The European Commission says that the EU could become the most attractive, secure and dynamic data-agile economy in the world. The Commission's new data strategy is for the EU to seize new opportunities in digitised industry and business-to-business artificial intelligence (AI) applications. However, the Commission has scrupulously avoided the vital question of whether GDPR is an obstacle to the EU's plans to become an AI hub. The European Commission announced its new EU data strategy with the publication of two papers in February 2020. These were a white paper on AI and a communication entitled, "A European strategy for data". The Commission acknowledges that "the availability of data is essential for training artificial intelligence systems … without data, there is no AI."

WIRE)-- Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced intelligent Clear-IQ Engine (AiCE) for the Vantage Galan 3T MR system, further expanding access to its new Deep Learning Reconstruction (DLR) technology. This technology, which is also available across a majority of Canon Medical's CT product portfolio, uses a deep learning algorithm to differentiate true MR signal from noise so that it can suppress noise while enhancing signal, forging a new frontier for MR image reconstruction. AiCE was trained using vast amounts of high-quality image data, and features a deep learning neural network that can reduce noise and boost signal to quickly deliver sharp, clear and distinct images, further opening doors for advancements in MR imaging. "AiCE utilizes a next generation approach to MR image reconstruction, further proving Canon Medical's leadership and commitment to innovation in diagnostic imaging," said Jonathan Furuyama, managing director, MR Business Unit, Canon Medical Systems USA, Inc. "With the expansion of this unique DLR method across modalities and into MR, we're elevating diagnostic imaging capabilities for our customers by bringing the power of AI to routine imaging to provide more possibilities in improving patient care than ever before." Canon Medical Systems USA, Inc., headquartered in Tustin, Calif., markets, sells, distributes and services radiology and cardiovascular systems, including CT, MR, ultrasound, X-ray and interventional X-ray equipment.

In the months since the novel coronavirus emerged in Wuhan, China, last December, almost 2,000 research papers have been published on the health effects of the new virus, possible treatments, and the dynamics of the resulting pandemic. This outpouring of research is a testament to the speed with which science can tackle big problems. But it also presents a headache for anyone wanting to stay up to date with the literature, or hoping to mine it for insight about the virus, its behavior, or possible treatments. Naturally, some believe that artificial intelligence may help. Monday, the White House announced a project in collaboration with tech companies and academics to make a huge amount of coronavirus research accessible to AI researchers and their algorithms for the first time.

U.S. health and technology specialists on Monday said they had launched a new collaborative venture to assemble a dataset of tens of thousands of scientific papers and literature on the coronavirus, which would then be analyzed by artificial intelligence programs to find patterns and answer questions raised by the World Health Organization about the pandemic. The dataset includes 29,000 articles, including 13,000 full-text pieces of medical literature, which will be made available on a special website allowing data scientists and artificial intelligence programmers to propose tools and software code that can unearth insights from the articles, White House officials and experts told reporters in a conference call. The venture came together after the White House Office of Science and Technology Policy issued a call to tech companies and research groups to figure out how artificial intelligence tools could be used to sift through thousands of research articles being published worldwide on the pandemic, said Lynn Parker, deputy chief technology officer at the White House office. With data scientists and machine language experts mining the literature compilation known as COVID-19 Open Research Dataset, experts and White House officials expect to get help developing vaccines, forming new guidelines on how long social distancing should be maintained and other insights, Michael Kratsios, the U.S. chief technology officer said. The venture includes the National Library of Medicine, which is part of the National Institutes of Health, Microsoft, Allen Institute of AI, Georgetown University's Center for Security and Emerging Technology, the Chan Zuckerberg Initiative (named for Mark Zuckerberg, Facebook's founder, and his wife Priscilla Chan), and Kaggle, which is a unit of Google.

Microsoft Research and Google Cloud have joined forces as part of an initiative to open up datasets that help researchers combat the Covid-19 novel coronavirus. Through the initiative, the Kaggle data science community acquired by Google cloud three years ago, Allen Institute for AI, Chan Zuckerberg Initiative (CZI), Georgetown University's Center for Security and Emerging Technology (CSET), Microsoft and the National Library of Medicine (NLM) at the National Institutes of Health have collaborated to release the Covid-19 Open Research Dataset (Cord-19). Michael Kratsios, US chief technology officer at The White House, called on the research community to collaborate to combat coronavirus. "Decisive action from America's science and technology enterprise is critical to prevent, detect, treat and develop solutions to Covid-19," he said. "The White House will continue to be a strong partner in this all-hands-on-deck approach. We thank each institution for voluntarily lending its expertise and innovation to this collaborative effort, and call on the US research community to put artificial intelligence [AI] technologies to work in answering key scientific questions about the novel Coronavirus. The collaborative effort includes a challenge aimed at encouraging researchers to provide new insights based on the reams of data being produced about coronavirus. "We are issuing a call to action to the world's artificial intelligence experts to develop text and data mining tools that can help the medical community develop answers to high-priority scientific questions.

Adversarial training (AT) has become a popular choice for training robust networks. However, by virtue of its formulation, AT tends to sacrifice clean accuracy heavily in favor of robustness. Furthermore, AT with a large perturbation budget can cause models to get stuck at poor local minima and behave like a constant function, always predicting the same class. To address the above concerns we propose Adversarial Training with Early Stopping (ATES). The design of ATES is guided by principles from curriculum learning that emphasizes on starting "easy" and gradually ramping up on the "difficulty" of training. We do so by early stopping the adversarial example generation step in AT, progressively increasing difficulty of the samples the network trains on. This stabilizes network training even for large perturbation budgets and allows the network to operate at a better clean accuracy versus robustness trade-off curve compared to AT. Functionally, this leads to a significant improvement in both clean accuracy and robustness for ATES models.

Min-max saddle point games appear in a wide range of applications in machine leaning and signal processing. Despite their wide applicability, theoretical studies are mostly limited to the special convex-concave structure. While some recent works generalized these results to special smooth non-convex cases, our understanding of non-smooth scenarios is still limited. In this work, we study special form of non-smooth min-max games when the objective function is (strongly) convex with respect to one of the player's decision variable. We show that a simple multi-step proximal gradient descent-ascent algorithm converges to $\epsilon$-first-order Nash equilibrium of the min-max game with the number of gradient evaluations being polynomial in $1/\epsilon$. We will also show that our notion of stationarity is stronger than existing ones in the literature. Finally, we evaluate the performance of the proposed algorithm through adversarial attack on a LASSO estimator.

This article deals with the IT security of connectionist artificial intelligence (AI) applications, focusing on threats to integrity, one of the three IT security goals. Such threats are for instance most relevant in prominent AI computer vision applications. In order to present a holistic view on the IT security goal integrity, many additional aspects such as interpretability, robustness and documentation are taken into account. A comprehensive list of threats and possible mitigations is presented by reviewing the state-of-the-art literature. AI-specific vulnerabilities such as adversarial attacks and poisoning attacks as well as their AI-specific root causes are discussed in detail. Additionally and in contrast to former reviews, the whole AI supply chain is analysed with respect to vulnerabilities, including the planning, data acquisition, training, evaluation and operation phases. The discussion of mitigations is likewise not restricted to the level of the AI system itself but rather advocates viewing AI systems in the context of their supply chains and their embeddings in larger IT infrastructures and hardware devices. Based on this and the observation that adaptive attackers may circumvent any single published AI-specific defence to date, the article concludes that single protective measures are not sufficient but rather multiple measures on different levels have to be combined to achieve a minimum level of IT security for AI applications.

An emerging form of AI known as neuromorphic computing has been used to recognize scents emitted by explosives, chemical weapons, and narcotics. Researchers from Intel and Cornell University made the breakthrough by equipping Intel's neuromorphic test chip Loihi with neural algorithms that mimic what happens in your brain when you smell something. This enabled the system to recognize the smell of each hazardous chemical from just a single sample. The study could pave the way to a vast range of applications of neuromorphic computing, which mimics the brain's basic mechanics to make machine learning more efficient. Intel believes the "electronic nose systems" could be used by airport security to detect weapons and explosives, by police and border control to find narcotics, by robots to monitor gases pimped out into the atmosphere, and by the makers of smoke detectors to improve their products.

The White House and others have released the free COVID-19 Open Research Dataset -- known as CORD-19 -- and are asking researchers around the world to apply AI techniques like natural language processing to drum up new insights, which could help us better understand and fight the coronavirus. Specifically, they want AI experts to develop text and data mining tools, which could help the medical community answer high-priorty questions about the novel coronavirus. With more than 29,000 scholarly articles, the freely available dataset is the "most extensive collection of machine readable coronavirus literature to date," says U.S. CTO Michael Kratsios. Broken down, 13,000 of the articles are available in their entirety and machine-readable, while 16,000 articles have partial text and summaries. It will be constantly updated with new information. It's the work of Microsoft Research, the Allen Institute for AI, the Chan Zuckerberg Initiative, the National Library of Medicine at the National Institutes of Health (NIH), the White House Office of Science and Technology (OSTP), Georgetown University's Center for Security and Emerging Technology, Cold Spring Harbor Laboratory and Google's Kaggle AI platform, which is also hosting the database and researchers' submissions.