Wow! Now that’s amazing! She has used it for years!
A woman has become the first human to receive a robotic limb fused with both her nervous and skeletal systems — and she’s being dubbed the “real bionic woman.”
Category: robotics/AI – Page 881
Elevating neuromorphic computing using laser-controlled filaments in vanadium dioxide
In a new Science Advances study, scientists from the University of Science and Technology of China have developed a dynamic network structure using laser-controlled conducting filaments for neuromorphic computing.
Neuromorphic computing is an emerging field of research that draws inspiration from the human brain to create efficient and intelligent computer systems. At its core, neuromorphic computing relies on artificial neural networks, which are computational models inspired by the neurons and synapses in the brain. But when it comes to creating the hardware, it can be a bit challenging.
Mott materials have emerged as suitable candidates for neuromorphic computing due to their unique transition properties. Mott transition involves a rapid change in electrical conductivity, often accompanied by a transition between insulating and metallic states.
Nanoelectronic device performs real-time AI classification without relying on the cloud
Forget the cloud. Northwestern University engineers have developed a new nanoelectronic device that can perform accurate machine-learning classification tasks in the most energy-efficient manner yet. Using 100-fold less energy than current technologies, the device can crunch large amounts of data and perform artificial intelligence (AI) tasks in real time without beaming data to the cloud for analysis.
With its tiny footprint, ultra-low power consumption and lack of lag time to receive analyses, the device is ideal for direct incorporation into wearable electronics (like smart watches and fitness trackers) for real-time data processing and near-instant diagnostics.
To test the concept, engineers used the device to classify large amounts of information from publicly available electrocardiogram (ECG) datasets. Not only could the device efficiently and correctly identify an irregular heartbeat, it also was able to determine the arrhythmia subtype from among six different categories with near 95% accuracy.
50 exaFLOPS supercomputer planned for 2025
Chip designer Tachyum has accepted a major purchase order from a U.S. company to build a new supercomputing system for AI. This will be based on its 5 nanometre (nm) “Prodigy” Universal Processor chip, delivering more than 50 exaFLOPS of performance.
Tachyum, founded in 2016 and headquartered in Santa Clara, California, claims to have developed a disruptive, ultra-low-power processor architecture that could revolutionise data centre, AI, and high-performance computing (HPC) markets.
New easy-to-use optical chip can self-configure to perform various functions
Researchers have developed an easy-to-use optical chip that can configure itself to achieve various functions. The positive real-valued matrix computation they have achieved gives the chip the potential to be used in applications requiring optical neural networks. Optical neural networks can be used for a variety of data-heavy tasks such as image classification, gesture interpretation and speech recognition.
Photonic integrated circuits that can be reconfigured after manufacturing to perform different functions have been developed previously. However, they tend to be difficult to configure because the user needs to understand the internal structure and principles of the chip and individually adjust its basic units.
“Our new chip can be treated as a black box, meaning users don’t need to understand its internal structure to change its function,” said research team leader Jianji Dong from Huazhong University of Science and Technology in China. “They only need to set a training objective, and, with computer control, the chip will self-configure to achieve the desired functionality based on the input and output.”
Researchers reconstruct speech from brain activity, illuminating complex neural processes
Speech production is a complex neural phenomenon that has left researchers explaining it tongue-tied. Separating out the complex web of neural regions controlling precise muscle movement in the mouth, jaw and tongue with the regions processing the auditory feedback of hearing your own voice is a complex problem, and one that has to be overcome for the next generation of speech-producing protheses.
Now, a team of researchers from New York University have made key discoveries that help untangle that web, and are using it to build vocal reconstruction technology that recreates the voices of patients who have lost their ability to speak.
The team, co-led by Adeen Flinker, Associate Professor of Biomedical Engineering at NYU Tandon and Neurology at NYU Grossman School of Medicine, and Yao Wang, Professor of Biomedical Engineering and Electrical and Computer Engineering at NYU Tandon, as well as a member of NYU WIRELESS, created and used complex neural networks to recreate speech from brain recordings, and then used that recreation to analyze the processes that drive human speech.
Exploring parameter shift for quantum Fisher information
In a recent publication in EPJ Quantum Technology, Le Bin Ho from Tohoku University’s Frontier Institute for Interdisciplinary Sciences has developed a technique called time-dependent stochastic parameter shift in the realm of quantum computing and quantum machine learning. This breakthrough method revolutionizes the estimation of gradients or derivatives of functions, a crucial step in many computational tasks.
Typically, computing derivatives requires dissecting the function and calculating the rate of change over a small interval. But even classical computers cannot keep dividing indefinitely. In contrast, quantum computers can accomplish this task without having to discrete the function. This feature is achievable because quantum computers operate in a realm known as “quantum space,” characterized by periodicity, and no need for endless subdivisions.
One way to illustrate this concept is by comparing the sizes of two elementary schools on a map. To do this, one might print out maps of the schools and then cut them into smaller pieces. After cutting, these pieces can be arranged into a line, with their total length compared (see Figure 1a). However, the pieces may not form a perfect rectangle, leading to inaccuracies. An infinite subdivision would be required to minimize these errors, an impractical solution, even for classical computers.
