Governments, corporations and even technology vendors are all grappling with major opportunities and challenges emerging as artificial intelligence moves from the lab into the boardroom. With any game-changing innovation the biggest challenge is often the scarcity of talent, not least in the field of AI where many of the advances emanated from academia. Often pioneers of this latest wave of disruption are PhD students from top global universities who see the opportunity to take a theory from the classroom and create a product or solution for the world. AI and deep learning, automation, predictive analytics, quantum computing and nano technology have all in some form started life in a lab or classroom - not in a traditional software development environment. The net result is that the new'rock stars' (well-paid technical talent) as they are so commonly called in Silicon Valley, are the PhD Research Scientists who are even fewer in number today than their predecessor software engineers.

The 11th edition of the Digital Transformation Review will be launched soon and it focuses on Artificial Intelligence (AI). AI has received significant bad press so why should we pay attention?.

We spend a lot of time talking to Alexa and Siri. Imagine if such artificial personalities were put inside a cute, adorable robot. That's what Alexander Reben has done. The artist created what he saw as the perfect interview machine to see how much he could get people to reveal to the robot. Reben's experiments with human robot interactions began when he was working on his Ph.D. in robotics at MIT.

A subreddit committed to intelligent understanding of the hypothetical moment in time when artificial intelligence progresses to the point of greater-than-human intelligence, radically changing civilization. This community studies the creation of superintelligence-- and predict it will happen in the near future, and that ultimately, deliberate action ought to be taken to ensure that the Singularity benefits humanity. The technological singularity, or simply the singularity, is a hypothetical moment in time when artificial intelligence will have progressed to the point of a greater-than-human intelligence. Because the capabilities of such an intelligence may be difficult for a human to comprehend, the technological singularity is often seen as an occurrence (akin to a gravitational singularity) beyond which the future course of human history is unpredictable or even unfathomable. The first use of the term "singularity" in this context was by mathematician John von Neumann.

To what extent have RL researchers tapped into Experimental Design theory from statisticians? They have focused on minimizing costs of data collection that optimizes predictability / explainability for all sorts of problems.

We view the creative arts as an industry indisputably connected to human ability. Writing a novel, connecting the dots in a piece of investigative journalism, painting a landscape, filming and editing a movie -- these are all creative pursuits that require the human touch. But is it possible that robots could learn to do these things too? Could creative jobs also be on the chopping block? One category where AI is currently being pushed to its creative limits is content marketing.

We consider the task of learning the parameters of a {\em single} component of a mixture model, for the case when we are given {\em side information} about that component, we call this the "search problem" in mixture models. We would like to solve this with computational and sample complexity lower than solving the overall original problem, where one learns parameters of all components. Our main contributions are the development of a simple but general model for the notion of side information, and a corresponding simple matrix-based algorithm for solving the search problem in this general setting. We then specialize this model and algorithm to four common scenarios: Gaussian mixture models, LDA topic models, subspace clustering, and mixed linear regression. For each one of these we show that if (and only if) the side information is informative, we obtain parameter estimates with greater accuracy, and also improved computation complexity than existing moment based mixture model algorithms (e.g. tensor methods). We also illustrate several natural ways one can obtain such side information, for specific problem instances. Our experiments on real data sets (NY Times, Yelp, BSDS500) further demonstrate the practicality of our algorithms showing significant improvement in runtime and accuracy.

The way you control Apple's smart speaker, the HomePod, is pretty cool. You just talk to it. But Siri, Apple's voice assistant, can't make the speaker do much beyond the basics and can only summon music from Apple-owned cloud services. That sets it behind the other products in the voice-activated speaker race--most notably those from Amazon or Google--which are capable, flexible, and largely platform-neutral when it comes to streaming. It's too bad Siri can't play internet radio or the deep cuts from your local MP3 library, because the speaker itself sounds absolutely amazing.

Several Facebook groups are dedicated to it, one with over 3,000 likes, New Yorker readers overwhelmingly selected it as the word to eliminate from the dictionary, and Jimmy Fallon sarcastically thanked it for being the worst word in the English language. When you ask people why this might be, there is no shortage of armchair theory: that there's something about the sounds involved, that it puts your face in a position similar to the facial expression of disgust, or that it reminds people of mold or sex. Psychologist Paul Thibodeau and his colleagues ran a study to sort it out. They gave participants a set of words and asked them to rate how, whether, and to what degree, each word made them uncomfortable. Twenty-one percent of the people in the study had an aversion to the unloved word.

Artificial intelligence is moving into movie production. Screenwriters denied the big budgets and formidable resources of the major film studios may soon have another option, thanks to a new algorithm that can generate a video simply by consuming a (very short) script. The new movies are far from Oscar-worthy, but a similar technique could one day find uses outside entertainment, by, say, helping a witness reconstruct a car crash or a crime. Artificial intelligence (AI) is getting much better at identifying the content of images and providing labels. So-called "generative" algorithms go the other way, producing images from labels (or brain scans). A few can even take a single movie frame and predict the next series of frames.