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.

For better or worse, Google Assistant can do it all. From mundane tasks like turning on your lights and setting reminders to convincingly mimicking human speech patterns, the AI helper is so capable it's scary. Its latest (unofficial) ability, though, is a bit more sinister. Artist Alexander Reben recently taught Assistant to fire a gun. Fortunately, the victim was an apple, not a living being.

Analyzing the performance of the "ground truth" agent will vary in difficulty based on the task, in terms of a stochastic vs. deterministic environment, how obvious the reward function is (as simple as distance traveled, or more difficult like the score in Tetris), etc. A "ground truth" agent implies perfect performance which is extremely difficult to obtain for any but the most simple environments. If you are mainly just interested in looking at how to model an agent's behaviors, then the performance of the "ground truth" agent maybe won't matter. But if the performance of the "ground truth" agent does matter (it is part of an evolutionary process or something), then perhaps you could do something like record your own actions at the task (if doable), or compare the score to that of some baseline. Can you share more details about your project, like the environment you're using, what exactly you're trying to get out of it, what the project is for, etc.? This will help to get you a better answer.

As of 1/15, based on historical growth rates, it is estimated that 90% of USA Uber driver-partners drive for UberX.

The growth of big data opens up opportunities for Investment Banks in terms of sources of information, whilst the development of Artificial Intelligence can turn this information into answers. In this webinar Leon Saunders Calvert, Sofia Spencer and Ryan Roser will discuss how machine learning can be used to predict M&A opportunity and Ryan will share the results of his work in this area.

With its paid Premium streaming service, YouTube has a steep climb to even compete against entrenched services like Netflix. First it has to get our attention, so for one of its most-anticipated series, Impulse, it came up with an interesting promotion. Google Assistant owners can access the first episode by saying "talk to Impulse." The AI device will give a brief sum-up of the show and ask for a password (it's "Henry" and a few variants). Once you do that, you'll get a link to the first unlisted episode on YouTube.