Andrew Moore's career path at Carnegie Mellon has become emblematic of the way the University fosters its star talent. He became a tenured professor at Carnegie Mellon in 2000. In 2006, Moore joined Google, where he was responsible for building a new engineering office. As a vice president of engineering, Andrew was responsible for Google Shopping, the company's retail segment. Moore returned to Carnegie Mellon in 2014 as the Dean of the Computer Science department.

Armed with an understanding of machine learning, ROSS Intelligence is going after LexisNexis and Thomson Reuters for ownership of legal research. The startup, founded in 2015 by Andrew Arruda, Jimoh Ovbiagele and Pargles Dall'Oglio at the University of Toronto, is announcing an $8.7 million Series A today led by iNovia Capital with participation from Comcast Ventures Catalyst Fund, Y Combinator Continuity Fund, Real Ventures, Dentons' NextLaw Labs and angels. At its core, ROSS is a platform that helps legal teams sort through case law to find details relevant to new cases. This process takes days and even weeks with standard keyword search, so ROSS is augmenting keyword search with machine learning to simultaneously speed up the research process and improve relevancy of items found. "Bluehill benchmarks Lexis's tech and they are finding 30 percent more relevant info with ROSS in less time," Andrew Arruda, co-founder and CEO of ROSS, explained to me in an interview.

Summary: Bengaluru: Google on Friday launched a day-long Mobile Developer Fest here to train young students in the latest mobile technologies. The event will also provide an opportunity for students to become part of Google Developer Student Clubs and University Innovation Fellows. Students can also participate in hands-on code labs sessions, and learn directly from Google certified developers. The event, which aims to train two million developers in the country, was conducted at the CMR Institute of Technology. The day-long fest gave computer science and engineering students insights across multiple product areas like Machine Learning, Firebase, Android and Progressive Web Apps. Bengaluru: Google on Friday launched a day-long Mobile Developer Fest here to train young students in the latest mobile technologies.

Nonnegative matrix factorization (NMF), a dimensionality reduction and factor analysis method, is a special case in which factor matrices have low-rank nonnegative constraints. Considering the stochastic learning in NMF, we specifically address the multiplicative update (MU) rule, which is the most popular, but which has slow convergence property. This present paper introduces on the stochastic MU rule a variance-reduced technique of stochastic gradient. Numerical comparisons suggest that our proposed algorithms robustly outperform state-of-the-art algorithms across different synthetic and real-world datasets.

This paper raises an implicit manifold learning perspective in Generative Adversarial Networks (GANs), by studying how the support of the learned distribution, modelled as a submanifold $\mathcal{M}_{\theta}$, perfectly match with $\mathcal{M}_{r}$, the support of the real data distribution. We show that optimizing Jensen-Shannon divergence forces $\mathcal{M}_{\theta}$ to perfectly match with $\mathcal{M}_{r}$, while optimizing Wasserstein distance does not. On the other hand, by comparing the gradients of the Jensen-Shannon divergence and the Wasserstein distances ($W_1$ and $W_2^2$) in their primal forms, we conjecture that Wasserstein $W_2^2$ may enjoy desirable properties such as reduced mode collapse. It is therefore interesting to design new distances that inherit the best from both distances.

Matrix factorization is a flexible approach to uncover latent factors in low-rank or sparse models. With sparse factors, it is used in dictionary learning, and has proven very effective for denoising and visual feature encoding in signal and computer vision [see e.g., 1]. When the data admit a low-rank structure, matrix factorization has proven very powerful for various tasks such as matrix completion [2, 3], word embedding [4, 5], or network models [6]. It is flexible enough to accommodate a large set of constraints and regularizations, and has gained significant attention in scientific domains where interpretability is a key aspect, such as genetics [7] and neuroscience [8]. In this paper, our goal is to adapt matrix-factorization techniques to huge-dimensional datasets, i.e., with large number of columns n and large number of rows p. Specifically, our work is motivated by the rapid increase in sensor resolution, as in hyperspectral imaging or fMRI, and the challenge that the resulting high-dimensional signals pose to current algorithms.

Machine learning libraries are becoming faster and more accessible with each passing year, showing no signs of slowing down. While traditionally Python has been the go-to language for machine learning, nowadays neural networks can run in any language, including JavaScript! The web ecosystem has made a lot of progress in recent times and although JavaScript and Node.js are still less performant than Python and Java, they are now powerful enough to handle many machine learning problems. Web languages also have the advantage of being super accessible - all you need to run a JavaScript ML project is your web browser. Most JavaScript machine learning libraries are fairly new and still in development, but they do exist and are ready for you to try them.

The consternation of radiologists about the impact of artificial intelligence is real--but so are the benefits of machine learning. Recent research showed that deep learning with a convolutional neural network (CNN) was successful in differentiating liver masses in CT. The retrospective study, published online Oct. 23 in Radiology, examined the diagnostic abilities of a deep learning method with a CNN. Researchers tested the CNN with 100 liver mass image sets from 2016, including 74 men and 26 women with the average age of 66 years old. "This preliminary study, which used 55, 536 image sets (1068 image sets augmented by a factor of 52) to obtain models, indicated that classifying liver masses into five categories can be accomplished with a high degree of accuracy by using a deep learning method with a CNN on dynamic contrast-enhanced CT images," wrote Koichiro Yasaka, MD, PhD, with the department of radiology at the University of Tokyo Hospital in Japan, and colleagues.

New technologies bring new products, which shift jobs across occupations: with the arrival of cars, the economy needed more assembly line workers and fewer blacksmiths. New technologies also bring new work processes, which shift skills in jobs: with the arrival of copiers, office workers needed to replace ink cartridges but not use carbon paper. Economic history is full of examples of new technologies causing such shifts. Workers often worry that new technologies will destroy old jobs without creating new ones. However, economic history suggests that job destruction and creation have always gone together, with a shift in jobs and skills that leaves most people still employed. Will artificial intelligence (AI) differ from past technologies in the way it shifts jobs and skills?

This post is authored by Matthew Calder, Senior Business Strategy Manager, and Ke Wang, Research Intern at Microsoft. There are more than 9,000 students enrolled in the Microsoft Introduction to C# course on edX.org. Although course staff can't offer the type of guidance available in an on-campus classroom setting, students can receive personalized help, thanks to a project from Microsoft Research. When a student's assignment contains mistakes, that student--within seconds--receives a message specific to their code submission. Beyond just informing the student that their program doesn't work, Microsoft has created a tool which automatically generates feedback that precisely identifies errors and even hints at how to correct them.