Lifeboat Foundation

In a new study in Nature Machine Intelligence, researchers Bojian Yin and Sander Bohté from the HBP partner Dutch National Research Institute for Mathematics and Computer Science (CWI) demonstrate a significant step towards artificial intelligence that can be used in local devices like smartphones and in VR-like applications, while protecting privacy.

They show how brain-like neurons combined with novel learning methods enable training fast and energy-efficient spiking neural networks on a large scale. Potential applications range from wearable AI to speech recognition and Augmented Reality.

While modern artificial neural networks are the backbone of the current AI revolution, they are only loosely inspired by networks of real, biological neurons such as our brain. The brain however is a much larger network, much more energy-efficient, and can respond ultra-fast when triggered by external events. Spiking neural networks are special types of neural networks that more closely mimic the working of biological neurons: the neurons of our nervous system communicate by exchanging electrical pulses, and they do so only sparingly.

We have various ways of seeing what the brain is up to, from low-resolution electrodes that track waves of activity that ripple across the brain, to implanted electrodes that can follow the activity of individual cells. Combined with a detailed knowledge of which regions of the brain are involved in specific processes, we’ve been able to do remarkable things, such as using functional MRI (fMRI) to determine what letter a person was looking at or an implant to control a robotic arm.

But today, researchers announced a new bit of mind reading that’s impressive in its scope. By combining fMRI brain imaging with a system that’s somewhat like the predictive text of cell phones, they’ve worked out the gist of the sentences a person is hearing in near real time. While the system doesn’t get the exact words right and makes a fair number of mistakes, it’s also flexible enough that it can reconstruct an imaginary monologue that goes on entirely within someone’s head.

The mysterious phenomenon of “spooky action at a distance,” which once troubled Einstein, could soon become as commonplace as the gyroscopes used to measure acceleration in smartphones.

A recent study in Nature Photonics.

<em>Nature Photonics</em> is a prestigious, peer-reviewed scientific journal that is published by the Nature Publishing Group. Launched in January 2007, the journal focuses on the field of photonics, which includes research into the science and technology of light generation, manipulation, and detection. Its content ranges from fundamental research to applied science, covering topics such as lasers, optical devices, photonics materials, and photonics for energy. In addition to research papers, <em>Nature Photonics</em> also publishes reviews, news, and commentary on significant developments in the photonics field. It is a highly respected publication and is widely read by researchers, academics, and professionals in the photonics and related fields.

Year 2021 face_with_colon_three This could be made into a smartphone device that could one day treat everything without the need for surgery or other ways that are not as safe.

Researchers are investigating potential uses for the cell reprogramming technology to treat brain disorders such as Alzheimer’s disease or autoimmune diseases.

Continue reading “Cell Therapy AIDS Stroke-Damaged Brain Repair, Restores 90% of Motor Function” »

In the ceaseless pursuit of energy-efficient computing, new devices designed at UC Santa Barbara show promise for enhancements in information processing and data storage.

Researchers in the lab of Kaustav Banerjee, a professor of electrical and computer engineering, have published a new paper describing several of these devices, “Quantum-engineered devices based on 2D materials for next-generation information processing and storage,” in the journal Advanced Materials. Arnab Pal, who recently received his doctorate, is the lead author.

Each device is intended to address challenges associated with conventional computing in a new way. All four operate at very low voltages and are characterized as being low leakage, as opposed to the conventional metal-oxide semiconductor field-effect transistors (MOSFETs) found in smartphones that drain power even when turned off. But because they are based on processing steps similar to those used to make MOSFETs, the new devices could be produced at scale using existing industry-standard manufacturing processes for semiconductors.

UNSW Sydney researchers have developed a chip-scale method using OLEDs to image magnetic fields, potentially transforming smartphones into portable quantum sensors. The technique is more scalable and doesn’t require laser input, making the device smaller and mass-producible. The technology could be used in remote medical diagnostics and material defect identification.

Smartphones could one day become portable quantum sensors thanks to a new chip-scale approach that uses organic light-emitting diodes (OLEDs) to image magnetic fields.

Researchers from the ARC Centre of Excellence in Exciton Science at UNSW Sydney have demonstrated that OLEDs, a type of semiconductor material commonly found in flat-screen televisions, smartphone screens, and other digital displays, can be used to map magnetic fields using magnetic resonance.

Top Apple engineers left the company late last year to join Google as concerns grow that the iPhone maker is falling behind on AI.

AI has just made its next big move! The Humane AI Wearable made its debut on a TED talk. Its coming after iPhones and Android smartphones!

The “spooky action at a distance” that once unnerved Einstein may be on its way to being as pedestrian as the gyroscopes that currently measure acceleration in smartphones.

Quantum entanglement significantly improves the precision of sensors that can be used to navigate without GPS, according to a new study in Nature Photonics.

“By exploiting entanglement, we improve both measurement sensitivity and how quickly we can make the measurement,” said Zheshen Zhang, associate professor of electrical and computer engineering at the University of Michigan and co-corresponding author of the study. The experiments were done at the University of Arizona, where Zhang was working at the time.