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Japan extends timeline for approving Fujifilm's Avigan drug for COVID-19

The Japan Times

The government has decided to postpone approving Fujifilm Holdings Corp.'s Avigan drug for the treatment of COVID-19 until June or later, health minister Katsunobu Kato said Tuesday. Prime Minister Shinzo Abe had said earlier this month he hoped the drug, known generically as favipiravir, would be approved some time in May if its efficacy and safety could be confirmed. But Kato told a news conference Tuesday that clinical tests on the drug would continue into next month or beyond, while noting that there was no change in the government's policy of approving the drug swiftly once its effectiveness is confirmed. Fujifilm shares slumped last week after it was reported that an interim study showed no clear evidence of efficacy for Avigan in COVID-19 cases. Researchers at Fujita Health University, which is conducting a clinical trial on the drug, said in a statement the interim study was done to ensure the scientific validity of the trial, not to determine the efficacy of the drug.


Hacked NES Power Glove is a thing of horrifying, wriggly beauty

Mashable

Makeup tutorials, woodworking tutorials, baking tutorials -- there are creators of every kind uploading lessons in their craft for the YouTube-watching masses. Still, it's probably safe to assume this guy is the only YouTuber DIY-ing a robotic hand from an NES Power Glove to remotely play a modular synth. On Sunday, musician-turned-inventor Sam Battle (aka Look Mum No Computer) released a 12-minute video showing off his revamped NES Power Glove and the musical feats it has achieved. A 1989 Nintendo creation that flopped big time, the Power Glove doesn't seem like it would be good for much. But Battle has transformed his into a remarkably tactile triumph in electrical engineering, capable of manipulating volume, tone, and pitch all with the curl of a finger.


Using machine learning to identify different types of brain injuries

AIHub

Researchers have developed an algorithm that can detect and identify different types of brain injuries. The team, from the University of Cambridge, Imperial College London and CONICET, have clinically validated and tested their method on large sets of CT scans and found that it was successfully able to detect, segment, quantify and differentiate different types of brain lesions. Their results, reported in The Lancet Digital Health, could be useful in large-scale research studies, for developing more personalised treatments for head injuries and, with further validation, could be useful in certain clinical scenarios, such as those where radiological expertise is at a premium. Head injury is a huge public health burden around the world and affects up to 60 million people each year. It is the leading cause of mortality in young adults.


'The lads were buzzing to kick a ball about with their mates' - Championship resumes training

BBC News

Players at Championship clubs were allowed to return to training on Monday - the first step towards the potential resumption of the second-tier season. On Friday, the English Football League provided safety protocols and guidance for clubs to follow upon their return. Players took part in non-contact sessions and trained in small groups. A total of 1,014 Championship players and staff were tested for coronavirus towards the end of last week, with two people testing positive. Hull City confirmed on Sunday that the two positive cases were from their club.


How to reverse-engineer a rainforest

Engadget

But 2019 was the year the earth burned. In Australia, the world watched in horror as bushfires destroyed 10.3 million hectares, marking the continent's most intense and destructive fire season in over 40 years. Earlier that fall, California saw more than 101,000 hectares destroyed, with damages upward of $80 billion. Alaska saw nearly a million. Record-breaking fires also hit Indonesia, Russia, Lebanon -- but nowhere saw the sheer mass of media coverage as the fires that tore through the Amazon nearly all last summer. By year's end, thousands of global media outlets had reported that Brazil's largest rainforest played host to more than 80,000 individual forest fires in 2019, resulting in an estimated 906,000 square hectares of environmental destruction. At the time, Brazil's National Institute for Space Research reported it was the fastest rate of burning since record keeping began in 2013. But amid the charred ruins of one of the largest oxygen-producing environments on the planet, a secret lies buried beneath the soil.


Machine Learning with SQL

#artificialintelligence

This post is about Machine Learning with SQL. It makes sense to build/run Machine Learning models where data stays -- in the database. Step by step info on how to get started. Python (and soon JavaScript with TensorFlow.js) is a dominant language for Machine Learning. There is a way to build/run Machine Learning models in SQL.


The Air Force's AI-Powered 'Skyborg' Drones Could Fly as Early as 2023

#artificialintelligence

The U.S. Air Force is finally pushing into the world of robot combat drones, vowing to fly the first of its "Skyborg" drones by 2023. The service envisions Skyborg as a merging of artificial intelligence with jet-powered drones. The result will be drones capable of flying alongside fighter jets, carrying out dangerous missions. Skyborg drones will be much cheaper than piloted aircraft, allowing the Air Force to grow its fleet at a lower cost. The Air Force, according to Defense News, will award a total of $400 million to one or more companies to develop different types of Skyborg drones.



turing test in AI : can machines think?

#artificialintelligence

Turing test in artificial intelligence is supposed to answer the complicated question of "Can machines think?" here we discuss the Turing test and the vision of Alan Turing about thinking machine.


How to Prevent Overfitting in Machine Learning Models

#artificialintelligence

Very deep neural networks with a huge number of parameters are very robust machine learning systems. But, in this type of massive networks, overfitting is a common serious problem. Learning how to deal with overfitting is essential to mastering machine learning. The fundamental issue in machine learning is the tension between optimization and generalization. Optimization refers to the process of adjusting a model to get the best performance possible on the training data (the learning in machine learning), whereas generalization refers to how well the trained model performs on the data that it has never seen before (test set).