It took millennia for Humanity to advance from the Stone Age to the Industrial Revolution. The pace of Human development continues to rapidly accelerate. The next Revolution is already overtaking society before many are ready for- or even aware of it. Both diametrically opposed predictions curiously are based on the premise that Humans will blithely give up control to either benevolent or diabolical machine'overlords'. There are arguments for both extremes, and both sides raise enough valid questions provoke meaningful dialogue on AI ethics and control. While History reminds us that extreme people and ideas often provide valid counterweight to over-enthusiasm and even zealotry from opposing people and views, they usually tend to be mere lane markers for History's true road.

Artificial intelligence is going to change everything in every industry. Not so fast, says Coursera president and co-founder Daphne Koller. Her education company markets itself as being ahead of technological disruption, but even it isn't so sure AI can do everything that a human college professor can do. "Instructors are valuable, first of all, in creating the content. That's really important," Koller said in an interview with Recode's Kara Swisher on the latest episode of Recode Decode.

Who do we become when we die? Our identities are such fragile, personally curated things: we go through life as children, then students and peers, and sometimes switch to a vocational preference. A person becomes farmer, shepherd, mechanic, soldier, baker, homemaker, brigand, or bandit, or backyard bomb-maker -- whichever strikes us by calling or necessity. People assume narrower identities, a lover for an afternoon, a wedding guest for a weekend. Perhaps it's the wrong wedding, the wrong place, the wrong people, and whatever mish-mash of identities, they end with a hellfire strike and a grim, clinical finality. The bodies become "military-age males," the rich matrixes of interwoven identities collapsed into two categories, a fatal guilt decided abroad in the moment of impact.

Additionally, the fraud detection and prevention programs that keep your bank account safe are also utilizing predictive analytics. The most sophisticated of these programs analyze your spending habits and compare each purchase against them. The purchases themselves also have fraudulence probability scores (a 1,000 online purchase paid to a company based in Timbuktu is more likely to be fraudulent than a 200 purchase at your local grocery store). If anything seems fishy, the bank sends you an alert to ensure your funds aren't compromised if fraud is, in fact, taking place.

Artificial intelligence may be the new face of medical diagnostics. For the first time, a flavor of A.I. called deep learning is being implemented in new ultrasound imaging equipment to aid in breast exams and help patients avoid unnecessary biopsies. A new feature in Samsung Medison's ultrasound system uses a deep-learning algorithm to make recommendations about whether a breast abnormality is benign or cancerous. The "S-Detect for Breast" feature is now included in an upgrade to the company's RS80A ultrasound system and is commercially available in parts of Europe, the Middle East and Korea and is pending FDA approval in the U.S., according to PR manager Doug Kim. Deep learning relies on large amounts of data to inform complex decision-making algorithms, has aided in everything from speech and image recognition software to pharmaceutical research.

After falling to the eventual NBA champs during the Eastern finals, the Toronto Raptors are hungry for a championship title. Thursday's draft will be crucial in crafting a winning lineup, and when it comes to deciding who makes the team, the Raptors will be able to consult their newest recruit: IBM's Watson. The partnership dates back to February, when the Raptors' parent company Maple Leaf Sports and Entertainment announced they were partnering with IBM, making the Raptors the first NBA team to use the Watson supercomputer to analyze players. "Watson doesn't answer questions of who the best trade pick would be--rather it compares them on different dimensions," explained Jon Lenchner, the scientist who led the IBM Sports Insights Central project. IBMers collaborate through interactive touch screens as part of IBM Sports Insights Central.

Samsung Medison's new ultrasound system quickly screens for abnormalities. Artificial intelligence may be the new face of medical diagnostics. For the first time, a flavor of A.I. called deep learning is being implemented in new ultrasound imaging equipment to aid in breast exams and help patients avoid unnecessary biopsies. A new feature in Samsung Medison's ultrasound system uses a deep-learning algorithm to make recommendations about whether a breast abnormality is benign or cancerous. The "S-Detect for Breast" feature is now included in an upgrade to the company's RS80A ultrasound system and is commercially available in parts of Europe, the Middle East and Korea and is pending FDA approval in the U.S., according to PR manager Doug Kim.

Computer vision systems typically learn how to recognize an object by analyzing images of thousands of examples. But scientists at Disney Research have shown that computers also can learn to recognize objects they have never seen before, based in part on studying vocabulary. People, after all, can get an idea of what things might look like based on reading a book. Similarly, a computer that already has been taught to recognize certain objects - apples, for instance - can analyze word use to get hints about the existence of fruits such as pears and peaches, and how they might differ from apples, said Leonid Sigal, senior research scientist at Disney Research. The knowledge that other fruits exist also is helpful in teaching the computer about important characteristics of apples themselves, he added.

NIIT, a global leader in skills and talent development, today launched a course in Web App Development with MEAN Stack under the DigiNxt Series. The company has recently ventured into Digital Transformation to offer pioneering programs to young aspirants wishing to enter the digital services industry, as well as to IT professionals wishing to reskill themselves for the new digital world. The cutting-edge program will use the student-centred pedagogy of project-based learning to help them carve a successful career in the emerging digital era. Some of the famous web applications like LinkedIn, Netflix, Uber, Paypal, etc. have been built using MEAN Stack. AngularJS, Node.js (MEAN) represents a group of open source technologies which are known to synergize well together, thereby empowering students to launch their own web and mobile apps.

By the end of this article, you'll understand how Numerai is using advances in cryptography like homomorphic encryption to allow for open participation in the problem of stock market efficiency. Over the last few years, machine learning algorithms solved big problems in computer vision. One such problem was getting an algorithm to learn how to recognize handwritten digits in the MNIST dataset. Everyone writes digits differently, so the problem was difficult for computers to grasp. When the dataset first became available in 1998, machine learning algorithms for computer vision were not very accurate.