TL;DR Baidu's TTS system now supports multi-speaker conditioning, and can learn new speakers with very little data (a la LyreBird). I'm really excited about the recent influx of neural-net TTS systems, but all of the them seem to be too slow for real time dialog, or not publicly available, or both. Hoping that one of them gets a high quality open-source implementation soon!

Once considered'science fiction', artificial intelligence is now increasingly used in newsrooms. Imagine you're a journalist in 2027. You get in your self-driving car and while your personal virtual assistant reads you the headlines, the car's sensors detect a deterioration in air quality. A quick statistical analysis determines it's unusual and significant. You launch your social media tracking program and notice that parents in a particular neighborhood are discussing their childrens' respiratory issues.

The research follows similar work published by a separate group at the University of Oxford earlier this month. Using related techniques, these scientists were able to create a lip-reading program called LipNet that achieved 93.4 percent accuracy in tests, compared to 52.3 percent human accuracy. However, LipNet was only tested on specially-recorded footage that used volunteers speaking formulaic sentences. By comparison, DeepMind's software -- known as "Watch, Listen, Attend, and Spell" -- was tested on far more challenging footage; transcribing natural, unscripted conversations from BBC politics shows. More than 5,000 hours of footage from TV shows including Newsnight, Question Time, and the World Today, was used to train DeepMind's "Watch, Listen, Attend, and Spell" program.

I'm working on a continuous state and action Q-learning algorithm, using radial basis nets to store the value function. It works OK, but I'm not completely happy with the way I'm doing the argmax to get the action. Basically I have a radial basis function and I have to find maximum. The function looks like this more or less. Things I've tried so far: Not that fast either, and prone to stuck in local maximum.

Compute Library for Deep Neural Networks (clDNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL inference on Intel HD Graphics Driver and Intel Iris graphics (also referred to as Intel Processor Graphics).

Welcome to Mossberg, a weekly commentary and reviews column on The Verge and Recode by veteran tech journalist Walt Mossberg, executive editor at The Verge and editor at large of Recode. This is my last weekly column for The Verge and Recode -- the last weekly column I plan to write anywhere. I've been doing these almost every week since 1991, starting at The Wall Street Journal, and during that time, I've been fortunate enough to get to know the makers of the tech revolution, and to ruminate -- and sometimes to fulminate -- about their creations. Now, as I prepare to retire at the end of that very long and world-changing stretch, it seems appropriate to ponder the sweep of consumer technology in that period, and what we can expect next. Let me start by revising the oft-quoted first line of my first Personal Technology column in the Journal on October 17, 1991: "Personal computers are just too hard to use, and it's not your fault." It was true then, and for many, many years thereafter.

That's how long radio astronomer Frank D. Drake pointed the 26-meter telescope at the National Radio Astronomy Observatory (NRAO) research facility in Green Bank, West Virginia, towards the heavens, looking for signs of intelligent life beyond Earth. He dubbed his efforts Project Ozma, in honor of the Queen of Oz from L. Frank Baum's famed children's book series. Between April and July of 1960, Drake recorded some 150 hours of tape speckled with radio noise. While no meaningful encoded signals or patterns emerged from those readings, Drake still earned himself a place in history for performing what would become the first scientific search for extraterrestrial intelligence in the modern era. Since then, research organizations around the world have performed nearly 100 SETI (search for extraterrestrial intelligence) experiments.

Robot" is never far from the pulse of current events. A couple of weeks ago, the dystopian computer hacker drama was in mid-shoot at an FBI field office when news broke that bureau Director James Comey had been fired. "One of the lines of dialogue is referencing Comey," Sam Esmail, the show's creator, recalled. "That was a little surreal and kind of an out-of-body experience." Esmail might as easily be describing the USA Network series, whose dark and downbeat second season unfolded much within the imagination of its mentally unstable antihero, Elliot Alderson (Rami Malek), as he fought to free himself from his manipulative alter-ego, Mr. Robot (Christian Slater). By season's end, fsociety -- the underground hacktivist group led by the sidelined Elliot -- was shattered, and a deadly, explosive plot hatched by Mr. Robot and the Dark Army, a cryptic Chinese organization, was about to go off. Elliot himself lay bleeding, shot by a character he believed to be a delusional figment. The show's license to be uncanny is endorsed by the headlines, argues its star. "As ridiculous as this sounds, I feel that I'm reading my scripts as if I could be reading the L.A. Times tomorrow," said Malek, speaking by phone during a lunch break from a location shoot on New York City's Broadway. "I would approach Sam and say, 'Do you really believe that this is possible?'

We are currently using basic Adam optimizer, but the training time increases dramatically after third epoch. We were not able to go beyond 4th epoch due to continual increase of training time. Our Model uses Keras 2.0. Training takes about an hour per epoch ( up to third epoch) on Nvidia Tesla K80. The model contains both classification and regression, concatenated at the end.

There are times when you know exactly what you want to watch. So when you're ready to launch a Stranger Things binge session, you can tell Google to do exactly that. Similar specificity can be performed with Spotify and Google Play Music playlists, albums, or titles in your Google Play Movies & TV catalogue. Sometimes I've found the program will start streaming right away, while other times it'll take you to the product screen and you'll still need to press play. Such is the inconsistency that happens at times when living in the future.