Like many of the other terms that crop up in conversations about artificial intelligence, neural network, which refers code designed to work like a brain, can be conceptually intimidating. Janelle Shane, however, makes the kind of neural networks that go viral. Her quirky creations autonomously stumble and grumble as they attempt to come up with names of Star Wars character, pick-up lines, and even recipes. Shane rightly warns that you should try the output of that last algorithm "at your own risk," though there's little danger that any human would attempt to: The network's recipe for Beothurtreed Tuna Pie, for example, includes such bafflingly unappetizing ingredients as "1 hard cooked apple mayonnaise" and "5 cup lumps; thinly sliced."
As Uber fitfully tries to reset its bad fortunes--see the recent intrigue in its search for a new CEO--Lyft is trying its hardest to catch up with its rival. Beyond positioning itself as the convenient ride-hailing company that isn't afflicted by a toxic tech-bro culture and also keeps an eye out for its drivers (it's allowed riders to tip drivers for years), Lyft wants to show that it can beat Uber at some of its other endeavors, including the most important one of all: innovating at the bleeding edge of transportation technology. While Uber has faced scandals, internal squabbles, and even a messy legal battle with Google's parent Alphabet over whether it stole aspects of its self-driving cars technology, Lyft has launched ambitious partnerships in that field with General Motors and even with Alphabet.
For science fiction, the multiverse is a gold mine of compelling story ideas. Some physicists like Kleban believe we could detect evidence of the multiverse through something called bubble collisions. The hunt for bubble collisions continues, although Kleban himself has acknowledged finding one would be a long shot. "If convincing evidence of the multiverse is found, it would be one of the biggest discoveries in human history akin to the Copernican revolution," he said.
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.