I've long been both paranoid and optimistic about the promise and potential of artificial intelligence to disrupt -- well, almost everything. Last year, I was struck by how fast machine learning was developing and I was concerned that both Nokia and I had been a little slow on the uptake. What could I do to educate myself and help the company along? As chairman of Nokia, I was fortunate to be able to worm my way onto the calendars of several of the world's top AI researchers. But I only understood bits and pieces of what they told me, and I became frustrated when some of my discussion partners seemed more intent on showing off their own advanced understanding of the topic than truly wanting me to get a handle on "how does it really work."

First, a couple of pointers to keep in mind when searching for datasets. Kaggle: A data science site that contains a variety of externally contributed interesting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even seattle pet licenses. Although the data sets are user-contributed, and thus have varying levels of cleanliness, the vast majority are clean. This site makes it possible to download data from multiple US government agencies.

Deep artificial neural networks (DNNs) are powerful tools for recognition and classification as they learn sophisticated mapping rules between the inputs and the outputs. However, the rules that learned by the majority of current DNNs used for pattern recognition are largely fixed and do not vary with different conditions. This limits the network's ability to work in more complex and dynamical situations in which the mapping rules themselves are not fixed but constantly change according to contexts, such as different environments and goals. Inspired by the role of the prefrontal cortex (PFC) in mediating context-dependent processing in the primate brain, here we propose a novel approach, involving a learning algorithm named orthogonal weights modification (OWM) with the addition of a PFC-like module, that enables networks to continually learn different mapping rules in a context-dependent way. We demonstrate that with OWM to protect previously acquired knowledge, the networks could sequentially learn up to thousands of different mapping rules without interference, and needing as few as $\sim$10 samples to learn each, reaching a human level ability in online, continual learning. In addition, by using a PFC-like module to enable contextual information to modulate the representation of sensory features, a network could sequentially learn different, context-specific mappings for identical stimuli. Taken together, these approaches allow us to teach a single network numerous context-dependent mapping rules in an online, continual manner. This would enable highly compact systems to gradually learn myriad of regularities of the real world and eventually behave appropriately within it.

Question Generation is the task of automatically creating questions from textual input. In this work we present a new Attentional Encoder--Decoder Recurrent Neural Network model for automatic question generation. Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels. The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution. In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence. Our model achieves state of the art results of 19.98 Bleu_4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin. A human evaluation also shows that these added features improve the quality of the generated questions.

The trends in entertainment are usually a very accurate measure of what'tickles the pickle' of people at a certain time. In the ancient world, people enjoyed gladiator fights or (pretty much at the opposite end) skillfully written plays. In the'roaring twenties' entertainment meant basically lush, shiny parties with loud music and no small amount of alcohol and other substances. Once the motion picture appeared that became the beacon of leisure and fun. Hollywood gave and is still producing both heartwarming and heart-stopping experiences. Judging by box office results, the success of movies as a favorite pastime is still on the rise.

Last time we began exploring the role of educators within networked learning environments, broadly using a theory by George Siemens called "Connectivism" as our base. Today we will further that exploration via a few definitions mooted by pedagogues over the years, each of which offers a useful insight into how the role of instructor needs to change in environments were content and information is freely available, but where collaboration, creativity and critical thinking needs to be ever more actively, and explicitly encouraged. Professor John Seely Brown, (PhD computer and communication sciences) is among a many other things a member of the American Academy of Arts and Sciences, the National Academy of Education, and a Fellow of the American Association for Artificial Intelligence. He is currently a visiting scholar at the University of Southern California, and has also published over 100 papers focusing on networks, collaborative innovation and networked learning. In his article, New Learning Environments and subsequent book, A New Culture of Learning, Professor Brown, among many, many other things, promotes the idea of studio or atelier learning.

Machine learning engineers--i.e., advanced programmers who develop artificial intelligence (AI) machines and systems that can learn and apply knowledge--are in high demand, as more companies adopt these technologies. These professionals perform sophisticated programming, and work with complex data sets and algorithms to train intelligent systems. While many fear that AI will soon replace jobs, at this phase in the technology's development, it is still creating positions like machine learning engineers, as companies need highly-skilled workers to develop and maintain a wide range of applications. To help those interested in the field better understand how to break into a career in machine learning, we compiled the most important details and resources. This guide on how to become a machine learning engineer will be updated on a regular basis.

People too often forget that IQ tests haven't been around that long. Indeed, such psychological measures are only about a century old. Early versions appeared in France with the work of Alfred Binet and Theodore Simon in 1905. However, these tests didn't become associated with genius until the measure moved from the Sorbonne in Paris to Stanford University in Northern California. There Professor Lewis M. Terman had it translated from French into English, and then standardized on sufficient numbers of children, to create what became known as the Stanford-Binet Intelligence Scale. The original motive behind these tests was to get a diagnostic to select children at the lower ends of the intelligence scale who might need special education to keep up with the school curriculum. But then Terman got a brilliant idea: Why not study a large sample of children who score at the top end of the scale?

Young children are spending record time on hand-held devices. According to new data published on Thursday, the amount of time under-5s devote to watching TV and video content via such appliances is now 2.8 hours, per day. That's an increase three out of four infants aged under five have regular access to a computer or mobile tablet. Last year, they spent 2.6 hours each day, but this has now risen to 2.8 hours. In addition, the majority of pre-school children also have their very own gadget.

How is your organization preparing for artificial intelligence (AI)? Ask this question of businesses investing in this field today, and the answer almost always comes down to "data"-- with leaders talking about "data preparations" or "data science talent acquisition." Related: Which Countries Are Ready for AI Adoption? While there would be no AI without data, enterprises that fail to ready the other side of the equation-- people-- don't just stunt their capacity for good AI, they risk sunk investment and jeopardize employee trust, brand backlash or worse. After all, people are the ones building, measuring, consuming and determining the success of AI in enterprise and consumer settings.