Humans become aroused when touching robots in sensitive places, a new study has found. Far from seeing robots as just computers, humans can become physiologically aroused from touching a human-shaped robot in private places like their eyes and buttocks, the Stanford study found. The results could have huge consequences for the creation of robots in the future, such as ones that people live or even have sex with. It might also help people create "robot stand-ins", that allow people to touch others when actually being there isn't an option, the researchers said. Scientists have taken a leaf out of the script of The Martian by showing how easy it would be to grow your own veg on the Red Planet.

In an effort to improve the social networking experience for users with visual impairments, Facebook has introduced a new feature in its iOS app to help them'see' photos. With the help of AI, the app automatically generates a description of each photo a user comes across. When they're using a screen reader on iOS, they'll be able to hear a list of items in the pictures, such as "Image may contain three people, smiling, outdoors." Get your company on stage at TNW Europe. The descriptions, called Automatic Alternative Text, are generated as image alt text.

Google, of course, can't give away all of its secrets. That's why TensorFlow's release into the wild only includes part of the code that allows it to run on a single machine. Despite being shared under what's called an Apache 2 license (meaning anyone is free to use it). These days, the big Internet giants frequently share the software sitting at the heart of their online operations. Open source accelerates the progress of technology.

Facebook is now using artificial intelligence to automatically generate captions for photos in the News Feed of people who can't see them. The tool is called Automatic Alternative Text, and it dovetails with text-to-speech engines that allow blind people to use Facebook in other ways. Using deep neural networks, the system can identify particular objects in a photo, from cars and boats to ice cream and pizza. It can pick out particular characteristics of the people in the photo, including smiles and beards and eyeglasses. And it can analyze a photo in a more general sense, determining that a photo depicts sun or ocean waves or snow.

A few days before Thanksgiving, George Hotz, a 26-year-old hacker, invites me to his house in San Francisco to check out a project he's been working on. He says it's a self-driving car that he had built in about a month. But when I turn up that morning, in his garage there's a white 2016 Acura ILX outfitted with a laser-based radar (lidar) system on the roof and a camera mounted near the rearview mirror. A tangle of electronics is attached to a wooden board where the glove compartment used to be, a joystick protrudes where you'd usually find a gearshift, and a 21.5-inch screen is attached to the center of the dash. "Tesla only has a 17-inch screen," Hotz says. He's been keeping the project to himself and is dying to show it off. Hotz fires up the vehicle's computer, which runs a version of the Linux operating system, and strings of numbers fill the screen. When he turns the wheel or puts the blinker on, a few numbers change, demonstrating that he's tapped into the Acura's internal controls. After about 20 minutes of this, and sensing my skepticism, Hotz decides there's really only one way to show what his creation can do. "Screw it," he says, turning on the engine. As a scrawny 17-year-old known online as "geohot," Hotz was the first person to hack Apple's iPhone, allowing anyone--well, anyone with a soldering iron and some software smarts--to use the phone on networks other than AT&T's.

It doesn't really have a name, and not much of a personality. But a robot created by Aviano middle and high schoolers performed well enough to qualify for the FIRST Tech Challenge World Championships. At a recent competition in Prague, the robot flipped over three times, said Julia Markel, the lead programmer on the 13-member team, which calls itself Robotica Santi Robotics Club. But it rallied enough to achieve one of the team's top goals: climb up a ramp and do a pull-up. "We were one of the only teams that were able to do that," said Dave Izzo, the club's adviser.

Codified narrative is the product of converting human-friendly narrative into computer-friendly code. In past blogs, I discussed my own approach towards this process of codification. Here, I will be covering the idea of spatial, temporal, and contextual distribution of codified narrative. I have never suggested that narrative can or should be used in place of quantitative data. However, I have reflected on how the quantitative regime has tended to dominate discourse; this has perhaps led to data being contextually constrained or deprived. Geography is a type of context that can shape the extent to which people interact with the world. Space provides a medium to distribute resources. It can be involved in forced confinement. An office full of cubicles demonstrates control and dominance over space.

Superfast quantum computers could transform the world of finance, advocates say. In a world where how fast you can assimilate and analyse data then act on it, makes the difference between profit and loss, computing speed is key. This is why banks, insurance firms, and hedge funds invest millions on technology to give them an edge when trading, and to offset human error. Quantum computers, that owe more to nuclear quantum mechanics than electronics, promise to be exponentially more powerful than traditional computers, holding out the tantalising prospect of near-perfect trading strategies and highly accurate forecasting and risk assessments. "Financial services is a data-rich environment," says Kevin Hanley, director of design at the Royal Bank of Scotland (RBS).

Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is crucial. The process of extracting features is highly linked to dimensionality reduction as it implies the transformation of the data from a sparse high-dimensional space, to higher level meaningful abstractions. This dissertation employs Neural Networks for distributed paragraph representations, and Latent Dirichlet Allocation to capture higher level features of paragraph vectors. Although Neural Networks for distributed paragraph representations are considered the state of the art for extracting paragraph vectors, we show that a quick topic analysis model such as Latent Dirichlet Allocation can provide meaningful features too. We evaluate the two methods on the CMU Movie Summary Corpus, a collection of 25,203 movie plot summaries extracted from Wikipedia. Finally, for both approaches, we use K-Nearest Neighbors to discover similar movies, and plot the projected representations using T-Distributed Stochastic Neighbor Embedding to depict the context similarities. These similarities, expressed as movie distances, can be used for movies recommendation. The recommended movies of this approach are compared with the recommended movies from IMDB, which use a collaborative filtering recommendation approach, to show that our two models could constitute either an alternative or a supplementary recommendation approach.

This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. A recurrent neural network generates individual words, thus reaping the benefits of discriminatively-trained vector representations. The discourse relations are represented with a latent variable, which can be predicted or marginalized, depending on the task. The resulting model can therefore employ a training objective that includes not only discourse relation classification, but also word prediction. As a result, it outperforms state-of- the-art alternatives for two tasks: implicit discourse relation classification in the Penn Discourse Treebank, and dialog act classification in the Switchboard corpus. Furthermore, by marginalizing over latent discourse relations at test time, we obtain a discourse informed language model, which improves over a strong LSTM baseline.