To process an image, for example, the lowest layer is fed the raw images. It notes things like the brightness and colours of individual pixels, and how those properties are distributed across the image. The next layer combines these observations into more abstract categories, identifying edges, shadows and the like. The layer after that will analyse those edges and shadows in turn, looking for combinations that signify features such as eyes, lips and ears. And these can then be combined into a representation of a face--and indeed not just any face, but even a new image of a particular face that the network has seen before.

Autonomous vehicles have infiltrated much of the military, from airborne surveillance to all manner of ground-based operations. But the Navy remains a mostly human-controlled operation--with the demand for robotic tech focused on conflicts in Iraq and Afghanistan, it simply hasn't trickled down to aquatic operations yet. But the Office of Naval Research thinks autonomous boats can have a major impact on the military's ocean-going efficiency and effectiveness. In a demonstration conducted this fall in the lower Chesapeake Bay, a fleet of small, human-free boats collectively patrolled a harbor, detected intruders, and even chased them away from the area they were protecting. The Navy first demonstrated the swarm in 2014, when the vessels were tasked with protecting a single ship.

In the past, soldiers went off to war and left their families behind. But drone pilots commute to work - and to war - each day. Vin Ray was given rare access to the only US Air Force base devoted entirely to flying drones, where he discovered the pilots' strange double life. If you're a drone pilot, there's a strong possibility you live in Las Vegas. And your commute to work is against the traffic.

Chatbots are automated text conversation computer programs that use artificial intelligence (AI) and natural language processing to talk to customers/users. Want to disappear from a fleeting relationship? Ghostbot is a burner text messaging bot that will ghost you away. Need something to do to pass the time? Send a text to the Casper mattress company's chatbot and talk about anything and everything.

CHENNAI: Google DeepMind's AlphaGo, an artificial intelligence programme developed using deep neural networks and machine learning techniques, hit global headlines last year when it beat South Korean Go grandmaster Lee Sedol to win the series 4-1. However, not many know that AlphaGo has consumed a whopping 30,000 watts of power to complete the task, while the human brain consumes around 20 watts! What gives the human brain such efficiency has so far proven elusive to replicate in computers. Not surprisingly, man's most defining organ is also the least understood. Although an adult human brain weighing 1.4 kg is made up of close to 100 billion neurons, scientists do not know how many different kinds of human neurons exist.

Dematerialization is going to continue. We'll see hardware that is thinner and lighter. New materials are coming on the market, like stretchable electronics. Imagine what that could do for wearable technologies. Everything is going to be computing in the future, whether it's your shoes, clothes, or the temporary tattoo that you wear to monitor your health.

This is Essential Politics, our daily look at California political and government news. Rosey Grier, a legendary Los Angeles Rams player, says he's thinking about running for governor of California . Uber's effort to use self-driving cars on San Francisco streets without a permit inspires one legislator to take action . A former Los Angeles planning commissioner becomes the twelfth person to enter the race to replace Rep. Xavier Becerra. Rosey Grier, a legendary Los Angeles Rams player, says he's thinking about running for governor of California .

New York state's top cops want to use machine-learning algorithms to detect drug dealers on social media networks like Instagram, a trend that "has become a severe problem in recent years," according to researchers from the University of Rochester and the New York Attorney General's office. Using social media to sell drugs began years ago and continues to this day. Newer networks like Tinder have become especially popular with drug dealers because they offer both sellers and customers a deal in close proximity. All of the networks rely on manual user reports to remove the illegal content in what has largely been a losing battle. The New York Attorney General's office co-authored new research on algorithms meant to examine millions of Instagram posts, spotlight drug dealers, and only then pass the suspects on to human officers for further investigation.

Nancy Brinker is a cancer advocate, a global consultant and founder of Susan G. Komen. Her opinions expressed in this article are her own. Elad Gil, Ph.D. is the chairman and co-founder of Color Genomics. The complexity of seeking a cure for cancer has vexed researchers for decades. While they've made remarkable progress, they are still waging a battle uphill as cancer remains one of the leading causes of death worldwide.

Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions, which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can "denoise" the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise-aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points over a state-of-the-art LSTM baseline (and into second place in the competition). Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.