The government aims to put a facial recognition system into practical use to prevent new coronavirus infections at large-scale events including the Tokyo Olympics and Paralympics, it was learned Friday. The government also hopes to improve the national capacity to conduct saliva-based polymerase chain reaction tests to simultaneously detect cases of influenza and novel coronavirus infection, informed sources said. The proposals are included in a draft program for developing new technologies for preventing coronavirus infection. The government will unveil the program shortly and carry out demonstration tests at relevant ministries and agencies. According to the draft, the government is looking at using security cameras equipped with a facial recognition system to record the movements of visitors to the Tokyo Games, which were postponed to 2021, and other large-scale events, the sources said.
Former Sony executive Ken Kutaragi, often called the "father" of the PlayStation video game system, has been named CEO of Tokyo-based startup Ascent Robotics. Ascent develops artificial-intelligence technologies for self-driving cars and other applications. Kutaragi said in a statement that he hopes to "drive the team forward on both the technology and business fronts." He joined the company's board in 2018. Ascent announced Kutaragi's appointment on Friday.
As the subway roared into Tokyo's Tsukishima Station a gust of wind tossed up a stray face mask, sending it sailing above the platform. Hisashi Taniguchi watched the piece of fabric fluttering about. He immediately pictured in his mind a microscopic view in which the wind dispersed -- in the air he was breathing -- countless viral particles that had been trapped between the mask's filters. There needs to be an efficient system to disinfect these public spaces, he thought. This was back in March, when the spread of COVID-19 was just starting to pick up speed in the capital.
Tokyo stocks took a downturn Thursday on the back of the yen's appreciation and overseas market falls. The 225-issue Nikkei average of the Tokyo Stock Exchange shed 156.16 points, or 0.67 percent, to close at 23,319.37, after rising 20.64 points Wednesday. The Topix index of all first section issues closed down 5.95 points, or 0.36 percent, at 1,638.40, following a 3.51-point rise the previous day. The market got off to a weaker start and plunged deeper in the morning, with sentiment chilled by the yen's strengthening against the dollar and a drop in the U.S. Dow Jones Industrial Average futures in off-hours trading, brokers said. Bearish performances of Chinese and other Asian shares also weighed on Tokyo stocks throughout the afternoon session, they added.
Google users contribute more than 20 million pieces of information on Maps every day – that's more than 200 contributions every second. The uncertainty of traffic can crash the algorithms predicting the best ETA. There is also a chance of new roads and buildings being built all the time. Though Google Maps gets its ETA right most of the time, there is still room for improvement. Researchers at Alphabet-owned DeepMind have partnered with the Google Maps team to improve the accuracy of the real-time ETAs by up to 50% in places like Berlin, Jakarta, São Paulo, Sydney, Tokyo, and Washington D.C.
TOKYO (THE YOMIURI SHIMBUN/ASIA NEWS NETWORK) - Products and services equipped with artificial intelligence (AI) are increasingly being used in our daily lives. They must be used wisely, by understanding the nature of AI. In AI, computers process large amounts of information to analyse them and make decisions. While conventional products operate within a range that was set beforehand, AI products can increase the accuracy of their operations by continuously accumulating user data and learning from that. A user has only to talk to smart speakers that use AI voice recognition for the speakers to perform such tasks as operating home appliances, or searching for necessary information and conveying that information to the user.
It has been nearly 13 years since Google Maps started providing traffic data to help people navigate their way around, alongside providing detail about whether the traffic along the route is heavy or light, the estimated travel time, and the estimated time of arrival (ETAs). In a bid to further enhance those traffic prediction capabilities, Google and Alphabet's AI research lab DeepMind have improved real-time ETAs by up to 50% in places such as Sydney, Tokyo, Berlin, Jakarta, Sao Paulo, and Washington DC by using a machine learning technique known as graph neural networks. Google Maps product manager Johann Lau said Google Maps uses aggregate location data and historical traffic patterns to understand traffic conditions to determine current traffic estimates, but it previously did not account for what traffic may look like if a traffic jam were to occur while on the journey. "Our ETA predictions already have a very high accuracy bar -- in fact, we see that our predictions have been consistently accurate for over 97% of trips … this technique is what enables Google Maps to better predict whether or not you'll be affected by a slowdown that may not have even started yet," he said in a blog post. The researchers at DeepMind said by using graph neural networks, this allowed Google Maps to incorporate "relational learning biases to model the connectivity structure of real-world road networks."
Google Maps helps users navigate over one billion kilometers in more than 200 countries and territories daily, and Google says its estimated time of arrival (ETA) predictions have been consistently accurate for over 97 percent of trips. That's not good enough for Google, though, so the company partnered with DeepMind to use machine learning to make its ETAs even more accurate. Before partnering with DeepMind, an Alphabet AI research lab, Google Maps used a combination of historical traffic patterns and live traffic conditions to understand current traffic patterns. The partners wanted to be able to predict future traffic patterns, so DeepMind developed a graphic neural network, which also considers data on the time of year, road quality, speed limits, accidents and closures. Thanks to that machine learning approach, Google Maps has improved the accuracy of real-time ETAs by up to 50 percent in places like Berlin, Jakarta, São Paulo, Sydney, Tokyo, and Washington D.C. Now, Google Maps can warn users about traffic jams before they exist.
In Tokyo, an eerily human-like robot called Aiko Chihira wears a colourful kimono and greets shoppers at the entrance of a glossy department store. At the nearby Uniqlo warehouse, AI machines have now replaced 90 percent of human staff and work day and night. When we thought of mass job losses in the fashion industry, we pictured a robotic future like this one, which – until March – felt years away from a British reality. However, the pandemic has accelerated a move towards automation that could otherwise have taken decade, with the British Fashion Council suggesting a quarter of a million industry jobs might be lost. Debenhams axed 2,500 on Tuesday after the 4,000 cuts the group made in April, while Burberry recently announced plans to cut 500 jobs worldwide, and M&S 950.
Fujitsu and Tokyo Shinagawa Hospital have announced a partnership to jointly develop artificial intelligence (AI) technology that can be used to help with early detection of COVID-19 pneumonia, even in cases where the possibility of infection is determined as low during initial examinations. The AI technology will be designed specifically to help examine chest CT scans, which are considered as an effective method for diagnosing novel coronavirus pneumonia, even when COVID-19 test results are negative, Fujitsu said. According to the Japanese tech giant, data collected from past CT scans of COVID-19 pneumonia patients from Tokyo Shinagawa Hospital will be used to train the AI technology to detect abnormal shadow patterns in the lungs. "When diagnosing COVID-19 pneumonia, patterns of abnormal opacities in the lungs, as well as the spread of shadows across the entire lung is important information," the company said. Applying AI to the analysis can help automate a process, according to Fujitsu, that normally requires doctors to visually check hundreds of chest CT images per patient to understand the characteristics of the lung.