His groundbreaking application of advanced artificial intelligence algorithms for the analysis, transformation and creation of music forms the basis of DigiTrax's disruptive approach to music composition, and has generated seven granted patents. As the software architect of The Music Builder platform, his pioneering vision is grounded in both theoretical and practical knowledge of music theory. Mr. Matusiak has garnered multiple awards in the field, while completing his studies at Leeds College of Music in the U.K.

Abstract: We demonstrate the first application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, our model is able to learn a policy for lane following in a handful of training episodes using a single monocular image as input. We provide a general and easy to obtain reward: the distance travelled by the vehicle without the safety driver taking control. We use a continuous, model-free deep reinforcement learning algorithm, with all exploration and optimisation performed on-vehicle. This demonstrates a new framework for autonomous driving which moves away from reliance on defined logical rules, mapping, and direct supervision.

Knowledge graphs encode uniquely identifiable entities to other entities or literal values by means of relationships, thus enabling semantically rich querying over the stored data. Typically, the semantics of such queries are often crisp thereby resulting in crisp answers. Query log statistics show that a majority of the queries issued to knowledge graphs are often entity centric queries. When a user needs additional answers the state-of-the-art in assisting users is to rewrite the original query resulting in a set of approximations. Several strategies have been proposed in past to address this. They typically move up the taxonomy to relax a specific element to a more generic element. Entities don't have a taxonomy and they end up being generalized. To address this issue, in this paper, we propose an entity centric reformulation strategy that utilizes schema information and entity features present in the graph to suggest rewrites. Once the features are identified, the entity in concern is reformulated as a set of features. Since entities can have a large number of features, we introduce strategies that select the top-k most relevant and {informative ranked features and augment them to the original query to create a valid reformulation. We then evaluate our approach by showing that our reformulation strategy produces results that are more informative when compared with state-of-the-art

Katherine Nails is a former eZanga Content Marketing Intern. She is a student at the University of Delaware, where she is pursuing a Bachelor of Arts in Media Communication, and minors in Journalism and Integrated Design. When she's not tracking down a source for her school's student newspaper, you can find her relaxing with a good book or movie.

A never-ending debate over artificial intelligence is whether this exciting, deeply-complicated software can really boost a company's bottom line. One way to find out: build a quick-and-easy neural network and plug it into your legacy system. In Sweden, engineers who once built the "fruit-accounting" mechanics to support hundreds of millions of Candy Crush users, have joined a startup that's claims it's cracked the problem. Senior engineers from a raft of other Swedish tech unicorns - Spotify, Klarna and Truecaller - have joined too, says their CEO, Luka Crnkovic-Friis. When it comes to attracting talent in the Nordics, "no one comes close," he says.

In "Attention Is All You Need" Vaswani et al. propose to scale the value of the dot-product attention score by 1/sqrt(d) before taking the softmax, where d is the key vector size. Clearly, this scaling should depend on the initial value of the weights that compute the key and query vectors, since the scaling is a reparametrization of these weight matrices, but unfortunately the paper does not specify how these weights are initialized. Trying to follow the rabbit hole that is tensor2tensor, which is supposed to be the reference implementation, it seems to use the default tf.layers.Conv2D initalizer which is undocumented, but people on teh interwebz say that it is Glorot uniform. Marian also uses Glorot uniform, while Sockeye uses the default MXNet initializer which I don't know what it is. Rant: the initializer should have been clearly specified in the paper, or at lest in the reference code.

Barbie becomes a robotics engineer, a new app for baby and Siri gets mad. You can now talk to Alexa through your iPhone, Barbie is becoming a robotics engineer and an app can tell you why your baby is crying -- the late-night comics talk about the newest rollouts related to technology, and why some of it isn't as impressive as you might think. Late-night comic Trevor Noah kicks things off by pointing out why, when it comes to parenting, apps can't replace good old fashioned common sense. Seth Meyers gives us the mansplaining Ken doll and Jimmy Fallon gets in some woman trouble. Find out why Siri tells him off in today's Best of Late Night, above.

In Disney movies, heroes soar, swing, stretch, and toss hammers and shields that always hit their marks. But in its parks, Disney's animatronic characters, while uncannily convincing and interactive, mostly just… sit there. Disney's new high-flying, animatronic stunt robots could change that. Developed by Disney Imagineers, the team that designs and builds Disney's theme parks resorts, and attractions, the new robots can be flung through the air, perform aerial stunts, and stick their landings, every time, TechCrunch reports. "The realization we came to after seeing where our characters are going on screen, whether they be Star Wars characters, or Pixar characters, or Marvel characters or our own animation characters, is that they're doing all these things that are really, really active," Tony Dohi, a lead Imagineer, tells TechCrunch.

Housed in a tall plastic cylinder, Amazon's Alexa is far from physically resembling a woman. Yet when asked about its gender, the system curiously responds it is "female in character." A closer look at recent developments in artificial intelligence shows Alexa is the rule rather than the exception. From Apple's Siri to Hanson Robotics' humanoid robot Sophia, it seems that the future is female indeed -- but not in the way we intended. Artificial intelligence and robotics may intend to free us from many human limitations, but it seems that gender stereotypes are not one of them.

TechCrunch has reported that Facebook is acquiring London-based startup, Bloomsbury AI, as part of its efforts to fight against fake news on the world's largest social network. Bloomsbury's product is an NLP engine that helps machines answer questions on information derived from documents. TechCrunch's sources report that Facebook plans to use the firm's team and technology in policing the platform, and combating the scourge of bullshit fake news stories that have proliferated since the 2016 US general election. This is easily the biggest UK AI acquisition this year, and one of the most interesting since Google sucked up the machine learning powerhouse DeepMind in 2014. The deal is believed to be valued between $23 and $30 million, which is a far cry from DeepMind's $500 million asking price.