Harry K. sits at his desk in Vancouver, Canada, scanning sepia-tinted swirls, loops and blobs on his computer screen. Every second or so, he jabs at his mouse and adds a fluorescent dot to the image. After a minute, a new image pops up in front of him. Harry is tagging images of cells removed from breast cancers. It's a painstaking job but not a difficult one, he says: "It's like playing Etch A Sketch or a video game where you color in certain dots." Harry found the gig on Crowdflower, a crowdworking platform. Usually that cell-tagging task would be the job of pathologists, who typically start their careers with annual salaries of around 200,000 -- an hourly wage of about 80. Harry, on the other hand, earns just four cents for annotating a batch of five images, which takes him between two to eight minutes. His hourly wage is about 60 cents. Granted, Harry can't perform most of the tasks in a pathologist's repertoire.

When you think of artificial intelligence, the first image that likely comes to mind is one of sentient robots that walk, talk and emote like humans. It's known as machine learning, and it revolves around enlisting computers in the task of sorting through the massive amounts of data that modern technology has allowed us to generate (a.k.a. One of the places machine learning is turning out to be the most beneficial is in the environmental sciences, which have generated huge amounts of information from monitoring Earth's various systems -- underground aquifers, the warming climate or animal migration, for example. A slew of projects have been popping up in this relatively new field, called computational sustainability, that combine data gathered about the environment with a computer's ability to discover trends and make predictions about the future of our planet. This is useful to scientists and policy-makers because it can help them develop plans for how to live and survive in our changing world.

With so many people taking their cues from the movies on what a future with artificial intelligence will look like, some who fear one day having robotic overlords will be heartened by research that Google is doing. Google DeepMind, a London-based artificial intelligence company that Google acquired in 2014, is working on what will be a kill switch for robots and other A.I. systems. The idea is that one day a smart machine might be able to override its own off button. If that's the case, then humans would need another way to gain the upper hand. "If an agent is operating in real-time under human supervision, now and then it may be necessary for a human operator to press the big red button to prevent the agent from continuing a harmful sequence of actions -- harmful either for the agent or for the environment," researchers wrote in a paper posted on the Machine Intelligence Research Institute website.

This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines such as physics, economics and social sciences, natural sciences, and engineering. It parallels the combination of theory and applications in Professor Strang's textbook Introduction to Linear Algebra. This course has been designed for independent study. It provides everything you will need to understand the concepts covered in the course.

Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. The aim of this class is to introduce the relevant models, skills, and tools, by combining mathematics with conceptual understanding and intuition.

In what could one day help find missing people in forests, a team of researchers used deep learning to train an autonomous drone to navigate a previously-unseen trail in a densely wooded forest completely on its own. The researchers from Dalle Molle Institute for Artificial Intelligence, the University of Zurich, and NCCR Robotics, mounted three GoPro cameras to a headset to train their deep neural network and took over 20,000 pictures from hours of trail hikes in the Swiss Alps. These images were then used alongside an NVIDIA GeForce GTX 580 GPU to teach the model what the boundaries of a hiking trail generally look like. Check out the autonomous drone in action in the video below. The researchers claim the resulting deep learning network is even better than humans at determining the correct direction of the trails on which it travels, guessing the correct direction of a trail with 85 percent accuracy.

Machine learning has become a central part of our life – as consumers, customers, and hopefully as researchers and practitioners! Whether we are applying predictive modeling techniques to our research or business problems, I believe we have one thing in common: We want to make "good" predictions! Fitting a model to our training data is one thing, but how do we know that it generalizes well to unseen data? How do we know that it doesn't simply memorize the data we fed it and fails to make good predictions on future samples, samples that it hasn't seen before? And how do we select a good model in the first place?

There is no need to worry about whether robots might start taking our jobs. Three of the world's 10 largest employers are already replacing tens of thousands of their workers with robots: Foxconn, a key manufacturing partner for Apple, Google, and Amazon, is the world's 10th largest employer and it has already replaced 60,000 workers with robots, according to a recent note written in part by analyst John Seagrimat CLSA. Walmart, the third-largest global employer with 2.1 million workers, wants to replace its warehouse stock-checkers with flying drones that can scan miles of shelves in a fraction of the time. And the US Department of Defense, the No.1 global employer, is already; flying the world's largest fleet of unmanned aerial vehicles - drones, basically - in its various Middle East conflicts. The US DoD has at least 7,362 RQ-11 Ravens in operation for instance.

You know when you just keep pressing the predictive text button on your mobile phone and the sentence just starts making less and less sense? Well, that type of functionality isn't just for absurdist poetry, you know; the team behind Sunspring used the same technology -- an LSTM Neural network, to be precise -- to write a screenplay. That is pretty funny in and of itself, of course, but then the team had an even better idea… What if they rounded up some actors (including Thomas Middleditch, who plays Pied Piper's Richard Hendricks in Silicon Valley) and turned it into a real movie? The team fed a ton of Sci-Fi movies into an AI… Then fed it some hallucinogenics and asked it to pen a screenplay. And it is absolutely, gloriously, intensely, bizarrely, completely fascinating.

The news that Christina Grimmie -- the 22-year-old singer who, as a New Jersey teen, made a name for herself on YouTube before broadening her fame in 2014 on Season 6 of "The Voice" – was shot and killed Friday while signing autographs for fans after a concert in Orlando, Fla., is tragic. But for fans of "The Voice" who watched Grimmie show off, during her time on the show, not only her impressive vocal chops and stage presence, but also her musical creativity, willingness to experiment and upbeat resilience, the loss must be heartbreaking. Those who watched Grimmie turn four chairs during her blind audition and then stick around to finish third on the show, behind only sweet, shy, country-singing runner-up Jake Worthington (of Team Blake Shelton) and silky-soulful winner Josh Kaufman (of Team Usher), knew she was an unusual talent. Grimmie's coach, Adam Levine, believed in her so fiercely that, at one point, he promised the audience she would end up winning the show. Then, when she didn't, he announced that he planned to sign her to his own label.