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Researchers at Penn Medicine and Intel Corporation led the largest-to-date global machine learning effort to securely aggregate knowledge from brain scans of 6,314 glioblastoma (GBM) patients at 71 sites around the globe and develop a model that can enhance identification and prediction of boundaries in three tumor sub-compartments, without compromising patient privacy. Their findings were published today in Nature Communications.

“This is the single largest and most diverse dataset of glioblastoma patients ever considered in the literature, and was made possible through federated learning,” said senior author Spyridon Bakas, Ph.D., an assistant professor of Pathology & Laboratory Medicine, and Radiology, at the Perelman School of Medicine at the University of Pennsylvania. “The more data we can feed into machine learning models, the more accurate they become, which in turn can improve our ability to understand, treat, and remove glioblastoma in patients with more precision.”

Researchers studying rare conditions, like GBM, an aggressive type of brain tumor, often have patient populations limited to their own institution or geographical location. Due to privacy protection legislation, such as the Health Insurance Portability and Accountability Act of 1996 (HIPAA) in the United States, and General Data Protection Regulation (GDPR) in Europe, data sharing collaborations across institutions without compromising data is a major obstacle for many healthcare providers.

These fundamental units of the brain and nervous system – composed of the cell body, the dendrites and the axon (a long, thin extension responsible for communicating with other cells) – receive sensory input from the external world, send motor commands to our muscles and for transform and relay the electrical signals at every step in between.

“Our novel method of creating ‘mini-brains’ opens the door to finding solutions for various neurological impairments”

Prof. Orit Shefi and doctoral student Reut Plen from the Kofkin Faculty of Engineering at Bar-Ilan University (BIU) have developed a novel technique to overcome this challenge using nanotechnology and magnetic manipulations – one of the most innovative approaches to creating neural networks. Their research was recently published in the peer-reviewed journal Advanced Functional Materials under the title “Bioengineering 3D Neural Networks Using Magnetic Manipulations.”

For decades, the growth of artificial intelligence has fascinated scholars and scientists alike. Today, intelligent machines aid and streamline our lives, but the next fifty or a hundred years may yield even more powerful AI, which could elevate or transform our species. If humans do create sophisticated, super-intelligent machines, how will the growth of artificial intelligence affect the future of humanity?
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Ray Kurzweil is an author, computer scientist, inventor, futurist and a director of engineering at Google. Kurzweil is a public advocate for the futurist and transhumanist movements, and gives public talks to share his optimistic outlook on life extension technologies and the future of nanotechnology, robotics, and biotechnology.

Recorded 2013

A team of researchers at DeepMind Technologies Ltd., has created an AI application called “DeepNash” that is able to play the game Stratego at an expert level. In their paper published in the journal Science, the group describes the unique approach they took to improve the app’s level of play.

Stratego is a two-player board game and is considered to be difficult to master. The goal for each player is to capture their opponent’s flag, which is hidden among their initial 40 game pieces. Each of the game pieces is marked with a power ranking—higher-ranked players defeat lower-ranked players in face-offs. Making the game more difficult is that neither player can see the markings on the opponent’s game pieces until they meet face-to-face.

Prior research has shown that the complexity of the game is higher than that of chess or go, with 10535 possible scenarios. This level of complexity makes it extremely challenging for computer experts attempting to create Stratego-playing AI systems. In this new effort, the researchers took a different approach, creating an app capable of beating most human and other AI systems.

More than a million people tuned in to Twitter Spaces to hear the world’s richest man speak while he was flying in his private jet on Saturday.

During the live podcast hosted by some independent media and social personalities following the publication of the “Twitter Files” last week, Elon Musk was questioned by a sizable group of Blue tick holders about a variety of subjects pertaining to the social networking platform.

Researchers in the CEST group have published a study demonstrating the effectiveness of machine learning methods to identify suitable perovskite solar cell materials. Perovskite solar cells are a novel technology gathering a lot of interest due to their high efficiency and potential for radically lower manufacturing costs when compared to the traditional silicon-based solar cells.

Despite their promising qualities, the commercialization of has been held back by their fast degradation under environmental stresses, such as heat and moisture. They also contain that can negatively impact the environment. The search for new perovskite materials that do not have these problems is ongoing, but the established experimental and computational research methods have not been able to handle the high number of material candidates that need to be tried and tested.

CEST members Jarno Laakso and Patrick Rinke, with from University of Turku and China, developed new machine learning-based methodology for rapidly predicting perovskite properties. This new approach accelerates computations and can be used to study perovskite alloys. These alloy materials contain many candidates for improved solar cell materials, but studying them has been difficult with conventional computational methods.