Search engine algorithms used by Google and Microsoft's Bing use similar technology to digest and summarize written content and compose new paragraphs. Voice assistants such as Amazon's Alexa rely on listening comprehension to answer questions posed by people. Google recently demonstrated an eerily human-like voice assistant that can call hair salons or restaurants to make appointments.

Jackson Schroeder is a recent graduate of Ohio University with a B.A. in Journalism from the E.W. Scripps School. He is originally from Savannah, Georgia. Jackson has covered a wide range of topics, including sustainability, technology, sports, culture, travel, and music. He plays bass and guitar, and enjoys playing and listening to live music in his free time.

The family of Expectation-Maximization (EM) algorithms provides a general approach to fitting flexible models for large and complex data. The expectation (E) step of EM-type algorithms is time-consuming in massive data applications because it requires multiple passes through the full data. We address this problem by proposing an asynchronous and distributed generalization of the EM called the Distributed EM (DEM). Using DEM, existing EM-type algorithms are easily extended to massive data settings by exploiting the divide-and-conquer technique and widely available computing power, such as grid computing. The DEM algorithm reserves two groups of computing processes called \emph{workers} and \emph{managers} for performing the E step and the maximization step (M step), respectively. The samples are randomly partitioned into a large number of disjoint subsets and are stored on the worker processes. The E step of DEM algorithm is performed in parallel on all the workers, and every worker communicates its results to the managers at the end of local E step. The managers perform the M step after they have received results from a $\gamma$-fraction of the workers, where $\gamma$ is a fixed constant in $(0, 1]$. The sequence of parameter estimates generated by the DEM algorithm retains the attractive properties of EM: convergence of the sequence of parameter estimates to a local mode and linear global rate of convergence. Across diverse simulations focused on linear mixed-effects models, the DEM algorithm is significantly faster than competing EM-type algorithms while having a similar accuracy. The DEM algorithm maintains its superior empirical performance on a movie ratings database consisting of 10 million ratings.

We present a neural sequence model designed specifically for symbolic music. The model is based on a learned edit distance mechanism which generalises a classic recursion from computer science, leading to a neural dynamic program. Repeated motifs are detected by learning the transformations between them. We represent the arising computational dependencies using a novel data structure, the edit tree; this perspective suggests natural approximations which afford the scaling up of our otherwise cubic time algorithm. We demonstrate our model on real and synthetic data; in all cases it outperforms a strong stacked long short-term memory benchmark.

When you think of the power and reach of voice through the lens of technology, you likely think back to simpler times--a time when whole families would gather in front of the radio and listen to Orson Welles' gripping narration of "War of the Worlds," or a time when the only contact you had with your crush was through a landline that you constantly had to fight off your annoying siblings to use. Telephony and radio were huge tech empires built on the power of voice, but as technology evolved, they quickly started becoming obsolete. Video killed the radio star, talking morphed into texting and voice as a tech powerhouse was cracking. But then the tides started turning yet again. Podcasts burst onto the scene and became a surprise hit.

Big data refers to large sets of info that are analyzed for trends and patterns that offer useful insights. Special emphasis is placed on analyzing people's behavior and interactions online. The challenge, however, is in figuring out the best way to process, analyze and make useful insights of the information gathered. Here is why big data is such a big deal. Data on the kind of entertainment we like (e.g., music, videos, movies) can be analyzed, and the insight drawn from it can be used to tailor make a unique experience just for us.

Google News is checked by millions of people on a daily basis looking for quick access to a range of coverage of a given event or issue. It was founded by software developer Krishna Bharat in 2002 in response to the scramble for news that followed the attacks on the World Trade Centre on 11 September 2001. The service collects and ranks all articles on a particular topic then making international headlines into clusters, allowing readers to choose which publication's account they read. But how does Google rank the content it shows? Rather than a physical team of news editors, Google relies on an algorithm whose methodology, like Colonel Sanders' recipe for fried chicken, is a closely guarded secret.

YouTube has launched its music offering across the world, hoping to take on streaming services such as Spotify. The company will make YouTube Music available in the UK and 16 other countries, it has announced. The launch comes at the same time as YouTube Premium, a paid-for service that offers people videos they would not normally be able to watch. YouTube is one of the primary destinations for music streaming on the internet. It is used to listen to both official and illegitimate uploads of songs, many of which can't be found on official streaming services.

One of the behaviors considered to be uniquely human is our creativity. While many animal species create visually stunning displays or constructions -- think of a spider's delicate web or the colorful, intricate structures built by bowerbirds -- they are typically created with a practical purpose in mind, such as snagging prey or seducing a mate. Humans, however, make art for its own sake, as a form of personal expression. And as computer engineers attempt to imbue artificial intelligence (AI) with humanlike capabilities and behaviors, a question arises: Can AI create art? The AMC series "Humans," which returns June 5 for its third season, is populated by Synths -- intelligent robots that resemble people, save for their unnaturally green eyes.

You probably use machine-learning systems every day without even knowing it. The technology gives us spam filters, our Facebook News Feeds, digital assistants, search engines, Netflix picks, Amazon recommendations, fraud detection systems, chatbots and more. And it's only going to become more pervasive. For forward-looking parents, it's time to get your kids on it. Software developer and dad Dale Lane has created Machine Learning for Kids, a collection of free projects that teach students how to build with this technology.