Lifeboat Foundation

Neural networks are learning algorithms that approximate the solution to a task by training with available data. However, it is usually unclear how exactly they accomplish this. Two young Basel physicists have now derived mathematical expressions that allow one to calculate the optimal solution without training a network. Their results not only give insight into how those learning algorithms work, but could also help to detect unknown phase transitions in physical systems in the future.

Neural networks are based on the principle of operation of the brain. Such computer algorithms learn to solve problems through repeated training and can, for example, distinguish objects or process spoken language.

For several years now, physicists have been trying to use neural networks to detect phase transitions as well. Phase transitions are familiar to us from everyday experience, for instance when water freezes to ice, but they also occur in more complex form between different phases of magnetic materials or quantum systems, where they are often difficult to detect.

Robotics and wearable devices might soon get a little smarter with the addition of a stretchy, wearable synaptic transistor developed by Penn State engineers. The device works like neurons in the brain to send signals to some cells and inhibit others in order to enhance and weaken the devices’ memories.

Led by Cunjiang Yu, Dorothy Quiggle Career Development Associate Professor of Engineering Science and Mechanics and associate professor of biomedical engineering and of materials science and engineering, the team designed the synaptic transistor to be integrated in robots or wearables and use artificial intelligence to optimize functions. The details were published Sept. 29 in Nature Electronics.

“Mirroring the human brain, robots and wearable devices using the synaptic transistor can use its artificial neurons to ‘learn’ and adapt their behaviors,” Yu said. “For example, if we burn our hand on a stove, it hurts, and we know to avoid touching it next time. The same results will be possible for devices that use the synaptic transistor, as the artificial intelligence is able to ‘learn’ and adapt to its environment.”

Sept 27 (Reuters) — An experimental Alzheimer’s drug made by Eisai Co Ltd (4523.T) and Biogen (BIIB.O) slowed cognitive and functional decline in a large trial of patients in the early stages of the disease, they said on Tuesday, potentially a rare win in a field littered with failed drugs.

Multiple drugmakers have so far tried and failed to find an effective treatment for the brain-wasting disease that affects about 55 million people globally. A breakthrough would be a major boost to similar studies being run by Roche and Eli Lilly.

Speaking of the Eisai-Biogen drug results announced late on Tuesday night, Ronald Petersen, director of the Mayo Clinic Alzheimer’s Disease Research Center in Rochester, Minnesota said: “It’s not a huge effect, but it’s a positive effect”.

Researchers have used a novel near-infrared light imaging technique to capture the first cross-sectional images of carbon dioxide in the exhaust plume of a commercial jet engine. This new state-of-the-art technology could help accelerate turbine combustion research aimed at developing engines and aviation fuels that are more environmentally friendly.

“This approach, which we call chemical species tomography, provides real-time spatially resolved information for carbon dioxide emissions from a large-scale commercial engine,” said research team leader Michael Lengden from the University of Strathclyde in the U.K. “This information has not been available before at this industrial scale and is a big improvement over the current industry-standard emissions measurement, which involves taking gas from the exhaust to a gas analyzer system in a different location.”

The researchers report the new research in Applied Optics. Chemical species tomography works much like the X-ray-based CT scans used in medicine, except that it uses near-infrared laser light tuned to the absorption wavelength of a target molecule and requires very fast imaging speeds to capture the dynamic processes of combustion.

The neurohormone oxytocin is well-known for promoting social bonds and generating pleasurable feelings, for example from art, exercise, or sex. But the hormone has many other functions, such as the regulation of lactation and uterine contractions in females, and the regulation of ejaculation, sperm transport, and testosterone production in males.

Now, researchers from Michigan State University show that in zebrafish and human cell cultures, oxytocin has yet another unsuspected function: It stimulates stem cells derived from the heart’s outer layer (epicardium) to migrate into its middle layer (myocardium) and there develop into cardiomyocytes, muscle cells that generate heart contractions. This discovery could one day be used to promote the regeneration of the human heart after a heart attack. The results are published in Frontiers in Cell and Developmental Biology.

“Here we show that oxytocin, a neuropeptide also known as the love hormone, is capable of activating heart repair mechanisms in injured hearts in zebrafish and human cell cultures, opening the door to potential new therapies for heart regeneration in humans,” said Dr. Aitor Aguirre, an assistant professor at the Department of Biomedical Engineering of Michigan State University, and the study’s senior author.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

Cybercriminals are growing ever more relentless and deft with their attacks, with data breaches and system disruptions due to cyberattacks rising every year. Therefore, finding and strengthening cybersecurity weak spots, or vulnerabilities, is key to thwarting these attacks.

A key vulnerability is apps. Many organizations rely on productivity software and apps built in-house or from IT service providers to be competitive in today’s market. However, while these solutions boost productivity and employee and customer experiences, many of them have weak security measures that can expose the organization to cyberattackers.

Scalable biofabrication of heart helical tissue pattern augments pumping function.

Michael V. Sefton [email protected] and Craig A. Simmons

Science.

Agility CEO Damion Shelton and CTO Jonathan Hurst discuss artificial intelligence and its role in robot control. They also discuss the capability of robot learning paired with physics-based locomotion, Cassie setting a new world record using learned policies for control, and an exploration of the future of robotics through Dall-E.

At Agility, we make robots that are made for work. Our robot Digit works alongside us in spaces designed for people. Digit handles the boring and repetitive tasks that are meant for a machine, which allows companies and their people to focus on the work that requires the human element.

Continue reading “Will Artificial Intelligence Drive Robots?” »

Two milliseconds—or two thousandths of a second—is an extraordinarily long time in the world of quantum computing. On these timescales the blink of an eye—at one 10th of a second—is like an eternity.

Now a team of researchers at UNSW Sydney has broken new ground in proving that ‘spin qubits’—properties of electrons representing the basic units of information in quantum computers—can hold information for up to two milliseconds. Known as ‘coherence time’, the duration of time that qubits can be manipulated in increasingly complicated calculations, the achievement is 100 times longer than previous benchmarks in the same quantum processor.

Continue reading “For the longest time: Quantum computing engineers set new standard in silicon chip performance” »