Although 11.5 million is a large number, most readers probably had no idea what went into drawing meaningful conclusions from that huge cache of documents. In fact, it took some 400 journalists at more than 100 news organizations an entire year to peruse the 2.6 terabytes of data in those documents and piece together the story of a company that helped the world's wealthiest people set up offshore bank accounts. In a lecture hosted by the University of Delaware Cybersecurity Initiative on Wednesday, April 6, computer scientist James Nolan used the Panama Papers as an example of the need for new machine learning techniques to address the problems associated with living in a data-rich, information-poor world. "Why can't we put that 2.6 terabytes through an algorithm and spit out relationships in a few hours?" he asked. Nolan emphasized the distinction between raw data which is collected from cameras, phones, sensors, satellites, written documents, cyber-logs, and other sources and information, which is the knowledge gained from studying data and teasing out relationships, resolving ambiguities, understanding scenes, and labeling events.

Engineers, safety advocates and even automakers have a safety message for federal regulators eager to get self-driving cars on the road: slow down. Fully self-driving cars may be the future of the automotive industry, but they aren't yet up to the demands of real-world driving, several people told the National Highway Traffic Safety Administration during a public meeting Friday. A slower, more deliberative approach may be needed instead of the agency's rapid timetable for producing guidance for deploying the vehicles, according to an auto industry trade association. In January, the federal agency announced that it would begin work on writing guidance for deploying the vehicles. Officials have promised to complete that guidance by July.

Deep-learning machines are conquering realm after realm of human expertise, but is there a difference between Them and Us? I think the only thing that distinguishes us from the machines is memory. It is what makes us human, says Rajeev Srinivasan. In the wake of the astonishing feat by Google's AlphaGo machine in defeating, nay thrashing 4-1 the world's best player of Go, it is time for us to wonder what it is that is truly human, that which distinguishes us from the machines. Deep-learning machines are conquering realm after realm of human expertise, from chess to natural language to Go to other domains, and there is no reason to imagine their progress will come to a halt any time soon.

Self-driving cars are more likely to be a threat than a boon to public safety because of unresolved technical issues, engineers and safety advocates told the government Friday, countering a push by innovators for expedited government approval. Even a trade association for automakers cautioned the National Highway Traffic Safety Administration at a public meeting that a slower, more deliberative approach may be needed than the agency's plan to provide in six months federal guidance for deploying the vehicles on roadways. There are risks to deviating from the government's traditional process of issuing regulations and standards, Paul Scullion, safety manager at the Association of Global Automakers, told a public meeting on self-driving cars hosted by NHTSA. Issuing new regulations takes an average of eight years, NHTSA has said. "While this process is often time-consuming, these procedural safeguards are in place for valid reasons," Scullion said.

We live in a world besotted with images: selfies, Instagram photos, video clips running on social media platforms that make it easy to instantly post images captured by smartphones and other devices. The explosion of images is making keyword searches less effective, prompting companies that sell photos and music to come up with new approaches to sorting through the haystack for the relevant image. With that in mind, image and music licensor Shutterstock Inc. (NYSE: SSTK) has launched new search features based on machine learning techniques and, specifically, its proprietary "convolutional" neural network technology. Convolutional neural networks are a machine-learning construct usually comprised of multiple layers that is often followed by more fully connected layers as in a standard multilayer neural network. Convolution nets are made up of neurons with "learnable" weights and biases.

A senior Egyptian al-Qaida figure fighting in Syria was killed in a U.S. drone strike this week, the latest to be killed in such attacks in Syria, a Syrian opposition monitoring group and relatives said Friday. The Britain-based Syrian Observatory for Human Rights said Rifai Ahmad Taha was killed in a strike Tuesday in the northwestern Idlib province. Before joining al-Qaida, Taha was a top figure in Egypt's notorious militant group Gamaa Islamiya, which massacred 58 foreign tourists in the ancient Egyptian city of Luxor in 1997. He was also allied with Osama bin Laden in Afghanistan. The Observatory's chief Rami Abdurrahman said several al-Qaida members, including Taha, were killed in Tuesday's strike.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Imagining a future where a machine decides which job you're best suited for is frankly frightening. Television shows like Futurama have done their job of reminding us why a future dictated by machine logic is probably best avoided. We understandably become uneasy with this idea because a machine does not think like a human. But what if a machine could think like a human - a more considerate version of Futurama's Bender - what then? Machines with cognitive intelligence are no longer the realm of science fiction.

You can call it a Virtual Personal Assistant, an Intelligent Agent, an Intelligent Interface or whatever you wish. The era of the assistant that began with Siri will eventually dominate the way people interact with mobile devices, computers, cars, wearables, appliances and every other piece of technology that requires complex human-machine interaction. Nearly all of the large Internet players have now launched or are working on some effort to win this next-generation paradigm and it's the earliest of days. Despite the massive uptake of assistants spurred by Apple's Siri, Google Now, and Microsoft Cortana, the market and technologies for this paradigm remain in their adolescence. Siri was the first chapter in a much longer, larger story that reminds me of the original iPhone launch in 2007.

About a dozen trucks from major manufacturers like Volvo and Daimler just completed a week of largely autonomous driving across Europe, the first such major exercise on the continent. The trucks set off from their bases in three European countries and completed their journeys in Rotterdam in the Netherlands today (Apr. One set of trucks, made by the Volkswagen subsidiary Scania, traveled more than 2,000 km and crossed four borders to get there. The trucks were taking part in the European Truck Platooning Challenge, organized by the Dutch government as one of the big events for its 2016 presidency of the European Union. While self-driving cars from Google or Ford get most of the credit for capturing the public imagination, commercial uses for autonomous or nearly autonomous vehicles, like tractors from John Deere, have been quietly putting the concept to work in a business setting. When trucks autonomously follow one another, it's called "platooning."