For 20 years she's been studying what transport gurus call shared mobility, dissecting factors that make it successful, like shorter wait times. Though you might think choosing how to get to work is simple, Shaheen will tell you that especially in urban areas, there are countless factors in play, from the time of day you're on the move to whether you own a smartphone. And more than any of the tech executives giving speeches lately about how we all should rethink our relationship to our cars, she knows just how revolutionary that could be. Walking the halls of one of the nation's oldest departments dedicated to transportation research, at the University of California, Berkeley, the engineering professor wonders how our lives would have turned out if the Model T had been marketed as something to be shared, not owned home by home. Would our vehicles still be idle an estimated 95% of the time?

In late 2013, Clay Bavor began experimenting with teleportation. He paired an Oculus Rift headset to a robotic arm, upon which he mounted a couple of GoPro cameras. When he moved his head, the thinking went, the cameras would mimic the movement, acting as a second pair of eyes. If it worked, he'd be able to "teleport" himself (or his eyes, at least) a few feet away. He still has a video of the first time he ever got it running: There's Bavor, tall and thin in a t-shirt and jeans, standing among the contraptions with the Rift on his face. He reaches out his arm, waving his hand in front of the cameras at his side while simultaneously seeing it in front of his face. "Whoaaa," he says to himself. This is like the craziest thing I've ever experienced." The so-called Teleportation Robot was just one of Bavor's many side hustles at Google. Technically, his job was to lead the company's apps teams--the folks who make Gmail and Drive and Docs. But he'd been enthralled by VR ever since he ...

Alphabet Inc., the parent company of Google, announced a software program Wednesday called AlphaGo that successfully beat European Go champion Fan Hui on a full-sized board five times in a row. Developed by researchers at Alphabet's DeepMind company, AlphaGo is considered a major landmark in the development of artificial intelligence. The game of Go, played on a 19 by 19 grid, has long thwarted computer scientists due to the vast number of available moves. "The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves," says the abstract to a paper about AlphaGo published in Nature. "This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away." To account for as many permutations of move sets as possible, AlphaGo uses two artificial neural networks that have millions of connections loosely modeled after the human brain.

There's no "Like" button, but if there was, consider it pressed. I've been working on a few algorthims for cross item comparision, where if click me is blue and banner is X size and the different combinations I want to test, So basically creating test sets over individual items which proves more coherent with web design. So I am more testing which random style sheet / page design still randomly provided and measured seems to get the most time, clicks, navigation, etc and pull all of those factors in for a scorecard I know seems like a bunch of work but once you have the scripts it works for any page / site you build from then on and having it automated saves so much time down the road, and talk about great stats to provide to your clients.

Presentation by Delip Rao: "…The models you create have power to get people arrested or vindicated, get loans approved or rejected, determine what interest rate should be charged for such loans, who should be shown to you in your long list of pursuits on your Tinder, what news do you read, who gets called for a job phone screen or even a college admission… the list goes on.

Facebook wants to use artificial intelligence to make it easier for people to find photos and videos on the social network. Facebook isn't working on artificial intelligence just to let you text with robots. It's also using the technology to do something much simpler: help you quickly find the photos you're searching for or the videos you want to see on the social network. The company showed off several new ways to use "machine learning," a form of AI where computers can learn to work without first being programmed, to search through its massive trove of photos and videos. "We're doing research on cracking the image open, and understanding it on an individual pixel level," Joaquin Candela, head of Facebook's Applied Machine Learning group, said Wednesday at F8, the company's annual conference for software developers.

As I'm typing this sentence, a beautiful program is running on my mac in the background. My model is learning to do something we never imagined a computer could do. It's not doing well enough yet, but it's getting better every day. Welcome to the 3rd part of this article! In case you haven't seen part I or part II yet, In this final piece, I'm going to walk you through a real example.

Artificial intelligence, machine learning, discovery algorithms… At this stage of human development we find ourselves gazing at a fantastic horizon, one where we have the opportunity to design intelligent entities -- robot brains. Will we design them in the image of our own neural networks? In today's research landscape, scientists are giving robots resilience and adaptability, deep learning and long-term modular memory. They're asking AI to perform complex tasks not so much with efficiency in mind as novelty. And guess what: it may change how we think about not only evolution, but the way we ourselves, as a network of individuals and organizations, approach innovation. Will we design for efficiency, our minds on reaching a fixed set of desired objectives -- or will we design for resilience, adaptability… emergence? We spoke with Jeff Clune of the Evolving AI Lab about how some of the emergent effects of machine learning are taking AI in a direction we didn't expect… and what it could potentially teach us about ourselves. Jeff's work has not gone unnoticed. He's been cited in Wired, the Atlantic, Nature, and Scientific American, to name a few.

Turning data into insight is one of the top business challenges today, and it becomes especially tricky when the data in question is unstructured. Artificial intelligence has a mixed track record there, but a young startup aims to get better results by bringing humans back into the picture. Spare5 on Wednesday released a new platform that applies a combination of human insight and machine learning to help companies make sense of unstructured data, including images, video, social media content, and text messages. The result, it says, are "game-changing insights delivered cost-effectively and at scale." The company's technology is now being used by companies including Expedia and Getty Images to enrich, clean and label unstructured data.

The battle for the future of computing is a battle to bring artificial intelligence to the mainstream – and Google is quietly overhauling a machine learning tool used to improve some of its most popular services including Google Translate and Google Photos. TensorFlow can be used to help teach computers how to process data in ways similar to how the human brain handles information. It is also open source, meaning Google has published and shared the code online so that external developers can use and improve it. The latest version, released by Google on Wednesday, adds a feature many TensorFlow users have asked for since the tool made its public debut in late 2015: the ability to operate on multiple devices. Instead of being limited by the processing capabilities of a single computer, it can use distributed networks to handle more complicated tasks – as if TensorFlow will now be able to use many brain cells instead of being confined to just one.