In a blog post published in collaboration with strategic consulting firm McKinsey & Company, the researchers said that they used a deep neural network to watch thousands of small slices of video--movies, TV, and short online features. For each slice, the neural network guessed which were the different elements that made a moment emotionally special, constructing an emotional arc. To test their accuracy, the team got human volunteers to watch the same clips, tagging their reaction and labeling which elements--from the music to the dialogue to the type of imagery shown on screen–had a stronger weight in their emotional response. This information helped them fine-tune the resulting model until it got really accurate at guessing what triggers human emotions. So what does a machine learning model see when it watches the opening act of Pixar's Up!?

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. There are gifts that are practical, gifts that are functional, and then there are gifts that are just… wowsers, that's beautiful. If you're looking to impress someone with a show-stopping present, this list of gadgets has been imbued with great design, and they're all really useful to boot. Good news: Beautiful does not always mean budget-breaking (though there are a few splurge-worthy items here too).

With federal regulators poised to repeal net neutrality rules this week, your internet service provider would be allowed to speed up delivery of some online content to your home or phone. Whether those fast lanes are coming, and what they ultimately deliver for Americans, is unclear. The concept, known as paid prioritization, involves a telecommunications company charging an additional fee to transport a video stream or other content at a higher speed through its network. The fee would most likely come from deals struck with websites such as Netflix willing to pay for a competitive advantage over an online rival. Or the fee could be charged to a company providing services that require reliably fast connections, such as self-driving vehicles or remote health monitoring of people with serious illnesses.

There's a revolution afoot, and you will know it by the stripes. Earlier this year, a group of Berkeley researchers released a pair of videos. In the second video, the horse is suddenly sporting a zebra's black-and-white pattern. The execution isn't flawless, but the stripes fit the horse so neatly that it throws the equine family tree into chaos. Turning a horse into a zebra is a nice stunt, but that's not all it is. It is also a sign of the growing power of machine learning algorithms to rewrite reality.

In classification tasks I can use the class score as a measure of the network confidence. If the network classifies an image as a cat with 0.99 score then the network has high confidence in its prediction compared to an image with a 0.60 score. Is there a similar mechanism to assess the confidence of a regression output? Intuitively, it seems that it should be doable. Say I'm building a model to predict the temperature from location and time of year.

Google is deepening its push into artificial intelligence (AI) by opening a research centre in China, even though its search services remain blocked in the country. Google said the facility would be the first its kind in Asia and would aim to employ local talent. Silicon Valley is focusing heavily on the future applications for AI. China has also indicated strong support for AI development and for catching up with the US. Research into artificial intelligence has the potential to improve a range of technologies, from self-driving cars and automated factories to translation products and facial recognition software.

Google is deepening its push into artificial intelligence (AI) by opening a research centre in China, even though its search services remain blocked in the country. Google said the facility will be the first its kind in Asia and will aim to employ local talent. Silicon Valley is focusing heavily on the future applications for AI. China has also indicated strong support for AI development and catching up with the US. Research into artificial intelligence has the potential to improve a range of technologies, from self-driving cars and automated factories to translation products and facial recognition software. In a blog post on the company's website, Google said the new research centre was an important part of its mission as an "AI first company".

To quote Eoin Treacy in'How to Profit from the Peaceful Rise of the Robots': "Don't think of robots as replacements for humans - think of them as things that will help make us better at tackling many of the problems we face." Never has this been more appropriate than now in the digital media industry. The three key problems that machines can fix are costs, monetisation and ad quality. As programmatic advertising evolves at a rapid rate, the need to deploy smart technology to continue to add value has increased. In August, the New York chapter of the IAB launched a working group to help the industry navigate the impact that artificial intelligence (AI) and machine learning (ML) will have on digital advertising.

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Flamingo's artificial intelligence (AI) technology has attracted domestic and international institutional investors and sophisticated investors to invest in the company.