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U.S. Police Already Using 'Spot' Robot From Boston Dynamics in the Real World

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Massachusetts State Police (MSP) has been quietly testing ways to use the four-legged Boston Dynamics robot known as Spot, according to new documents obtained by the American Civil Liberties Union of Massachusetts. And while Spot isn't equipped with a weapon just yet, the documents provide a terrifying peek at our RoboCop future. This browser does not support the video element. The Spot robot, which was officially made available for lease to businesses last month, has been in use by MSP since at least April 2019 and has engaged in at least two police "incidents," though it's not clear what those incidents may have been. It's also not clear whether the robots were being operated by a human controller or how much autonomous action the robots are allowed.


Hyundai Motor Group works with leading AI scholars

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Hyundai Motor Group said Thursday it has scouted Tomaso Poggio and Daniela Rus, experts on artificial intelligence, to work together on various projects of AI technology development. Poggio and Rus are serving as technology consultants and have been giving advice on utilizing AI to build planning and technological strategies for new business models, to establish a global research organization and to set investment directions for research infrastructures. Poggio, who heads the Center for Brains, Minds and Machine at Massachusetts Institute of Technology, is considered as one of the founders of computational neuroscience. Rus, a renowned roboticist who is also from MIT, is director of the Computer Science and Artificial Intelligence Laboratory. She is a class of 2002 MacArthur fellow and has conducted research on robots and autonomous driving, according to the automaker.


MIT researcher held up as model of how algorithms can benefit humanity

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In June, when MIT artificial intelligence researcher Regina Barzilay went to Massachusetts General Hospital for a mammogram, her data were run through a deep learning model designed to assess her risk of developing breast cancer, which she had been diagnosed with once before. The workings of the algorithm, which predicted that her risk was low, were familiar: Barzilay helped build that very model, after being spurred by her 2014 cancer diagnosis to pivot her research to health care. Barzilay's work in AI, which ranges from tools for early cancer detection to platforms to identify new antibiotics, is increasingly garnering recognition: On Wednesday, the Association for the Advancement of Artificial Intelligence named Barzilay as the inaugural recipient of a new annual award honoring an individual developing or promoting AI for the good of society. The award comes with a $1 million prize sponsored by the Chinese education technology company Squirrel AI Learning. While there are already prizes in the AI field, notably the Turing Award for computer scientists, those existing awards are typically "more focused on scientific, technical contributions and ideas," said Yolanda Gil, a past president of AAAI and an AI researcher at the University of Southern California.


AI Technique Copies Human Memory To Minimize Data Storage Burden

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Artificial intelligence (AI) experts at the University of Massachusetts Amherst and the Baylor College of Medicine report that they have successfully addressed what they call a "major, long-standing obstacle to increasing AI capabilities" by drawing inspiration from a human brain memory mechanism known as "replay." First author and postdoctoral researcher Gido van de Ven and principal investigator Andreas Tolias at Baylor, with Hava Siegelmann at UMass Amherst, write in Nature Communications that they have developed a new method to protect - "surprisingly efficiently" - deep neural networks from "catastrophic forgetting" - upon learning new lessons, the networks forget what they had learned before. Siegelmann and colleagues point out that deep neural networks are the main drivers behind recent AI advances, but progress is held back by this forgetting. They write, "One solution would be to store previously encountered examples and revisit them when learning something new. Although such'replay' or'rehearsal' solves catastrophic forgetting," they add, "constantly retraining on all previously learned tasks is highly inefficient and the amount of data that would have to be stored becomes unmanageable quickly."


The brain's memory abilities inspire AI experts in making neural networks less 'forgetful'

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Artificial intelligence (AI) experts at the University of Massachusetts Amherst and the Baylor College of Medicine report that they have successfully addressed what they call a "major, long-standing obstacle to increasing AI capabilities" by drawing inspiration from a human brain memory mechanism known as "replay." First author and postdoctoral researcher Gido van de Ven and principal investigator Andreas Tolias at Baylor, with Hava Siegelmann at UMass Amherst, write in Nature Communications that they have developed a new method to protect--"surprisingly efficiently"--deep neural networks from "catastrophic forgetting;" upon learning new lessons, the networks forget what they had learned before. Siegelmann and colleagues point out that deep neural networks are the main drivers behind recent AI advances, but progress is held back by this forgetting. They write, "One solution would be to store previously encountered examples and revisit them when learning something new. Although such'replay' or'rehearsal' solves catastrophic forgetting," they add, "Constantly retraining on all previously learned tasks is highly inefficient and the amount of data that would have to be stored becomes unmanageable quickly."


Artificial intelligence system developed to help better select embryos for implantation

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For many people who are struggling to conceive, in-vitro fertilization (IVF) can offer a life-changing solution. But the average success rate for IVF is only about 30 percent. Investigators from Brigham and Women's Hospital and Massachusetts General Hospital are developing an artificial intelligence system with the goal of improving IVF success by helping embryologists objectively select embryos most likely to result in a healthy birth. Using thousands of embryo image examples and deep-learning artificial intelligence (AI), the team developed a system that was able to differentiate and identify embryos with the highest potential for success significantly better than 15 experienced embryologists from five different fertility centers across the United States. Results of their study are published in eLife.


Biopharma companies utilizing artificial intelligence for drug research

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Biopharma companies are relying more and more on artificial intelligence and machine learning (AI/ML) to help them uncover the intricacies of disease mechanisms and open up strategies to develop novel medicines for treatment. As a result, the BioWorld Artificial Intelligence price-weighted index, which includes biopharmaceutical companies, medical devices and health care services companies, has climbed in value and is currently up almost 37% year-to-date. Fueling the index has been biopharmaceutical company Bioxcel Therapeutics Inc., of New Haven, Conn., which is utilizing artificial intelligence to identify improved therapies in neuroscience and immuno-oncology. Its shares (NASDAQ:BTAI) have been on a tear so far this year, gaining a whopping 229%, catalyzed by significant clinical progress in its product pipeline. In July, the company reported that it had initiated an expanded access program at Massachusetts General Hospital (MGH) to provide its alpha 2A adrenoceptor agonist, BXCL-501, a sublingual thin-film formulation of dexmedetomidine, to individuals diagnosed with COVID-19 who are critically ill in the intensive care unit and may require calming or arousable sedation.


Massachusetts suspends Boston-based coronavirus testing lab Orig3n after nearly 400 false positives

Boston Herald

The state has suspended Boston-based COVID-19 testing lab Orig3n Laboratory after it produced nearly 400 false positive results. Public health officials became aware in early August of an "unusually high positivity rate" among the lab's test results and requested that Orig3n stop testing for the virus as of Aug. 8. Specimens were sent to an independent lab to be retested as part of a state Department of Public Health investigation, and the results showed at least 383 false positives. On Aug. 27, the state Department of Public Health notified Orig3n of "three significant certification deficiencies that put patients at immediate risk of harm," according to a DPH spokeswoman. They included the failure of the lab's director to provide overall management, issues with the extraction phase of testing, and a failure to meet analytic requirements such as documenting the daily sanitizing of equipment used for coronavirus testing. A statement of deficiency was issued on Sept. 4. The lab must now respond with a written plan of correction by Sept. 14, "and if action is not taken it can face sanctions," DPH said.


UC Berkeley researchers develop artificial intelligence to remove shadows from photos

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Whether eating out at a restaurant or taking a hike in nature, UC Berkeley doctoral candidate Cecilia Zhang always has a camera at hand. As a lover of visual media, Zhang noticed that individuals are becoming increasingly reliant on mobile phones to take photos and wanted to find a way to bridge the gap between casual portraits and those produced in a professional studio. In order to fulfill this need and push casual photography forward, Zhang and researchers at the Massachusetts Institute of Technology, Google and UC Berkeley have developed a way to minimize natural and facial shadows from portraits using artificial intelligence, or AI. "After going through thousands of casual portraits in the internet, I realized there's a large issue with lighting and shadows," Zhang said. "Most people don't have access to professional equipment and can't get the environment to bend to their needs."


Photonics Processor Aimed at AI Inference

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Silicon photonics is exhibiting greater innovation as requirements grow to enable faster, lower-power chip interconnects for traditionally power-hungry applications like AI inferencing. With that in mind, scientists at Massachusetts Institute of Technology launched a startup in 2017 called Lightmatter Inc. to develop silicon photonic processors. Another goal was leveraging optical computing to "decouple" AI processing from Moore's law scaling that according to the company founders literally produces more heat than light. Lightmatter announced an AI photonic "test chip" during this week's Hot Chips conference positioned as an AI inference accelerator using light to process and transport data. The 3D module incorporates a 12- and 90-nm ASIC, the latter supporting photonics processing steps such as laser monitoring and light distribution.