Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.

We propose a simple discrete time semi-supervised graph embedding approach to link prediction in dynamic networks. The learned embedding reflects information from both the temporal and cross-sectional network structures, which is performed by defining the loss function as a weighted sum of the supervised loss from past dynamics and the unsupervised loss of predicting the neighborhood context in the current network. Our model is also capable of learning different embeddings for both formation and dissolution dynamics. These key aspects contributes to the predictive performance of our model and we provide experiments with three real--world dynamic networks showing that our method is comparable to state of the art methods in link formation prediction and outperforms state of the art baseline methods in link dissolution prediction.

Carl Vondrick is a doctoral candidate and researcher at MIT, where he studies computer vision and machine learning. His research focuses include leveraging large-scale data with minimal annotation and its applications to predictive vision and scene understanding. Recently his work has received a lot of media attention, including features in Forbes, Wired, CNN and PopSci, and other media outlets worldwide. As part of his work with MIT CSAIL, Carl built a deep learning vision system for AI to learn and understand human behaviour and interactions, using popular TV shows like The Office, Desperate Housewives, and YouTube videos. The resulting algorithm analyzes videos, then uses what it learns to predict how humans will behave.

Shivon Zilis will participate in a panel discussion at Strata Hadoop World New York 2016, "Where's the puck headed?," considering the big trends in big data and explaining what the field will look like down the road. A year ago today, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive.

Elon Musk famously called it "our greatest existential threat". Physicist Stephen Hawking said that, limited by slow biological evolution, humans wouldn't be able to compete and would be superseded. But the technology that sparked those fears - artificial intelligence - is also being touted as the biggest potential advance in our history. A recent international study found that 50 per cent of experts questioned believe that artificial intelligence - or AI - will be smarter than humans within the next 24 years. And 90 per cent of those surveyed believed that milestone would be reached within 60 years.

Chicago police use algorithmic systems to predict which people are most likely to be involved in a shooting, but they have proved largely ineffective. This week, the White House published its report on the future of artificial intelligence (AI) -- a product of four workshops held between May and July 2016 in Seattle, Pittsburgh, Washington DC and New York City (see go.nature.com/2dx8rv6). During these events (which we helped to organize), many of the world's leading thinkers from diverse fields discussed how AI will change the way we live. Dozens of presentations revealed the promise of using progress in machine learning and other AI techniques to perform a range of complex tasks in every day life. These ranged from the identification of skin alterations that are indicative of early-stage cancer to the reduction of energy costs for data centres. The workshops also highlighted a major blind spot in thinking about AI.

Fisher plays a recently divorced mother of two teens and out-of-work art critic determined to cook a traditional festive dinner with all the trimmings in her sunny Southern California home for her smartphone-addicted friends and extended family. But taming the turkey proves to be the least of her challenges when her neighborhood's cell reception suddenly goes dead, which proceeds to bring out the worst in some already less than exemplary behavior from her preoccupied houseguests. Unfortunately many viewers will have experienced their own connectivity issues long before those characters do. Although there's a genuinely cozy rapport between Fisher and Stevens, the other cast members, including Daphne Zuniga, Nick Court, Natasha Gregson Wagner and Michael Muhney, have a tougher time trying to make all the overwritten, self-consciously quirky dialogue believably their own. Filmmaker Russell Brown clearly had something pertinent he wished to say about our plugged-in, tuned-out obsession with the Internet and was obviously going for a Luis Buñuel-Robert Altman style of social commentary here.

Humanity may still be years if not decades away from producing sentient artificial intelligence. But with the rise of machine-learning services in our smartphones and other devices, one type of narrow, specialized AI has become all the rage. And the research on this branch of AI is only accelerating. In fact, as more industries and policymakers awaken to the benefits of machine learning, two countries appear to be pulling away in the research race. The results will probably have significant implications for the future of AI.

Google needs to make "Parse for AI" to wedge themselves deeply into apps even when on other's platforms/cloud. I've been interested in this space for a while. A broad prediction I have for the coming years is that, as a developer, you won't need to be proficient in machine learning to take advantage of its power. The technology is becoming increasingly democratized and opening up access to millions of new developers. Eventually, you won't even need to know how to program to perform data analysis with ML.

Artificial intelligence, intelligence as exhibited by machines, is not something that is new to this world. Nearly twenty years ago IBM's supercomputer Deep Blue beat world chess champion Gary Kasparov. The win was symbolically significant and a sign that artificial intelligence was catching up with human intelligence. Fast forward 20 years and the application of AI technologies is something we encounter on a regular basis. For example, manufacturing and the use of robots in assembly and packaging has revolutionised how our favourite products are made.