Fox News senior White House correspondent Peter Doocy reports on President Donald Trump's response to a viral video that allegedly showed a plastic bag being thrown out of a White House residence window on'The Story.' President Donald Trump has dismissed a bizarre viral video showing mystery objects being hurled from a White House window as fake. The footage appeared to show someone repeatedly throwing objects from the top floor of the White House onto the lawn below. At a packed press conference, the president told how the mansion's windows were sealed and bulletproof and suggested the clip was made by AI. The short video began circulating widely on social media over the weekend and racked up thousands of views while fueling speculation online. People debated whether the clip showed a staffer, while others floated conspiracy theories about hidden activities inside the Washington, D.C., mansion with items lobbed from what many speculated was the Lincoln Bedroom. At first, a White House official claimed the footage involved a contractor carrying out routine maintenance while Trump was away.

Tesla CEO Elon Musk stands in front of the damaged Cybertruck after it fails a demonstration of its durability.Ringo H.W. Chiu / AP At a live delivery event this November, where Elon Musk awkwardly opened the door for about a dozen new Cybertruck owners, he told the world: "The apocalypse can come along any moment, and here at Tesla, we have the best in apocalypse technology." Then he showed a video of the vehicle being pummeled by a machine gun, quipping, "If you're ever in an argument with another car, you will win." And then he sold a bunch of Cybertrucks. Two million have been preordered--and 500 delivered--for over 60,000 a pop. Some soon proved that they couldn't survive a test drive, let alone a ride with Mad Max.

If you use a tool where it hasn't been verified safe, any mess you make is your fault… AI is a tool like any other, so the same rule applies. Instead, force machine learning and AI systems to earn your trust. If you want to teach with examples, the examples have to be good. If you want to trust your student's ability, the test has to be good. Always keep in mind that you don't know anything about the safety of your system outside the conditions you checked it in, so check it carefully!

If you use a tool where it hasn't been verified safe, any mess you make is your fault… AI is a tool like any other, so the same rule applies. Instead, force machine learning and AI systems to earn your trust. If you want to teach with examples, the examples have to be good. If you want to trust your student's ability, the test has to be good. Always keep in mind that you don't know anything about the safety of your system outside the conditions you checked it in, so check it carefully!

The Createch stage will offer an upbeat vision of how converging creativity and technology can improve how we connect, create, and consume.Discover the pioneers re-imagining TV, theatre, fashion, music, and community, and inventing new possibilities for audiences, creatives, and investors.Between sessions, short films will showcase innovative creativity to keep you stimulated and entertained. CogX is hosted by Charlie Muirhead Co-Founder and CEO, and Co-Founder Tabitha Goldstaub. Find out more at: https://cogx.co/ CogX is an award-winning Festival with its roots in artificial intelligence. The fourth edition, June 8th to 10th 2020, adds a Virtual first experience and Global Leadership Summit, and builds on the huge success of the 2019 event, which brought together over 20,000 visitors.

In part one of this series we covered some of the history and theoretical basics of Artificial Neural Networks (ANNs). Now it's time to look at what changed to lead us to where we are today. In the pre-cloud era, time, cost and computational constraints meant that large scale research was prohibitively difficult. It was also unclear exactly how to scale ANNs out to hundreds of layers and thousands of neurons. And even if it was possible, could you get any results worth using? So given how difficult it was, it's worth asking what the motivation was in the first place.

In this article and the next, we'll discuss some of the history and basics of Deep Learning to serve as a kind of primer for the coverage of the International Conference on Machine Learning recently held in Sydney. Much of the discussion centres around recent advances in Deep Learning and the purpose here is to give some background, basic terminology and provide some context. Deep Learning is part of Machine Learning which is itself a sub-field of the field of Artificial Intelligence. It's generally accepted that there are three broad categories or types of Machine Learning: Supervised, Unsupervised and Reinforcement (these are fuzzy boundaries, other categories exist and definitions vary but we'll stick with these for now). In Unsupervised Learning we don't make any assumptions about the data we have, the processes that generated it nor any underlying structure/constraints.

Every gamer has a tale of the worst game they ever played. It may have been the utterly catastrophic sequel to a much-loved classic, a rushed tie-in with a favourite movie, or an experimental new release from a favourite studio. But we've all had that moment of excitedly unwrapping the box, shoving the disc (or cartridge) into the machine and then ... then comes the horrific realisation that you have wasted £45 on the interactive equivalent of a late-career M. Night Shyamalan movie. To create this list, four veteran game writers gathered together to relive our own experiences of this gut-wrenching phenomenon. As hundreds of games are released every year, there are certainly thousands of unmitigated digital disasters that very few people have had to suffer because they sank faster than a pair of solid-iron water wings.