Real-time machine learning has access to a continuous flow of transactional data, but what it really needs in order to be effective is a continuous flow of labeled transactional data, and accurate labeling introduces latency. During this year, I heard and read a lot about real-time machine learning. People usually provide this appealing business scenario when discussing credit card fraud detection systems. They say that they can continuously update credit card fraud detection model in real-time (See "What is Apache Spark?", "…real-time use cases…" and "Real time machine learning"). It looks fantastic but not realistic to me.

Symantec Corp. (SYMC) recently announced its latest Symantec Endpoint Protection 14 technology. "Artificial intelligence fused with critical endpoint technologies deliver the most complete endpoint security on the planet," boasts the cybersecurity industry pioneer. As headline data breaches and cybercrime news reach organizations worldwide, Symantec is positioned well to benefit from its cuttingedge integrated cyberdefense prevention and detection. The firm's Endpoint Protection 14 marks the lightest and strongest endpoint protection Symantec has on the market, "providing everything from file reputation and behavioral analysis to advanced machine learning Al," the company says. Endpoint Protection 14 stands as the only solution on the market merging essential endpoint technologies with advanced machine learning and memory exploit mitigation in a single agent.

Data is everywhere – masses of it. And it's helping businesses to make better decisions across departments. Marketing can utilise data to discover the effectiveness of email campaigns, finance can analyse past trends to make predictions and projections for the future, and sales can target their follow-up with detailed information on prospective customers. But data is only useful when business tools transform it into valuable information. Data intelligence through algorithms and analytics make business data relatable. The most advanced solutions require enormous amounts of data to be able to offer accurate insight to users.

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.

A viral Facebook post has been shared by more than a million people – but there's one very important catch. The "Standing Rock check-in post claims that police monitoring protests over the North Dakota Access Pipeline will be obstructed if people check in to the area on Facebook. The local sheriffs have been using Facebook "to find out who is at Standing Rock in order to target them in attempts to disrupt the prayer camps", the message reads, and so falsely checking in can stop them from doing so. But both local sheriffs and the Sacred Stone Camp that is the centre of the protests have said that the message doesn't actually interrupt any ongoing surveillance or monitoring operations, and isn't likely to make any immediate difference to the protests. Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo. A Toyota Motors employee demonstrates a smartphone app with the company's pocket plug-in hybrid (PHV) ...

People who use Facebook live longer, according to a new study. Using the site to stay in touch with people appears to suggest that using the site is associated with living longer. Scientists have long known that people have stronger social networks and relationships also tend to live longer. The new study shows that the same is true of the online world. "Interacting online seems to be healthy when the online activity is moderate and complements interactions offline," said author William Hobbs in a statement.

Who within organizations should be responsible for robotics security? Today's security threats have expanded in scope and seriousness. There can now be millions -- or even billions -- of dollars at risk when information security isn't handled properly. As robotics becomes increasingly intertwined with other facets of IT such as the cloud, mobile devices, data analytics and the Internet of Things (IoT), concerns about the risk of data theft and other negative impacts are legitimate. But the security issue goes beyond traditional risks such as the loss or theft of information or the interruption of services.

Evolving technologies and the growing numbers of "always on", "always connected" devices, tools and commodities are giving the instigators of cyber-threats increased opportunities for access and interference. With statistics suggesting that assaults on individuals, corporations, and government bodies account for almost $400 billion in lost revenue annually, and some 90% of companies admitting to having been victim to some kind of attack – figures that translate to 18 individuals per second being affected by cyber-crime – countering these threats is a real and ongoing concern, for enterprises. Security personnel are finding themselves overwhelmed by the multiplicity of attack vectors and tools available to the cyber-criminals, and are increasingly looking to a new ally, in the quest for cyber security: AI.

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.

But that is not to say it is perfect by any stretch of the imagination. "Deep learning has led to some big advances in computer vision, natural language processing, and other areas," Tommi Jaakkola, a Massachusetts Institute of Technology professor of electrical engineering and computer science, told Digital Trends. "It's tremendously flexible in terms of learning input/output mappings, but the flexibility and power comes at a cost. That is it that it's very difficult to work out why it is performing a certain prediction in a particular context." This black-boxed lack of transparency would be one thing if deep learning systems were still confined to being lab experiments, but they are not.