Magenta will use TensorFlow, the machine-learning engine that Google built and opened up to the public at the end of 2015, to determine whether AI systems can be trained to create original pieces of music, art, or video. Much in the same way that Google opened up TensorFlow, Eck said Magenta will make available its tools to the public. Roberts also showed off a simple digital synthesizer program he'd been working on, where an AI could listen to notes that he played, and play back a more complete melody from those notes: The goal of the project, Eck suggested, could well be to create a system that could give a listener "musical chills" with entirely new pieces of music, on a regular basis, as they sit listening to computer-generated music from the comfort of their couch at home. Eck said the inspiration for Magenta had come from other Google Brain projects, like Google DeepDream, where AI systems were trained on image databases to "fill in the gaps" in pictures, trying to find structures in images that weren't necessarily present in the images themselves.

Magenta will use TensorFlow, the machine-learning engine that Google built and opened up to the public at the end of 2015, to determine whether AI systems can be trained to create original pieces of music, art, or video. Much in the same way that Google opened up TensorFlow, Eck said Magenta will make available its tools to the public. Roberts also showed off a simple digital synthesizer program he'd been working on, where an AI could listen to notes that he played, and play back a more complete melody from those notes: The goal of the project, Eck suggested, could well be to create a system that could give a listener "musical chills" with entirely new pieces of music, on a regular basis, as they sit listening to computer-generated music from the comfort of their couch at home. Eck said the inspiration for Magenta had come from other Google Brain projects, like Google DeepDream, where AI systems were trained on image databases to "fill in the gaps" in pictures, trying to find structures in images that weren't necessarily present in the images themselves.

Google wants to put the art back in artificial intelligence. During the last session at Moogfest, a four-day music and technology festival, in Durham, North Carolina, Douglas Eck, a researcher on Google Brain, the company's artificial-intelligence research project, outlined a new group that's going to focus on figuring out if computers can truly create. The group, called Magenta, will launch more publicly at the start of June, but attendees at Moogfest were given a taste of what it's going to be working on. Magenta will use TensorFlow, the machine-learning engine that Google built and opened up to the public at the end of 2015, to determine whether AI systems can be trained to create original pieces of music, art, or video. This is no simple task, given that even the most advanced artificially intelligent systems have enough trouble copying the styles of existing artists and musicians, let alone coming up with entirely new ideas themselves.

Volkswagen, acknowledging the growing automotive importance of machine learning, has acquired a stake in the German Research Center for Artificial Intelligence (DFKI). "Artificial intelligence (AI) is a key technology for autonomous driving and therefore an investment in our future," VW Group CEO Matthias Mueller said in a press statement. "We want to forge ahead with AI research in the automotive industry and beyond." VW also hopes its involvement in DFKI, one of the world's biggest AI research institutes, will help it with the digitalization of plants and a range of corporate processes. Many carmakers have been expanding their AI activities as an industry-wide effort to build autonomous vehicles is stretching current computer capacity to its limits.

Computer users have long relied on Nvidia Corp.'s technology to paint virtual worlds on the screen as they gunned down videogame enemies. Now some researchers are betting it can also help save lives--of real people. Massachusetts General Hospital recently established a center in Boston that plans to use Nvidia chips to help an artificial-intelligence system spot anomalies on CT scans and other medical images, jobs now carried out by human radiologists. The project, drawing on a database of 10 billion existing images, is...

Machine Learning today tends to be "open-loop" – collect tons of data offline, process them in batches and generate insights for eventual action. There is an emerging category of ML business use cases that are called "In-Stream Analytics (ISA)". Here, the data is processed as soon as it arrives and insights are generated quickly. However, action may be taken offline and the effects of the actions are not immediately incorporated back into the learning process. If we did, it is an example of a "closed-loop" system – we will call this approach "Adaptive Machine Learning" or AML.

Douglas Eck, a researcher at Google, talks about Magenta during at panel at Moogfest. If Google's artificial intelligence can paint its dreams, why not make other kinds of art? On June 1, Google is set to launch Magenta, a research project to explore using artificial intelligence to create art, and make that process easier for TensorFlow users. The group has about six researchers now, and will invite other academics to help try to solve the problem of creative machines. The project exists within Google Brain group.

He bobbed his head with the groove, and leaned way in when he wanted to play more complicated melodies, rocking and rolling with the beat of the jam. This wasn't a your average jazz band member, though--this was Shimon, a four-armed robot marimba player built by the Georgia Institute of Technology to be able to listen to music, improvise, and play along with human musicians. At a performance at Moogfest, a four-day music and technology festival in Durham, North Carolina, Gil Weinberg, the lead researcher at Georgia Tech's Center for Music Technology, demonstrated what he and his lab have been working on for the past 12 years. Their efforts have aimed at augmenting the creative capabilities of humans with robotics. That can mean robots like Shimon, which uses machine-learning programs trained on music theory and a wide range of musical styles, from chamber music to dubstep, to be able to add a superhuman element to musical performances, playing chord structures that would be physically impossible for humans to hit. But it can also mean robotic enhancements for humans: At the concert, Weinberg introduced Jason Barnes, a drummer who lost the lower part of his right arm a few years ago.

Google revealed a handful of slick new products this week that showed off its impressive artificial intelligence and machine learning tech chops. Its new conversational assistant, for example, will take its traditional search product to the next level, letting users ask it questions, find suggestions, or book services through text chat in its new messaging app Allo or voice, in its new smart speaker, Home. Google's building on many years of research, development, and data collecting here and it shows. Although Home is essentially a follow-up to Amazon's Echo speaker, Google has much more experience with voice search and natural language processing. Ask Home a question, like "How tall is Steph Curry?" and you can follow-up with "What's his jersey number?" and Home will know who you're still talking about, where Alexa can only handle single queries.

The giant hedge fund, which manages 35 billion, is as much a technology company as it is a hedge fund. It uses advanced technologies to find investment opportunities, and it just hosted its annual artificial intelligence competition. One of those technological applications involves using natural-language-processing techniques to analyze the Fed minutes, such as those set for release Wednesday afternoon. "Historically, interpretations of those minutes required art, so Fed watchers pontificated and critiqued," the firm said in a note. "Now natural language processing techniques can translate those minutes into relatively objective data."