Proponents of autonomous vehicles are in a sticky situation. Self-driving technology is expected to have a tremendous impact on public health and reduce the 1.25 million deaths every year on global roads. At the same time, this emerging technology is a threat to the employment of the millions who are paid to sit behind the wheel -- from truck drivers to cab drivers and delivery workers. Baidu chief scientist Andrew Ng, an expert in the world of artificial intelligence, acknowledges the unemployment concerns, but he sees a way forward that offers society the benefits of autonomous vehicles and blunts the negative impact of job losses. "I feel a strong moral responsibility or obligation to try to make self-driving cars a reality as quickly as possible," Ng said in a visit to The Washington Post.

Artificial intelligence and autonomous cars are no longer exclusive to science fiction. Google's self-driving cars have driven more than five million kilometres, while some Teslas can drive themselves under certain conditions, and Singapore could see a fully autonomous taxi hit the streets by the end of the year. While all these initiatives are focused on what's outside the car, on equipping and teaching machines to understand and react to the outside world, it's also important to understand the people inside. The solution they created – a camera that can understand when drivers are fatigued or distracted, has helped save thousands of lives. The company traces its origins to a group of roboticists at the Australian National University in 1997.

The conventional wisdom is that intelligent systems, while good with numbers and maybe facts, are not going to be able to cope with the world of judgment and decision-making. The common assumption is that computers will not be able to deal with the nuance of reasoning that drives the solely human ability to assess what is happening in the world and then make reasoned decisions in reaction to that assessment. And herein lies my problem -- the assumption hidden in this belief is that humans are actually good at this sort of reasoning. And it's not clear that this is true. In particular, we seem prone to reasoning mistakes based on biases in decision-making that hinder us every day.

The writer argues that artificial intelligence will steal our jobs – and it may be the best thing that will ever happen to us. We are living on the brink of a fourth industrial revolution argued Klaus Schwab, the Executive Chairman of the World Economic Forum, recently. Like the revolutions of the past – from steam engine to electricity and digitalization, the coming earthquake will fundamentally alter the way we work. The supreme commander of the incoming revolution is artificial intelligence. Two aspects differentiate the coming tsunami from the past ones.

Britain is on the brink of a robotics revolution. Advances in technology are unleashing a new age where computers handle many tasks previously carried out by humans. From automated manufacturing to software that does complex legal work, business is adapting to the robot economy. Some worry that this will lead to a jobs apocalypse as "thinking machines" replace workers. Others are optimistic that robots will free workers from mundane tasks and allow them to concentrate on higher-level creative and strategic work.

With all of the discussion about Big Data these days, there is frequest reference to the 3 V's that represent the top big data challenges: Volume, Velocity, and Variety. These 3 V's generally refer to the size of the dataset (Volume), the rate at which data is flowing into (or out of) your systems (Velocity), and the complexity (dimensionality) of the data (Variety). Most practitioners agree that big data volume is indeed huge, but that is not necessarily big data's biggest challenge, at least not in terms of data storage capacities, which are growing rapidly also and keeping pace with data volume. The velocity of big data is also a very big challenge, though primarily for applications and use cases that specifically demand near-real-time analysis and response to dynamic data streams. However, unlike volume and velocity, most will agree that the variety (complexity) of the data is truly big data's biggest mega-challenge at all scales and in most applications.

After being stranded in the Mediterranean for three days, fear had overcome Alou Sango. "I thought that we would all die, because there was nothing left, the petrol had finished," he says of his journey from Libya. Like thousands before him, Sango boarded an overcrowded boat to escape the country's turmoil after being unable to return to his native Mali. But after days at sea the captain lost his way and, without a GPS position to give to the Italian authorities, the 100 or so passengers were losing hope. Their rubber dinghy was finally spotted by a Chinese vessel, which picked up the migrants and took them to Italy, where Sango, now 24, is studying through a Rome-based charity, Sant'Egidio Community.

WASHINGTON – The Supreme Court on Thursday approved a rule change that would allow U.S. judges to issue search warrants for access to computers located in any jurisdiction when their location is unknown, despite opposition from civil liberties groups who say it will greatly expand the FBI's hacking authority. U.S. Chief Justice John Roberts transmitted the rules to Congress, which will have until Dec. 1 to reject or modify the changes to the federal rules of criminal procedure. If Congress does not act, the rules would take effect automatically. U.S. officials have portrayed the change as a common-sense administrative revision that is needed because criminals increasingly use technology to hide their true locations. The U.S. Justice Department, which has pushed the rule change since 2013, has described it as a minor modification needed to modernize the criminal code for the digital age, and has said it would not permit searches or seizures that are not already legal.

How do you train a car to drive itself? Let it watch real drivers. Engineers from graphics processing unit (GPU) company Nvidia designed a system that learned how to drive after watching humans drive for a total of 72 hours, as reported by NetworkWorld. The likely conclusion of the system's success is that driverless cars are coming faster than most of us expected. The details of how Nvidia trained two test cars are in a file titled End to End Learning for Self-Driving Cars.

ARTIFICIAL INTELLIGENCE: Inside OpenAI, Elon Musk's Wild Plan to Set Artificial Intelligence Free Cade Metz WIRED "In the rarefied world of AI research, the brightest minds aren't driven by--or at least not only by--the next product cycle or profit margin. They want to make AI better, and making AI better doesn't happen when you keep your latest findings to yourself." ROBOTICS: When a Robot Kills, Is It Murder or Product Liability? Ryan Calo Slate "There is a fundamental similarity between the question of whether a robot can be responsible and the question of whether a robot should enjoy rights...We wouldn't say of a driverless car that it possesses a responsibility to keep its passengers safe, only that it is designed to do so. But somehow, we feel comfortable saying that a driverless car is responsible for an accident."