The Artificial Intelligence (AI) Award is run by the Accelerated Access Collaborative (AAC) in partnership with NHSX and the National Institute for Health Research (NIHR). It will make £140million available over three years to accelerate the testing and evaluation of the most promising AI technologies which meet the strategic aims set out in the NHS Long Term Plan. The Award will support technologies across the spectrum of development: from initial feasibility to evaluation within the NHS. Initially, it will focus on four key areas: screening, diagnosis, decision support and improving system efficiency. The Award forms a key part of the AAC's ambition to establish a globally leading testing infrastructure for innovation in the UK.

Britain has decided to allow Chinese tech giant Huawei to supply new high-speed network equipment, dealing a setback to the U.S. government and its global campaign to press allies into banning the company. The government's decision on Tuesday is the first by a major U.S. ally on the issue, which has seen intense lobbying from the Trump administration and China as the two vie for technological dominance. The British government said it is excluding "high risk" companies from supplying the sensitive "core" parts of the new fifth-generation, or 5G, networks. The core is the brain that keeps track, among other things, of smartphones connecting to networks and helps manage data traffic. But Britain will allow high risk suppliers to provide up to 35% of the less risky radio access network of antennas and base stations.

Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods. Existing works relied on either inter- or intra-region connectivities to generate neighborhood representations but failed to fully utilize the informative yet heterogeneous data within neighborhoods. In this work, we propose Urban2Vec, an unsupervised multi-modal framework which incorporates both street view imagery and point-of-interest (POI) data to learn neighborhood embeddings. Specifically, we use a convolutional neural network to extract visual features from street view images while preserving geospatial similarity. Furthermore, we model each POI as a bag-of-words containing its category, rating, and review information. Analog to document embedding in natural language processing, we establish the semantic similarity between neighborhood ("document") and the words from its surrounding POIs in the vector space. By jointly encoding visual, textual, and geospatial information into the neighborhood representation, Urban2Vec can achieve performances better than baseline models and comparable to fully-supervised methods in downstream prediction tasks. Extensive experiments on three U.S. metropolitan areas also demonstrate the model interpretability, generalization capability, and its value in neighborhood similarity analysis.

ABSTRACT Spoken language identification (LID) technologies have improved in recent years from discriminating largely distinct languages to discriminating highly similar languages or even dialects of the same language. One aspect that has been mostly neglected, however, is discrimination of languages for multilingual speakers, despite being a primary target audience of many systems that utilize LID technologies. As we show in this work, LID systems can have a high average accuracy for most combinations of languages while greatly underper-forming for others when accented speech is present. We address this by using coarser-grained targets for the acoustic LID model and integrating its outputs with interaction context signals in a context-aware model to tailor the system to each user. This combined system achieves an average 97% accuracy across all language combinations while improving worst-case accuracy by over 60% relative to our baseline.

LONDON – Britain decided Tuesday to allow Chinese tech giant Huawei to supply new high-speed network equipment, ignoring the U.S. government's warnings that it would sever intelligence cooperation if the company was not banned. Britain's decision is the first by a major U.S. ally in Europe, and follows intense lobbying from the Trump administration and China as the two vie for technological dominance. It sets up a diplomatic clash with the Americans, who claim that British sovereignty is at risk because the company could give the Chinese government access to data, an allegation Huawei denies. "We would never take decisions that threaten our national security or the security of our Five Eyes partners," Foreign Secretary Dominic Raab said, referring a security arrangement in which Britain, the United States, Australia, Canada and New Zealand, share intelligence. "We know more about Huawei and the risks that it poses than any other country in the world."

When a mysterious illness first pops up, it can be difficult for governments and public health officials to gather information quickly and coordinate a response. But new artificial intelligence technology can automatically mine through news reports and online content from around the world, helping experts recognize anomalies that could lead to a potential epidemic or, worse, a pandemic. In other words, our new AI overlords might actually help us survive the next plague. These new AI capabilities are on full display with the recent coronavirus outbreak, which was identified early by a Canadian firm called BlueDot, which is one of a number of companies that use data to evaluate public health risks. The company, which says it conducts "automated infectious disease surveillance," notified its customers about the new form of coronavirus at the end of December, days before both the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) sent out official notices, as reported by Wired.

Rising sea levels are not just predicted to change the landscape of the US, but it will also reshape where millions of people call home. Scientist used artificial intelligence to map where people will migrate once their coastal residence are under six-feet of water. The technology estimates nearly 13 million Americans will be forced to move by the end of the century, with many heading inland to land-locked cities such as Atlanta, Houston, Dallas, Denver and Las Vegas. The model also predicts suburban and rural areas in the Midwest will experience disproportionately large influx of people relative to their smaller local populations. The technology estimates nearly 13 million Americans will be forced to move by the end of the century, with many heading inland to land-locked cities such as Atlanta, Houston, Dallas, Denver and Las Vegas.

The American Council for Technology and Industry Advisory Council (ACT-IAC), the premier public-private partnership dedicated to advancing government through the application of information technology, officially announced the release of the "Artificial Intelligence (AI) Playbook for the U.S. Federal Government." It was produced through a collaborative, volunteer effort by a working group of 133 leaders from government and industry plus academia and associations, hosted by the ACT-IAC Emerging Technology Community of Interest (COI). "The AI Playbook is designed to help the United States Federal Government achieve successful outcomes and reduce risk in its understanding and application of AI technologies," said David Wennergren, CEO of ACT-IAC, "and this important work directly supports the President's Management Agenda (PMA), Cross Agency Priority (CAP) Goal 6 – Shifting from Low-Value to High-Value Work." The Playbook also follows the General Service Administration's Office of Government-wide Policy Modernization and Migration Management (M3) framework used for Shared Services. AI has the power to accelerate government services in fields as diverse as medical research and disaster recovery to help save lives and improve quality of service in impactful ways.

As we move towards a future where we lean on cybersecurity much more in our daily lives, it's important to be aware of the differences in the types of AI being used for network security. Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars. However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies. Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning). Supervised Learning relies on a process of labeling in order to "understand" information.

TALLAHASSEE, FLORIDA – Florida could turn to the sky to fight Burmese pythons on the ground under a bill a Senate committee unanimously approved Monday to allow two state agencies to use drones in the effort to eradicate invasive plants and animals. The bill would create an exception to a current law that prohibits law enforcement from using drones to gather information and bans state agencies from using drones to gather images on private land. It would allow the Florida Fish and Wildlife Conservation Commission and the Florida Forest Service to fly drones to manage and eradicate invasion species on public lands. Sen. Ben Albritton said he has been told that drones equipped with lidar, which stands for "light detection and ranging," might be able to identify pythons. "As you know, chasing those nasty critters down there in the Everglades is a difficult task," Albritton said.