboticist, embedded engineer, and AI practitioner will soon face: when intelligence becomes local, autonomous, and physically embodied, what mechanisms ensure that accountability remains local too? Support the show If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!

    Who is Liable for Onboard AI?

  8. May 28

    Squeezing AI into your Pocket

    Send us Fan Mail By 2026, language models have moved off the cloud and onto the device in your pocket. What was a research demonstration two years ago is now a routine engineering capability, and the centre of gravity for artificial intelligence has begun to migrate from distant data centres to local silicon. The episode traces the four engineering moves that made this possible. Quantization, which shrinks a model by storing its parameters with less precision. Optimized key-value caches, which let a model hold a long conversation without exhausting memory. Neural Processing Units, the dedicated AI accelerators now standard in flagship phones. And specialized frameworks such as LiteRT-LM and llama.cpp, which finally make all three usable from a single application. The consequences reach further than performance figures. Privacy becomes the default rather than a feature, because data never leaves the device. The cost structure of AI applications changes, because there are no per-query cloud fees. And the link between training capital and deployment capability begins to decouple, opening the door for small teams to ship genuine intelligence on hardware they already control. Support the show If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!

    Squeezing AI into your Pocket
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“Intelligence at the Deep Edge” is a podcast exploring the fascinating intersection of embedded systems and artificial intelligence. Dive into the world of cutting-edge technology as we discuss how AI is revolutionizing edge devices, enabling smarter sensors, efficient machine learning models, and real-time decision-making at the edge. Discover more on Embedded AI (https://medium.com/embedded-ai) — our companion publication where we detail the ideas, projects, and breakthroughs featured on the podcast. Help support the podcast - https://www.buzzsprout.com/2429696/support

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