The government is considering having hospitals introduce a facial recognition system to identify patients using My Number tax and social security identification cards as health insurance cards, sources have said. The move is designed to help prevent the fraudulent use of My Number cards and promote the spread of online procedures for public services. The use of My Number cards as health insurance cards at hospitals will become possible as early as March 2021 as the Diet passed related legislation last month. But such use is raising concerns among hospitals about potential problems that could arise if hospital staff accept My Number cards from patients for identification purposes. The government is considering having patients themselves take care of the identification procedures for the cards. Some companies, including NEC Corp. and Panasonic Corp., already sell facial recognition systems.

We've all read the news and heard the scaremongering stories around potential flaws and biases in Artificial Intelligence (AI) systems. Despite the scepticism, businesses are undoubtedly using the technology to streamline work processes, automate timely tasks, and completely reimagine the way individuals work altogether. But to truly harness the potential of AI, we need to move past the speculation and foster a workforce that unites the power of both humans and emerging technologies. 'Augmented Intelligence' spans across business intelligence and automated data analytics, and encourages businesses to put human intuition in the middle of data analytics and advanced algorithms. Currently, AI innovation is at its peak, with numerous business intelligence and data analytics technologies springing up each week. It is a sector that is rife with competition and with a thirst to constantly innovate.

The arms race between cybercriminals and cybersecurity professionals continues to escalate. And anyone watching the trajectory of this perpetual game of one-upmanship can see that this is a race towards implementing AI in the service of each side's goals. For instance, a report by Nokia revealed that AI-powered botnets look for vulnerabilities in Android devices, then load data-stealing malware that is only detected after the damage has been done. Networks now incorporate multi-cloud environments that are dynamic and often temporary, SD-WAN connections to branch offices to support critical business applications, and an increasingly mobile workforce. At the same time, devices are proliferating inside networks at an unprecedented pace, from a multitude of different end-user devices to IoT technology.

Companies across the world are leveraging Artificial Intelligence to communicate with customers, extract relevant insights out big data and solve some of the most complex problems. AI is everywhere and the way it is rising, it is prophesied to create2.3 million jobs by 2020. However, even though AI seems to be the next big tide in the industry, many companies are not able to make the best out of this sought after technology. Maybe because they are lacking someone who would be able to make some serious decisions regarding this technology. And this calls for the much-needed job of a Chief Artificial Intelligence Officer (CAIO).

Artificial intelligence (AI), a branch of computer science that is transforming scientific inquiry and industry, could now speed the development of safe, clean and virtually limitless fusion energy for generating electricity. A major step in this direction is under way at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University, where a team of scientists working with a Harvard graduate student is for the first time applying deep learning -- a powerful new version of the machine learning form of AI -- to forecast sudden disruptions that can halt fusion reactions and damage the doughnut-shaped tokamaks that house the reactions. "This research opens a promising new chapter in the effort to bring unlimited energy to Earth," Steve Cowley, director of PPPL, said of the findings, which are reported in the current issue of Nature magazine. "Artificial intelligence is exploding across the sciences and now it's beginning to contribute to the worldwide quest for fusion power." Fusion, which drives the sun and stars, is the fusing of light elements in the form of plasma -- the hot, charged state of matter composed of free electrons and atomic nuclei -- that generates energy.

Faculty members Ian Kerr and Teresa Scassa have been appointed to the Government of Canada's new Advisory Council on Artificial Intelligence, joining a prestigious group of leading Canadian researchers and business executives to provide advice on how Canada can become a global leader in artificial intelligence (AI) advancements while ensuring that AI policy and practice reflect Canadian values. As stated in the press release from the Ministry of Innovation, Science and Economic Development Canada, "Artificial intelligence (AI) is a set of complex and powerful technologies that will touch or transform every sector and industry in Canada. It has the power to help us address some of our most challenging problems, from improving Canadians' health to fighting climate change. It will also introduce new sources of job creation and sustainable economic growth." The advisory council will be tasked with ensuring that Canada is approaching the transformative power of AI in an intelligent human-centric way, with attention given to human rights, transparency and openness.

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A variety of machine learning applications expect to achieve rapid learning from a limited number of labeled data. However, the success of most current models is the result of heavy training on big data. Meta-learning addresses this problem by extracting common knowledge across different tasks that can be quickly adapted to new tasks. However, they do not fully explore weakly-supervised information, which is usually free or cheap to collect. In this paper, we show that weakly-labeled data can significantly improve the performance of meta-learning on few-shot classification. We propose prototype propagation network (PPN) trained on few-shot tasks together with data annotated by coarse-label. Given a category graph of the targeted fine-classes and some weakly-labeled coarse-classes, PPN learns an attention mechanism which propagates the prototype of one class to another on the graph, so that the K-nearest neighbor (KNN) classifier defined on the propagated prototypes results in high accuracy across different few-shot tasks. The training tasks are generated by subgraph sampling, and the training objective is obtained by accumulating the level-wise classification loss on the subgraph. The resulting graph of prototypes can be continually re-used and updated for new tasks and classes. We also introduce two practical test/inference settings which differ according to whether the test task can leverage any weakly-supervised information as in training. On two benchmarks, PPN significantly outperforms most recent few-shot learning methods in different settings, even when they are also allowed to train on weakly-labeled data.

If you think you've got a bad case of the travel bug, get this: Dr. John Halamka travels 400,000 miles a year. Halamka is chief information officer at Harvard's Beth Israel Deaconess Medical Center, a professor at Harvard Medical School, and a practicing emergency physician. In a talk at Singularity University's Exponential Medicine last week, Halamka shared what he sees as the biggest healthcare problems the world is facing, and the most promising technological solutions from a systems perspective. "In traveling 400,000 miles you get to see lots of different cultures and lots of different people," he said. "And the problems are really the same all over the world. Maybe the cultural context is different or the infrastructure is different, but the problems are very similar."

Artificial Intelligence (AI) technology has the capability to be one of, if not the, most impactful technologies ever and at the moment, like almost every other government on the planet, the US government has no idea how to properly regulate it. But what the US does know is that it doesn't want other countries using its own AI technology against it, especially in the event of war as we continue to see the emergence of autonomous AI powered weapons systems, from Chinese "fire and forget" cruise missiles to fully autonomous Russian nuclear submarines. As a result a new proposal published recently by the Department of Commerce lists a wide range of AI technologies that could potentially require a license to sell to certain countries, and the categories of restricted "exports" are as diverse as Machine Vision and Natural Language Processing tech. As you'd expect though it also lists military specific products like adaptive camouflage and surveillance technology. The small number of countries these regulations would target includes one of the biggest names in AI – China, who last year announced that they want to be world leaders in AI by 2030.