Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues. However, the conventional reward Rouge-L simply looks for exact n-grams matches between candidates and annotated references, which inevitably makes the generated sentences repetitive and incoherent. In this paper, instead of Rouge-L, we explore the practicability of utilizing the distributional semantics to measure the matching degrees. With distributional semantics, sentence-level evaluation can be obtained, and semantically-correct phrases can also be generated without being limited to the surface form of the reference sentences. Human judgments on Gigaword and CNN/Daily Mail datasets show that our proposed distributional semantics reward (DSR) has distinct superiority in capturing the lexical and compositional diversity of natural language.

When I spoke at the UK China business forum last month, I discussed this topic in response to an audience question. In the current climate of nationalistic fervour, I see the same question asked in many guises. As a disclosure, these are personal views. Like many in Europe, I take the view of building bridges and of collaboration. I have worked with Chinese AI companies (in robotics) and also enjoyed working with excellent Chinese students who I have mentored.

BEIRUT – Unknown warplanes targeted overnight an arms depot and posts of Iranian-backed militias in eastern Syria, near the Iraqi border, killing at least 18 fighters, Syrian opposition activists said Monday. The strikes come amid rising tensions in the Middle East and the crisis between Iran and the U.S. in the wake of the collapsing nuclear deal between Tehran and world powers. An official with an Iranian-backed militia in Iraq blamed Israel for the airstrikes that hit in the eastern Syrian town of Boukamal. There was no immediate comment from Israel. Israeli Prime Minister Benjamin Netanyahu said last month that Iran has no immunity anywhere and that the Israeli military "will act -- and currently are acting -- against them."

In recent years, the increase in accessible, large-scale computing power and storage has ushered in a new dawn for artificial intelligence (AI), with the technology appearing to finally catch up with the existing algorithms to bring machine learning (ML) to realisation. In this article, we discuss some of the key applications of ML that have shown success in clinical research. Moreover, we consider how machine learning is impacting on clinical trials, utilising decades of structured clinical trial data alongside real-world data (RWD) and other valuable data sources to support clinical trial design, execution and analysis. Combining computational skills and drug development experience, data science teams can support the pharma and biotech industry to generate business value through the application of machine learning. For ML algorithms to be successful they require large, quality data sets for their application.

Yanping grew up in China, and moved to Canada to complete her PhD at the University of Sherbrooke in Machine Learning and Data Mining. While completing her studies, she had strong a strong interest in further researching Machine Learning, which led her to pursue becoming a professor in Natural Language Understanding (NLU) at Xiamen University. In 2015, she decided to move to the AI hub, also known as Montreal, to dive into the industry dimension of her specialization. With NLU being one of the key technologies of our platform, Kepler, we got down to the nitty gritty with Yanping, who gave us a behind-the-scenes look at what her life as an NLU research scientist is actually like. NLU allows computers to understand the structure and meaning of human language.

Technology developed using artificial intelligence (AI) could identify people at high risk of a fatal heart attack at least 5 years before it strikes, according to new research funded by the British Heart Foundation (BHF). The findings are being presented at the European Society of Cardiology (ESC) Congress in Paris and published in the European Heart Journal. Researchers at the University of Oxford have developed a new biomarker, or'fingerprint', called the fat radiomic profile (FRP), using machine learning. The fingerprint detects biological red flags in the perivascular space lining blood vessels which supply blood to the heart. It identifies inflammation, scarring and changes to these blood vessels, which are all pointers to a future heart attack.

What do you think when you think of artificial intelligence (AI)? Until recently, generations of people only viewed AI technology through the lens of popular culture depictions. And one major touchstone in their experiences was the movie 2001: A Space Odyssey, which celebrates its 50th anniversary this year. It was the highest-grossing film in North America in 1968, selected to be preserved in the U.S. Library of Congress National Film Registry, won an Academy Award for visual effects and a Hugo Award--one of science fiction's highest honors--for Best Dramatic Presentation. Despite its accolades, the movie drew a mixed reception of awe and confusion over its depiction of evolution and the meaning of existence.

The field of artificial intelligence (AI) has progressed rapidly in recent years, matching or, in some cases, even surpassing human accuracy at tasks such as image recognition, reading comprehension, and translating text. The intersection of AI and GIS is creating massive opportunities. AI, machine learning, and deep learning are helping us make our world better by increasing crop yields through precision agriculture, fighting crime by deploying predictive policing models, and predicting when the next big storm will hit so we can be better equipped to handle it. Broadly speaking, AI is the ability of computers to perform tasks that typically require some level of human intelligence. Machine learning is one type of engine that makes this possible.

PLYMOUTH, Mass., September 9, 2019 -- Microway, a leading provider of computational clusters, servers, and workstations for AI and HPC applications, announces it has provided Oregon State University with six NVIDIA DGX-2 supercomputer systems, deployment services, and bringup expertise. Each DGX-2 packs 16 fully connected Tesla V100 GPUs, giving Oregon State a linked network of the world's most powerful AI systems powered by 96 GPU accelerators. The new, massively increased computing capabilities at the College of Engineering resolved a significant campus hardware gap and helped support cutting-edge research on medical imaging, nuclear science, bridge construction, robotics, and driverless vehicles. When planning expanded capability, university faculty and administrators determined they needed enough GPU capacity to serve the diverse needs of undergraduate classes and research workloads, plus lightning-fast storage. The University selected the NVIDIA DGX-2 platform for its immense power, technical support services, and the Docker images with NVIDIA's NGC containerized software.

Kleyman brings more than 15 years of experience to his role as Executive Vice President of Digital Solutions at Switch. Using the latest innovations such as AI, machine learning, blockchain, DevOps, cloud, and advanced technologies, Kleyman delivers solutions to customers that help them achieve their business goals and remain competitive in their market. An active member in the technology industry, he was ranked number 16 globally in the Onalytica study that reviewed the top 100 most influential individuals in the cloud landscape; and number 4 in another Onalytica study that reviewed the industry's top Data Security Experts. His published and referenced work can be found on ITPro Today, Data Center Knowledge, InformationWeek, Network Computing, AFCOM, TechTarget, Dark Reading, Forbes, CBS Interactive, Slashdot, and more.