They continue to target computer networks and damage their infrastructure. Now, a combined team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx has developed a powerful artificial intelligence system called AI2 which works significantly better than any existing cyber-attack detection system. The system has been tested on 3.6 billion log lines or pieces of data that reveal major system activities triggered by millions of users over a period of three months. Researchers have found that new tool can detect cyber-attacks with 85% accuracy which is roughly three times better than the previous benchmark. Moreover, it reduces the number of'false positives' – an event wrongly identified as threat – by a factor of 5. Conventional security systems are either virtual machine-based or humanly operated but none of them has proven overwhelmingly successful at encountering cyber-attacks.

Visitors to Australia's federal parliament are often surprised by the robust verbal confrontation between the government and the opposition – technically known as questions without notice, more commonly as question time. A theatrical highpoint of every sitting day, question time is part intellectual cage fight, part kindergarten spat – and all psychological warfare. Political journalists watch the hour-long question time as drought-stricken farmers view the clouds. They look for signs, they read the climate. But what if you were interested in facts?

First there was Artificial Intelligence (AI), then came machine learning... neural networks and finally cognitive computing technology. But then came multilingual cognitive computing technology. Cogito Studio is a product for developing customised semantic applications for text analytics, including information analysis, categorisation and extraction. Developed by Expert System in the US state of Maryland, Cogito Studio combines a cocktail of AI algorithms for simulating the human ability to read and understand language (semantics) and deep learning techniques (machine learning) to help optimise the creation of applications that are advanced, intelligent and intuitive.

Diplomats from around the world met in Geneva last week for the United Nations' third Informal Expert Meeting on lethal autonomous weapons systems (LAWS), commonly dubbed "killer robots". Their aim was to make progress on deciding how, or if, LAWS should be regulated under international humanitarian law. A range of views were expressed at the meeting, from Pakistan being in favour of a full ban, to the UK favouring no new regulation for LAWS, and several positions in between. Despite the range of views on offer, there was some common ground. It is generally agreed that LAWS are governed by international humanitarian law.

HEFFNER: I'm Alexander Heffner, your host on The Open Mind. TED Talk curator Chris Anderson joined us recently to consider the danger of artificial intelligence, namely its potential to drive away or make obsolete the moral compass of human beings and civilization as we know it. Of course sometimes, we're our own worst enemy, and we would rather not embrace the present reality. So I've invited today the leading ethicist in the arena of innovation. He's going to help us understand the term techno sapiens as he calls it, with our drones, our supercomputers, our designer babies, and now our 3D printers too. Wendell Wallach is the author of A Dangerous Master: How to Keep Technology From Slipping Beyond Our Control.

A new artificial intelligence system developed by researchers at MIT merges human and machine capabilities to hunt potential cyber-attacks and weed out false positives. Called AI2, the platform acts as a virtual analyst and has so far proven its ability to detect 85 percent of attacks. As the system presents its findings to human analysts, feedback is incorporated to continually improve its detection rates. A new artificial intelligence system developed by researchers at MIT merges human and machine capabilities to hunt potential cyber-attacks and weed out false positives. This allows it to detect suspicious activity, which is then presented to the human analysts for confirmation.

You could be sent to prison and charged with a felony for shooting a drone from the sky. According to the federal law, 18 USC S 32, anyone who willfully'sets fire to, damages, destroys, or wrecks an aircraft' will be fined or imprisoned no more than 20 years or both. And the FAA says drones fall into the category of'aircraft' and threatening anyone operating a drone is also punishable with jail time. According to the federal law, 18 USC S 32, anyone who willfully'sets fire to, damages, destroys, or wrecks an aircraft' will be fined or imprisoned no more than 20 years or both. And experts say drones fall into the category of'aircraft' and threatening anyone operating a drone falls is also punishable with jail time The law says that if you attempt to shoot down a flying robot from the sky, you could face up to two decades behind bars, and/or be handed a fine up to a quarter of a million dollars.

Food safety is vital to the well-being of society; therefore, it is important to inspect food products to ensure minimal health risks are present. The presence of certain species of insects, especially storage beetles, is a reliable indicator of possible contamination during storage and food processing. However, the current approach of identifying species by visual examination of insect fragments is rather subjective and time-consuming. To aid this inspection process, we have developed in collaboration with FDA food analysts some image analysis-based machine intelligence to achieve species identification with up to 90% accuracy. The current project is a continuation of this development effort. Here we present an image analysis environment that allows practical deployment of the machine intelligence on computers with limited processing power and memory. Using this environment, users can prepare input sets by selecting images for analysis, and inspect these images through the integrated panning and zooming capabilities. After species analysis, the results panel allows the user to compare the analyzed images with reference images of the proposed species. Further additions to this environment should include a log of previously analyzed images, and eventually extend to interaction with a central cloud repository of images through a web-based interface.

As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difficult challenge for the artificial intelligence (AI) research community. This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China.

We demonstrate an implementation of Markov Argumentation Random Fields (MARFs), a novel formalism combining elements of formal argumentation theory and probabilistic graphical models. In doing so MARFs provide a principled technique for the merger of probabilistic graphical models and non-monotonic reasoning, supporting human reasoning in ``messy’’ domains where the knowledge about conflicts should be applied. Our implementation takes the form of a graphical tool which supports users in interpreting complex information. We have evaluated our implementation in the domain of intelligence analysis, where analysts must reason and determine likelihoods of events using information obtained from conflicting sources.