More people now live in cities than at any other time in history, and their number is predicted to increase: 66% of the world's population will live in urban areas by 2050. To house all these people, innovators are developing solutions for smaller, more efficient, and environmentally sustainable abodes that incorporate digital technologies. The house of the future is small and smart. Jeff Wilson founded Kasita, based in Austin, Texas, after spending a year living in a 33-square-foot converted dumpster to experience the challenges of (very) small living for himself. Kasita's homes combine design minimalism with smart home technology that makes new housing efficient to run, inexpensive to own, simple to maintain, and quick to install (in as little as a day).

Video games can get a pretty raw deal in the news. At worst, we see stories claiming links between playing violent games and some of the worst aspects of humanity, or that games are robbing children of time spent in nature. At best, we hear news stories where games are regarded with a certain distain; something to be smirked at, and not taken seriously. But these sorts of stories completely miss the varied, rich and nuanced experiences that playing games can afford. For many children and adults with disabilities, simply being able to pick up a controller and coordinate fine motor movements can be a difficult, even impossible task.

The goodness of fit for data reduction techniques such as MDS and t-SNE can be easily assessed with Shepard diagrams. A Shepard diagram compares how far apart your data points are before and after you transform them (ie: goodness-of-fit) as a scatter plot. Shepard diagrams can be used for data reduction techniques like principal components analysis (PCA), multidimensional scaling (MDS), or t-SNE. In this post, I illustrate goodness of fit with Shapard diagrams using a simple example which maps the locations of cities in Europe using t-SNE and MDS. You will see that the t-SNE approach, which is not designed to preserve all distances in the data, produces an odd-looking map of Europe and a distorted Shepard diagram.

Just five years ago, artificial intelligence-enabled computers could barely recognize images fed to them, much less analyze them anything like people can. But suddenly, they've turned the tables. "In 2011 their error rate was 26 percent," says Jeff Dean, chief of the Google Brain project, which along with other tech giants has helped lead a recent revolution in image recognition as well as speech recognition and self-driving cars. Now, he says, computers' ability to view and analyze images (pictured) exceeds what human eyes can do. "If you'd have told me that would be a possible just a few years ago, I would've never believed you," Dean said during an appearance at a research event in Heidelberg, Germany.

Slamby uses Azure's unique capabilities to deliver award-winning data management services. Founded in 2013 in Debrecen, Hungary, Slamby-Semantics is an award winning international IT solution firm developing technology that can understand and categorize written language. Specialized for E-commerce solutions, classified ads, and job portal categorization, Slamby's semantics technology is capable of learning, remembering, and using acquired knowledge to resolve common, every-day tasks – from sorting advertisements, to complex textual analysis, to simple customer service tasks. Slamby's unique ability to quickly analyze and sort complex text frees people from the tedious process of organizing and categorizing their work, enabling them to be more productive. "We are providing a ready-to-use, instant data management service that can process domain specific data," says CEO and Founder Peter Mezei, "Our products are completely language-independent, unlike other solutions in this market, and we think that gives us a big leg up on the competition."

Pete Warden wants you to throw your voice-recognition hardware in the trash. And then buy more--and more, and more. This Google engineer is on a quest to make voice recognition dirt cheap. His idea is simple enough: cut down the neural networks that are usually used to process sound until they're efficient enough to run on cheap, lightweight chips. "What I want is a 50-cent chip that can do simple voice recognition and run for a year on a coin battery," he explained during last week's Arm Research Summit in Cambridge, U.K. "We're not there yet … but I really think this is doable with even the current technology that we have now." At such a low price, the hardware would effectively become disposable, opening up uses that have previously been unimaginable.

Artificial Intelligence (AI) has been touted as the next major disruptor of the financial services sector. Shankar Narayanan, Head of UK & Ireland at Tata Consultancy Services (TCS), reflects on how the novel technology is transforming the banking landscape. Today, there is one innovation, above all else, that is shaping the future of the financial services (FS) sector through the entire value chain, whether a retail bank or a global financial institution – and this is Artificial Intelligence (AI). It's difficult to read any analyst or trends reports about the future of banking and FS without mentions of AI innovation. In many respects, this is because AI is a tool that's already having a significant impact.

The General Data Protection Regulation (GDPR) is a wide-ranging and complex regulation intended to strengthen and unify data protection for all individuals within the European Union (EU). A year ago I blogged about the data governance ramifications of GDPR, and in this blog I'll focus on another facet of GDPR to talk about a related analytics topic: explainable artificial intelligence (AI). First, let's start with GDPR. Article 22 of GDPR, "Automated individual decision-making, including profiling," concerns the use of data in decision-making that affects individuals, such as a person applying for a loan. The data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her." Point 2 of Article 22 describes exclusions (including situations involving the person's explicit consent, such as applying for a loan), but the key issue for our discussion here is in point 3: "…the data controller shall implement suitable measures to safeguard the data subject's rights and freedoms and legitimate interests, at least the right to obtain human intervention on the part of the controller, to express his or her point of view and to contest the decision."

Peer review, evaluation, and selection is a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of procedures or mechanisms for peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.

A standard recipe for spoken language recognition is to apply a Gaussian back-end to i-vectors. This ignores the uncertainty in the i-vector extraction, which could be important especially for short utterances. A recent paper by Cumani, Plchot and Fer proposes a solution to propagate that uncertainty into the backend. We propose an alternative method of propagating the uncertainty.