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.

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.

China has rich talent sources and great potential on AI or artificial intelligence thanks to strong mathematics and IT research as well as local firms' expansion into AI, said LinkedIn in a report yesterday after the AlphaGo's winning in a machine-vs-human Go match. There are totally 250,000 people work and research on AI globally, mainly in the United States, Europe, India and China, according to LinkedIn, the world's largest business social network. Besides the eye-catching AlphaGo, the artificial intelligence is already part of people's lives, from applications used in navigation, Siri and Google Translate to robots deployed in manufacturing and stock-advice software. AI technologies have been evolving steadily alongside the development of the Internet, big data, cloud computing and graphic chips. The United States has the most AI talents with the top employers Google, developer of AlphaGo, Microsoft, Amazon, IBM and Apple.

At Facebook's recent annual developer conference, Marc Zuckerberg outlined the social network's artificial intelligence (AI) plans to "build systems that are better than people in perception." He then demonstrated an impressive image recognition technology for the blind that can "see" what's going on in a picture and explain it out loud. From programs that help the visually impaired and safety features in cars that detect large animals to auto-organizing untagged photo collections and extracting business insights from socially shared pictures, the benefits of image recognition, or computer vision, are only just beginning to make their way into the world -- but they're doing so with increasing frequency and depth. It's busy enough that the upcoming LDV Vision Summit, an annual conference dedicated to all things visual tech, from VR and cameras to medical imaging and content analysis, is already in its third year. "The advancements in computer vision these days are creating tremendous new opportunities in analyzing images that are exponentially impacting every business vertical, from automotive to advertising to augmented reality," says Evan Nisselson of LDV Capital, which organizes the summit.

Computer vision technology will help you find the best stock images of whatever strikes your fancy. It's in a European city somewhere, with narrow cobblestone streets, and the fence is in front of an old-looking brick building. The bike is shiny and blue, with a basket, sort of old fashioned. You can't see the sky, but you can tell it's a somewhat sunny day. There's no way I could possibly find a picture of a scene like this one on the Internet. Sure, I can type in keywords like "blue bike next to fence in Europe" and it will show me some results that are tangentially related if I'm lucky.

WIRE)--VirtusaPolaris, the market-facing brand of Virtusa Corporation and Polaris Consulting & Services, Ltd. and a leading worldwide provider of information technology (IT) consulting and outsourcing services, and WorkFusion, the leading smart process automation (SPA) provider, today announced a partnership to deliver new smart automation solutions for the banking and financial services (BFS) market. The combination of VirtusaPolaris' deep BFS industry and process expertise and WorkFusion's cutting edge platform will help clients reduce operational costs, while improving quality, productivity and agility. "Most financial services organizations continue to struggle with inefficient legacy systems that have not kept pace with the change in business and regulations, introducing gaps in process automation that negatively impact efficiency of business operations. Many of these gaps are low complexity high volume routine process steps and most organizations have deployed large operational workforces, frequently offshore, to handle these processes," said Bob Graham, global solutions head, Banking and Financial Services at VirtusaPolaris. "WorkFusion's combination of robotic and cognitive automation supported by VirtusaPolaris' expert consulting and implementation services allow customers to improve quality through greater accuracy and the removal of human error, reduce costs through rapid automation of manual tasks, and accelerate time to market with our proven delivery approach."

Dreams are successions of images, ideas, emotions, and sensations that occur usually involuntarily in the mind during certain stages of sleep.[1] The content and purpose of dreams are not definitively understood, though they have been a topic of scientific speculation, as well as a subject of philosophical and religious interest, throughout recorded history. The scientific study of dreams is called oneirology.[2] Dreams mainly occur in the rapid-eye movement (REM) stage of sleep--when brain activity is high and resembles that of being awake. REM sleep is revealed by continuous movements of the eyes during sleep. At times, dreams may occur during other stages of sleep. However, these dreams tend to be much less vivid or memorable.[3] The length of a dream can vary; they may last for a few seconds, or approximately 20–30 minutes.[3] People are more likely to remember the dream if they are awakened during the REM phase. The average person has three to five dreams per night, and some may have up to seven;[4] however, most dreams are immediately or quickly forgotten.[5] Dreams tend to last longer as the night progresses. During a full eight-hour night sleep, most dreams occur in the typical two hours of REM.[6] In modern times, dreams have been seen as a connection to the unconscious mind. They range from normal and ordinary to overly surreal and bizarre. Dreams can have varying natures, such as being frightening, exciting, magical, melancholic, adventurous, or sexual. The events in dreams are generally outside the control of the dreamer, with the exception of lucid dreaming, where the dreamer is self-aware.[7]

We present algorithms to effectively represent a set of Markov decision processes (MDPs), whose optimal policies have already been learned, by a smaller source subset for lifelong, policy-reuse-based transfer learning in reinforcement learning. This is necessary when the number of previous tasks is large and the cost of measuring similarity counteracts the benefit of transfer. The source subset forms an `$\epsilon$-net' over the original set of MDPs, in the sense that for each previous MDP $M_p$, there is a source $M^s$ whose optimal policy has $<\epsilon$ regret in $M_p$. Our contributions are as follows. We present EXP-3-Transfer, a principled policy-reuse algorithm that optimally reuses a given source policy set when learning for a new MDP. We present a framework to cluster the previous MDPs to extract a source subset. The framework consists of (i) a distance $d_V$ over MDPs to measure policy-based similarity between MDPs; (ii) a cost function $g(\cdot)$ that uses $d_V$ to measure how good a particular clustering is for generating useful source tasks for EXP-3-Transfer and (iii) a provably convergent algorithm, MHAV, for finding the optimal clustering. We validate our algorithms through experiments in a surveillance domain.

Robotics scientists at the US's Stanford University have achieved a remarkable first: they have successfully sent an automated avatar – which they describe as a robo-mermaid – down to an ancient shipwreck to retrieve a vase from the sunken vessel. La Lune, the flagship of Louis XIV of France, sank 20 miles off the south coast city of Toulon in 1664. Only a few dozen of the hundreds of men on board survived. The wreck, which lies at a depth of 100 metres, had never been disturbed until the OceanOne robot craft reached it two weeks ago and recovered the grapefruit-size vase. The humanoid diving robot was piloted, using virtual reality techniques, by Oussama Khatib, professor of computer science at Stanford.

How does the image-recognition technology in a self-driving car respond to a blurred shape suddenly appearing on the road? Researchers from KU Leuven, Belgium, have shown that machines can learn to respond to unfamiliar objects like human beings would. Imagine heading home in your self-driving car. The rain is falling in torrents and visibility is poor. All of a sudden, a blurred shape appears on the road.