One of the first key takeaways from Vanderbilt Law School's conference on Thursday about artificial intelligence is that the term doesn't carry much value in the scientific community. "A.I. is whatever we can't do this year," David Lewis, a speaker who holds a PhD in computer science, said in between panel sessions. Lewis estimated we're currently experiencing the second or third wave of "A.I. hype," in which everyone uses the term to describe their technology. That's happened before, he said, and then it went out of style as a marketing buzzword. "By 2020, it'll have a negative connotation again," he predicted.

Let's say you're scrolling through your favorite social media app and you come across a series of pictures of a man in a tuxedo and a woman in a long white dress. An automated image captioning system might describe that scene as "a picture of a man and a woman," or maybe even "a bride and a groom." But a person might look at the pictures and think, "Wow, my friends got married! As image captioning tools get increasingly good at correctly recognizing the objects in an image, a group of researchers is taking the technology one step further. They are working on a system that can automatically describe a series of images in the same kind of way that a human would, by focusing not just on the items in the picture but also what's happening and how it might make a person feel. "Captioning is about taking concrete objects and putting them together in a literal description," said Margaret Mitchell, a Microsoft researcher who is leading the research project. "What I've been calling visual ...

Google is expanding its free software to now include self-learning smart computers. TensorFlow, the company bringing this software to Google users, will allow for anyone with access to computer software to create their own smart computer from scratch that can program itself. Users can customize the settings to specify what programs they want the computer to learn, and it takes off from there. Learned skills can range anywhere from drawing and talking to recognizing pictures. Making these programs available to programmers aids the next frontier for many tech vendors, as "machine-learning tech" is allowing them to better integrate services into their apps.

Actor and writer Wil Wheaton wants to "add an A to the STEM acronym and make it STEAM." He'll be speaking at the USA Science and Engineering Festival April 16-17 in Washington about why he thinks the arts should be represented in the acronym commonly used when referring to the science, technology, engineering and math fields. Wheaton, 43, best known for his role as Wesley Crusher on "Star Trek: The Next Generation" in the 1980s and '90s and more recently as a fictionalized version of himself on "The Big Bang Theory," says that he has always been fascinated by science and technology, and has made it a goal of his to ensure that kids get the encouragement they need to pursue those fields. Wheaton spoke with U.S. News by phone about why he got involved in the festival, how science fiction and fact have shaped his life and career and why he thinks it should be "science, technology, engineering, arts and math." How did you get involved with the USA Science and Engineering Festival?

One of the first key takeaways from Vanderbilt Law School's conference on Thursday about artificial intelligence is that the term doesn't carry much value in the scientific community. "A.I. is whatever we can't do this year," David Lewis, a speaker who holds a PhD in computer science, said in between panel sessions. Lewis estimated we're currently experiencing the second or third wave of "A.I. hype," in which everyone uses the term to describe their technology. That's happened before, he said, and then it went out of style as a marketing buzzword. "By 2020, it'll have a negative connotation again," he predicted.

AI experts believe that computers will write as well as humans within the next 15 years. This means that any student will be able to input a poorly-written essay into a software program, which will analyze the text and reconstruct it as well-written, grammatically correct text. Since we use calculators as an extension of our minds, shouldn't we also use AI software to become better writers? This is not a hypothetical question. Across the world, teams of computer scientists are racing at a breakneck speed to construct advanced artificial intelligence that can automate thinking and writing. Last month, AlphaGo, the artificial intelligence program created by Google, beat the world-champion Lee Sodel in Go, a game that is so complex that there are more choices available in a single game than there are atoms in the entire universe.

Microsoft is notching up its Azure Media services platform by a couple of notches. The company is now going to implement its machine learning tools into its collection of cloud-based tools for video workflows. Now, you may wonder at the apparent non-existence of a relation between videos and machine learning. Machine learning after all, is used for data analysis. It can't be used with videos, right?

In 2009, Hal Varian (Google's Chief Economist) famously joked that "the sexiest job in the next 10 years will be Statistics". Fast forward to 2016, and it's abundantly clear that he was right (and how!) Compare that with, say, what the average web developer gets paid: 67,097. Companies are churning out exponentially more data every day yet struggling to derive value from it. According to McKinsey, by 2018, the US alone will face a shortage of 150,000 data analysts and an additional 1.5 million data-savvy managers. But you know this stuff.

"I never thought that I would be accepted into a human school," the robot said upon hearing of his successful enrollment at Hisashi High School in Waseda, Fukushima Prefecture. He also promised to "try my best," TASS reported. Pepper comes to the school with an impressive array of language skills, speaking both Japanese and English. He will mostly take part in English classes, though the school has told Pepper than he can also visit other classes and activities. Teachers believe learning alongside Pepper will be a positive experience for students, encouraging their desire to learn new information.

This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally-linear Gaussian regression model between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods.