Behavioral biometrics that uses machine learning is behind new features being added to IBM's Trusteer Pinpoint Detect platform, which financial institutions use to head off crooks who may have stolen the username and password of legitimate account holders. The new feature looks for anomalies between legitimate users' normal mouse gestures and those of the current user, and over time refines the accuracy of its analysis, says Brooke Satti Charles, Financial Crime Prevention Strategist for IBM Security. That analysis creates a risk score that banks can use to decide whether an ongoing transaction is fraudulent and trigger an alert. The institutions have to decide what to do about the alerts, but they could cut off the transaction or require further ID before the customer is allowed to continue, she says. The platform already checks the geolocation and IP address of customers as they login in order to detect fraudulent use, and behavior biometrics is an enhancement.

Elena is a Market Research Analyst at LTP. She is a research professional with a background in social sciences and extensive experience in consumer behavior studies and marketing analytics. She is passionate about technologies enabling financial inclusion for underprivileged and vulnerable groups of the population around the world.

The idea of artificial intelligence (AI) -- autonomous computers that can learn independently -- makes some people extremely uneasy, regardless of what the computers in question might be doing. Those individuals probably wouldn't find it reassuring to hear that a group of researchers is deliberately training computers to get better at scaring people witless. The project, appropriately enough, is named "Nightmare Machine." Digital innovators in the U.S. and Australia partnered to create an algorithm that would enable a computer to understand what makes certain images frightening, and then use that data to transform any photo, no matter how harmless-looking, into the stuff of nightmares. Images created by Nightmare Machine are unsettling, to say the least.

WASHINGTON – It's a scary world out there, risk experts agree, but they say Donald Trump and Hillary Clinton often focus on the wrong dangers -- fixing on hazards that are unlikely, or unlikely to cause massive pain. The Associated Press asked 21 risk experts to analyze the presidential campaign and list what they consider the five biggest threats to the world. Climate change topped the list with 17 mentions, often as the top threat. It was followed by use of nuclear weapons, pandemics, cyberattacks and problems with high technology. Neither Trump's signature issues of immigration and terrorism nor Clinton's major concerns, financial insecurity and gun violence, made the list. "I have not heard or read about any significant deliberations of the major risks that face our country today and tomorrow.

From stopping cyberattacks to operating autonomous vehicles to visually searching through a wine database, 140 startups using AI as a core part of their products raised $958M in funding in Q3'16, making it the second-highest quarter for funding after Q2'16. Since 2012, deals and dollars to AI startups have been on a steady rise, and this year is extending that trend. Our AI category includes companies applying AI solutions to verticals like healthcare, security, advertising, and finance as well as those developing general-purpose AI tech. Our list excludes robotics (hardware-focused) and AR/VR startups, which we've analyzed separately here and here. Our analysis includes all equity funding rounds and convertible notes.

Think of a regression model mapping a number of features onto a real number (potentially a probability). The resulting real number can then be mapped on one of two classes, depending on whether this predicted number is greater or lower than some choosable threshold. Let's take for example a logistic regression and data on the survivorship of the Titanic accident to introduce the relevant concepts which will lead naturally to the ROC (Receiver Operating Characteristic) and its AUC or AUROC (Area Under ROC Curve). Every record in the data set represents a passenger – providing information on her/his age, gender, class, number of siblings/spouses aboard (sibsp), number of parents/children aboard (parch) and, of course, whether s/he survived the accident. The logistic regression model is tested on batches of 10 cases with a model trained on the remaining N-10 cases – the test batches form a partition of the data. In short, Leave-10-out CV has been applied to arrive at more accurate estimation of the out-of-sample error rates.

Lancaster University is developing artificial intelligence (AI) software for servers that could potentially drive down the amount of energy consumed by datacentres. A collection of our most popular articles on datacentre management, including: Cloud vs. Colocation: Why both make sense for the enterprise right now; AWS at 10: How the cloud giant shook up enterprise IT and Life on the edge: The benefits of using micro datacenters This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers. You also agree that your personal information may be transferred and processed in the United States, and that you have read and agree to the Terms of Use and the Privacy Policy.

WASHINGTON – The Pentagon says a U.S. airstrike in Syria last month killed what the Defense Department describes as a senior al-Qaida leader who once had ties to Osama bin Laden. Jeff Davis, identifies the target as Haydar Kirkan. The spokesman says Kirkan oversaw the planning of attacks against Western targets outside of Syria, including in Turkey. Davis says a U.S. drone carried out the airstrike Oct. 17 in the vicinity of Idlib, in western Syria. The U.S. has previously announced that in the week after that airstrike, it hit al-Qaida targets in Yemen and Afghanistan.

Cybersecurity companies are increasingly looking to artificial intelligence tech to improve defense systems and create the next generation of cyber protection. These trends are driving demand for automated cybersecurity, i.e. AI-driven software that can use machine learning and other technologies to differentiate benign or harmful activity on a system or network. We used CB Insights data to understand when artificial intelligence began to be linked to cybersecurity, and we identify 13 companies to watch at the intersection of AI and security. To inform our analysis in the charts below, we used the Trends tool on the CB Insights Platform, which analyzes millions of media articles to track technology trends.

Using Azure, gave them an immediate global reach in a way unthinkable just a few years earlier. Their mission is to bring big data analytics to utilities and smart cities, and one of their focus areas is electric utilities and smart grids. It is a story that combines drones with intelligent software to prevent power blackouts, or as eSmart puts it "making Azure intelligence mobile". The economic impact of blackouts is massive, and the scale of power grids is huge. According to Grid Resilience Report, US Department of Energy, 2011, the average annual cost of power outages caused by severe weather in the USA is anywhere between $18-33 billion.