Artificial intelligence boosters predict a brave new world of flying cars and cancer cures. Detractors worry about a future where humans are enslaved to an evil race of robot overlords. Veteran AI scientist Eric Horvitz and Doomsday Clock guru Lawrence Krauss, seeking a middle ground, gathered a group of experts in the Arizona desert to discuss the worst that could possibly happen -- and how to stop it. Their workshop took place last weekend at Arizona State University with funding from Tesla Inc. co-founder Elon Musk and Skype co-founder Jaan Tallinn. Officially dubbed "Envisioning and Addressing Adverse AI Outcomes," it was a kind of AI doomsday games that organized some 40 scientists, cyber-security experts and policy wonks into groups of attackers -- the red team -- and defenders -- blue team -- playing out AI-gone-very-wrong scenarios, ranging from stock-market manipulation to global warfare.

Are you willing to learn about Deep Learning for Cyber Security? Join the webinar to learn more! In this webinar, Steven Hutt, Consultant in Deep Learning and Financial Risk, will provide an overview of network anomaly detection. This webinar will be of interest to Data Scientists, Software Engineers and Entrepreneurs in the areas of Connected Cars, Internet of Things/Industrial Internet, Medical Devices, Financial Technology (blockchain) and predictive apps/APIs of all sorts. Steven Hutt is a consultant in Deep Learning and Financial Risk, currently working in Cyber Security and Algorithmic Trading.

Brevan Jorgenson's grandma kept her cool when he took her for a nighttime spin in the Honda Civic he's modified to drive itself on the highway. A homemade device in place of the rear-view mirror can control the brakes, accelerator, and steering, and it uses a camera to identify road markings and other cars. "She wasn't really flabbergasted--I think because she's seen so much from technology by now," says Jorgenson, a senior at the University of Nebraska, Omaha. Others are more wary of the system, which he built using plans and software downloaded from the Internet, plus about $700 in parts. Jorgenson says the fact that he closely supervises his homebrew autopilot hasn't convinced his girlfriend to trust the gadget's driving.

BENGALURU: Microsoft chief executive officer Satya Nadella said "an enlightened immigration policy" has enabled him to live the American dream, even as he emphasised that governments had the right to determine immigration and trade policies. "American values have always been about inclusion and diversity. It's a land of immigrants," said Nadella, who was born in Hyderabad and completed his bachelor's degree in engineering from the Manipal Institute of Technology, before moving to the US in 1988. "American technology reaching me in India is what made it even possible to dream the dream. And then, the enlightened American immigration policy is what led me to live the dream. And I think those are things we will always advocate for... the American dream and the American enlightened immigration policy, especially for high-skilled workers, is something that I'm optimistic about," Nadella told ETin an interview.

If you'll be attending the RSA Conference in February and are looking to learn some hacking tips and tricks, Cylance CEO and President Stuart McClure will be presenting on February 15th at 1:30 PM on Hacking Exposed NextGen. Stuart will demonstrate some live exploits and real-life hacking on everyday technology, and will also show you how to prevent them using simple countermeasures. We urge you to join Stuart for this amazing presentation at the Marriott Marquis in Yerba Buena 5. Reserve your seat today! In the meantime, get ready for RSA by watching Stuart's recent CBS News interview, during which he discusses why "who did the hacking" is a less important question than how to stop it: STUART: "Our approach is really quite different than anything else you'll find out there. We take a purely mathematical approach. We believe that you can actually identify attacks long before they ever start, and truly prevent them. This is done through what we call AI prediction. We've trained computers over millions and millions of files and attacks, to learn exactly what makes them up - the DNA of these attacks. By understanding the DNA mathematically, now we can prevent and protect against future attacks. So it looks like we're predicting attacks, when really, we've just learned through AI machine learning what the DNA of these attacks is. We see ourselves today as applying artificial intelligence to cybersecurity in a truly preventative and predictive way. We see the company expanding far beyond cybersecurity. The techniques that we are using are very applicable to other areas and fields of study. Anything that you want to try and classify effectively. For example, another application might be healthcare, or diagnostics - for example an MRI scan. Being able to detect early forms of cancer or disease. They take large amounts of data that you and I would fall asleep trying to process, and they never forget what they process. And they can learn from that to make decisions going forward."

How many people could self-driving cars kill before we would no longer tolerate them? This once-hypothetical question is now taking on greater urgency, particularly among policymakers in Washington. The promise of autonomous vehicles is that they will make our roads safer and more efficient, but no technology is without its shortcomings and unintended consequences -- in this instance, potentially fatal consequences. "What if we can build a car that's 10 times as safe, which means 3,500 people die on the roads each year. Would we accept that?" asks John Hanson, a spokesman for the Toyota Research Institute, which is developing the automaker's self-driving technology.

Recent breakthroughs in machine learning and artificial intelligence mean AI-enabled technologies are gaining traction. The billion-dollar cybersecurity industry is no exception, as vendors begin to scale and automate their processes intelligently - all while locked into the early stages of a security arms race with professional hackers. A recent report from analyst firm ABI Research estimates that machine learning in cybersecurity will enormously bolster spending in big data, intelligence and analytics, reaching as much as $96 billion (£71.9 billion) by 2021. Vendors are likely to find buyers in large enterprises, and more than likely, across industries that are especially prone to attack: think government and defence, banking, and across the technology sector. At the moment, ABI's report says, User and Entity Behavioural Analytics - using machine learning for threat detection by analysing data at scale - is the driving force. "Using static machine learning models to detect previously unknown malware is the only use case I'm aware of that offers clear evidence of effective results," says cybersecurity analyst at 451 Research, Adrian Sanabria.

As Maurice Conti has said in one of his presentations, we have arrived to the augmented era, in which Artificial Intelligence gains a new role. Until now all our tools were passive: we told them what to do and they did it. But the future holds a new era where artificial intelligence will come up with its own designs, its own ideas, its own products. Many tasks could soon be executed with the cooperation of robots, AI and humans, robots providing perfect execution, AI delivering the design the design and humans making the necessary decisions. But to get to this point it is crucial to build a nervous system for robots through sensors.

Rather than focus on attack signatures, these AI solutions look for anomalous network behavior, flagging when a machine goes rogue or if user activity or traffic patterns appear unusual. "A really simple example is someone with high privilege who attempts to get onto a system at a time of day or night that they never normally log in and potentially from a geolocation or a machine that they don't log in from," said Kelley. Another example would be a "really rapid transfer of a lot of data," especially if that data consists of the "corporate crown jewels." Such red-flags allow admins to quickly catch high-priority malware infections and network compromises before they can cause irreparable damage. IBM calls this kind of machine learning "cognitive with a little'c'" – which the company was already practicing prior to Watson.

John Nhial was barely a teenager when he was grabbed by a Sudanese guerrilla army and forced to become a child soldier. He spent four years fighting, blasting away on guns almost too heavy to hold, until one day the inevitable happened: he was seriously injured, treading on a landmine while he was on morning patrol. "I stepped on it and it exploded," he recalled. "It threw me up and down again – and then I tried to look for my leg and found that there was no foot." His comrades carried him back to base camp, but there was hardly any medical care available. It took 25 days before he received proper treatment, during which time he developed tetanus down one side of his body.