Researchers develop energy-efficient supercomputing with neural networks and charge density waves.
Researchers are creating efficient systems using neural networks and charge density waves to reduce supercomputing’s massive energy use.
Continue reading “US uses neural networks to make energy-efficient supercomputers” »
Startup Riverlane helped continue what has been a strong year for venture funding in the quantum computing industry.
The U.K.-based firm — which specializes in quantum error correction technology — raised a $75 million Series C led by Planet First Partners. The round also includes participation from ETF Partners, EDBI, Cambridge Innovation Capital, Amadeus Capital Partners, the National Security Strategic Investment Fund and Altair
The company’s tech helps quantum computers perform without succumbing to eventual errors. Such computers typically can only perform a few hundred quantum operations before failure.
Even if we can dodge a disaster in orbit by responsibly de-orbiting derelict satellites, many scientists are concerned that the number of objects circling our planet could still do harm: When they deorbit, they could deposit a significant flux of metals that could alter the chemical makeup of Earth’s atmosphere.
“Effects on astronomy are just the tip of the iceberg,” said Barentine, who says we may be fast approaching a turning point where tragedy becomes imminent, either in space due to a collision or on the ground from falling debris. “Space policy-making moves far too slowly to effectively deal with all of this.”
“Right now, there’s not a lot to look forward to that is positive,” he added. “If the New Space Age goes badly in the end, history will not look favorably on it.”
In a collaboration with EPFL Lausanne, ETH Zurich and the University of Southern California researchers at the Paul Scherrer Institute PSI have used X-rays to look inside a microchip with higher precision than ever before. The image resolution of 4 nanometers marks a new world record. The high-resolution three-dimensional images of the type they produced will enable advances in both information technology and the life sciences.
The researchers are reporting their findings in the current issue of the journal Nature (“High-performance 4 nm resolution X-ray tomography using burst ptychography”).
View inside a state-of-the-art computer chip. Their newly developed ptychographic technique allowed the researchers to map the three-dimensional structure of this engineering marvel. The picture shows the different layers that make up the microchip. The coarser structures can be seen at the top. The microchip becomes increasingly complex as you move down through the layers – making the connections there visible requires a resolution of just a few nanometers. (Image: Tomas Aidukas, Paul Scherrer Institute)
Brain-computer interfaces (BCIs) can translate a person’s brainwaves into action, but these devices are vulnerable to hacking.
Artificial neural networks—algorithms inspired by biological brains—are at the center of modern artificial intelligence, behind both chatbots and image generators. But with their many neurons, they can be black boxes, their inner workings uninterpretable to users.
Researchers have now created a fundamentally new way to make neural networks that in some ways surpasses traditional systems. These new networks are more interpretable and also more accurate, proponents say, even when they’re smaller. Their developers say the way they learn to represent physics data concisely could help scientists uncover new laws of nature.
Now, scientists in China have developed robots that give human-like realistic expressions.
The humanoid robot with highly expressive facial features is developed by Liu Xiaofeng, a professor at Hohai University in east China’s Jiangsu Province, and his research team.
For the development of this robot, the research team developed a new algorithm for generating facial expressions on humanoid robots.
Nanoelectronics.
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He writes that AI is now exceeding the human brain at several cognitive tasks and that it will eventually do all things far better than even the most expert humans.
These new machines can learn, reason, plan and act with intention, and they are becoming far smarter far faster than most people, save Kurzweil, could have predicted.
Soon, he forecasts, they will be indistinguishable from human brains, before accelerating past them in nearly every way.
283,119 views • Mar 13, 2024 • ✪ Members first on March 12, 2024 • #sciencenews #technology #science
