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 researchers developed predictive-vision software that uses machine learning to anticipate what actions should follow a given set of video frames. They grabbed thousands of videos showing humans greeting each other, and fed those videos into the algorithm. To test how much the machine was learning about human behavior, the researchers presented the computer with single frames that showed meet-ups between characters on TV sitcoms it had never seen, including "The Big Bang Theory," "Desperate Housewives" and "The Office."

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.

As readers of my blog will know, I want to learn more about machine learning. I've managed to run some samples, and I've built my own first little samples. It feels like the next step is to understand more about the different algorithms, for example when to pick which one and how to tune the parameters to achieve the best results. To learn more, I've started to watch the first hours of the awesome courses below. The courses are a great introduction to machine learning and very different from most other videos I found which often seem to assume you are already a data scientist.

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.

In the coming decade, as X Prize strives to achieve its impact mission through incentive competitions and crowd-sourcing, we see tremendous opportunity in this emerging generation of problem solvers to use AI to solve humanitys grandest challenges," X Prize CEO Marcus Shingles said in a statement. "The IBM Watson AI X Prize is intended to promote and progress the notion of AI for impact among the global bold innovator crowd, both the established community of practitioners, as well as encourage newcomers to experiment and ultimately demonstrate how AI can be used as a tool for good.

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.

Streaming service Sling TV is now available on Apple TV's fourth-generation set-top box. Apple is buffing up its Apple TV set-top box in hopes of making it a more popular choice for cord cutters. The purveyor of iPhones and iPads is playing catch-up in the Net video streaming device competition. But some Apple TV advances announced last week could give it a boost. Sling just added Comedy Central to that basic package.

Every time our customers visit the Zalando Fashion Store, we want to serve them personalised product recommendations, depending on their preferences. Others love ankle boots, while some prefer sneakers. Whilst some follow the latest trends and others prefer the classic style. In a nutshell, the task of a personalised recommender system involves building user profiles from their behavior and predicting which product recommendations will be most relevant to such profiles. Intuitively, the user profile specifies properties such as how much interest the customer has in sportswear, or whether flat heels are preferred over high heel shoes.

First let's create a very simple dataset. We have some class labels y. 500 are from class 0, and 500 are from class 1, and they are randomly ordered. And we have 100 variables x that we want to use to predict y. 99 of them are just random noise, but one of them is highly correlated with the class label. Now let's say we want to classify the points using linear discriminant analysis. The final 1 in the output indicates that variable 100 is, as expected, the best predictor of y among the variables in x.

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".