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Category: computing – Page 238

Pat Bennett’s prescription is a bit more complicated than “Take a couple of aspirins and call me in the morning.” But a quartet of baby-aspirin-sized sensors implanted in her brain are aimed at addressing a condition that’s frustrated her and others: the loss of the ability to speak intelligibly. The devices transmit signals from a couple of speech-related regions in Bennett’s brain to state-of-the-art software that decodes her brain activity and converts it to text displayed on a computer screen.

Bennett, now 68, is a former human resources director and onetime equestrian who jogged daily. In 2012, she was diagnosed with amyotrophic lateral sclerosis, a progressive neurodegenerative disease that attacks neurons controlling movement, causing physical weakness and eventual paralysis.

Our brains remember how to formulate words even if the muscles responsible for saying them out loud are incapacitated. A brain-computer hookup is making the dream of restoring speech a reality.

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The human brain, with its intricate network of approximately 86 billion neurons, is arguably among the most complex specimens scientists have ever encountered. It holds an immense, yet currently immeasurable, wealth of information, positioning it as the pinnacle of computational devices.

Grasping this level of intricacy is challenging, making it essential for us to employ advanced technologies that can decode the minute, intricate interactions happening within the brain at microscopic levels. Thus, imaging emerges as a pivotal instrument in the realm of neuroscience.

The new imaging and virtual reconstruction technology developed by Johann Danzl’s group at ISTA is a big leap in imaging brain activity and is aptly named LIONESS – Live Information Optimized Nanoscopy Enabling Saturated Segmentation. LIONESS is a pipeline to image, reconstruct, and analyze live brain tissue with a comprehensiveness and spatial resolution not possible until now.

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This is leading to even better brain engineering 👏 🙌 👌 😀 😄.

Computer-augmented brains, cures to blindness, and rebuilding the brain after injury all sound like science fiction. Today, these disruptive technologies aren’t just for Netflix, “Terminator,” and comic book fodder — in recent years, these advances are closer to reality than some might realize, and they have the ability to revolutionize neurological care.

Neurologic disease is now the world’s leading cause of disability, and upwards of 11 million people have some form of permanent neurological problem from traumatic brain injuries and stroke. For example, if a traumatic brain injury has damaged the motor cortex — the region of the brain involved in voluntary movements — patients could become paralyzed, without hope of regaining full function. Or some stroke patients can suffer from aphasia, the inability to speak or understand language, due to damage to the brain regions that control speech and language comprehension.

Thanks to recent advances, sometimes lasting neurologic disease can be prevented. For example, if a stroke patient is seen quickly enough, life-threatening or-altering damage can be avoided, but it’s not always possible. Current treatments to most neurologic disease are fairly limited, as most therapies, including medications, aim to improve symptoms but can’t completely recover lost brain function.

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We often believe computers are more efficient than humans. After all, computers can complete a complex math equation in a moment and can also recall the name of that one actor we keep forgetting. However, human brains can process complicated layers of information quickly, accurately, and with almost no energy input: recognizing a face after only seeing it once or instantly knowing the difference between a mountain and the ocean. These simple human tasks require enormous processing and energy input from computers, and even then, with varying degrees of accuracy.

Creating brain-like computers with minimal energy requirements would revolutionize nearly every aspect of modern life. Funded by the Department of Energy, Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C) — a nationwide consortium led by the University of California San Diego — has been at the forefront of this research.

UC San Diego Assistant Professor of Physics Alex Frañó is co-director of Q-MEEN-C and thinks of the center’s work in phases. In the first phase, he worked closely with President Emeritus of University of California and Professor of Physics Robert Dynes, as well as Rutgers Professor of Engineering Shriram Ramanathan. Together, their teams were successful in finding ways to create or mimic the properties of a single brain element (such as a neuron or synapse) in a quantum material.

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Most of today’s EVs use lithium-ion batteries, the same kind you’ll find in your smartphone or laptop. These batteries all have two electrodes (one positive and one negative), and the negative one is usually made of graphite.

While the battery is being charged, the lithium ions flow from the side of the battery with the positive electrode to the side with the negative electrode. If the charging happens too fast, the flow can be disrupted, causing the battery to short circuit.

StoreDot’s EV battery replaces the graphite electrode with one made from nanoparticles based on the chemical element germanium — this allows the ions to flow more smoothly and quickly, enabling a faster charge.

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Advance lays the groundwork for miniature devices for spectroscopy, communications, and quantum computing. Researchers have created chip-based photonic resonators that operate in the ultraviolet (UV) and visible regions of the spectrum and exhibit a record low UV light loss. The new resonators lay the groundwork for increasing the size, complexity, and fidelity of UV photonic integrated circuit (PIC) design, which could enable new miniature chip-based devices for applications such as spectroscopic sensing, underwater communication, and quantum information processing.

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Founded in 2021, Virginia-based Procyon Photonics is a startup aiming to change the future of computing hardware with its focus on optical computing. What makes the company unique is that their entire team consists of current high school students, and its co-founder, CEO, and CTO, Sathvik Redrouthu, holds the distinction of being the world’s youngest CEO in the photonic and optical computing sector.

Optical computing represents an innovative leap from traditional computing, which relies on electrons moving through wires and transistors. Instead, this relatively nascent field seeks to harness photons — particles of light — as the fundamental elements in computational processes. The promise of optical computing is compelling enough that industry giants like IBM and Microsoft, among others, are heavily investing in its research and development.

Procyon is attempting to differentiate itself in this competitive landscape not just by its youth, but with their technology. The team is pioneering a unique, industry-leading optical chip, and has published a conference paper detailing how a specialized form of matrix algebra could be executed on an optoelectronic chip.

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In a press release, Bujack, who creates scientific visualizations at Los Alamos National Laboratory, called the current mathematical models used for color perceptions incorrect and requiring a “paradigm shift.”

A surprise finding

Being able to accurately model human color perception has a tremendous impact on automating image processing, computer graphics, and visualization. Bujack’s team first set out to develop algorithms that would automatically enhance color maps used in data visualization to make it easier to read them.

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Femtotech: Computing at the femtometer scale using quarks and gluons.
How the properties of quarks and gluons can be used (in principle) to perform computation at the femtometer (10^−15 meter) scale.

I’ve been thinking on and off for two decades about the possibility of a femtotech. Now that nanotech is well established, and well funded, I feel that the time is right to start thinking about the possibility of a femtotech.

You may ask, “What about picotech?” — technology at the picometer (10-12m) scale. The simple answer to this question is that nature provides nothing at the picometer scale. An atom is about 10–10 m in size.

The next smallest thing in nature is the nucleus, which is about 100,000 times smaller, i.e., 10–15 m in size — a femtometer, or “fermi.” A nucleus is composed of protons and neutrons (i.e., “nucleons”), which we now know are composed of 3 quarks, which are bound (“glued”) together by massless (photon-like) particles called “gluons.”

Hence if one wanted to start thinking about a possible femtotech, one would probably need to start looking at how quarks and gluons behave, and see if these behaviors might be manipulated in such a way as to create a technology, i.e., computation and engineering (building stuff).

In this essay, I concentrate on the computation side, since my background is in computer science. Before I started ARCing (After Retirement Careering), I was a computer science professor who gave himself zero chance of getting a grant from conservative NSF or military funders in the U.S. to speculate on the possibilities of a femtotech. But now that I’m no longer a “wager,” I’m free to do what I like, and can join the billion strong “army” of ARCers, to pursue my own passions.

Continue reading “Hugo de Garis — Innovating Beyond the Nanoscale, Femtometer Scale Technology” | >