Artificial Intelligence (AI) has triggered all sorts of views about its future and the impact on mankind.

It may not be the'Dillo shuttle many Austinites remember, but a new downtown circulator operated by autonomous vehicles will launch this fall in Austin. The autonomous, or self-driving, shuttles are part of a new technology-infused approach to transit that Capital Metro has been exploring in the past couple of years. "We just really want to start connecting the community closer together through technology and the services Capital Metro provides," President and CEO Randy Clarke said. Capital Metro will operate the pilot shuttle program, which has yet to be named, for 12 months, and rides will be free during that period. The agency will also be seeking bids from autonomous vehicle manufacturers to lease six vehicles for the program.

"Microsoft Monday" is a weekly column that focuses on all things Microsoft. In the process of rebranding MSN News, Microsoft has launched a news app for Android and iOS called Microsoft News. The MSN brand will remain on the website, but Microsoft will not be using that brand for the Android and iOS news apps going forward. Some of the features built into the Microsoft News app includes a dark mode, integration with Android and iOS widgets, continuous reading and news alerts. And the Microsoft News service will also be used for powering the news on Microsoft Edge, the News app in Windows 10, Skype, Xbox and Outlook.com.

Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds.

Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which train and generate at the note level, are currently the more prevalent approach; these models can capture long-range dependencies of melodic structure, but fail to grasp the nuances and richness of raw audio generations. Raw audio models, such as DeepMind's WaveNet, train directly on sampled audio waveforms, allowing them to produce realistic-sounding, albeit unstructured music. In this paper, we propose an automatic music generation methodology combining both of these approaches to create structured, realistic-sounding compositions. We consider a Long Short Term Memory network to learn the melodic structure of different styles of music, and then use the unique symbolic generations from this model as a conditioning input to a WaveNet-based raw audio generator, creating a model for automatic, novel music. We then evaluate this approach by showcasing results of this work.

James Pamment has written that for most of the 20th century the term public diplomacy was associated with the term propaganda. According to the Oxford Dictionary propaganda relates to information, especially of a biased or misleading nature, used to promote a political cause or point of view. During the 21st century, the field of public diplomacy faced a conceptual shift known as the "new" public diplomacy. This shift saw the goals of public diplomacy change from influence and opinion formation to creating long lasting relationships with foreign populations. These relationships, built on dialogue and two way interactions, could be used to create a receptive environment for a country's foreign policy.

Evan Ackerman is a journalist who has been covering robotics (and some other stuff) for the last 11 years. Last time he counted, he'd written well over 6,000 articles, which seems like a lot. Tim Smith is the CEO of Element Public Relations, a boutique PR firm in San Francisco specializing in emerging technologies. Element has particular expertise in robotics, given their work with Willow Garage, Open Robotics, Fetch Robotics, Simbe Robotics, and others. For this article, Evan and Tim sent each other some questions about what it's like for a journalist to work with PR firms, and what it's like for PR firms to work with journalists.

The upcoming Assassin's Creed movie has been slowly gathering steam for a good long while now, but as of today, Ubisoft has announced that the film has officially begun production, and has a release date to boot, December 21, 2016. Ubisoft has an unprecedented level of influence over the film for a video game studio, and is actually co-publishing the movie with New Regency. It's been considered by many fans to be one of the best new hopes for creating a video game-based film that might actually turn out to be high quality, first given Ubisoft's involvement, and second, based on the fact that the esteemed Michael Fassbender has been attached to the project for a while now. The actor will be playing both the present day lead, and his Assassin ancestor in the genetic flashbacks which will assuredly make up most of the film. Also this week it was announced that the cast has expanded to include Oscar-winner Marion Cotillard in an unspecified role, lending further credence to the film's potential quality.

Machine learning and artificial intelligence are technologies that have evolved rapidly in the last decade. Most people today are familiar with intelligent voice assistants, streaming video platforms that recommend personalized content, and vehicle navigation systems that suggest best routes in real time to avoid traffic, all examples of artificial intelligence and machine learning in the consumer world. While consumer-facing machine learning often has a narrow focus, enterprise machine learning solutions must cater to many different types of businesses, all of which measure success differently. A consumer can expect Netflix to learn her movie preferences by tracking what she and people similar to her click on, a solution that can be built to be fairly generic. However, enterprise machine learning solutions rarely work as seamlessly right out of the box.

Early applications of deep learning to imagery and video focused primarily on cataloging that content, identifying the objects, activities, locations and text within. As neural algorithms have improved and new techniques developed, there has been an increasing focus on using deep learning approaches to actually generate completely new visual content autonomously. The cost of such AI generated synthetic content is dropping rapidly to the point that in the very near future you'll likely be able to generate it on your smartphone with a click of a button. These images and videos are a far cry from the ham-handed Photoshop jobs of yesteryear, with some appearing nearly flawless even to a trained eye. At the same time, we've taught society that "seeing is believing" and to put far more trust in the visual material we see online – an image might be miscaptioned, but the picture itself is likely real.