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Category: robotics/AI – Page 1,546

A digital spinal cord that streams your thoughts | Thomas Oxley | TEDxSydney

In what could be the first direct link between AI and the human brain, interventional neurologist Thomas Oxley reveals the world’s first minimally invasive digital spinal cord. He shares the exciting story behind the ongoing development of this unique wireless device that can interpret signals from the brain for patients with paralysis without the need for open brain surgery or direct contact with brain tissue. Endovascular neurologist Thomas Oxley’s 2016 research demonstrated the potential for a neural recording device to be engineered onto a stent and implanted into a blood vessel in the brain, without the need for open brain surgery.

This research has progressively attracted investment, with completion of a Series A fundraiser in 2017. His company’s technology, the Stentrode, currently under FDA review, is planned for a first in human trial. Patients with tetraplegia due to spinal cord injury, stroke and ALS will be recruited into a trial of direct brain control over a suite of assistive technologies. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

A face recognition framework based on vision transformers

Face recognition tools are computational models that can identify specific people in images, as well as CCTV or video footage. These tools are already being used in a wide range of real-world settings, for instance aiding law enforcement and border control agents in their criminal investigations and surveillance efforts, and for authentication and biometric applications. While most existing models perform remarkably well, there may still be much room for improvement.

Researchers at Queen Mary University of London have recently created a new and promising for face recognition. This architecture, presented in a paper pre-published on arXiv, is based on a strategy to extract from images that differs from most of those proposed so far.

“Holistic methods using (CNNs) and margin-based losses have dominated research on face recognition,” Zhonglin Sun and Georgios Tzimiropoulos, the two researchers who carried out the study, told TechXplore.

Using machine learning to better understand how water behaves

Water has puzzled scientists for decades. For the last 30 years or so, they have theorized that when cooled down to a very low temperature like-100C, water might be able to separate into two liquid phases of different densities. Like oil and water, these phases don’t mix and may help explain some of water’s other strange behavior, like how it becomes less dense as it cools.

It’s almost impossible to study this phenomenon in a lab, though, because crystallizes into ice so quickly at such low temperatures. Now, new research from the Georgia Institute of Technology uses machine learning models to better understand water’s phase changes, opening more avenues for a better theoretical understanding of various substances. With this technique, the researchers found strong computational evidence in support of water’s liquid-liquid transition that can be applied to real-world systems that use water to operate.

“We are doing this with very detailed quantum chemistry calculations that are trying to be as close as possible to the real physics and physical chemistry of water,” said Thomas Gartner, an assistant professor in the School of Chemical and Biomolecular Engineering at Georgia Tech. “This is the first time anyone has been able to study this transition with this level of accuracy.”

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