A hundred and thirty three years--that's how long Virginia-based Newport News Shipbuilding has been in the business of manufacturing ships. As the sole developer of U.S. Navy aircraft carriers, the company has constructed more than 30 warships, including the world's first and largest nuclear-powered carrier, which weighs 100,000 tons and comprises 300 million parts. Traditionally, building a ship like this might require 30 million to 40 million man-hours. Digital transformation has upended the ship building business, said Bharat Amin, VP and chief information officer at Newport News Shipbuilding, on stage at Siemens' Spotlight on Innovation, an annual technology conference held recently in Orlando. "You hear about smart cities, we want to be a smart shipyard," Amin told the audience.

Five years after Google acquired DeepMind, the health and artificial intelligence group is unveiling its biggest breakthrough yet in health care. Its technology is able to predict if a patient has potentially fatal kidney injuries 48 hours before many symptoms can be recognized by doctors. In a paper published on Wednesday in the journal Nature, DeepMind researchers said their algorithms correctly predicted 90 percent of acute kidney injuries that would end up requiring dialysis. The work was the result of a project with the U.S. Department of Veteran Affairs to help doctors get a head start on treatment. "We've been really excited for the potential of using AI to support clinicians moving care from reactive to proactive and preventative," said Dominic King, DeepMind's co-founder and clinical lead, in an interview.

As the Joint Chiefs of Staff gathered in Key West, Florida for a private meeting in March 1948, the first U.S. secretary of defense, James Forrestal, posed a simple question: "Who will do what with what?" The Air Force and Navy tussled over strategic nuclear bombers, while the Army and Marine Corps bickered over limitations to their respective end strengths. The resulting Key West agreement defined the primary functions of the services for the Cold War, but it didn't end the debate -- far from it. Interservice rivalries flared over atomic weapons and the "missile gap." Turf battles wasted critical time and money and introduced new dangers in command and control.

Government usually isn't the place to look for innovation in IT or new technologies like artificial intelligence. But Ott Velsberg might change your mind. As Estonia's chief data officer, the 28-year-old graduate student is overseeing the tiny Baltic nation's push to insert artificial intelligence and machine learning into services provided to its 1.3 million citizens. "We want the government to be as lean as possible," says the wiry, bespectacled Velsberg, an Estonian who is writing his PhD thesis at Sweden's Umeå University on using the Internet of Things and sensor data in government services. Estonia's government hired Velsberg last August to run a new project to introduce AI into various ministries to streamline services offered to residents.

The media makes sensationalist claims about AI, but let's take a closer look at the facts: AI is not a trend! Cisco has been doing it for years to help businesses across the globe quickly and easily identify banking trojans, botnets, phishing and ransomware. A taxonomy of AI algorithms used in cyber threat detection What AI can and can't do for your organisation How to leverage AI as a preventative measure to help detect & uncover threats before they hit your business Best practices on how to incorporate AI into your threat detection and defence systems What AI can and can't do for your organisation During this webinar, we'll go beyond the hype and show you real-world examples of AI algorithms that helps us keep our customers safe on the internet, anywhere their users go. We'll cover anomaly detection, clustering, belief propagation, deep learning, and more. See how Cisco Umbrella uses AI to effectively detect current and emerging threats.

These days, the latest developments in artificial intelligence (AI) research always get plenty of attention, but an AI researcher at the U.S. Naval Research Laboratory believes one AI technique might be getting a little too much. Ranjeev Mittu heads NRL's Information Management and Decision Architectures Branch and has been working in the AI field for more than two decades. "I think people have focused on an area of machine learning--deep learning (aka deep networks)--and less so on the variety of other artificial intelligence techniques," Mittu said. "The biggest limitation of deep networks is that a complete understanding of how these networks arrive at a solution is still far from reality." Deep learning is a machine learning technique that can be used to recognize patterns, such as identifying a collection of pixels as an image of a dog.

Cities are an engine for human prosperity. By putting people and businesses in close proximity, cities serve as the vital hubs to exchange goods, services, and even ideas. Each year, more and more people move to cities and their surrounding metropolitan areas to take advantage of the opportunities available in these denser spaces. Technology is essential to make cities work. While putting people in close proximity has certain advantages, there are also costs associated with fitting so many people and related activities into the same place. Whether it's multistory buildings, aqueducts and water pipes, or lattice-like road networks, cities inspire people to develop new technologies that respond to the urban challenges of their day. Today, we can see the responses made possible by the advances of the second industrial revolution, namely steel and electricity. Multistory buildings and skyscrapers responded to our demand for proximity to do business in the same locations.

You could be forgiven for thinking that A.I. will soon replace human physicians based on headlines such as "The A.I. Doctor Will See You Now," "Your Future Doctor May Not Be Human," and "This A.I. Just Beat Human Doctors on a Clinical Exam." But experts say the reality is more of a collaboration than an ousting: Patients could soon find their lives partly in the hands of A.I. services working alongside human clinicians. There is no shortage of optimism about A.I. in the medical community. But many also caution the hype surrounding A.I. has yet to be realized in real clinical settings. There are also different visions for how A.I. services could make the biggest impact.

Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user for different events. The majority of existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process. Existing methods that incorporate interactive user feedback focus on historical datasets. Therefore, classifiers cannot be interactively retrained for specific events or user-dependent needs in real-time. This limits real-time situational awareness, as streaming data that is incorrectly classified cannot be corrected immediately, permitting the possibility for important incoming data to be incorrectly classified as well. We present a novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements. We computationally evaluate our classification model adapted to learn at interactive rates. Our results show that our approach outperforms state-of-the-art machine learning models. In addition, we integrate our framework with the extended Social Media Analytics and Reporting Toolkit (SMART) 2.0 system, allowing the use of our interactive learning framework within a visual analytics system tailored for real-time situational awareness. To demonstrate our framework's effectiveness, we provide domain expert feedback from first responders who used the extended SMART 2.0 system.

When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve. Due to de-identification requirements, however, current experimental practices for public EHR benchmarks (such as the MIMIC-III critical care dataset) are time agnostic, assigning care records to train or test sets without regard for the actual dates of care. As a result, current benchmarks cannot assess how well models trained on one year generalise to another. In this work, we obtain a Limited Data Use Agreement to access year of care for each record in MIMIC and show that all tested state-of-the-art models decay in prediction quality when trained on historical data and tested on future data, particularly in response to a system-wide record-keeping change in 2008 (0.29 drop in AUROC for mortality prediction, 0.10 drop in AUROC for length-of-stay prediction with a random forest classifier). We further develop a simple yet effective mitigation strategy: by aggregating raw features into expert-defined clinical concepts, we see only a 0.06 drop in AUROC for mortality prediction and a 0.03 drop in AUROC for length-of-stay prediction. We demonstrate that this aggregation strategy outperforms other automatic feature preprocessing techniques aimed at increasing robustness to data drift. We release our aggregated representations and code to encourage more deployable clinical prediction models.