Clune says that GAN is composed of two neural networks playing a game of cops and robbers--or cops and forgers, rather. These neural networks are commonly referred to as "deep neural networks"--they take data and combine them through a series of many transformations. The high number of transformations is what makes a neural network "deep." The other network, the cop, is tasked with judging the veracity of the forgery.
What perhaps sets these new brands apart from their predecessors, then, is their push to expand the visibility of the queer community. For instance, one user might not know much about another offline, but he might know little things about him from having scrolled through his geotagged social media page. He might even recognize him from his profile photos walking down the street, or in the audience of, say, a recent panel about digital content by and for the queer community. Far from keeping queer men on the fringes, these apps are fueling a novel knowingness among users--on the app, yes, but also offline, when users go out to create and engage with open communities.
Uber and others--Google and Tesla and the auto companies--have invested a lot of money in developing technology for self-driving cars because technologists believe that the technology is still good and will eventually become so pervasive that most of us won't drive cars around anymore. If you're the company that controls that technology, then you could in theory control the transportation network that runs that technology. That's why it's investing a lot of money in its own self-driving car technology. General Motors is investing a lot of money.
Interestingly, that suggests the truck driver could have foreseen the crash--if he had assumed the car would maintain a constant speed. The witness said the Model S was visible to the truck over the crest of a rise in the freeway for "several seconds" before the truck began its left turn. The implication is that the driver either didn't see the Model S coming or assumed--perhaps naturally, given the truck's size and visibility--that the Tesla's driver would brake or change lanes to avoid the collision. That seems like a safe assumption when the driver is human but perhaps less so when the driver is an automated system.
The U.S. has a racial wealth gap problem. By one estimate, at current levels of wealth growth it would take 228 years for the average black family to catch up with levels of wealth among white families. Thomas Shapiro explains some of the surprising reasons parity remains so elusive in his book, Toxic Inequality: How America's Wealth Gap Destroys Mobility, Deepens the Racial Divide, and Threatens Our Future.
This new project is reminiscent of Hieroglyph, a project from Arizona State University that is similarly aimed at leveraging science fiction to make positive change in the real world. Like the Hieroglyph project, the Science Fiction Advisory Council will be launching with a short story collection. In July, XPRIZE plans to publish an online anthology of original science-fiction stories by members of the advisory council recounting the experiences of passengers on a fictional flight from Tokyo to San Francisco who are mysteriously transported 20 years into the future. The stories, published at Seat14C.com, will presumably include visions of some of the "preferred future states" that XPRIZE seeks to identify, and will be followed by quarterly meetings of the advisers as they build out their roadmaps for avoiding dystopia and reaching those better futures.
While we have recently seen important advancements in machine learning and artificial intelligence, this work has been largely driven by consumer applications, with Google, IBM, Facebook, Microsoft, and Amazon leading the way. These companies have developed deep learning models that achieve near-human performance on certain tasks, even as their workings are largely incomprehensible to human users. Most people use the Amazon Echo without an understanding of the A.I. If your Echo doesn't understand you, you simply repeat the sentence until it does or you give up.
Emily St. John Mandel's best-selling 2014 novel, Station Eleven, wears its wish fulfillment on its sleeve. Most of the planet's population gets killed off by a superflu, leaving the survivors to pick through the remnants while fending off marauders and creepy doomsday cults. The novel focuses on a band of traveling players who cater to the public's renewed appreciation of Shakespeare and Beethoven now that the mass-media trash that once diverted them has been switched off. Scenes of the troupe's adventures alternate with scenes from before the pandemic, all related to the life of an actor, Arthur Leander, who dies on the eve of the outbreak.
Why commit to owning a car that will run for 11 or 12 years when you can make a less onerous short-term commitment and then upgrade substantially in a few years without paying significantly more. This dynamic is particularly evident when it comes to the most technologically advanced cars, plug-in electrics and battery-powered vehicles. Thanks to rising competition and volumes, and to significant advances in battery design and production, the state of the art is improving rapidly. It's not quite like that with cars, but a similar dynamic is at work.
Maternal-fetal medicine specialists like us are tasked with caring for women with "high-risk" pregnancies, usually defined as pregnancies complicated by chronic or acute maternal illness, fetal concerns, or problems related to pregnancy itself (e.g. Our nuclear event--one of the worst things that can happen when we practice--is a mother dying. Maternal mortality in the United States is rare but, sadly, nowhere near rare enough: Data collected from 1990–2015 show that the number of maternal deaths per 100,000 births has increased from 16.9 1990 to 26.4 in 2015. Not only are more American mothers dying than in our peer countries, but we're one of the only developed countries where the death rate is increasing, not decreasing.