As robots become more life-like, questions have been raised about whether its morally acceptable to kick them, bully them or program them to kill. Taking this dilemma to the next level, researchers have studied how these machines make us feel, sexually and emotionally. Scientists programmed a human-shaped robot to ask volunteers to touch it in 13 different places. While some of these requests presented no difficulties, people were hesitant to touch'private' areas including the robot's'buttocks'. Scientists at Stanford University programmed a human-shaped robot (pictured left) to ask volunteers (pictured right) to touch it in 13 different places.

The rise of the robot writers is here. Computers are replacing human writers without you realising it. Hal just took my job. The Associated Press (AP) already uses them. These robot journalists don't need insurance or a pension, pay or annual leave – or even sleep. This lets publications react to unforeseen events such as earthquakes, in real time, without stirring a human hack from their slumbers.

The engineers at 1 Hacker Way trained their object recognition tech by feeding it millions of images as examples. This kind of machine learning is called neural network, and it was how Google taught AlphaGo the ancient game of Go and how one of our editors trained his computer to write Engadget articles. The team then made sure that alt text describes photos in a specific order, starting with (the number of) people, then objects and then scenes in the background. People tend to post mostly images on Facebook these days to the point that browsing the News Feed has become a very visual experience. Someone who can't see all those could feel excluded, but this technology could help "the blind community experience Facebook the same way others enjoy it."

BB-8, the adorable yet heroic droid who made his debut in the latest Star Wars film, is now also the poster child of a hot youth-focused tech trend: toys that improve over time. In this case, the toy robot now has the ability to react to screenings of "The Force Awakens." On Tuesday, Sphero pushed out a smartphone app update giving our current favorite toy robot the ability to listen and respond to the movie's dialog. Before you start the movie--which was just released on home video--you'll need to update then open iPhone or Android Sphero BB-8 control app, then connect it to the baseball-sized robot. You'll want to keep BB-8 in his charging cradle, so he doesn't roll off your table or couch.

Enabling the feature is easy, though you'll need an iPhone or iPad to use it right now. To use it, ask Siri to "turn on VoiceOver," a built-in iOS feature that describes out loud whatever is on your screen. You can also tap into Settings, then "General" and "Accessibility" to manually flip on VoiceOver. Representatives for Facebook say the feature will come to other platforms in the near future. While they didn't specify which, the demo shown to The Huffington Post ran on a laptop.

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.

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.

As robotics and 3-D printing technologies become more accessible to home tinkerers, men are (of course) building robots of beautiful women. Anyone who's turned on a TV in the past decade shouldn't be surprised to learn that one of the first--and creepiest--examples of this development involves movie star Scarlett Johansson. News broke on Friday about a Hong Kong designer who made a robot that looks just like the award-winning actress--although Ricky Ma, the robot's creator, wouldn't name the actress he modeled the bot on, choosing instead to it Mark 1. It took Ma eighteen months and over 50,000 to complete the project, which he constructed on his patio with a 3-D printer and software that he taught himself how to use. The question, however, is one of precedent.

I have 34 years as an engineer here at Intel. Almost all but about the last, I don't know, four or five has been mainly on the manufacturing side; all of our silicon manufacturing. Which, in many ways makes you a maker because you're producing a million chips a day. When I look at a 3D printer, I look at it as not only what can I build with it, but I understand exactly how that machine works. I could take it apart and put it back together.

We live in times, where science fiction authors are struggling to keep up with reality. In recent years, there has been an explosion of research and experiments that deal with creativity and A.I. Almost every week, there is a new bot that paints, writes stories, composes music, designs objects or builds houses: Artificial Intelligence systems performing creative tasks? Our research started by wondering about this phenomenon and playfully experimenting with it. This lead to an in-depth investigation, of what we call "CreativeAI". This document is the first chapter of our adventure into CreativeAI, aiming at establishing a backstory and language we can use to talk about this intricate subject.