China has been the hottest story in robotics for the past year. With the Made in China 2025 plan, Xi Jinping's government literally came up with a roadmap for dominating the global robotics industry. An executive guide to the technology and market drivers behind the $135 billion robotics market. But is the hype warranted? Have recent trade tensions with the U.S. affected China's automation push?

Invest India and the UAE Minister for Artificial Intelligence (AI) signed a Memorandum of Understanding (MoU) for India – UAE Artificial Intelligence Bridge in New Delhi. This partnership will generate an estimated USD 20 billion in economic benefits during the next decade for both countries. The MoU will spur development across areas like Blockchain, AI and Analytics as data and processing will be a catalyst for innovation and business growth and serve as the backbone of more effective and efficient service delivery systems. By 2035 AI can potentially add USD 957 billion to the Indian economy. The MoU was signed in the presence of Minister of Commerce & Industry and Civil Aviation, Suresh Prabhu and H.E. Ahmad Sultan Al Falahi, Minister Plenipotentiary – Commercial Attache, UAE Embassy at the India leg of GovHack series of World Government Summit.

The other drivers wouldn't have noticed anything unusual as the two sleek limousines with German license plates joined the traffic on France's Autoroute 1. But what they were witnessing -- on that sunny, fall day in 1994 -- was something many of them would have dismissed as just plain crazy. It had taken a few phone calls from the German car lobby to get the French authorities to give the go-ahead. But here they were: two gray Mercedes 500 SELs, accelerating up to 130 kilometers per hour, changing lanes and reacting to other cars -- autonomously, with an onboard computer system controlling the steering wheel, the gas pedal and the brakes. Decades before Google, Tesla and Uber got into the self-driving car business, a team of German engineers led by a scientist named Ernst Dickmanns had developed a car that could navigate French commuter traffic on its own. The story of Dickmann's invention, and how it came to be all but forgotten, is a neat illustration how technology sometimes progresses: not in small steady steps, but in booms and busts, in unlikely advances and inevitable retreats --"one step forward and three steps back," as one AI researcher put it. It's also a warning of sorts, about the expectations we place on artificial intelligence and the limits of some of the data-driven approaches being used today.

Light, on the other hand, travels 186,282 miles in a second. Imagine the possibilities if we were that quick-witted. Well, computers are getting there. Researchers from UCLA on Thursday revealed a 3D-printed, optical neural network that allows computers to solve complex mathematical computations at the speed of light. In other words, we don't stand a chance.

Information Age discusses how artificial intelligence is transforming the NHS, and its potential in other sectors too with Charles A. Taylor, founder and chief technology officer at HeartFlow. The potential for AI in healthcare is tremendous as it increasingly becomes integrated into the healthcare ecosystem. AI is transforming the way doctors deliver cost-effective, high-quality diagnostic and treatment services to their patients. For example, the technology can identify patterns and anomalies in diagnostic data from medical scans at a speed and volume that humans are simply unable to replicate. The processing power of AI has applications far beyond providing simple diagnoses.

"Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world." The first step to understanding machine learning is understanding what kinds of problems it intends to solve, based on the foregoing definition. It is principally concerned with mapping data to mathematical models -- allowing us to make inferences (predictions) about measurable phenomena in the world. From the machine learning model's predictions, we can then make rational, informed decisions with increased empirical certainty. Take, for example, the adaptive brightness on your phone screen. Modern phones have front- and rear-facing cameras that allow the phone to constantly detect the intensity of ambient light, and then adjust the brightness of the screen to make it more pleasant for viewing.

Technological innovation is moving at an ever-accelerating pace, and this comes with vast benefits and inevitable changes to our way of life. One downside is that machine learning and automation are already replacing jobs, and this will increase rapidly. It also has the potential to replace much of that income with Universal Basic Income (UBI), or government cash handouts to all adult citizens, perhaps starting with covering some element of taxes and rising in the range of $100,000/year per citizen within the next 20 years. Proponents of UBI include well-known figures such as Mark Zuckerberg, Richard Branson and Elon Musk. Musk stated last year that he believed job loss would be so severe due to automation that some form of UBI will be necessary to support our society.

Federal officials overseeing billions in benefit payments to millions of Canadians are hoping artificial intelligence can resolve ongoing snags in the system. The government is looking to "push the boundaries" of what artificial intelligence can do to improve a variety of services, including the pace of benefit decisions to Canadians applying for disability pensions, say documents obtained by The Canadian Press under the access to information law. Employment and Social Development Canada is currently facing processes that are "slow, inefficient, inconsistent, and prone to error," reads a presentation about the AI efforts. Instead of being able to proactively change the way federal services are delivered to Canadians, the documents say, officials are bogged down with millions of "low value" issues that need to be taken care of, crowding out more critical work. Among the "low value" work are about 50,000 tasks required to issue tax forms to Canada Pension Plan and old age security recipients.

Boston Dynamics is preparing to build its terrifying army of robot dogs, according to a Saturday report in Inverse that the company has set a target date of July 2019 as the time it will be ready to manufacture 1,000 of its compact SpotMini models annually. SpotMini is the smallest variant of Boston Dynamics' many different models of robo-dogs yet at approximately two feet, nine inches tall. It weighs "around 66 pounds" and has an hour and a half battery life, per TechCrunch, and the company has recently demonstrated all kinds of functionalities like opening doors for other robots and increasingly complicated navigational skills. While the company already announced plans to launch commercially in 2019 with a limited run of robots already in pre-production, Inverse's report has some new details, such as that the SpotMini is intended to eventually become a multi-use platform of sorts: The overarching goal for the 26-year-old company is to become the what Android operating system is for phones: a versatile foundation for limitless applications. The attachment point where the SpotMini's robotic arm stems from its body could in the future hold a variety of attachments "to be designed and produced by third parties," per Fortune, making it more versatile.

An attacker should find some issues inside the system to break it. Primarily, there are two different ways to discover vulnerabilities: 1) check for known issues by known payloads and 2) generate new payloads by fuzzing to discover new issues. The first approach is as simple as following a checklist. The vulnerability tool, in this case, should check all the items one by one. The second one is more interesting.