The country on the cutting edge of facial recognition technology and the amazing ways it can be put to use is definitely China. While the Chinese government and many of the country's current systems, large population (more than 1.3 billion citizens) and centralized identity data bases might make adopting facial recognition technology easier than in Europe or the United States, Chinese-based technology companies are also leaders in investing and building useful innovations to find new ways to profit from the use of computer vision. Unlike your fingerprint, your faceprint can be scanned at a distance. Your individual faceprint is a unique code that is applicable to you. It's created by measuring distances between points on your face such as the width of your nose or the distance between your eyes.

Maoyan Entertainment, China's biggest online movie ticketing platform, is seeking to raise as much as US$345 million in a Hong Kong initial … By target, the hottest sectors were tech, entertainment and culture, e-commerce, … as regulators relaxed restrictions on M&A and IPO approvals. Tencent and Alibaba, China's biggest tech groups and two of the country's most acquisitive buyers, have stepped on the brakes after a … Let's start with Honor, a sub-brand of Chinese tech giant Huawei, … with super low-cost phones made by Chinese tech giant TCL under the … China's push for technology self-reliance faces reality check, says … The problem is that a large proportion of suppliers in China's technology industry are foreign based, often with headquarters in Taiwan, South … Tencent Holdings, the largest video game publisher in the world and owner of China's top messaging app WeChat, made 163 investments in … China has been scrambling to catch up to the U.S. cloud computing … intelligence research and so-called "smart cities," which generate a lot of … As the trade war between China and the United States rumbles on, its focus has shifted from deficits and surpluses towards more technological .. The Alibaba effect: Chinese e-tailer tightens grip over 600m lives … It is also a world leader in technologies like artificial intelligence. Artificial Intelligence Is Powerful--And Misunderstood. The potential applications for AI are extremely exciting. World's most valuable AI startup SenseTime unveils self-driving center … Xiaomi Corp. will invest at least 10 billion yuan ($1.5 billion) on artificial intelligenceand smart devices over the next five years, as the … According to the Chinese media, the joint venture between Riot and Tencent will be established in Shanghai.

The Army envisions acquiring technologies that use machine learning to autonomously detect and address software vulnerabilities and network misconfigurations – routine mistakes that could offer attackers an entry point onto its systems. Another reason organizations are turning to AI-powered cyber defenses: to counter the threat posed by intelligent cyber weapons. In February 2018, a group of more than two dozen researchers representing the Washington-based Center for a New American Security, the universities of Oxford and Cambridge, and nonprofit organizations including the Electronic Frontier Foundation and OpenAI, issued a groundbreaking report warning that AI technologies could amplify the destructive power available to nation-states and criminal enterprises. The report outlines dozens of ways attackers could use artificial intelligence to their advantage, from generating automated spear-phishing attacks capable of reliably fooling their human targets, to triggering ransomware attacks using voice or facial recognition, to designing malware that mimics normal user behavior to evade detection. Although there haven't yet been confirmed cases of AI-enabled cyberattacks, the researchers conclude that, "the pace of progress in AI suggests the likelihood of cyber attacks leveraging machine learning capabilities in the wild soon, if they have not done so already." Pentagon officials appear to be taking the threat seriously.

Under the concept, operators, government agencies and individual citizens would have access to the data. The recent test results are expected to provide momentum for proposed package delivery to consumers and many other drone uses currently stalled by regulatory hurdles. U.S. air-safety and law-enforcement officials have balked at approving extensive commercial drone operations without reliable identification techniques. In addition to Wing, which is slated to demonstrate fledgling-package delivery procedures in Virginia this year, the flights included drone-service companies AirMap Inc. and Kittyhawk. With three of the burgeoning industry's leading companies backing the approach and promising to step up testing, proponents hope to persuade the Federal Aviation Administration to loosen flight restrictions before completion of full-fledged rule making expected to take years.

A new machine-learning model developed by a West Virginia University student has potential applications in the energy, environmental and health-care fields. The model, which can be used to predict adsorption energies -- i.e., adhesive capabilities in gold nanoparticles -- was developed by Gihan Panapitiya, a doctoral physics student from Sri Lanka. Gold nanoparticles have historically been used by artists to bring out vibrant colors via their interaction with light. Now they are increasingly used in high-technology applications such as electronic conductors and others. "Machine learning recently came into the spotlight, and we wanted to do something linking machine learning with gold nanoparticles as catalysts," Panapitiya said.

Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML are an active focus of research. A central problem in this context is that both the quality of interpretability methods as well as trust in ML predictions are difficult to measure. Yet evaluations, comparisons and improvements of trust and interpretability require quantifiable measures. Here we propose a quantitative measure for the quality of interpretability methods. Based on that we derive a quantitative measure of trust in ML decisions. Building on previous work we propose to measure intuitive understanding of algorithmic decisions using the information transfer rate at which humans replicate ML model predictions. We provide empirical evidence from crowdsourcing experiments that the proposed metric robustly differentiates interpretability methods. The proposed metric also demonstrates the value of interpretability for ML assisted human decision making: in our experiments providing explanations more than doubled productivity in annotation tasks. However unbiased human judgement is critical for doctors, judges, policy makers and others. Here we derive a trust metric that identifies when human decisions are overly biased towards ML predictions. Our results complement existing qualitative work on trust and interpretability by quantifiable measures that can serve as objectives for further improving methods in this field of research.

The hysteria about the future of artificial intelligence (AI) is everywhere. There seems to be no shortage of sensationalist news about how AI could cure diseases, accelerate human innovation and improve human creativity. Just looking at the media headlines, you might think that we are already living in a future where AI has infiltrated every aspect of society. While it is undeniable that AI has opened up a wealth of promising opportunities, it has also led to the emergence of a mindset that can be best described as "AI solutionism". This is the philosophy that, given enough data, machine learning algorithms can solve all of humanity's problems.

Life-saving vaccines by could soon be delivered by drone. On-demand drone deliveries of vaccines have several advantages over more traditional means of transport to remote locations and Vanuatu's government has commissioned two companies to test such a programme.

The Pentagon has made big plans to adopt artificial intelligence across the department, but two very large hurdles stand in the way of that goal, its new chief data officer said Wednesday: structuring data and recruiting the talent to manage it. "You can't feed the algorithms if you don't have data. Solid, clean data in large volumes, well-tagged and well organized," Michael Conlin said at the ACT-IAC Artificial Intelligence and Intelligent. "People will tell you that the machine learning algorithms, AI technologies can clean the data for you. The Department of Defense has no shortage of data to pull from, but for it to be of any use to the AI capabilities, the department has to make sure that data is recorded in consistent, machine-readable formats for accuracy and to ensure it doesn't present the algorithms with unintended bias, Conlin said. "The more data you have to train your algorithms, the more accurate the algorithms are and the faster you get your results," he said. As an example, he detailed the department's efforts to improve the flight readiness of aircraft by tracking the lifecycle of parts that are replaced frequently versus those that can be sustained for longer -- dubbed "lemons" and "peaches," respectively. Conlin said the department tracked the serial numbers for the parts from aircraft maintenance records and determined with 99.9 percent accuracy which parts were lemons and which were peaches after nine maintenance stops on each part. The problem stems from the data itself, however. Because department officials used the serial numbers to identify the lemons versus peaches, Conlin said, only 25 percent of the data was useful. "Some [records] had a blank or a'To be completed later,' or'I don't know,' or something that wasn't the serial number," he said. "So you couldn't connect the maintenance records together in order to be able to identify to nine consistent maintenance activities." Considering that Silicon Valley is focused more on delivering AI solutions tailored for specific use cases rather than enterprisewide applications, as well as the growing importance of edge computing, the quality of the structured data becomes that much more important. Equally important is the Pentagon's need for data scientists to help oversee the AI systems, Conlin said. But the challenge is the current federal workforce structure isn't designed for the job. We don't have the career path for data professionals, let alone data scientists," he said.

For some organizations, harnessing artificial intelligence's full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. While testing the waters this way may deliver valuable insights, it likely won't be enough to make your company a market maker (rather than a fast follower). To become a true AI-fueled organization, a company may need to fundamentally rethink the way humans and machines interact within working environments. Executives should also consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making. Likewise, AI could drive new offerings and business models. These are not minor steps, but as AI technologies standardize rapidly across industries, becoming an AI-fueled organization will likely be more than a strategy for success--it could be table stakes for survival. In his new book The AI Advantage, Deloitte Analytics senior adviser Thomas H. Davenport describes three stages in the journey that companies can take toward achieving full utilization of artificial intelligence.1 In the first stage, which Davenport calls assisted intelligence, companies harness large-scale data programs, the power of the cloud, and science-based approaches to make data-driven business decisions. Today, companies at the vanguard of the AI revolution are already working toward the next stage--augmented intelligence--in which machine learning (ML) capabilities layered on top of existing information management systems work to augment human analytical competencies. According to Davenport, in the coming years, more companies will progress toward autonomous intelligence, the third AI utilization stage, in which processes are digitized and automated to a degree whereby machines, bots, and systems can directly act upon intelligence derived from them. The journey from the assisted to augmented intelligence stages, and then on to fully autonomous intelligence, is part of a growing trend in which companies transform themselves into "AI-fueled organizations."