From IBM's chess-playing supercomputer to self-driving cars Artificial Intelligence (AI) is frothing up towards the crest of the hype cycle. With remarkable progress around big data, better algorithms and deep neural networks now available, politicians and academics alike fret about the possibility that robots will take control and human intelligence will become an artifact of a slower, more arcane era. Before you brace yourself against the onslaught of non-humans, consider this: AI is still a toddler, technologically speaking. Hype blossoms on misinformation, and what many outsiders call AI today is really machine learning, which is a subset of something much bigger. While machine learning powers now-familiar devices like Netflix recommendations and Nest's self-programmed thermostat, the AI industry as a whole has a long way to go before robots replace people on a large scale.

I feel rather uneasy at this application of DCGANs. Yes, we know they are great at hallucinating details and creating single plausible reconstructions of high perceptual detail, so the perceptual ratings do not surprise anyone, but that's not the same thing as making accurate diagnoses, is it?

I was wondering if some of the ideas behind VAEs could be used for simple regression tasks as well? The hope being that it would provide some error estimates for the neural networks predictions.

Long before Nov. 8, 2016, research scientist Dean Pomerleau was concerned about fake news. His Facebook News Feed had been filled with political disinformation during the presidential campaign, and he saw that the stories appeared to be influencing reader's attitudes toward the candidates. Though skeptical, he wondered whether it were possible to create a machine learning tool that could flag fake news stories, and discussed the idea with colleagues on Twitter. Then he issued a challenge: he bet that it were not possible to do, and asked his colleagues could prove him wrong. Delip Rao, the founder of Joostware, which builds Artificial Intelligence products, contacted Pomerleau and offered his help – and thus began the Fake News Challenge.

We've seen many buzzwordy innovations in technology over the last decade, from cloud computing to big data to microservices and beyond - but artificial intelligence (AI) by far has the most buzzword baggage. On the one hand, AI is perhaps the most revolutionary set of innovations since the transistor. But on the other, the bad press surrounding it continues to mount, perhaps even faster than the innovations themselves. We didn't suffer this kind of PR nightmare with the cloud, or the web, or even client/server. In fact, AI has an unprecedented set of PR challenges that threaten to sink the entire movement.

Apple is expected to release iOS 11 this fall alongside the iPhone 8; however, the company could give the world a preview of features that could be included in the update during the Worldwide Developers Conference next week. The update for iPhones and iPads could come with features for Siri and Apple Music. Here's a roundup of expected iOS 11 features and other rumors: For iOS 11, Apple is rumored to be revamping Siri with contextual learning abilities and deep integration with iMessage and iCloud, Israeli site the Verifier reported in March. The virtual assistant is expected to get an improved level of artificial intelligence. Siri reportedly will be able to follow through with voice commands, have the ability to learn a user's habits and offer action options based on context of content, somewhat resembling Samsung's voice assistant Bixby.

People's faces change all the time. During a conversation, a person's facial expressions and head angle change, and over time, people's appearances also change if they lose weight, grow a beard, or change their hairstyle. If we know someone, we can recognize them despite these changes - but if a face is unfamiliar, research has shown that people are generally very bad at matching together two images of the same face. If we know someone, we can recognize them despite changes in facial expressions - but if a face is unfamiliar, research has shown that people are generally very bad at matching together two images of the same face. Pictured is Jim Carrey in the film'A Series of Unfortunate Events' How the human visual system manages to overcome the challenge of face changes and allow us to recognize people it still mostly unknown.

Imagine a world where anyone could create a photo-realistic fake video in which whoever they choose can be made to say whatever they want. Add to that the ability to write a script and have a machine recite it back with the perfectly indistinguishable intonation of the person featured. We are officially moving closer to that world. A Montreal-based AI startup has recently revealed a new voice imitation technology that could signal the end of trusting your ears, meaning pretty soon there could be a cloud of doubt over literally every "recording" you see and hear. Upgrade to a Plus subscription today, and read the site without ads.

Science fiction has been instrumental in creating the future from the very beginning. Real-life manipulator hands, originally created for the nuclear industry, were named after Robert Heinlein's short story, "Waldo." It makes a lot of sense, then, that when the Xprize program partnered with All Nippon Airlines (ANA) to "imagine a bold vision of the future," it would look to celebrated science fiction novelists, writers, filmmakers, producers and screenwriters. The collaboration has produced the Science Fiction Council, a group comprised of high-octane sci-fi storytellers from nine countries, including luminaries like Margaret Atwood, Cory Doctorow, Andy Weir, Charles Stross, Ernest Cline and Nancy Kress. The Xprize organization runs prize competitions to encourage and support solutions to humanity's biggest challenges, like clean water, moon flights, Star Trek-inspired tricorders and even artificial intelligence.

The paper is here: https://arxiv.org/abs/1702.08835v2 My first pass seems to say that, when the authors report competitive data, it's on simple datasets compared to simple (sometimes exceedingly so, e.g. LeNet-5) deep networks, and when gcForest is compared to even moderately modern (e.g., AlexNet) architectures, the performance is nowhere near competitive. Is this worth reading in depth?