LONDON – Beijing dismissed as "ridiculous" a Harvard Medical School study of hospital traffic and search engine data that suggested the novel coronavirus may already have been spreading in China last August, and scientists said it offered no convincing evidence of when the outbreak began. The research, which has not been peer-reviewed by other scientists, used satellite imagery of hospital parking lots in Wuhan -- where the disease was first identified in late 2019 -- and data for symptom-related queries on search engines for terms such as "cough" and "diarrhea." The study's authors said increased hospital traffic and symptom search data in Wuhan preceded the documented start of the coronavirus pandemic, in December 2019. "While we cannot confirm if the increased volume was directly related to the new virus, our evidence supports other recent work showing that emergence happened before identification at the Huanan Seafood market (in Wuhan)," they said. Paul Digard, an expert in virology at the University of Edinburgh, said that using search engine data and satellite imagery of hospital traffic to detect disease outbreaks "is an interesting idea with some validity."

Toshiba Corp. has developed an artificial intelligence-based system to provide real-time video subtitles during online classes. The system transcribes teachers' speeches into subtitles, allowing students to quickly check parts they missed and review lessons afterward. Amid the new coronavirus outbreak, many universities and other educational institutions have introduced online education. Creating an environment to help students' understanding in online classes has become an important challenge. Toshiba, a major Japanese electronics and machinery maker, will conduct system verification tests at Keio University and Hosei University with the aim of putting the system into practical use in a year at the earliest.

At 23, Shunji Sugaya had what he calls a "life-changing episode." It was March 2000, and Sugaya had just won an award at a business contest where Masayoshi Son, the founder of what was then called SoftBank Corp., was a judge. He sent Son an email to thank him, the two met up, and before long SoftBank offered to buy Sugaya's idea for $2.8 million or for Sugaya to join the company and receive stock options. "It gave me a big boost in confidence, as I was a student -- I was so happy I could dance," he said. "We were very grateful for the offer but we politely declined and decided to do it ourselves."

IBM announced this week that it would stop selling its facial recognition technology to customers including police departments. The move prompted calls for other tech firms, like Amazon and Microsoft, to do the same. IBM announced this week that it would stop selling its facial recognition technology to customers including police departments. The move prompted calls for other tech firms, like Amazon and Microsoft, to do the same. IBM will no longer provide facial recognition technology to police departments for mass surveillance and racial profiling, Arvind Krishna, IBM's chief executive, wrote in a letter to Congress.

Boston would become the largest U.S. city east of San Francisco to ban the use of facial-recognition technology by any city agency if a local law proposed by two city councilors is adopted. "It would mean that the Boston city government, including Boston police and any other department, could not use any state surveillance system," City Councilor-at-Large Michelle Wu said at a press briefing before a hearing that drew more than 100 people to weigh in on the proposal. "To be clear, Boston police already … have said that they do not use it today." The ban would prevent any city agency from using face surveillance software and ensure that people are not subject to unregulated, mass surveillance in public spaces, such as at protests like the ones that have roiled Boston and other cities since last month's killing of George Floyd, an unarmed black man, at the hands of a white Minneapolis police officer. The technology some police departments use when looking for suspects furthers racial inequity by identifying people of color at a higher rate, City Councilor Ricardo Arroyo said.

Beijing has dismissed as "ridiculous" a Harvard Medical School study of hospital traffic and search engine data that suggested the new coronavirus may already have been spreading in China last August, and scientists said it offered no convincing evidence of when the outbreak began. Chinese Foreign Ministry spokeswoman Hua Chunying, asked about the research at a news briefing on Tuesday, said: "I think it is ridiculous, incredibly ridiculous, to come up with this conclusion based on superficial observations such as traffic volume." The research, which has not been peer-reviewed by other scientists, used satellite imagery of hospital parking lots in Wuhan - where the disease was first identified in late 2019 - and data for symptom-related queries on search engines for things such as "cough" and "diarrhoea". The study's authors said increased hospital traffic and symptom search data in Wuhan preceded the documented start of the coronavirus pandemic in December 2019. "While we cannot confirm if the increased volume was directly related to the new virus, our evidence supports other recent work showing that emergence happened before identification at the Huanan Seafood market (in Wuhan)," they said.

A team of global experts compiled this list of best reinforcement courses, classes, tutorials, training, and certification programs available online. This list includes both free and paid courses to help you learn reinforcement learning. Also, it is ideal for beginners, intermediates, and experts. Offered by the University of Alberta, this reinforcement learning specialization program consists of four different courses that will help you explore the power of adaptive learning systems and artificial intelligence. In this program, you will learn how reinforcement learning solutions can help you solve real-world problems via trial-and-error interaction by implementing a complete RL solution from beginning to end.

"We study spiking neural networks, which are systems that learn much as living brains do," said Los Alamos National Laboratory computer scientist Yijing Watkins. "We were fascinated by the prospect of training a neuromorphic processor in a manner analogous to how humans and other biological systems learn from their environment during childhood development." Watkins and her research team found that the network simulations became unstable after continuous periods of unsupervised learning. When they exposed the networks to states that are analogous to the waves that living brains experience during sleep, stability was restored. "It was as though we were giving the neural networks the equivalent of a good night's rest," said Watkins.

India is gearing up for the next generation computers. This move also reinforces the Digital India and Make in India vision of the Prime Minister Narendra Modi. The Government of India (GoI) has launched the National Supercomputing Mission (NSM) to connect the national academia with research institutes. With its peak computing power and high memory compute nodes supercomputers are expected to open out new avenues of scientific research and innovation. The first phase of NSM has already begun.

Artificial intelligence designed to function like a human could require periods of rest similar to those needed by biological brains. Researchers at Los Alamos National Laboratory in the US discovered that neural networks experienced benefits that were "the equivalent of a good night's rest" when exposed to an artificial analogue of sleep. "We were fascinated by the prospect of training a neuromorphic processor in a manner analogous to how humans and other biological systems learn from their environment during childhood development," said Yijing Watkins, a computer scientist at Los Alamos. The discovery was made by the team of researchers while working on a form of artificial intelligence designed to mimic how humans learn to see. The AI became unstable during long periods of unsupervised learning, as it attempted to classify objects using their dictionary definitions without having any prior examples to compare them to.