Here's Why This New Documentary On Artificial Intelligence Is Troubling Netflix's new Chilling Adventures of Sabrina is far darker than the 90's sitcom. Marvel's Daredevil is Netflix's best superhero series hands down. Season 3 has just made it even better. The hit show Tom Clancy's Jack Ryan on Amazon is challenging viewers to second guess their ethical presuppositions. CBS takes things to a higher level with its new show "God Friended Me."

Samsung has been teasing the release of a foldable phone since 2014 but the groundbreaking device may now be just a week away, as rumours abound that it will be unveiled at a conference hosted by the South Korean electronics giant. Potentially named the Galaxy X or Galaxy F, it is believed the bendy gadget will be demonstrated at the electronics giant's developer conference beginning on 7 November, with a potential release date set for some time in 2019. Samsung has been working with YouTube and Netflix to figure out ways to deliver content on the folding screen, The Wall Street Journal reported. The foldable screen may even have name – the Infinity-V – after a separate leak revealed Samsung had trademarked the name with the Korean intellectual property office. A spokesperson for Samsung was not immediately available for comment.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Oregon State's Cassie dressed up as an AT-ST from Star Wars. AT-ST stands for "All Terrain Scout Transport," which is basically accurate for Cassie, too.

Sensor fusion is the use of sensory data from multiple sources, combined into one comprehensive result for more accurate, complete, and dependable, business intelligence and security applications. "IoT devices, security cameras and other data capture sensors are practically everywhere," said Nadir Ali, CEO of Inpixon. "The challenge is to filter the information captured by those devices, so it can be processed and analyzed in a meaningful way. Inpixon has extensive experience in radio frequency data fusion, which we believe we can leverage due to the similarities between radio waves and the light waves captured by CCTV cameras. "Location is the lynchpin," continued Mr. Ali. "To know what's going on in your building -- for security purposes, for sales or customer service purposes, or for applications like location-based marketing or augmented reality -- you must know the location of persons and electronic devices in your space.

Data scientists at 20th Century Fox and Google Cloud have developed machine-learning software that can analyze movie trailers and predict how likely people are to see those movies in theaters. A recent preprint research paper breaks down how the program, named Merlin, can now recognize objects and patterns in a trailer to understand movie scenes. Merlin can scan trailers and spot objects like "man with beard," "gun," "car," and decide whether the movie is an action flick or a crime drama based on the context in which those objects appear. "A trailer with a long close-up shot of a character is more likely for a drama movie," the study's authors write, "whereas a trailer with quick but frequent shots is more likely for an action movie." Merlin can use its knowledge of common tropes in trailers to understand how sequences of actions in trailers play into our expectations for genre films.

I've run a lot of machine learning/A.I. projects as toys, and even a few not very complex ones in production. Normally they run on the CPU, and in only one instance did I use a GPU ... the projects simply didn't require it. Sometimes however, you come across something you need to try out, and it needs a STONKIN BIG MOTHA of a machine to really get its teeth stuck in. I had to do that recently and found the quickest way to get started was to spin up what I needed using the pre-configured Azure Deep Learning environment, then drop it when I was finished. This article walks through the process that is actually rather pleasantly simple.

It's never been quite clear, she says, whether the phrase--which is frequently the entire output of a student's first computer program--is supposed to be attributed to the program, awakening for the first time, or to the programmer, announcing their triumphant first creation. Perhaps for this reason, "Hello World" calls to mind a dialogue between human and machine, one which has never been more relevant than it is today. Her book, called Hello World, published in September, walks us through a rapidly computerizing world. Fry is both optimistic and excited--along with her Ph.D. students at the University of College, London, she has worked on many algorithms herself--and cautious. In conversation and in her book, she issues a call to arms: We need to make algorithms transparent, regulated, and forgiving of the flawed creatures that converse with them.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. An AI-made portrait sold for $432,500 at a famous auction last week. This was a story that was widely discussed in tech media in the past week, with some suggesting the development marked a threat for human artists. This is just one of the many stories of progress in deep learning that triggers sensational headlines about AI manifesting artistic creativity that is on par with humans. "AI songwriting has arrived" and "AI will soon write better novels than humans" are just some of the stories that have surfaced on mainstream media in the past few months.

Personal electronic devices such as smartphones give access to a broad range of behavioral signals that can be used to learn about the characteristics and preferences of individuals. In this study we explore the connection between demographic and psychological attributes and digital records for a cohort of 7,633 people, closely representative of the US population with respect to gender, age, geographical distribution, education, and income. We collected self-reported assessments on validated psychometric questionnaires based on both the Moral Foundations and Basic Human Values theories, and combined this information with passively-collected multi-modal digital data from web browsing behavior, smartphone usage and demographic data. Then, we designed a machine learning framework to infer both the demographic and psychological attributes from the behavioral data. In a cross-validated setting, our model is found to predict demographic attributes with good accuracy (weighted AUC scores of 0.90 for gender, 0.71 for age, 0.74 for ethnicity). Our weighted AUC scores for Moral Foundation attributes (0.66) and Human Values attributes (0.60) suggest that accurate prediction of complex psychometric attributes is more challenging but feasible. This connection might prove useful for designing personalized services, communication strategies, and interventions, and can be used to sketch a portrait of people with similar worldviews.

In this work, we address the problem of modifying textual attributes of sentences. Given an input sentence and a set of attribute labels, we attempt to generate sentences that are compatible with the conditioning information. To ensure that the model generates content compatible sentences, we introduce a reconstruction loss which interpolates between auto-encoding and back-translation loss components. We propose an adversarial loss to enforce generated samples to be attribute compatible and realistic. Through quantitative, qualitative and human evaluations we demonstrate that our model is capable of generating fluent sentences that better reflect the conditioning information compared to prior methods. We further demonstrate that the model is capable of simultaneously controlling multiple attributes.