From smartphones to self-driving cars, Nvidia's Tegra chips have been used in a wide variety of devices and products. Now, the next-generation Tegra is just around the corner. Details about a chip called "Tegra-next" will be shared next month at the Hot Chips conference in Cupertino. Tegra-next will succeed the Tegra X1 chip, which was announced in early 2015. An Nvidia spokesman confirmed the talk will be about the next-generation Tegra chip, but didn't share further details.

PokéDates, a new service from the dating firm Project Fixup, is here to help. Pokémon Go, the mobile game that's taken the country by storm, has inspired its own dedicated dating service. Those who want to sign up to find a partner in gaming and in life can navigate their way to gopokedates.com, Amid all the reports of injuries, robberies and people running into police cars, there also have been anecdotes of love found among the pocket monsters. PokéDates users also will have to include extra Pokémon Go-related information, such as when you'll be available to go searching for monsters and love.

In a galaxy known as New York, in a drab age before Trekkies and light sabers, there lived a curious boy who liked comic books, time travel and Elvis. He read Isaac Asimov and tuned in to John Zacherle, this Phantom of the Opera-type guy with scraggly hair and a creepy laugh who introduced horror movies on Channel 9, which, if you were a kid at the time, was something close to splendid. This boy, let's call him Jeffrey Walker, who incidentally would later watch "2001: A Space Odyssey" 29 times, tended toward the imaginative. His mother was a beauty queen, his father a clothier. Walker grew up to be many things, including an actor who played poker with Jesus Christ, shot a nude scene with Al Pacino and was hugged by Natalie Wood.

The Smart Recruits is a data science & machine learning competition. This competition will test your programming, analytics and business understanding skills. Machine Learning is being extensively used these days in building predictive models. Availability of wide range of algorithms have empowered data scientists to discover innovative solutions to a problem. This time the problem set gets tougher and better.

Kathryn Hume bio, Director of Sales and Marketing, and Nick Vermeer (bio), Research Engineer, at Fast Forward Labs (helping organizations accelerate their data science and machine intelligence capabilities), join hosts Pavan Bahl, Rob Sanchez and Marc Raco. Hume and Vermeer discuss the genesis of Fast Forward Labs, how innovation changed what the Labs mean from then to now, natural language generation, learning how much distance there is to go with Artificial Intelligence and language, machine learning vs. AI, difficulties with Amy on scheduling and other platforms, a bot to mimic Alexis Ohanian, how much sample size matters, and image recognition. A conversation on use cases in fashion businesses, CRMs, collaborative filtering, a focus on brick and mortar, using satellite data for cars in parking lots and credit card activity, RFID and privacy laws, and the surprising best way to block an RFID signal. Attention directing research, the next frontier vs. solving today's problems, how Fast Forward labs is like an outsourced R & D department and mentor to grow internally, purple unicorns, being "nerd best friends", and a mechanism for applied research. Off the Grid Questions cover a death-inspired poem, jeweler screwdrivers vs. a computer, Long Island corn, crop rotation bar talk, reactive illuminated dance costuming, thermochromic paints, and fast jet skiing.

In the last few years, you cannot have a discussion around technology without those terms entering the conversation. They have been major technology disruptors impacting all aspects of the business. Change seems to occur at breakneck speeds and shows no sign of slowing. Today, it appears the one constant in technology is change. Constant change requires constant innovation which thereby introduces more new technologies.

Stories about racist Twitter accounts and crashing self-driving cars can make us think that artificial intelligence (AI) is a work in progress. But while these headline-grabbing mistakes reveal the frontiers of AI, versions of this technology are already invisibly embedded in many systems that we use everyday. These everyday uses include everything from fraud detection systems that monitor credit card transactions to email filters that learn not to swamp your inbox with spam. You've probably already interacted with an AI system today without even knowing it and probably enjoyed the experience. One increasingly common form of AI can be found in chatbots, a type of software that lets you interact with it by having a conversation.

When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. A computer algorithm issues orders to buy a stock and floods the market with hundreds or thousands of apparently separate orders to buy the same stock. Other algorithms take note of this sudden demand and start raising their buy and sell offers, confident that the market is demanding a higher price. The first algorithm registers this response and sells its shares of stock for the newly higher price, making a tidy profit.

When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. A computer algorithm issues orders to buy a stock and floods the market with hundreds or thousands of apparently separate orders to buy the same stock. Other algorithms take note of this sudden demand and start raising their buy and sell offers, confident that the market is demanding a higher price. The first algorithm registers this response and sells its shares of stock for the newly higher price, making a tidy profit.

Some of society's brightest minds have warned that artificial intelligence (AI) may lead to dangerous unintended consequences, yet leaders of the U.S. intelligence community--with its vast budgets and profound capabilities--have yet to decide who within these organizations is responsible for the ethics of their AI creations. When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. If scenario-based exercises were conducted, the intelligence community provides no guidelines for deciding when a risk is too great and a system should not be built and assigns no authority to make such decisions. Intelligence agencies use advanced algorithms to interpret the meaning of intercepted communications, identify persons of interest and anticipate major events within troves of data too large for humans to analyze. If artificial intelligence is the ability of computers to create intelligence that humans alone could not have achieved, then the U.S. intelligence community invests in machines with such capabilities.