When it comes to detecting OS X malware, the future may not be rooted in machine learning algorithms, but patterns and heatmap visualization, a researcher posits. In an academic paper published by Virus Bulletin on Monday, Vincent Van Mieghem, a former student at the Delft University of Technology in the Netherlands, describes how a recurring pattern he observed in OS X system calls can be used to indicate the presence of malware. Van Mieghem wrote the paper, "Behavioral Detection and Prevention of Malware on OS X," (.PDF) while interning at Fox-IT but has since moved on to PricewaterhouseCoopers' cybersecurity division. By the numbers, the detection method Van Mieghem concocted is a success; it detected infections from 100 percent of malware samples found on OS X systems at the time. The method apparently leaves little room for error too; it resulted in a scant 0 percent to 20 percent false positive rate, depending on the user, according to the paper.

AI can help to bridge the gap between brands and their audiences, connecting them in direct and practical ways. This fuels the question, however, about how AI can and should be used, and where its benefits can be applied most effectively. The news of the first film trailer created using AI brought into focus the rapid pace with which new applications are being surfaced. But it should be noted that what the AI actually did was to select scenes from which the trailer would be created, based on its analysis of hundreds of previous horror movie trailers. The task of assembling those scenes, editing them and adding music and effects to tell a coherent story (i.e. the actual creative task) fell to a person.

If you are a fan of Iron Man or the Avengers franchise, you know that when Tony Stark is talking to Jarvis, he is talking with Artificial Intelligence or AI. According to computer science, an'intelligent machine' is a rational agent who perceives its environment and takes those actions, which will maximize its chances of success for the specified goal. In simple terms, intelligence that is exhibited by the machines is termed as'Artificial Intelligence' or AI. Ever wondered how Google Now, Siri, and Cortana help you find information? They are all AI applications used on different platforms.

Wells Fargo -- The bank will eliminate product sales goals for retail bankers and strengthen oversight, controls, and training. Wells Fargo will be the subject of a Senate Banking Committee hearing into its sales practices, after the bank settled a case with regulators by paying a 185 million penalty. The panel wants to question CEO John Stumpf about the practices that led to the case. UnitedHealth Group -- The health insurer's Optum unit and Quest Diagnostics formed a partnership aimed at reducing billing complexity, as well as providing more transparency into health care costs. Separately, UnitedHealth was rated as a "top pick" at Jefferies because of attractive valuation and a best-in-class management team.

Lumiata, a healthcare analytics company, said it will launch a tool called Risk Matrix that takes a patient's electronics record, combines it with artificial intelligence and risk algorithms to predict health over time for people with chronic conditions. The software, designed for insurers and healthcare providers, is designed to analyze models based on 175 million patient record years and provide clinical rationale for each prediction. Risk Matrix provides real-time predictions for more than 20 major diseases including congestive heart failure and diabetes. Insurers and companies are offering programs for chronic conditions, but often identify too many people as risks.

Why we urgently need to measure AI's societal impacts How will artificial intelligence systems change the way we live? This is a tough question: on one hand, AI tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption, audio processing, and leukemia detection. There is extraordinary potential to do much more in the future. On the other hand, AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people's everyday lives. AI and decision-support systems are embedded in a wide array of social institutions, from influencing who is released from jail to shaping the news we see.

Deep Learning, Machine Learning, Natural Language Processing, Computer Vision, Machine Reasoning, and Strong AI Artificial intelligence (AI) technologies are quickly gaining mindshare among corporate executives around the world, driving a proliferation of use cases that touch virtually every industry. AI technologies, which include deep learning, machine learning, natural language processing (NLP), and computer vision, among others, are designed to endow computers with human-like faculties such as hearing, seeing, reasoning, and learning. But AI enables computers to do some things better than humans, especially when it comes to processing very large amounts of data quickly, efficiently, and accurately. Investment is flowing rapidly into the development of AI technologies, both at the startup level as well as within some of the world's leading technology companies. Meanwhile, innovation in AI algorithms is progressing at a breakneck pace and industry players are scrambling to determine the optimal platforms to deliver AI-enabled services on a cost-effective basis across an ever-expanding set of use cases.

Recent accidents involving Tesla cars may have been a setback for self-driving cars, but Nvidia believes a fast computer under the hood could make autonomous cars and cabs truly viable. The company's new Drive PX 2 model is a palm-sized computer for autonomous cars that will marry mapping with artificial intelligence for automated highway and point-to-point driving. The computer's horsepower will help a car navigate, avoid collisions and make driving decisions. The Drive PX 2 could be attractive to companies like Uber, which want to deploy autonomous cars as taxis. The computer is also targeted at car makers looking to develop fully or partially autonomous cars, which would typically need human intervention.

Uninformed Search: This is used when creating an action sequence that doesn't account for any changes along the way. Heuristic Functions: These allow for decisions to be made without accurate or complete information. Adversarial or Moving Agent Search: This is used when there are other entities making decisions that influence one another. Piotr Gmytrasiewicz, associate professor in the department of computer science at the University of Illinois at Chicago, teaches three courses: Artificial Intelligence 1, Artificial Intelligence 2 and Applied Artificial Intelligence. Artificial Intelligence 1 covers logic-based approaches, while Artificial Intelligence 2 showcases numerical and mathematically focused approaches based on probability theory.

Decades ago, before the Internet was a gleam in Uncle Sam's eye, computers were stupid. Not in the sense of being primitive, though they were also that, but in the literal sense of being internally braindead. So stupid, in fact, that we nicknamed this prehistoric, biz-angled species of keyboard-and-monitor "dumb terminals"--slang for intentionally complexity-allergic vessels into which content was beamed by sophisticated, centrally located machines that filled rooms like squadrons of refrigerators. In old-school parlance, we called those machines "mainframes." In today-speak, you might call them cloud servers.