Comcast Corp. CEO Brian Roberts said Friday that he believes one of the biggest business trends will be "artificial intelligence," in which computers do tasks once done by people, leading to smart cities and smart cars. He didn't say how he thought artificial intelligence could transform Comcast, but he noted that "there's always a dark side to that kind of change." In a question-and-answer format, Roberts spoke conversationally to about 1,200 executives, lawyers, city and state officials, and others at the annual Greater Philadelphia Chamber of Commerce breakfast . Before Roberts' remarks, Drexel University president John Fry officially took over as the chamber's board chairman, replacing Exelon Corp.'s Dennis O'Brien. Fry said he believed that Philadelphia could be one of the world's 25 top-tier cities, but that civic leaders couldn't be complacent because "as we sit down here for breakfast, [competing cities] are preparing to eat our lunch."

Computers that learn for themselves are with us now. As they become more common in'high-stakes' applications like robotic surgery, terrorism detection and driverless cars, researchers ask what can be done to make sure we can trust them. There would always be a first death in a driverless car and it happened in May 2016. Joshua Brown had engaged the autopilot system in his Tesla when a tractor-trailor drove across the road in front of him. It seems that neither he nor the sensors in the autopilot noticed the white-sided truck against a brightly lit sky, with tragic results.

The classic guide for entrepreneurs preparing a pitch is Sequoia's Business Plan Template. This post aims to be a mere addendum to that in the age of machine learning. Why do investors spend so much time focusing on'differentiation'? Because the job of an investor is to allocate money to its best use. Investors shouldn't allocate money to a company unless it is crystal clear that the company is the best one to solve a particularly valuable problem.

JOINING "Hamilton", a Broadway show, and concerts by Adele, a British soul diva, on the list of tickets-to-kill-for in New York is a screening in an ugly new office building that recently popped-up in the East Village, a place best known for offbeat culture. There is a ten-week-long queue to see simulations by Watson, IBM's cognitive artificial-intelligence platform. Initially known for stunts such as beating human contestants on "Jeopardy!", a quiz show, Watson has been seeking a wider audience. It has found a vast potential one in the world of financial regulation. Rules have become so sprawling and mysterious that even regulators have begun asking for a map.

Yahoo's recently open sourced neural network, open_nsfw, is a fine tuned Residual Network which scores images on a scale of to on its suitability for use in the workplace.

How will AI shape the average North American city by 2030? A panel of experts assembled as part of a century-long study into the impact of AI thinks its effects will be profound. The One Hundred Year Study on Artificial Intelligence is the brainchild of Eric Horvitz, a computer scientist, former president of the Association for the Advancement of Artificial Intelligence, and managing director of Microsoft Research's main Redmond lab. Every five years a panel of experts will assess the current state of AI and its future directions. The first panel, comprised of experts in AI, law, political science, policy, and economics, was launched last fall and decided to frame their report around the impact AI will have on the average American city.

We @ProjectJunoAI are big fans of landscapes. That's why we've created a machine intelligence landscape focused entirely on Europe [1]. Europe deserves a landscape of its own to highlight its talent and expertise. Until recently, its contribution to the innovation and commercialisation of machine intelligence technologies has been under-appreciated. We now see growing self-confidence borne of the success, and continued presence, of local acquired startups like VocalIQ, Swiftkey, Deepmind, Magic Pony Technology, and PredictionIO.

For example, one team of researchers has built a simple reservoir computer out of a bucket of water. They demonstrated that, after stimulating the water with mechanical probes, they could train a camera watching the water's surface to read the distinctive ripple patterns that formed. They then worked out the calculation that linked the probe movements with the ripple pattern, and then used it to perform some simple logical operations. Fundamentally, the water itself was transforming the input from the probes into a useful output –- and that is the great insight.

Quora Questions are part of a partnership between Newsweek and Quora, through which we'll be posting relevant and interesting answers from Quora contributors throughout the week. The one way Amazon can win over Google in the long run given Google's AI superiority in this space is by partnering better with device manufacturers. To start off, I don't think the battle between Amazon Echo and Google Home is as important as the one between Amazon Alexa, which is their voice assistance service, and the Google (Voice) Assistant. Both Amazon Echo and Google Home are speakers with voice assistance built into them. The speakers are fairly standard, as speakers go. The real magic happens in the voice assistants that sit in the cloud and do the heavy lifting on behalf of these devices.

I can't offer much in terms of other entry level recommendations, but I can recommend you learn to utilize the resource pages on the coursera course. The way the andrew NG course is set up is that you more or less try to have an idea of how these algorithms work at a conceptual level through the videos, then when you go to programming assignments, you can skip a lot of the prep work and focus on implementing the machine learning algorithms. Now those algorithms might be a little hard to follow at first, which is okay and expected, and that's where the lecture notes and/or wiki come in. From the wiki you can more or less translate the math formulas into code syntax and the assignments are more or less complete. The weeks build off each other so as you learn how to do one part, they do a little less prep work for you so you have to learn how to do another part, and so forth.