What would you do if you had the super-power to accurately answer, in a few milliseconds, a multiple-choice question with a billion choices? Would you design the next generation of Web search engines, which could predict which of the billions of documents might be relevant to a given query? Would you build the next generation of retail recommender systems that have things delivered to your doorstep just as you need them? Or would you try and predict the next word about to be uttered by U.S. President Donald Trump? The objective in extreme classification, a new research area in machine learning, is to develop algorithms with such capabilities.

Samuel Greengard is an author and journalist based in West Linn, OR, USA.

In June, Amazon announced it was close to being able to offer for package deliveries by drone for its Prime Air service. That same month, Uber said it plans to test food delivery by aerial drone in crowded cities. And drone delivery company Flytrex already touts the ability to deliver drinks via unmanned vehicle on the golf course. Despite such announcements, drones are not crowding the skies over major cities and population centers just yet. But that may be about to change.

Artificial intelligence has the potential to radically change health care. Imagine a not too distant future when the focus shifts away from disease to how we stay healthy. At birth, everyone would get a thorough, multifaceted baseline profile, including screening for genetic and rare diseases. Then, over their lifetimes, cost-effective, minimally invasive clinical-grade devices could accurately monitor a range of biometrics such as heart rate, blood pressure, temperature and glucose levels, in addition to environmental factors such as exposure to pathogens and toxins, and behavioral factors like sleep and activity patterns. This biometric, genetic, environmental and behavioral information could be coupled with social data and used to create AI models.

Security researchers developed skills for both Google Home and Amazon Echo devices that could eavesdrop on people. Smart speakers already face privacy concerns, but now security researchers have found that malicious apps designed to eavesdrop can sneak through Google's and Amazon's vetting processes. On Sunday, Security Research Labs disclosed its findings after developing eight voice apps that could listen in on people's conversations through Amazon's Echo and Google's Nest devices. All of the apps passed through the companies' reviews for third-party apps. The research was first reported by CNET sister site ZDNet.

Find all our Student Opinion questions here. Last week, a robotic hand successfully solved a Rubik's Cube. While that feat might seem like a fun parlor trick, it's a sign that robots are being programmed to learn and not just memorize. Robots are already playing important roles inside retail giants like Amazon and manufacturing companies like Foxconn by completing very specific, repetitive tasks. But many believe that machine learning will ultimately allow robots to master a much wider array of more complex functions.

In the fall of 2017, Sam Bowman, a computational linguist at New York University, figured that computers still weren't very good at understanding the written word. Sure, they had become decent at simulating that understanding in certain narrow domains, like automatic translation or sentiment analysis (for example, determining if a sentence sounds "mean or nice," he said). But Bowman wanted measurable evidence of the genuine article: bona fide, human-style reading comprehension in English. So he came up with a test. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research develop ments and trends in mathe matics and the physical and life sciences.

Matternet CEO Andreas Raptopoulos walks next to an operator carrying a drone used to deliver medical specimens after a flight in March at WakeMed Hospital in Raleigh, N.C. Matternet CEO Andreas Raptopoulos walks next to an operator carrying a drone used to deliver medical specimens after a flight in March at WakeMed Hospital in Raleigh, N.C. Underneath it is a metal box -- smaller than a shoebox -- with vials of blood samples inside of it that are now heading across the campus to the lab for analysis, guided by a drone operator on the ground. "This facility happens to be across a very busy road from our main campus hospital," says Stuart Ginn, an ENT surgeon and medical director of innovations at WakeMed. But when taken by carrier on foot or by car, he says "the logistics of getting those samples across often resulted in about a 45-minute time of delivery."

Oscar-nominated actor Samuel L. Jackson is lending his iconic voice to Amazon's Alexa – profanities and all. During Amazon's event to unveil new products and services Wednesday, the online shopping giant announced that Jackson will be the first celebrity voice for its Alexa virtual assistant and was created using neural text-to-speech technology. There will be both an explicit version and a clean version when the feature launches later this year. The Alexa "skill" will cost 99 cents as an introductory offer. After the introductory period, the price will be $4.99, according to the product page.

Yale University researchers have developed a way to leverage neural networks to reveal patterns of activity of individual cells from multiple individuals. Researchers at Yale University have developed a method of leveraging artificial intelligence (AI) neural networks to reveal larger patterns of activity of individual cells that come from several individuals. The AI neural network, called SAUCIE (Sparse Autoencoder for Clustering, Imputation, and Embedding), can reveal minute cellular differences within individuals, as well as broader patterns that describe how the body functions. The new method will allow researchers to identify larger clusters of cellular activity that could shed light on the basis of a host's pathogens. For example, the team used SAUCIE to analyze 20 million cells from 60 patients and identify rare Gamma-Delta T cell types that regulate how the body responds to the virus that causes Dengue fever.