MIT researchers have developed a computer system that independently adds realistic sounds to silent videos. Although the technology is nascent, it's a step toward automating sound effects for movies. In a series of videos of drumsticks striking things -- including sidewalks, grass, and metal surfaces -- the computer learned to pair a fitting sound effect, such as the sound of a drumstick hitting a piece of wood or rustling leaves. The findings are an example of the power of deep learning, a type of artificial intelligence whose application is trendy in tech circles. With deep learning, a computer system learns to recognize patterns in huge piles of data and applies what it learns in useful ways.

Renewable energy like solar and wind power are changing the way we generate electricity. Our energy production is becoming cleaner and cheaper, and in many countries renewables are starting to overtake fossil fuels as the primary power source. But one of the biggest problems with renewables has yet to be solved: what happens if it's cloudy? More specifically, the problem is that renewable energy sources can never provide a constant source of power. No matter how many solar panels you build, they all provide zero power when the sun goes down.

There's even a subreddit dedicated to adding realistic color to old black and white images. It can be a time-consuming process and you need to be very familiar with Photoshop -- or similar software -- to do it convincingly. However, Algorithmia has implemented the Colorful Image Colorization algorithm created by Richard Zhang, Philip Isola and Alexei A. Efros. This algorithm automates the process of colorization by leveraging Algorithmia's cloud-based GPU network of hosted trained deep learning models. Deep learning is itself a fascinating and challenging field in computing. The idea is to create algorithms which can accurately model high-level abstractions that are typically exclusive to the human brain, such as recognizing and understanding how to accurately color a black and white image based on contextual understanding, for example.

With recent developments like the launch of Facebook's chatbot store, Apple's acquisition of Emotient, and the release of Viv, a virtual assistant from the founders of Siri, there's no doubt that artificial intelligence (AI) has started to quickly proliferate across consumer applications. Nonetheless, many of these new applications are still more novelty than necessity as their functionality is rudimentary at best, though we've come to rely on them daily – from Amazon product recommendations to Facebook facial recognition (auto tagging). Until consumer-facing AI can usher in new technological advancements that provide deeper and more human-like interaction, it still has a long way to go before it reaches its tipping point. Enterprise AI, however, offers immediate applications that help solve problems that many companies and workers face today, such as data overload. Companies across a wide range of industries have already taken advantage of AI capabilities in order to help improve both internal and external processes.

Google has acquired Moodstocks, a Paris-based startup that specialises in smartphone image recognition as part of its continued efforts to boost its own artificial intelligence (AI) research, development and capabilities. Announced in a blog post on 6 July, Vincent Simonet, head of Google's research and development (R&D) centre in Paris, says the tech giant's latest purchase is proof of its commitment to the promising sector. "Many Google services use machine learning to make them simpler and more useful in everyday life such as Google Translate, Smart Reply Inbox, or the Google app," Simonet wrote in French. "We have made great strides in terms of visual recognition: Now you can search in Google Pictures such as'party' or'beach' and the application will offer you good pictures without you needing to categorise them manually. But there is still much to do in this area. And this is where Moodstocks comes in."

In 2014, the Pew Research Center asked a panel of 1,896 experts if artificial intelligence would destroy more jobs than it created within a decade. The answer, including responses from Google's chief economist and MIT computer scientists, was definitive: they have no idea (pdf). The group came down almost 50-50 on both sides of the argument. What's virtually certain is that AI will cause upheaval in the labor markets. And the best way to combat that may be more of the same.

WATSON 2016: It's a placard you're unlikely to see in anyone's front lawn in the final months leading up to Election Day. You won't see the supercomputer on televised debates or speaking at national conventions. Watson never even leaves its room. None of this has stopped Watson's presidential campaign manager, Aaron Siegel, from stumping on behalf of IBM's hyper-intelligent, Jeopardy-winning supercomputer. An artist and design lecturer at the University of Southern California by day, Siegel began his campaign to elect an artificial intelligence to the highest office in the land last March, right around the time Republicans began announcing their candidacies en masse. Siegel was appalled by the general media spectacle surrounding the presidential race.

About 12% of women in the US will develop invasive breast cancer over the course of their lifetime, and there are expected to be close to 250,000 new cases of breast cancer in 2016 alone. With a breast cancer diagnosis comes fear, anxiety, stress, added expenses and--sometimes--a missed diagnosis. Approximately 5% to 17% of breast cancers are missed by a radiologist. Mammograms can also result in a false positive or an overdiagnosis, which leads to additional and unnecessary cancer treatment for the patient. According to a study by Health Affairs, between 22% and 31% of all diagnosed breast cancers are overdiagnosed, resulting in an additional 4 billion in health-care spending annually.

Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up.

How an ecommerce company used predictive analytics to improve customer retention. Acquiring new customers is much more expensive than retaining current ones. Keeping customers from unsubscribing from a company's services or from choosing another company's solution is therefore a challenge that should be at the top of every corporate agenda. A way to address this challenge is through predictive customer churn prevention, in which data is used to find out which customers are likely to churn in order to win them back -- before they are gone. Showroomprivé.com, an ecommerce company founded in 2006, sought ways to employ machine learning approaches to retain more customers.