The results are in from the biggest computer face-recognition contest to date. Everyone from government agencies to police forces are looking for software to track us in airports or spot us in CCTV images. But much of this technology is developed behind closed doors – how can we know if any of it really works? To answer this question, the Intelligence Advanced Research Projects Activity (IARPA) and the US National Institute of Standards and Technology (NIST) have been running the biggest face-recognition competition to date. The Face Recognition Prize Challenge tested two tasks: face verification and face search. Face verification is what phone manufacturers such as Apple – whose iPhone X, out last week, can be unlocked with your face – are trying to master.

Artificial intelligence and machine learning dominate so much conversation about cybersecurity that any CISO is faced with the necessity of explaining this family of technologies to the board. This is always challenging, especially with technologies so heavily hyped, and so liable to easy misunderstanding. As one panelist (Sriram Chandrasekar, Co-Head, AI Investments, Point72 Venture) put it, his role as a venture capitalist is to discern "the faint shimmer of snake oil" that so often rides atop presentations about artificial intelligence." The panel took up the task of framing AI in ways that would be accessible to boards, and that would give them a realistic sense of what AI is, does, and doesn't do. The panel was moderated by Dr. Reggie Brothers, Chertoff Group Principal and former senior science and technology executive at both the US Departments of Defense and Homeland Security. In addition to Chandrasekar, the panelists included Walid Ali (Senior Director, Artificial ...

As Russia's government develops a digital economy, organisations are stepping up the use of artificial intelligence (AI) and machine learning technologies. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.

This might come across surprising for lot many AI enthusiasts out there, but the technology actually fosters the capability to create fake audio and video, which is difficult to distinguish from reality. In a recent feat, scientists at University of Washington created an AI software that could generate highly realistic fake videos of former president Barack Obama using existing audio and video clips of him. The tool essentially takes audio files, converts them into realistic mouth movements, and then grafts those movements onto existing video. The resultant video shows someone saying something they didn't. University of Washington scientists had previously revealed that the tool could be utilized for generating digital doppelgangers of anyone by simply analyzing their images.

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In brief: Ray Kurzweil, chief engineer for Google and famous futurist, spoke in a discussion held at the Council on Foreign Relations on Friday. He emphasized how AI would enhance humankind, despite the possibility of "difficult episodes." Amidst all the talk about how artificial intelligence (AI) is threatening society with great harm--beginning with taking over human-held jobs and then, eventually, becoming more intelligent and taking over the entire world--some experts believe that AI shouldn't be feared. Foremost among these experts is Google's director of engineering and notable "future teller" Ray Kurzweil, who has said time and again that the technological singularity won't necessarily go down as expected. Kurzweil discussed the future of AI at the Council on Foreign Relations (CFR) in Washington, D.C. on Friday.

Experts are looking at ways to create AI which will ultimately benefit humanity and Google's director of engineering, Ray Kurzweil, backs up that theory. However, he insists mankind will have to endure "difficult episodes" on the way to achieving machine learning which falls in line with our goals. Mr Kurzweil said at the Council on Foreign Relations (CFR) in Washington, DC, that technology is a "double-edged sword" which has helped and hindered humans, and he expects the same of AI. The 69-year old said: "Technology has always been a double-edged sword. Fire kept us warm, cooked our food and burned down our houses.

With the adoption of new technology for conducting work in the digital world, it is only normal for new technology to start coming out as possible solutions to cyber threats. One such area that has been in the spotlight lately is artificial intelligence (AI) and how it can help businesses to improve cybersecurity and protect against recent growing threats that come from ransomware and phishing. One recent organization that has talked about such benefits, particularly for the healthcare industry in combatting ransomware, is the Institute for Critical Infrastructure Technology (ICIT) in their paper, "How to Crush the Health Sector's Ransomware Pandemic: The Machine Learning Based Artificial Intelligence Revolution Starts Now!" ICIT talks about how machine learning based AI throughout the Internet of Things (IoT) microcosm could help to detect, respond to, and predict cyber threats. ICIT finds that if the healthcare industry adopts sophisticated algorithmic defenses such as machine learning (ML) or AI solutions it will give them an edge against the average attacker and finally put them a step ahead of their risks. Since healthcare organizations are already using cognitive and AI solutions for big data analytics and clinical apps, all they need to do now is to adopt similar solutions but with cybersecurity in mind.

The cybersecurity threat landscape is ever-changing and augmenting SIEM systems offers federal agencies much-needed agility to deliver 24/7 insight. Leveraging a next generation advanced analytics and machine learning platform alongside a SIEM provides a strong future-proofed infrastructure that can empower agencies as they undergo efforts to beef up cybersecurity by optimizing SIEM deployments, opening up the possibilities for machine learning and analytic use cases. Significantly reducing the cost of storing and ingesting data with open source technology can allow agencies to expand their enterprise visibility, break vendor lock-in and provide the analytics they require to stay safe.

Many countries are suffering from severe air pollution. Understanding how different air pollutants accumulate and propagate is critical to making relevant public policies. In this paper, we use urban big data (air quality data and meteorological data) to identify the \emph{spatiotemporal (ST) causal pathways} for air pollutants. This problem is challenging because: (1) there are numerous noisy and low-pollution periods in the raw air quality data, which may lead to unreliable causality analysis, (2) for large-scale data in the ST space, the computational complexity of constructing a causal structure is very high, and (3) the \emph{ST causal pathways} are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present \emph{p-Causality}, a novel pattern-aided causality analysis approach that combines the strengths of \emph{pattern mining} and \emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST causal pathways}. First, \emph{Pattern mining} helps suppress the noise by capturing frequent evolving patterns (FEPs) of each monitoring sensor, and greatly reduce the complexity by selecting the pattern-matched sensors as "causers". Then, \emph{Bayesian learning} carefully encodes the local and ST causal relations with a Gaussian Bayesian network (GBN)-based graphical model, which also integrates environmental influences to minimize biases in the final results. We evaluate our approach with three real-world data sets containing 982 air quality sensors, in three regions of China from 01-Jun-2013 to 19-Dec-2015. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.