Machines that "learn" and make decisions on their own are proliferating in our daily lives via social networks and smartphones, and experts are already thinking about how we can engineer them so that they don't go rogue. So far, suggestions have ranged from "training" self-learning machines to ignore certain kinds of information that might teach them racism or sexism, to coding them with values like empathy and respect. But according to some new work from researchers at the Universidad Autónoma de Madrid,as well as other schools in Spain, the US, and Australia, once an AI becomes "superintelligent"--think Ex Machina--it will be impossible to contain it. Well, the researchers use the word "incomputable" in their paper, posted on the ArXiv preprint server, which in the world of theoretical computer science is perhaps even more damning. The crux of the matter is the "halting problem" devised by Alan Turing, which holds that no algorithm is able to correctly predict whether another algorithm will run forever or whether it will eventually halt--that is, stop running.

Daniel Dennett (1942 –) is an American philosopher, writer and cognitive scientist whose research is in the philosophy of mind, philosophy of science and philosophy of biology, particularly as those fields relate to evolutionary biology and cognitive science. He is currently the Co-director of the Center for Cognitive Studies, the Austin B. Fletcher Professor of Philosophy, and a University Professor at Tufts University. He received his PhD from Oxford University in 1965 where he studied under the eminent philosopher Gilbert Ryle. In his book, DARWIN'S DANGEROUS IDEA: EVOLUTION AND THE MEANINGS OF LIFE, Dennett present a thought experiment that defends strong artificial intelligence (SAI)--one that matches or exceeds human intelligence.[1] Dennett asks you to suppose that you want to live in the 25th century and the only available technology for that purpose involves putting your body in a cryonic chamber where you will be frozen in a deep coma and later awakened.

In May, a Tesla TSLA 0.39 % "autopilot" enthusiast in Florida became the first known fatality in a self-driving car. But this was no ordinary accident. The car performed exactly as designed, and the (non)driver's failure to take any corrective action could reasonably have been foreseen by the manufacturer. This unwelcome yet widely anticipated milestone may set back progress on what promises to be one of the most valuable technologies of the 21st century. In its rush to get hot new products into consumers' hands, Tesla--along with many other car manufacturers--has pursued a flawed vision of the future, one in which tomorrow's technology is simply layered on top of today's.

Breakthroughs in genetic fuzzy systems, most notably the development of the Genetic Fuzzy Tree methodology, have allowed fuzzy logic based Artificial Intelligences to be developed that can be applied to incredibly complex problems. The ability to have extreme performance and computational efficiency as well as to be robust to uncertainties and randomness, adaptable to changing scenarios, verified and validated to follow safety specifications and operating doctrines via formal methods, and easily designed and implemented are just some of the strengths that this type of control brings. Within this white paper, the authors introduce ALPHA, an Artificial Intelligence that controls flights of Unmanned Combat Aerial Vehicles in aerial combat missions within an extreme-fidelity simulation environment. To this day, this represents the most complex application of a fuzzy-logic based Artificial Intelligence to an Unmanned Combat Aerial Vehicle control problem. While development is on-going, the version of ALPHA presented withinwas assessed by Colonel (retired)Gene Lee who described ALPHA as "the most aggressive, responsive, dynamic and credible AI (he's) seen-to-date."

A robot carrying an explosive device was used to kill one of the shooters in Thursday night's horrific violence in Dallas, Texas, in what many law enforcement and other experts are calling the first such use of robotics technology by U.S. police. Five police officers were killed and seven others were wounded, along with two civilians, during a demonstration protesting the recent deaths of two African-American men at the hands of police in other cities. Micah Johnson, the man suspected of shooting the officers, was killed by remotely detonated explosives on the robot after a standoff and failed negotiations with police. Toby Walsh, a professor of artificial intelligence at the University of New South Wales, cautions against seeing this use of a robot as a nightmarish science-fiction scenario--because the robot was being operated by a human via remote control. "In [that] sense, it was no more taking us down the road to killer robots than the remote-controlled Predator drones flying above the skies of Iraq, Pakistan and elsewhere," Walsh told Scientific American in an email.

Microsoft has officially made Project Malmo, its Minecraft-based artificial intelligence research platform, available to all through Github. The open-source system was previously only available to a small group of researchers. The system, which had until now only been open to a small group of computer scientists in private preview, is primarily designed to help researchers develop sophisticated, more general artificial intelligence, or AI, that can do things like learn, hold conversations, make decisions and complete complex tasks. Minecraft may seem an odd choice for studying artificial intelligence, but Microsoft notes that researchers involved in the private preview were keen on its "endless possibilities for collaboration and exploration" as an ideal testing ground for AI research. In addition, Microsoft notes that the public launch heralds the arrival of new tools to the platform, like the ability to run experiments at a much faster pace (called overclocking), that should be a boon to researchers.

Google DeepMind is training its artificial intelligence software to detect early signs of eye diseases. It's aimed at detecting two diseases -- age-related macular degeneration and diabetic retinopathy. The latter can lead to blindness if not detected early. DeepMind is analyzing about 1 million eye scans to develop an algorithm that can catch early warning signs of the conditions. "If you have an OCT scan done, a machine learning algorithm will be able to tell you if it's urgent versus something that's not so urgent. The company has caught flak from privacy critics in the past.

There's nothing quite like a cold pint of beer to remind you that everything will be alright with the world - but what about a hoppy, alcoholic beverage that's been finely brewed by a booze-obsessed artificial intelligence? That's what the IntelligentX Brewing Company is offering with its new AI Beer. Yes, a brewery has designed a series of algorithms that actively improve its beer recipes over time - in fact, the company's Pale, Amber, Black and Golden varieties have been tweaked 11 times since the AI went live. So does an AI make a bottle of beer better? IntelligentX says it's all about using an online feedback system that uses questions ranked from one to 10 to determine customer responses to its products.

Most of the new functionality will likely benefit third-party brands who will use the chatbots to offer services. The update lets developers add cards for in-conversation functionality, letting folks do things like reserve hotel rooms without having to leave chat. They can also drop in authentication for users to carry over account and payment information. The last big addition is adding natural language processing to bots automatically, improving their ability to interpret the gobbledygook humans try to pass off as conversation. Microsoft opened the API-style Skype Bot Platform for developers to build their own back in March.

Todor Primov, life sciences product manager at Ontotext, discusses some of the ways artificial intelligence (AI) is changing the face of healthcare. A common joke among certain circles is that AI can be defined as "whatever we haven't done yet". While the current popularity of AI among investors, startups and the wider media is unparalleled, we need to retain focus on what exactly AI means, and its specific sector-based applications. In healthcare and pharmaceutical research, 'AI-like' technologies hold huge promise, but what are the problems these solutions are purporting to solve, and where are the business opportunities? Data volume is a problem in the healthcare sector too.