The Ministry of Economy, Trade and Industry will request a total budget of ¥1.4 trillion for fiscal 2019, up 9.9 percent from the previous year, sources with knowledge of the matter said Thursday. With the increased budget for the year starting in April next year, the ministry aims to promote cashless transactions for small companies and the development of advanced technologies such as artificial intelligence. Specifically, the ministry plans to spend ¥3 billion to help small businesses and other entities introduce cashless payment terminals and to standardize quick response, or QR, codes that currently vary among companies, according to the sources. For research and development of AI, next-generation computers and other technologies, the ministry hopes to allocate ¥27.9 billion. It plans to spend ¥1.8 billion for the cultivation of human resources, promoting the provision of refresher programs, for example, the sources said. To invigorate businesses in the field of social security, the ministry intends to boost spending to ¥2 billion from ¥600 million in fiscal 2018.

But more and more innovation is now coming from other regions, and there are reasons to believe the AI revolution might first take off elsewhere. Due to its size, capital, and cultural landscape, China, in particular, seems poised to lead AI evolution. China's economic growth since the 1980s has been staggering, and government investment has been key toward creating new technologies in the country. In July of 2017, the Chinese government resolved to make the nation the world's leader in AI by 2030. Although any government can make similarly bold claims, China's track record in recent years has been remarkable, with the nation now home to some of the world's most extensive and modern infrastructure.

RIO DE JANEIRO – New aerial images give a rare glimpse of an isolated tribe in Brazil's Amazon, showing 16 people walking through jungle as well as a deforested area with a crop. In a clip released Tuesday night, one of the tribespeople appears to be carrying a bow and arrow. Brazil's agency for indigenous affairs, Funai, said it captured the drone shots during an expedition last year to monitor isolated communities, but only released them now to protect their study. Researchers monitored the tribe in Vale do Javari, an indigenous territory in the southwestern part of the state of Amazonas. There are 11 confirmed isolated groups in the area -- more than anywhere else in Brazil.

The U.S. Army may establish an artificial intelligence task force over the next 90 days in an effort to help develop needed expertise and better prepare for the service for the future of warfare, says Lt. Gen. Bruce Crawford, USA, Army chief information officer. The service also is creating a cloud computing advisory board. Gen. Crawford mentioned the potential task force during the AFCEA TechNet Augusta conference in Augusta, Georgia. He tied the task force to the Defense Department's creation of an Joint Artificial Intelligence Center (JAIC), an effort being led by the department's CIO, Dana Deasy. "Our leadership across the department, in response to the National Defense Strategy, is in the process of standing up a joint AI center," told the conference audience.

Abstract--Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks. We consider the use of DL for radio signal (modulation) classification tasks, and present practical methods for the crafting of white-box and universal black-box adversarial attacks in that application. We show that these attacks can considerably reduce the classification performance, with extremely small perturbations of the input. In particular, these attacks are significantly more powerful than classical jamming attacks, which raises significant security and robustness concerns in the use of DLbased algorithms for the wireless physical layer. Deep learning (DL), implemented through deep neural networks (DNNs), represents a machine-learning paradigm that has been extremely successful in the last decade, especially in computer vision and natural language processing applications [1].

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.

We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the same underlying causal mechanisms. We compare our algorithms with those in the extant literature using extensive simulation studies based on large-scale voter persuasion experiments and the MNIST database. Our methods can perform an order of magnitude better than existing benchmarks while using a fraction of the data.

In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. The report, entitled "Artificial Intelligence and Life in 2030," examines eight domains of typical urban settings on which AI is likely to have impact over the coming years: transportation, home and service robots, healthcare, education, public safety and security, low-resource communities, employment and workplace, and entertainment. It aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential and to help guide decisions in industry and governments, as well as to inform research and development in the field. This article by the chair of the 2016 Study Panel and the inaugural chair of the AI100 Standing Committee describes the origins of this ambitious longitudinal study, discusses the framing of the inaugural report, and presents the report's main findings. It concludes with a brief description of the AI100 project's ongoing efforts and planned next steps.

A recent article in Bloomberg magazine called data science "America's hottest job." In response to increasing demand by industry, government and academia for highly trained data scientists, the University of Virginia's Data Science Institute is launching an online version of its Master of Science in Data Science program next summer. Through a collaboration with Noodle Partners, a company that provides online education management support, the degree can be earned entirely online, and will mirror the curriculum of the Data Science Institute's residential M.S.D.S. program. Currently, 49 students are enrolled in UVA's residential program and 20 more are working toward joint MBA/M.S. in Data Science degrees. The online M.S.D.S. program initially will enroll about 30 students, and that number is likely to grow each semester as the program modestly expands.

In July, Sigal Samuel wrote about new artificial-intelligence modeling projects that may help predict policy outcomes--particularly around issues of religious pluralism. In his book Sapiens, Yuval Noah Harari defines two classes of chaotic systems. In level-one chaotic systems, the rules can be complex, but they operate deterministically and produce a result based on the initial conditions. Level-two chaotic systems react to predictions about themselves and therefore can never be predicted accurately. A recent example might be the 2016 U.S. presidential election, where FBI Director James Comey assumed that the candidate Hillary Clinton was going to win; he claims that he used this prediction to justify the release of information to the public, in order to guard against the losing candidate claiming bias on the part of the FBI.