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Industrial IoT Protocol Fragmentation Hides A Real Lock-In Cost

OPC UA and MQTT Sparkplug B are now the accepted answer to industrial IoT protocol fragmentation. McKinsey estimates interoperability is required to capture around 40% of IoT’s total potential value.

But adopting both standards doesn’t end fragmentation. It relocates it to the edge gateway.

Machines still speak OPC UA. An edge gateway still has to translate that into Sparkplug B before data reaches the broker. That gateway is usually a specific vendor’s software, running specific translation logic, with mapping rules that may not be exportable.

A unified namespace built on someone else’s closed gateway is still a point of control the buyer doesn’t own.

The fix isn’t just picking the right protocols. It’s checking who owns the layer that bridges them — and requiring an exportable, documented mapping configuration before signing.

Full analysis:

#IndustrialIoT #IIoT #OPC UA #MQTT #SmartFactory

Continue reading “Industrial IoT Protocol Fragmentation Hides A Real Lock-In Cost” | >

Danko Nikolic and John Smart on AI, Practopoiesis, and Autopoiesis

Three months before ChatGPT changed everything, I sat down for two hours with a neuroscientist and a complex systems theorist to ask a question most of the #AI industry still isn’t asking:

What if we’re building intelligence the wrong way?

Danko Nikolic ran a lab at the Max Planck Institute for Brain Research. He went into neuroscience because he was frustrated with the limits of artificial neural networks. John Smart co-founded the Evo-Devo Universe research community and has spent decades studying how complex adaptive systems actually grow.

One argues we need to copy the brain more deeply. The other argues we need to copy life itself. Neither thinks bigger models alone get us there.

In 2022 that sounded contrarian. In 2026, with trillion-dollar bets riding on scale and #AIAlignment still unsolved, it’s a question worth asking again: is bigger enough, or are we missing something only biology can teach?

We get into practopoiesis and where intelligence comes from, the power-law problem with piling on parameters, whether we need hardware that breaks from von Neumann, and why both of them think emotion and #consciousness can’t be bolted on later.

Continue reading “Danko Nikolic and John Smart on AI, Practopoiesis, and Autopoiesis” | >

Performance of the spin qubit shuttling architecture for a surface code implementation

Berat Yenilen, Arnau Sala, Hendrik Bluhm, Markus Müller, and Manuel Rispler, Quantum 10, 2219 (2026). Qubit shuttling promises to advance some quantum computing platforms to the qubit register sizes needed for effective quantum error correction (QEC), but also introduces additional errors whose impact must be evaluated. The established method to investigate the performance of QEC codes in a realistic scenario is to employ a standard noise model known as circuit-level noise, where all quantum operations are modeled as noisy. In the present work, we take this noise model and single out the effect of shuttling errors by introducing them as an additional so-called error location. This hardware abstraction is motivated by the SpinBus architecture and allows a systematic numerical investigation to map out the resulting two-dimensional parameter space. To this end, we take the Surface code and perform large scale simulations, most notably extracting the threshold across said two-dimensional parameter space. We study two scenarios for shuttling errors, depolarization on the one hand and dephasing on the other hand. For a purely dephasing shuttling error, we find a threshold of several percent, provided that all other operations have a high fidelity. The qubit overhead needed to reach a logical error rate of $10^{-12}$ (known as the “teraquop” regime [23] increases only moderately for shuttling error rates up to about 1% per shuttling operation. The error rates at which practically useful, i.e. well below threshold error correction is predicted to be possible are comfortably higher than what is expected to be achievable for spin qubits. Our results thus show that it is reasonable to expect shuttling operations to fall below threshold already at surprisingly large error rates. With realistic efforts in the near term, this offers positive prospects for spin qubit based quantum processors as a viable avenue for scalable fault-tolerant error-corrected quantum computing.

Exploring the shapes of exotic nuclei

In the conventional diagram of an atom, the center depicts a nucleus—a spherical cluster of protons and neutrons. But that sphere is a simplification: nuclei can deform into exotic shapes that appear more like a pear, football or Frisbee. Understanding why and when nuclei distort is crucial for predicting and modeling how they will behave.

Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a new detector, CHICOX (Compact Heavy Ion Counter version X), that opens up a new era of extremely sensitive studies of nuclear shapes.

“Nuclear theorists are working toward a comprehensive, predictive model of nuclei and how they behave. The data we measure with CHICOX are a good test of these models,” said LLNL scientist Daniel Rhodes.

Cosmic-ray particles help probe powerful electric fields inside thunderstorms

An instrument built to study cosmic rays has turned out to be a powerful tool for investigating thunderstorms. Earlier observations with the GRAPES-3 muon telescope revealed that thunderclouds can develop electrical potential differences far greater than those ever measured directly. But they also uncovered a puzzle: the instrument detected many more thunderstorm events in the eastern part of its field of view than in the western part. A new study by the same team, published in the Journal of Cosmology and Astroparticle Physics, has now explained why.

The asymmetry does not mean that thunderstorms are actually more common to the east. Instead, it arises from the way the geomagnetic field affects the cosmic rays traveling through Earth’s magnetosphere, making GRAPES-3 more sensitive to thunderstorms from some directions than others. The result strengthens the case for using these cosmic-ray particles as a natural probe of thunderstorm electricity.

Cosmic rays are a continuous flux of extremely high-energy particles arriving at Earth from outer space. Most primary cosmic rays are protons. Because they are electrically charged, their trajectories are deflected by Earth’s magnetic field. When they enter the atmosphere, they collide with atomic nuclei in the air, generating showers of secondary particles.

Google DeepMind develops invisible watermarks for AI-designed proteins

Watermarks have long protected everything from bank notes and fine art to digital photographs and software, helping prove authenticity and trace an object’s origin. Their main function is to protect copyright and verify authenticity.

That same traceability is needed in synthetic biology, where AI is now a useful research tool. To address this, Google DeepMind has introduced SynthIDBio, a method for embedding invisible signatures directly into biological sequences and 3D structures without interfering with their function.

AI is actively designing new functional proteins and predicting their three-dimensional shapes. But along with the benefits come a host of potential problems. These include biosecurity risks, such as the potential misuse of AI-designed biological molecules, and the spread of fake or misleading scientific data.

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