For large-scale corporations, predicting how users will react to an app redesign or a new website feature is a months-long, intensive process that requires commissioning a study, recruiting participants for said study, and poring over the results before making a final decision. It's a labor-intensive undertaking, and one New York-based Alpha aims to streamline with the help of artificial intelligence. Alpha, which this week announced a $10 million funding round led by Crosslink Capital Ventures, Calibrate Ventures, Spider Capital, and Cendana Capital, is an insights platform that helps app developers, media companies, retailers, and others chart product roadmaps quickly and easily. "Ours is the first on-demand product to deliver insights for any sort of business platform," founder and CEO Thor Ernstsson told VentureBeat in a phone interview. Ernstsson, a former lead architect at mobile games company Zynga and CTO of UnitedHealth subsidiary Audax Health, has experienced the benefits of rapid experimentation firsthand and sought to distill those gains into a highly scalable product.

It seems like the world is getting smarter around us. Technologies seem to know what we are going to do before we do it. Our phones know what restaurants are nearby. Our computers know just what we have been thinking about buying. Even our TVs and Kindles know what we want next to entertain us.

Delhi-based last mile delivery startup NOW Delivery is exploring artificial intelligence and developing a voice-based platform to help small merchants for orders. The company offers horizontal delivery solutions to hyperlocal business. "We are using artificial intelligence and machine learning for our order allocation which means we are devising an algorithm which would match the rider with the order. Apart from that we are also developing a voice-based platform for smaller merchants. There are voice-based speakers in the market, so the idea is to create a simple voice-based platform where a person can announce an order and get captured on our platform and we later passed it on to our rider," said Vivek Pandey, founder and CEO, NOW Delivery.

Around the time Leonardo Da Vinci was painting the Mona Lisa, he was also writing his Codex on the Flight of Birds, a roughly 35,000-word exploration of the ways in which man might take to the air. His illustrations included diagrams positing pre-Newtonian theories of physics, a rudimentary plan for a flying machine and many, many sketches of birds in flight. The Mona Lisa, with her secretive smile, is a universe of intimacy captured on a relatively small panel of wood. But the landscape behind his captivating subject shows the world as you would see it from atop a tall hill--or from the vantage point you would get if you had hitched a ride on the back of a giant bird. Even as da Vinci was perfecting one way of seeing a face, he was dreaming of other ways of looking.

"I don't think Homo sapiens-type people will exist in 10 or 20 years' time," Getnet Assefa, 31, speculates as he gazes into the reconstructed eye sockets of Lucy, one of the oldest and most famous hominid skeletons known, at the National Museum of Ethiopia. "Slowly the biological species will disappear and then we will become a fully synthetic species," Assefa says. "I believe [we] can inspire the Ethiopian youth to actually get really engaged in AI and feel like it's their thing." "Perception, memory, emotion, intelligence, dreams -- everything that we value now -- will not be there," he adds. Assefa is a computer scientist, a futurist, and a utopian -- but a pragmatic one at that. He is founder and chief executive of iCog, the first artificial intelligence (AI) lab in Ethiopia, and a stone's throw from the home of Lucy.

We present a computational framework for the semantic interpretation of symmetry in naturalistic scenes. Key features include a human-centred representation, and a declarative, explainable interpretation model supporting deep semantic question-answering founded on an integration of methods in knowledge representation and computer vision. In the backdrop of the visual arts, we showcase the framework's capability to generate human-centred, queryable, relational structures, also evaluating the framework with an empirical study on the human perception of visual symmetry. Our framework represents and is driven by the application of foundational Vision and KR methods in the psychological and social sciences.

We consider a particular instance of a common problem in recommender systems: using a database of book reviews to inform user-targeted recommendations. In our dataset, books are categorized into genres and sub-genres. To exploit this nested taxonomy, we use a hierarchical model that enables information pooling across across similar items at many levels within the genre hierarchy. The main challenge in deploying this model is computational: the data sizes are large, and fitting the model at scale using off-the-shelf maximum likelihood procedures is prohibitive. To get around this computational bottleneck, we extend a moment-based fitting procedure proposed for fitting single-level hierarchical models to the general case of arbitrarily deep hierarchies. This extension is an order of magnetite faster than standard maximum likelihood procedures. The fitting method can be deployed beyond recommender systems to general contexts with deeply-nested hierarchical generalized linear mixed models.

We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label. We use a deep lattice network model so we can architect the model structure to enhance interpretability, and add monotonicity constraints between inputs-and-outputs. We then use the proposed set function to automate the engineering of dense, interpretable features from sparse categorical features, which we call semantic feature engine. Experiments on real-world data show the achieved accuracy is similar to deep sets or deep neural networks, and is easier to debug and understand.

In a constantly changing world, companies develop before global changes come into force. Innovations are the key to effective results, yet, not so long ago, improving user experience and content personalization was not vital. Let's look at how the things were several years ago. The way online businesses acted was similar to offline stores. Websites' content remained the same for each visitor.

As publishers grapple with how to best make use of the troves of audience data at their disposal, a growing number are handing brands the keys to in-house data and artificial intelligence tools that could change the way ads and sponsored content are sold. The New York Times, Group Nine Media and the Washington Post are among the media companies that have taken advantage of data science projects built for editorial purposes to give advertisers a clearer picture of who's consuming their content and how to best speak to them. Publishers hope programs like these might help them gain back ground from tech giants like Facebook and Google that dominate the ads industry through targeting precision. The Times debuted a unit earlier this year called nytDEMO that encompasses two new data-crunching tools. One, called "Project Feels," is meant to gauge and analyze readers' emotional reaction to articles and videos through a crowdsourced survey tool.