RE•WORK will host its annual East Coast events on Deep Learning and the Internet of Things in Boston on 12 & 13 May. Over 300 machine learning and IoT enthusiasts and experts will come together to hear keynote presentations, panel discussions, fireside chats and to explore the startup showcase area. The Deep Learning Summit brings together leaders from industry, academia and startups to explore advances in deep learning methods and techniques, as well as their business applications in areas including finance, manufacturing, healthcare & transportation. Confirmed speakers and presentations include Yoshua Bengio, Full Professor at Université de Montréal; Tony Jebara, Director of Machine Learning Research at Netflix; Vivienne Sze, Assistant Professor at MIT; Naveen Rao, CEO & Co-Founder of Nervana Systems, and more. "Today's models can be trained on huge quantities of data, but that's not enough," says Bengio, who together with LeCun and Google's Geoffrey Hinton is one of the original musketeers of deep learning.

John Holden is a journalist specializing in science, tech and innovation. His work has appeared mainly in the Irish Times. Cornered at a party by some bore talking about his unimaginative app that will "change the world." All seemingly convincing excuses to leave have already been used by the rest of the group (who were all there just a second ago). The last refuge of the rude scoundrel -- the smartphone -- is in your jacket pocket in the cloakroom all the way across the room. So you just have to suck it up and simulate enthusiasm for this guy's pitch.

Scientists in California have developed a microscope that uses machine learning and photonic time stretch to locate cancer cells more efficiently. The microscope, invented by UCLA scientists, uses artificial intelligence (AI) to analyse 36 million images per second, more accurately identifying cancer cells present in blood samples. Currently, doctors track cancerous cells by adding biochemicals to blood samples, which then add labels to them for later detection. However, this can damage the cells, making the samples useless. Other techniques include identifying cancer cells by their physical characteristics, but they can be inaccurate.

Some of the most powerful people in the world have gathered this week to discuss the most pressing issues affecting humanity. And the overwhelming conclusion is that the robots are coming. At the Milken Institute's Global Conference in California, at least four panels focused ontechnology taking over markets to mining, and most importantly,jobs. Some of the most powerful people in the world have gathered this week to discuss the most pressing issues affecting humanity, and the overwhelming conclusion is the robots are coming. At the Milken Institute's Global Conference in California, four panels focused on technology taking over markets and jobs (stock image) 'Most of the benefits we see from automation is about higherquality and fewer errors, but in many cases it does reducelabor,' Michael Chui, a partner at the McKinsey GlobalInstitute, said on Tuesday during a panel on'Is Any Job TrulySafe?'

At the core of the project is the idea of using machines to identify launcher-shaped objects buried within the staggering amount of digital imagery collected by US spy satellites, manned and unmanned aircraft. A senior official in the Department of Defense explained that it is this vast amount of data that makes manual research inefficient. "What was largely a manual process for intelligence analysts has to become an automated one," he said, cited by Defense One. The ultimate goal is to train computers spot what are called transporter-erector-launchers (TELs). North Korea used these kinds of launchers during missile tests conducted over the last few months.

Back in 2009 a Finnish company called Rovio launched its 52nd video game. Its premise was simple: players would use their smartphone touchscreen – still a relative novelty two years after the first iPhone came out – to control a catapult. Swine flu was in the news, so the enemies would be pigs. That game reportedly cost less than 100,000 to make. The numbers involved in The Angry Birds Movie, which arrives in cinemas 13 May, are rather larger.

In the U.S., individuals give more than 200 billion dollars to over 50 thousand charities each year, yet how people make these choices is not well understood. In this study, we use data from CharityNavigator.org and web browsing data from Bing toolbar to understand charitable giving choices. Our main goal is to use data on charities' overhead expenses to better understand efficiency in the charity marketplace. A preliminary analysis indicates that the average donor is "wasting" more than 15% of their contribution by opting for poorly run organizations as opposed to higher rated charities in the same Charity Navigator categorical group. However, charities within these groups may not represent good substitutes for each other. We use text analysis to identify substitutes for charities based on their stated missions and validate these substitutes with crowd-sourced labels. Using these similarity scores, we simulate market outcomes using web browsing and revenue data. With more realistic similarity requirements, the estimated loss drops by 75%—much of what looked like inefficient giving can be explained by crowd-validated similarity requirements that are not fulfilled by most charities within the same category. A choice experiment helps us further investigate the extent to which a recommendation system could impact the market. The results indicate that money could be redirected away from the long-tail of inefficient organizations. If widely adopted, the savings would be in the billions of dollars, highlighting the role the web could have in shaping this important market.

Embeddings are generic representations that are useful for many NLP tasks. In this paper, we introduce DENSIFIER, a method that learns an orthogonal transformation of the embedding space that focuses the information relevant for a task in an ultradense subspace of a dimensionality that is smaller by a factor of 100 than the original space. We show that ultradense embeddings generated by DENSIFIER reach state of the art on a lexicon creation task in which words are annotated with three types of lexical information - sentiment, concreteness and frequency. On the SemEval2015 10B sentiment analysis task we show that no information is lost when the ultradense subspace is used, but training is an order of magnitude more efficient due to the compactness of the ultradense space.

While many expect that the use of advanced learning technologies like intelligent tutoring systems (ITSs) will substitute for human teaching and thus reduce the influence of teachers on student outcomes, studies consistently show that outcomes vary substantially across teachers and schools (Pane et al. 2010; Pane et al. 2014; Ritter et al. 2007a; Koedinger et al. 1997; Koedinger and Sueker 2014). Despite these findings, there have been few efforts (e.g., Schofield 1995) to understand the mechanisms by which teacher practices influence student learning on such systems. We present analyses of Carnegie Learning’s Cognitive Tutor ITS data from a large school district in the southeastern United States, which present a variety of usage and implementation profiles that illuminate disparities in deployments in practical, day-to-day educational settings. We focus on differential effectiveness of teachers’ implementations and how implementations may drive learner efficiency in ITS usage, affecting long term learning outcomes. These results are consistent with previous studies of predictors and causes of learning outcomes for students using Cognitive Tutor. We provide recommendations for practitioners seeking to deploy intelligent learning technologies in real world settings.

Elderly patients, aged 65 or older, make up 13.5% of the U.S. population, but represent 45.2% of the top 10% of healthcare utilizers, in terms of expenditures. Middle-aged Americans, aged 45 to 64 make up another 37.0% of that category. Given the high demand for healthcare services by the aforementioned population, it is important to identify high-cost users of healthcare systems and, more importantly, ineffective utilization patterns to highlight where targeted interventions could be placed to improve care delivery. In this work, we present a novel multi-level framework applying machine learning (ML) methods (i.e., random forest regression and hierarchical clustering) to group patients with similar utilization profiles into clusters. We use a vector space model to characterize a patient’s utilization profile as the number of visits to different care providers and prescribed medications. We applied the proposed methods using the 2013 Medical Expenditures Panel Survey (MEPS) dataset. We identified clusters of healthcare utilization patterns of elderly and middle-aged adults in the United States, and assessed the general and clinical characteristics associated with these utilization patterns. Our results demonstrate the effectiveness of the proposed framework to model healthcare utilization patterns. Understanding of these patterns can be used to guide healthcare policy-making and practice.