Just as there are some (admittedly imperfect) technological solutions that attempt to prevent image software like Photoshop from being used to counterfeit money, there may be technological solutions that can mitigate the worst impacts of AI-enabled forgery. For instance, cameras could be designed that would hash encrypted video files in a block chain. This would not prevent later editing and forgery, but it would allow definitive, cryptographically secured evidence that a given version of a video or audio file existed at a given date. Though lay people would still struggle to know the truth, this might allow sophisticated investigators to definitively confirm that at least some versions were edited, since their hash date would be later than the original. This is but one potential research avenue to limit the impact of AI-enabled forgery.

Mathematicians at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new approach to machine learning aimed at experimental imaging data. Rather than relying on the tens or hundreds of thousands of images used by typical machine learning methods, this new approach "learns" much more quickly and requires far fewer images. Daniël Pelt and James Sethian of Berkeley Lab's Center for Advanced Mathematics for Energy Research Applications (CAMERA) turned the usual machine learning perspective on its head by developing what they call a "Mixed-Scale Dense Convolution Neural Network (MS-D)" that requires far fewer parameters than traditional methods, converges quickly, and has the ability to "learn" from a remarkably small training set. Their approach is already being used to extract biological structure from cell images, and is poised to provide a major new computational tool to analyze data across a wide range of research areas. As experimental facilities generate higher resolution images at higher speeds, scientists can struggle to manage and analyze the resulting data, which is often done painstakingly by hand.

Imagine that, in 20 or 30 years, a company creates the first artificially intelligent humanoid robot. She looks like a person, talks like a person, interacts like a person. If you were to meet Ava, you could relate to her even though you know she's a robot. Ava is a fully conscious, fully self-aware being: she communicates; she wants things; she improves herself. She is also, importantly, far more intelligent than her human creators.

AI, IoT, big data, and cybersecurity threats are already dominating technology headlines and predictions for 2018. At the enterprise level, addressing these challenges will have implications for end-user computing (EUC) as the landscape of where and how we work continues to evolve.

We consider the task of learning the parameters of a {\em single} component of a mixture model, for the case when we are given {\em side information} about that component, we call this the "search problem" in mixture models. We would like to solve this with computational and sample complexity lower than solving the overall original problem, where one learns parameters of all components. Our main contributions are the development of a simple but general model for the notion of side information, and a corresponding simple matrix-based algorithm for solving the search problem in this general setting. We then specialize this model and algorithm to four common scenarios: Gaussian mixture models, LDA topic models, subspace clustering, and mixed linear regression. For each one of these we show that if (and only if) the side information is informative, we obtain parameter estimates with greater accuracy, and also improved computation complexity than existing moment based mixture model algorithms (e.g. tensor methods). We also illustrate several natural ways one can obtain such side information, for specific problem instances. Our experiments on real data sets (NY Times, Yelp, BSDS500) further demonstrate the practicality of our algorithms showing significant improvement in runtime and accuracy.

The Department of Veterans Affairs has announced a research partnership with Alphabet subsidiary DeepMind that will tackle issues concerning patient deterioration during hospital care. Using a dataset comprised of 700,000 historical, de-personalized health records, the machine learning platform will help the VA identify risk factors for deterioration while predicting its onset. "Medicine is more than treating patients' problems," VA Secretary David J. Shulkin, MD, said in a statement. "Clinicians need to be able to identify risks to help prevent disease. This collaboration is an opportunity to advance the quality of care for our nation's veterans by predicting deterioration and applying interventions early."

Once large law firms had armies of first-year law graduates, combing documents for relevant information; now machines largely do it. New artificial intelligence diagnosed lung cancer 50 percent more accurately than radiology experts last year. And the U.S. Postal Service plans to deploy autonomous trucks by 2025. These are signs of big change, precipitated by a wave of new artificial intelligence resulting from a perfect storm of investments and development these past five years. And coming developments will increasingly enable machines to do more mental and physical tasks faster, better and cheaper than humans.

With malware growing increasingly sophisticated, CISOs are turning to artificial intelligence (AI) to combat cyberthreats and protect their companies' assets, according to a Wednesday security report from Cisco. Some 39% of CISOs said their organizations are reliant on automation for cybersecurity, the report found. Another 34% said they are reliant on machine learning, and 32% said they are highly reliant on AI. Encryption, while meant to improve security, is also causing challenges and confusion for cyber defenders, the report found. As of October 2017, 50% of global web traffic--both legitimate and malicious--was encrypted. This makes it more difficult to identify and monitor potential threats, because encryption offers malicious actors a tool to conceal command-and-control activity, giving them more time to operate and wreak havoc.

Google subsidiary DeepMind announced today that it's working with the U.S. Department of Veterans Affairs to use machine learning in an attempt to predict when patients will deteriorate during a hospital stay. Deterioration (when a patient's condition worsens) is a significant issue, since care providers can miss warning signs for potentially lethal conditions that arise as part of other treatment. DeepMind and the VA aim to tackle Acute Kidney Injury (AKI), which, as the name implies, occurs when a person's kidneys temporarily stop working as well as they should. That can mean kidney failure, or just injury that reduces kidney function. AKI can be fatal if untreated.

GraphGrail Ai, the world's first AI-based natural language processing platform with a DApps marketplace is passing through its TGE stage and has already garnered the acclaim of several prominent rating agencies with high rankings. Such results were made possible thanks to the extensive experience that the GraphGrail Ai development team has in implementing AI solutions for businesses and government agencies. To demonstrate and consolidate the experience of the GraphGrail Ai team for our followers, we have compiled a list of prominent cases that the development team had participated in over the last few years prior to undertaking the implementation of their solutions on blockchain systems. The GraphGrail Ai team was involved in the development and launch of a service that searches for extremist statements based on the legislation of the Russian Federation for the Rostov Center for Forensic Expertise. The system was used extensively and had successfully detected dangerous publications.