Artificial intelligence is one of the Seattle area's fastest-growing tech frontiers, so it only makes sense for the field to get its own category at the GeekWire Awards. Recognizing innovations in AI and its allied technologies, ranging from computer vision to machine learning and natural language processing, has always been a part of the big part of the awards, of course. In fact, some of 2019's contenders for the top AI prize have shown up as finalists in previous years. The split shines a tighter spotlight on two areas of technology where the Pacific Northwest stands out. The five finalists in this new category -- Highspot, Mighty AI, Olis Robotics, Textio and Xnor -- have already made names for themselves.

In his book Our Mathematical Universe: My Quest for the Ultimate Nature of Reality, Max Tegmark writes that "time is not an illusion, but the flow of time is." In this month's issue of Nautilus, which looks at the concept of flow through various portals in science, we revisited our 2014 video interview with Tegmark (transcribed below for the first time), in which the professor of physics at the Massachusetts Institute of Technology explains why the feeling of time is one thing and the math quite another. That Tegmark, also the author of 2017's Life 3.0: Being Human in the Age of Artificial Intelligence, takes the keenest pleasure from peering into the world through the kaleidoscope of his physics toolbox is amply clear. During our interview, leaning out of his chair, waving his arms, pouring his water bottle onto the carpeted hotel floor to drive home a point, he was in a constant state of animation, much like the objects (both microscopic and gargantuan) that he studies. You say time doesn't flow, but our subjective perception is that it does. Where do we go wrong?

In Japan's time-scarce, results-oriented society, people no longer feel they can find a life partner through traditional dating methods, and are instead turning to internet matchmaking options to better their chances of meeting a compatible companion. Rather than visiting a dating agency, attending matchmaking parties or actually finding a partner the old-fashioned way through "a chance encounter," people are peering into their screens in hopes that artificial intelligence will help them find a match made in heaven. The companies are not focused on delivering a solely digital date, however, as some also host events where prospective partners can meet in person to see if the profile picture meets reality. Makoto Yamada, 30, who works in the western Tokyo suburb of Tachikawa, married Sayaka, 33, a university research fellow, in June last year after meeting through the Pairs online matching service run by Tokyo-based Eureka Inc. Both learned of the matchmaking service through social media ads and signed up without giving it a second thought.

How to Do It is Slate's sex advice column. Send your questions for Stoya and Rich to howtodoit@slate.com. I have been sexually active since I was 17. I am now 29 years old. A majority of the sex I had between 17 and 21 was only when I was drunk, so I don't remember most of it, but I know I didn't climax.

On a recent Tuesday evening, the experimental musician Julianna Barwick checked into Sister City, a new two-hundred-room boutique hotel on the Lower East Side of Manhattan. If you're having the sort of day that makes you want to minimize human interaction, Sister City is a merciful oasis: there are self-service registration kiosks in the lobby, and each floor features a supply closet containing the sorts of sundries that you'd usually have to request from the concierge. The lobby has sparse but careful décor--clean white walls, cherry-wood furniture, floor tiles in muted shades of green and gray--suggesting a Scandinavian sauna, or perhaps the careful serenity of a Japanese stationery store; the vibe is "Serenity Now!" filtered through Instagram. Barwick, who has long, dark hair and inquisitive eyes, is using the sky immediately above the hotel as a source for a new composition. A camera mounted to the roof of the building sends information about the goings-on in the airspace above the hotel (rain, clouds, pigeons, airplanes, wind, sun, moonlight, drones, helicopters, constellations, what have you) to Pereira's program, which uses Microsoft's artificial intelligence to cue sounds written and recorded by Barwick.

Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systems cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we introduce the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals. Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures. At the end of each round, competitors will submit containerized versions of their learning algorithms and they will then be trained/evaluated from scratch on a hold-out dataset-environment pair for a total of 4-days on a prespecified hardware platform.

It's common knowledge that Barack Obama met the woman who eventually became his wife, Michelle Robinson, when he came to work at her law firm as a summer associate. George W. Bush met the future Mrs. Bush, who was Laura Welch back then, at a barbecue and took her mini-golfing the next day. And we all remember that Bill and Hillary Clinton were law school sweethearts. The historical record is full of these president-and-first-lady origin stories: Harry Truman was just 6 when he met the woman he would go on to marry, in church. So it's only natural to ask how the current crop of presidential candidates' how-they-met stories stack up.

You've probably heard of the idea of a queer "scene," perhaps most often from people who don't care for it. But what, exactly, is this scene? Is there more than one? What happens when a scene evolves--or when it doesn't? These are the questions we've gathered a group of writers to consider for an Outward special issue on "The Scene" in LGBTQ life today.

Ask a layman about artificial intelligence and they might point to sci-fi villains such as HAL from 2001: A Space Odyssey or the Terminator. But the co-founders of the AI Now Institute, Meredith Whittaker and Kate Crawford, want to change the conversation. Instead of talking about far-flung super-intelligent AI, they argued on the latest episode of Recode Decode, we should be talking about the ways AI is affecting people right now, in everything from education to policing to hiring. Rather than killer robots, you should be concerned about what happens to your résumé when it hits a program like the one Amazon tried to build. "They took two years to design, essentially, an AI automatic résumé scanner," Crawford said. "And they found that it was so biased against any female applicant that if you even had the word'woman' on your résumé that it went to the bottom of the pile." That's a classic example of what Crawford calls "dirty data." Even though people think of algorithms as being ...

Gross spent 43 years on the faculty of Princeton's psychology department where the university credited him with revolutionizing understanding of sensory processing and pattern recognition. Princeton called his work "foundational to the field of cognitive neuroscience" when he retired in 2013.