Researchers have taught a computer to do a better-than-expected job of predicting what characters on TV shows will do, just by forcing the machine to study 600 hours' worth of YouTube videos. The experiment could serve as a commentary on the state of research into artificial intelligence, or on the predictability of sitcom plots. It also calls to mind the scenes from countless science-fiction movies where the alien gets up to speed on human culture just by watching TV. MIT's Carl Vondrick and his colleagues are due to present the results of their experiment next week at the International Conference on Computer Vision and Pattern Recognition in Las Vegas. The researchers developed predictive-vision software that uses machine learning to anticipate what actions should follow a given set of video frames.

Researchers have developed a deep learning algorithm capable of successfully predicting what will happen in a video clip based on one still clip from the footage. The Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology (MIT) made the breakthrough in predictive vision by training an algorithm using 600 hundred hours of YouTube videos. By searching for patterns and recognizable objects like hands and faces, the algorithm was able to predict human interactions such as hugging, kissing, shaking hands or high fiving. The research is set to be presented this week at the International Conference on Computer Vision and Pattern Recognition (CVPR). "Humans automatically learn to anticipate actions through experience, which is what made us interested in trying to imbue computers with the same sort of common sense," said MIT PhD student and the paper's first author Carl Vondrick.

The machine was, and still is, my constant partner. I need her in order to translate the creative thoughts in my head into tangible ideas I can share with the world. Transitioning to design from a modern dance career in my twenties, I never thought a machine would be my accomplice for innovation. Machines have rapidly developed intelligence in this generation and their capabilities are changing the products we design. The process in which they are designed will also need to evolve.

Mario Herger is the CEO of consultancy firm Enterprise Garage. Not only is he a cutie, he is the last person to get a driver license. I admit: I don't know if Liam will be the last person to get a driver license. It could be Sophia or Ethan. This person may live right around the corner in your neighborhood. But one thing is certain: The last person to get a driver license is already born -- the speed of technology development and recent announcements confirm that.

Twitter has bought London-based AI startup Magic Pony Technology for a reported 150m ( 102m) as the company moves to strengthen its position in image-sharing, video and live video. Founded in 2014, Magic Pony uses machine learning to build improved systems for visual processing. The company said it was excited to be joining forces with Twitter "to improve the visual experiences that are delivered across their apps". Twitter's chief executive, Jack Dorsey, said Magic Pony's technology would be used to enhance live and video offerings and "opens up a whole lot of exciting creative possibilities for Twitter". Dorsey said the team included "11 PhDs with expertise across computer vision, machine learning, high-performance computing and computational neuroscience".

AS DOOMSDAY SCENARIOS go, it does not sound terribly frightening. The "paperclip maximiser" is a thought experiment proposed by Nick Bostrom, a philosopher at Oxford University. Imagine an artificial intelligence, he says, which decides to amass as many paperclips as possible. It devotes all its energy to acquiring paperclips, and to improving itself so that it can get paperclips in new ways, while resisting any attempt to divert it from this goal. Eventually it "starts transforming first all of Earth and then increasing portions of space into paperclip manufacturing facilities". This apparently silly scenario is intended to make the serious point that AIs need not have human-like motives or psyches.

In his quest to transform the way the Pentagon wages war, Defense Secretary Ashton Carter has turned to Silicon Valley, hoping its experimental culture, innovation and sense of urgency would rub off on the rigid bureaucracy he runs. Carter has made several trips to the region and appointed Eric Schmidt, the chairman of Google's parent company to an advisory board. And recently he sat down at the Pentagon with Elon Musk to see what suggestions the billionaire founder of Tesla and SpaceX might have to make the nation's military more efficient and daring. "Having an incentive structure that rewards innovation is extremely important," he said in an interview after the meeting. Whatever you reward will happen." The Pentagon finds itself in a new arms race, struggling to keep pace with forms of combat that are fought with bytes as well as bullets. The technological advancements disrupting established business sectors are now shaking up the world of war - where robots, swarming drones and ...

If you think your mind is the only safe place left for all your secrets, think again, because scientists are making real steps towards reading your thoughts and putting them on a screen for everyone to see. A team from the University of Oregon has built a system that can read people's thoughts via brain scans, and reconstruct the faces they were visualising in their heads. As you'll soon see, the results were pretty damn creepy. "We can take someone's memory - which is typically something internal and private - and we can pull it out from their brains," one of the team, neuroscientist Brice Kuhl, told Brian Resnick at Vox. The researchers selected 23 volunteers, and compiled a set of 1,000 colour photos of random people's faces.

Rolls-Royce this week unveiled a white paper on autonomous ships at an Amsterdam symposium, predicting the commercialization of the technology by the end of the decade. It's not if; it's when," Oskar Levander, Rolls-Royce's vice president for marine innovation, told the Autonomous Ship Technology Symposium 2016 Tuesday as the company issued its white paper, "Advanced Autonomous Waterborne Applications Initiative." "The technologies needed to make remote and autonomous ships a reality exist. The AAWA project is testing sensor arrays in a range of operating and climatic conditions in Finland and has created a simulated autonomous ship control system which allows the behavior of the complete communication system to be explored. We will see a remote-controlled ship in commercial use by the end of the decade," Levander said. The technology, which Rolls-Royce said has the support of shipowners and operators, was tested on Finferries' 213-foot double-ended ferry, the Stella, which operates between Korpo and Houtskär. Also working on the project is ESL Shipping Ltd., which is testing implications for cargo vessels. "Our solutions in ship intelligence will deliver multifaceted enhancements in vessel performance and operation for our customers.

Consider TPOT your Data Science Assistant. TPOT is a Python tool that automatically creates and optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there. TPOT is built on top of scikit-learn, so all of the code it generates should look familiar... if you're familiar with scikit-learn, anyway.