Just like a knife can be used to slice a fruit as well as to commit a murder, artificial intelligence can be used for improving healthcare, but also for discrimination based on facial features and complexion; 3D printing can make organs as well as guns. Technologies are creating new jobs but also leading to displacing workers. Companies complain of difficulty in finding people with requisite skills, even as millions of graduates (and, even more others) remain jobless. Narendra Jadhav, prolific author and Rajya Sabha MP, explores these conundrums in New Age Technology and Industrial Revolution 4.0 and argues for development of a rubric of conducive public policies alongside development and deployment of technology. The book starts with an overview of technologies like AI, augmented reality (AR), additive manufacturing (aka 3D printing) and blockchain. Jadhav puts these within the realm of education, healthcare, digital payments, national security and jobs to discern policy aspects pertaining to economic growth, social inequalities and yes, financial services and banking.

One of the highlights of this year's CEATEC (Combined Exhibition of Advanced Technologies) is Society 5.0 Town, an exhibition area where companies from diverse sectors such as retail, banking, construction, transport and local governments will showcase their technologies to connect people, information and things to enhance people's lives. Society 5.0 is a concept created in Japan and is defined as "a human-centered society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space," according to the Cabinet Office website. The town's theme is in line with Japan's push to become a global leader in utilizing technology to create a community that maximizes energy efficiency and residents' convenience, as well as welfare. Dozens of predominantly Japanese companies will participate in Society 5.0 Town at CEATEC, to be held at Makuhari Messe in Chiba Prefecture from Oct. 15 to 18. Participants range from ANA Holdings Inc., Mitsubishi Estate Co. and Taisei Corp., among others. "We are a company about mobility. We want to solve mobility-related challenges. If you want to go somewhere, you have to go to an airport and fly. Avatar saves people from having to do that," said Akira Fukabori, director of the Avatar Division.

London, China is a leading force in Artificial Intelligence (AI) technology and blocking AI research with the country will do more harm than good for humanity, Microsoft CEO Satya Nadella said. In an interview with the BBC, Nadella said that despite national security concerns, backing out of China would "hurt more" than it solved. "A lot of AI research happens in the open and the world benefits from knowledge being open," he said. Quoting Microsoft President Brad Smith, Nadella said: "We know any technology can be a tool or a weapon. The question is, how do you ensure that these weapons don't get created? I think there are multiple mechanisms."

On fields across the world, phones, tablets, drones, and other technologies are changing how food is grown. Through these devices, artificial intelligence (AI)--technology able to perform tasks that require human intelligence--may help farmers use the techniques they already know and trust on a bigger scale. And Big Data--data sets that reveal telling patterns about growth, yield, weather, and more--may help farmers make better decisions before crises strike. According to the report Refresh: Food Tech, From Soil to Supper released in 2018, AI and Big Data may help produce more food, use less water, limit resource consumption, redirect food waste, and lower food prices--all while improving the lives and incomes of farmers and food producers. "Recent advances have the potential for big breakthroughs in the ways we grow, store, transport, distribute, and consume food," says the Refresh Report.

By the end of this decade, a milestone is reached in artificial intelligence, with computers now routinely passing the Turing Test.** This test is conducted by a human judge who is made to engage in a natural language conversation with one human and one machine, each of which tries to appear human. Participants are placed in isolated locations. For several decades, information technology had seen exponential growth – leading to vast improvements in computer processing power, memory, bandwidth, voice recognition, image recognition, deep learning and other software algorithms. By the end of the 2020s, it has reached the stage where an independent judge is literally unable to tell which is the real human and which is not.* Answers to certain "obscure" questions posed by the judge may appear childlike from the AI – but they are humanlike nonetheless.*

Using publicly available traffic camera data in New York City, we quantify time-dependent patterns in aggregate pedestrian foot traffic. These patterns exhibit repeatable diurnal behaviors that differ for weekdays and weekends but are broadly consistent across neighborhoods in the borough of Manhattan. Weekday patterns contain a characteristic 3-peak structure with increased foot traffic around 9:00am, 12:00-1:00pm, and 5:00pm aligned with the "9-to-5" work day in which pedestrians are on the street during their morning commute, during lunch hour, and then during their evening commute. Weekend days do not show a peaked structure, but rather increase steadily until sunset. Our study period of June 28, 2017 to September 11, 2017 contains two holidays, the 4th of July and Labor Day, and their foot traffic patterns are quantitatively similar to weekend days despite the fact that they fell on weekdays. Projecting all days in our study period onto the weekday/weekend phase space (by regressing against the average weekday and weekend day) we find that Friday foot traffic can be represented as a mixture of both the 3-peak weekday structure and non-peaked weekend structure. We also show that anomalies in the foot traffic patterns can be used for detection of events and network-level disruptions. Finally, we show that clustering of foot traffic time series generates associations between cameras that are spatially aligned with Manhattan neighborhood boundaries indicating that foot traffic dynamics encode information about neighborhood character.

Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at the document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at the fragment level with eighteen propaganda techniques and we propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.

A team of scientists led by researchers at the University of Georgia Center for Food Safety in Griffin has developed a machine-learning approach that could lead to quicker identification of the animal source of certain Salmonella outbreaks. In the research, published in the January 2019 issue of Emerging Infectious Diseases, Xiangyu Deng and his colleagues used more than a thousand genomes to predict the animal sources, especially livestock, of Salmonella Typhimurium. Deng, an assistant professor of food microbiology at the center, and Shaokang Zhang, a postdoctoral associate with the center, led the project, which also included experts from the Centers for Disease Control and Prevention, the U.S. Food and Drug Administration, the Minnesota Department of Health and the Translational Genomics Research Institute. According to the Foodborne Disease Outbreak Surveillance System, close to 3,000 outbreaks of foodborne illness were reported in the U.S. from 2009 to 2015. Of those, 900 -- or 30 percent -- were caused by different serotypes of Salmonella, including Typhimurium, Deng said.

With the number of cameras drastically increasing in our world, facial recognition is rapidly taking flight. Facial recognition is a biometric faceprint, where artificial intelligence maps an individual's face mathematically. This faceprint is then stored in data. Later, the stored data can be compared with a live or digital image using deep learning to verify someone's identity. Facial recognition can be integrated anywhere there is a camera. As a result, the number of applications for facial recognition is growing rapidly, from security to retail and consumer electronics to government services.

Several technology services contractors and consulting firms have won spots on a potential five-year, $49M contract to provide the Department of Health and Human Services' program support center with artificial intelligence and automation tools designed to facilitate operations, the Washington Business Journal reported Thursday. More than 50 awardees will vie for task orders to supply AI, machine learning, blockchain, robotic process automation and other technology platforms to PSC under the Intelligent Automation/Artificial Intelligence program. Those companies include General Dynamics' (NYSE: GD) information technology business, Booz Allen Hamilton (NYSE: BAH), Leidos (NYSE: LDOS), Accenture Federal Services, Northrop Grumman (NYSE: NOC), CGI Federal, Unisys (NYSE: UIS), Parsons' (NYSE: PSN) government services arm, Deloitte, Guidehouse and Octo Consulting Group. HHS issued in January a solicitation for the IAAI IDIQ contract, which has functional areas that include systems design, applied ideation and design support and prototyping. The PSC is a provider of shared services across HHS and 23 other agencies covering logistics, administrative operations, procurement, real estate, occupational health and financial management.