Companies today are awash in data, but current tools and processes are not enabling them to keep it secure. That's according to Informatica CEO Anil Chakravarthy, whose says his company -- which has traditionally focused on data management and integration -- is embarking on a major push to go further into data security. "You hear about breaches all the time -- just imagine all the ones you're not hearing about," Chakravarthy said in a recent interview. "Data security today is an unsolved problem for customers." Last year, Informatica launched a product called Secure@Source that promises a data-centric approach to information security by helping organizations identify and visualize sensitive data wherever it resides.

LONDON - FEBRUARY 03: (FILE PHOTO) In this photo illustration the facebook logo is reflected in the eye of a girl on February 3, 2008 in London, England. Social networking site'Facebook' reaches it's 5th birthday this month. It was founded in 2004 by Mark Zuckerberg from his dorm room at Harvard University with the aim to help students keep in touch over the internet. Within 24 hours 1,200 Harvard students had signed up. The site now has 150 million active users worldwide. Facebook is opening up its image-recognition artificial intelligence research to the public.

But a machine sees none of this; to a machine, it's just a bunch of pixels. It's up to computer-vision technology like the one developed at FAIR to segment each object out. Considering that real-world objects come in so many shapes and sizes as well as the fact that photos are subject to varying backgrounds and lighting conditions, it's easy to see why visual recognition is so complex. The answer, Dollar writes, lies in deep convolutional neural networks that are "trained rather than designed." The networks essentially learn from millions of annotated examples over time to identify the objects. "The first stage would be to look at different parts of the image that could be interesting," he says. "The second step is to then say, 'OK, that's a sheep,' or'that's a dog.' "Our whole goal is to get at all the pixels, to get at all the information in the image," he says.

There's little that comes off as "natural" in "Natural Selection," a stiffly heavy-handed, drawn-out, faith-based drama about a Christ-like teen struggling to find his true path. Arriving in a new town, soft-spoken Tyler (Mason Dye) and his alcoholic mother ("Bluebloods" regular Amy Carlson) have chosen the proverbial fresh start, but there are telling signs that it won't be a smooth transition. Tyler is immediately targeted by the bullies at his new high school, which, curiously, doesn't appear to have a single character of color among the student body, while his mom is finding it difficult to extricate herself from the throes of depression in the wake of her husband's suicide. He's taken under the manipulative wing of the troubled Indrid (Ryan Munzert), an outsider with a dark soul (how else to explain his non-creationist views?) who chastises Tyler for having too much faith in people. You know from the repeated close-ups of all those guns mounted in the display cabinet at Indrid's home that his profound disenchantment with the universe isn't going to end well, but first-time writer-director Chad Scheifele forcefully prolongs the inevitable.

This article originally appeared in the French publication Viuz from an interview held with MediaMath CMO Joanna O'Connell at Cannes Lions. It has been translated into English below. What does MediaMath do and how do you differentiate from your competitors? MediaMath was founded in 2007 and, since the beginning, our goal has been to revolutionise traditional marketing in a mobile and data-driven world. We provide marketers with the best tools to take advantage of this new world.

Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.

Big data engineers often face a conceptual task – how to extract knowledge from the information stored. For a long period of time our company has been developing natural language processing (NLP) methods of analyzing and mining data (http://www.intellexer.com/). In our practice, we faced storages with millions of manually created documents/reports and the lack of possibilities for systemizing the information obtained at different times, in different places and without a uniform standard for presenting data. There is no single and easy answer. Unfortunately, it's impossible to offer a universally applicable method that would satisfy all the customer's needs.

Intel, long the most dominant chip vendor for PCs, is making a high-profile transition away from them and toward such growth markets as the internet of things (IoT), drones, the cloud, artificial intelligence (AI) and machine learning, virtual and augmented reality, network connectivity and the data center. Essentially, the company wants the billions of connected devices that will make up the IoT to run on Intel technology, and for its products to drive the systems that connect them to the cloud. "We are transforming into the company that is powering the cloud, connecting smart devices and making new experiences possible based on all that today," Intel CEO Brian Krzanich told the more than 6,000 people who traveled to San Francisco last week for the annual Intel Developer Forum (IDF). In the past, the show had been packed with new PCs and servers powered by the company's latest and greater processors. However, this year's IDF had a decidedly different vibe to it, with fewer PCs on display and more drones, cars and other connected devices on the floor.

AI has been making a lot of progress lately by almost any standard. It has quietly become part of our world, powering markets, websites, factories, business processes and soon our houses, our cars and everything around us. But the biggest recent successes have also come with surprising failures. Tesla impressed the world by launching a self driving car, but then crashed in cases a human would have easily handled. AlphaGo beat the human champion Go player years before most experts thought possible, but completely collapsed after its opponent played an unusual move.

This is the year of the chatbot. With the rise of artificial intelligence (AI), bots are responding to queries, helping users find products, and even troubleshooting customer service issues. Artificial intelligence dealmaking has exploded recently, leaping to a new quarterly record of over 140 deals in Q1â 16. This year especially, artificial intelligence (AI) has had a renaissance â " Tesla pushed their self-driving autopilot out to all eligible cars, and Google and Facebook have both announced large investments in AI research. The race is on to get driverless trucks on the roads, and experts say the impact on professional drivers â is going to be hugeâ .. read more.