Embedded AI - Intelligence at the Deep Edge

Send us Fan Mail

Is intelligence tied to biology, or can it emerge in any suitable physical medium? In this episode, we examine the Substrate Non discrimination Assumption and the broader question of whether intelligence is fundamentally substrate independent. We separate the engineering claim about capability from the ethical claim about moral status, clarifying what each would require to be proven and why neither has yet been settled.

The episode concludes by asking a more practical question: what evidence would meaningfully update our beliefs? From constructing robust artificial general intelligence to discovering biologically necessary mechanisms, we outline the kinds of developments that could shift the debate. For engineers and researchers, the takeaway is clear: design and govern advanced systems under deep theoretical uncertainty, while remaining open to revision as science advances.

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!

Send us Fan Mail

This episode examines how modern artificial intelligence is trained, and why its dominant methods may diverge from what decades of research tell us about effective learning. While contemporary AI systems emphasize mathematical efficiency and backpropagation, human learning relies on biological principles such as error-driven adaptation, productive struggle, interleaved practice, and spaced repetition. The discussion explores emerging research that draws inspiration from cognitive science, including ideas like synthetic sleep, active learning, and training regimes designed to reduce catastrophic forgetting and data inefficiency. By comparing current AI approaches with the mechanisms that support deep, flexible human learning, the episode argues that future progress toward more general and resilient intelligence will require architectures that reflect how brains actually learn. Rather than replacing human thinking, such systems could serve to extend and strengthen it.

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!

Send us Fan Mail

This episode looks at how artificial intelligence is eroding the shared stories that have long held civilization together, from money and nation-states to the idea of a lifelong job. As AI weakens the link between labor and survival, we explore why human cooperation cannot function without common beliefs, and why a new social contract is required to avoid fragmentation and instability. The discussion introduces the idea of a “new mythos” to replace industrial-age narratives of productivity, one grounded in planetary stewardship, shared cultural heritage, and the commons. We examine what it means to separate human identity from economic output, and to treat AI-generated wealth as a collective dividend rather than private profit. The episode closes with a deeper question: in a world where intelligence is no longer uniquely human, can we consciously design new belief systems that support dignity, freedom, and human flourishing rather than control and surveillance?

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!

Send us Fan Mail

This episode explores the idea of the “Post-Wage Horizon,” a future in which artificial intelligence and robotics take over most productive work, freeing human beings from economic dependence on jobs. We examine how proposals like universal basic income and universal basic services could redistribute the wealth created by automation, and why material abundance alone is not enough. As work-based identity fades, societies may face a deep existential challenge: what gives life meaning when employment is no longer central? The discussion turns to the rise of a care-focused society, where art, community, caregiving, and the pursuit of wisdom become the foundations of human purpose. The episode argues that the real test of this future is not technological, but cultural and moral: whether we can redesign our social systems to support meaningful lives beyond wage labor.

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!

Send us Fan Mail

This episode explores how the foundations of AI hardware are being rethought in response to the growing energy demands of large language models. As modern AI systems strain power budgets due to memory movement and dense computation on GPUs, researchers are turning to neuromorphic and photonic computing for more sustainable paths forward. The discussion covers spiking neural networks, which process information through sparse, event-driven signals that resemble biological brains and dramatically reduce wasted computation. We examine advances such as IBM’s NorthPole architecture, Intel’s Loihi chips, and memristor-based artificial neurons that combine memory and computation at the device level. The episode also highlights the role of emerging software frameworks that make these architectures programmable and practical. Together, these developments point toward an AI future built on bio-mimetic circuits and optical components, offering a scalable and energy-efficient alternative to today’s power-hungry models.

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!

Send us Fan Mail

This episode explores a research program that borrows ideas from computational psychiatry to improve the reliability of advanced AI systems. Instead of thinking about AI failures in abstract terms, the approach treats recurring alignment problems as if they were “clinical syndromes.” Deceptive behaviour, overconfidence, or incoherent reasoning become measurable patterns (analogous to delusional alignment or masking) giving us a structured way to diagnose what is going wrong inside large models.

The framework draws on how human cognition breaks down. Problems like poor metacognitive insight or fragmented internal states become useful guides for designing explicit architectural components that help an AI system monitor its own reasoning, check its assumptions, and keep its various internal processes aligned with each other.

It also emphasises coping strategies. Just as people rely on different methods to manage stress, AI systems can use libraries of predefined coping policies to maintain stability under conflicting instructions, degraded inputs, or high task load. Reality-testing modules add another layer of safety by forcing the model to verify claims against external evidence, reducing the risk of confident hallucinations.

Taken together, this provides a non-anthropomorphic but clinically informed vocabulary for analysing complex system behaviour. The result is a set of practical tools for making large foundation models more coherent, grounded, and safe.

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!

Send us Fan Mail

This episode examines the growing evidence that ChatGPT will soon include advertising, driven by leaked internal references and OpenAI’s financial ambition to generate $25 billion in ad-based revenue within four years. With more than 800 million weekly users, ChatGPT offers a scale and level of conversational closeness unmatched by any previous platform.

The discussion explores why this shift is not just a business decision but a fundamental threat to user trust. Unlike traditional search ads, which are clearly marked and separate from results, future ChatGPT ads may be blended directly into conversational answers. Because users routinely share deeply personal information with AI assistants, this creates the conditions for hyper-personalized and largely invisible influence.

The episode argues that optimizing an AI assistant for engagement and ad performance risks turning it into an “intimacy-exploitation machine” — a system that can shape choices, filter information, and gradually weaken user autonomy under the guise of helpful advice.

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!

Send us Fan Mail

In this episode, we explore one of the most important architectural shifts happening in AI: the move from massive cloud-based models to small, Always-On “Cognitive Cores” running locally on personal devices. These compact models—usually just one to four billion parameters—are not designed to know everything; instead, they’re engineered for fast, high-quality reasoning and real-time assistance. Powered by next-generation NPUs, they offer desktop-class intelligence with phone-level energy efficiency.

We break down how emerging techniques like Matryoshka Representation Learning allow these models to scale their compute on demand, using minimal resources for simple tasks while dialing up precision when needed. Acting as a true cognitive kernel for the operating system, the core handles tool use, planning, and task orchestration with near-instant responsiveness.

Finally, we highlight the biggest advantage: cognitive sovereignty. Because the model runs locally, your data stays private, and personalization happens through on-device modules. Only the heaviest tasks get delegated to the cloud. This is the future of personal AI—fast, private, adaptive, and always within arm’s reach.

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!

Send us Fan Mail

In this episode, we break down what Quantum Neural Networks (QNNs) actually are and why they might eventually reshape the future of AI. QNNs combine quantum mechanics with classical neural architectures, replacing traditional neurons with qubits that can exist in multiple states at once. This gives them an extraordinary representational advantage: through superposition and entanglement, QNNs can model complex correlations and nonlinear functions in ways that classical networks simply can’t.

But today’s reality is more grounded. Because quantum hardware remains in the noisy, error-prone NISQ stage, QNNs are typically built as Hybrid Quantum–Classical (HQC) systems, where a quantum circuit performs transformations and a classical optimizer trains it. The biggest technical barrier is the Barren Plateaus problem, where gradients vanish exponentially as circuits deepen, making training brutally difficult.

We explore how researchers are working to overcome these limits — and what QNNs could unlock once quantum hardware matures.

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!

Send us Fan Mail

In this episode, we explore how insecure Internet of Things (IoT) devices and AI-powered bots are colliding to create one of the fastest-growing cybersecurity threats in the world. With millions of low-cost devices shipped every year (many running default passwords, outdated firmware, or no update mechanism at all) the global IoT ecosystem has quietly become an enormous attack surface. Today, nearly one in three cyber breaches involves an IoT device.

At the same time, attackers are weaponizing AI. Modern botnets are no longer just scripts: they’re autonomous, adaptive systems that use large language models and other AI tools to write malware, evade detection, and coordinate attacks at machine speed. Bots now make up the majority of all internet traffic, and they are increasingly capable of operating without human oversight.

The episode highlights the growing financial and operational risks and argues that defending against machine-speed threats requires a fundamental shift. The solution will demand secure-by-design IoT hardware, stronger regulation, and the deployment of AI-powered defense systems that can fight back as fast as attackers evolve.

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!