Last month artificial intelligence technology reached a widely heralded milestone when a Google computer program called AlphaGo defeated the Go master Lee Se-dol at the ancient board game in a five-game series. Impressive, but can AliphaGo do this? On April 8, a computer program developed by Alibaba Group will attempt to predict the winner of a popular Chinese reality TV show by analyzing not potential moves on a glorified checkerboard but a range of complex and amorphous factors such as social media feedback, responses from a studio audience, and the "energy" of performers. The reality show in question is "I'm a Singer," an annual singing competition that pits well-known Asian pop stars--this week's season-ending finale features CoCo Lee, Hacken Lee, Jeff Chang and Joey Yung--against each other. Alibaba's cloud-computing arm, Alibaba Cloud, is using the competition to showcase a program it developed in-house called Apsara-I (Ai) that is able to gather insights from a multitude of inputs, can learn by analyzing data and even has the potential to understand human emotions, according to the company.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

Pick a photograph from the Web at random. Now try to work out where it was taken using only the image itself. If the image shows a famous building or landmark, such as the Eiffel Tower or Niagara Falls, the task is straightforward. But the job becomes significantly harder when the image lacks specific location cues or is taken indoors or shows a pet or food or some other detail. Nevertheless, humans are surprisingly good at this task.

Researchers at Stanford University want to develop a more agile drone--one that's able to avoid crashes and gracefully dodge moving hurdles. In order to do so, they developed an obstacle avoidance system that uses a drone-mounted camera to feed information to a computer, which processes information in real time. And to teach their drone agility and gauge its reaction time, they trained it against a fencer. Popular Science explains that the drone, created by researchers Ross Allen and Marco Pavone of Stanford University's Department of Aeronautics and Astronautics, uses machine learning to improve its abilities. Researchers began fencing with the drone to measure its agility and fine-tune its obstacle-avoidance system.

Ever wondered how your bank knew to call you when a thief attempted to buy a flat-screen TV on your Visa credit card? Or why your mobile banking app sent you an alert after a series of unusual transactions occurred on your Visa account at a gas station hundreds of miles from your home? Whether you insert, swipe, touch, click, or wave to make a payment, our predictive analytics, known as Visa Advanced Authorization, are monitoring in real-time for suspicious activity. Since it was launched just over a decade ago, Visa Advanced Authorization has grown increasingly effective at spotting the tiny percentage of suspicious transactions from the roughly 150 million payments that flow through the Visa network each day. In fact, Visa's system-wide fraud rate has declined by two-thirds over the last two decades--to less than 6 cents out of every 100 transacted--even as transaction volume has increased by more than 1,000 percent.

The SETI Institute has partnered with NASA HQ, NVIDIA and the Asteroid Grand Challenge for a summer research project aimed at pairing young planetary scientists with early-career machine learning software developers to "hack" various datasets pertaining to the tracking and cataloging of Near Earth Objects. The idea is to see what can be gleaned from such datasets by applying some of the latest developments in machine learning to analyze the data in new and innovative ways. SETI is looking for more applicants to the project from the planetary science community, where the target is PhD candidates or postdocs. The project will run for 6 weeks this summer, and will be hosted at the SETI Institute. Participants will be housed at dormitories at NASA Ames.

Without a doubt, Bill Cunningham has an incredible ability for discerning clothing. One may wonder how he got that way. On top of being quite gifted, someone like Bill must have also taken notice of a lot of outfits throughout his 60-year career as a photographer. Assuming Bill works every day of the year (which isn't a bad approximation) and shoots 10 outfits an hour for 8 hours a day, that number is well over a million. Here's the motivating question: If we presented the same number of clothing images to an artificial neural network, can it learn to see the world of fashion like Bill Cunningham does? Said in a less sensationalized way, what we're proposing is training a neural network to recognize clothing from images and find us visually similar ones.

To create an inclusive community that successfully tackles issues like discrimination, we need open lines of communication between faculty and students. When that communication breaks down, a misunderstanding can turn ugly and prevent real progress. We saw an example of this recently when Professor Satyen Kale assigned his machine learning class a project: to train classifiers on the New York Police Department's stop-and-frisk dataset. Stop-and-frisk was a controversial NYPD interrogation program that disproportionately targeted young black and Hispanic men and was ruled unconstitutional and discriminatory by a federal court in 2013. The assignment, satirically titled "Help design RoboCop!" was an exercise using records of searches conducted by the NYPD.

In just a few years, it will provide customer service in airports, hospitals, train stations and other facilities, speaking four languages so that it can even serve the masses of foreign tourists streaming into Japan. It is Hitachi's Emiew3 -- a smaller, faster and more agile competitor unveiled Friday. The new robot marks the third generation, and first commercially viable member, of a series that began with an experimental model in 2005. The company seeks to put it on the market in 2018. In a demonstration Friday, an Emiew3 prototype surveyed its surroundings and approached an actress playing a lost foreigner.

In 2003, the wife of a 55-year-old Vietnamese carpenter named Le Van died. Heartbroken, he dug up her grave, cast her body in clay and slept next to "her" for five years. The story is unsettling, but there's also something universal about his struggle to let go. Many grieving people feel an emotional connection to things that represent dead loved ones, such as headstones, urns and shrines, according to grief counselors. In the future, people may take that phenomenon to stunning new heights: Artificial intelligence experts predict that humans will replace dead relatives with synthetic robot clones, complete with a digital copy of that person's brain. "It's like when people stuff a pet cat or dog.