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Novel ‘speech clock’ can estimate a person’s chronological age

A large study shows that machine-learning clocks based on speech can estimate chronological age and may also offer a window into brain aging, biological aging, cognitive health, cumulative burden, and dementia.

The new study, just published in leading international journal Science Advances, outlines that researchers have developed a “speech clock” that can estimate a person’s chronological age from hundreds of acoustic and linguistic characteristics of their speech. The difference between a person’s actual age and their speech-predicted age (called the speech age gap) was also associated with multiple independent markers of biological aging, brain health, cognition, social adversity, and dementia.

The study analyzed 2,928 Spanish-speaking participants from Argentina, Chile, Colombia, Mexico, and Peru, including healthy adults and people with mild cognitive impairment, Alzheimer’s disease, and different forms of frontotemporal dementia. Rather than looking at a single property of the voice, the researchers used machine learning to analyse hundreds of features capturing how people speak and what they say: incorporating speech rate and pauses, pitch, emotional content, vocabulary, semantic precision, and the amount and organisation of verbal output. Machine-learning models combined these features to estimate chronological age and generate an individual speech age gap.

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.

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