Researchers were able to predict the results of human rights cases with 79 percent accuracy by using artificial intelligence to analyze the factual sections of published human rights judgments. The study, published in PeerJ Computer Science, found that the outcomes were best predicted by analyzing the "circumstances" section of a case--which includes factual background--along with the topics covered by the case and the language used, according to a press release. Publications covering the findings include the Wall Street Journal Law Blog, Law.com The researchers examined 584 cases before the European Court of Human Rights with a machine-learning algorithm. They found that the court's judgments were highly correlated to facts rather than legal arguments.

MasterCard (MA) is taking payments, the last -- and sometimes, the most painful -- part of shopping into the future, replacing card swipes with robots, artificial intelligence and the ubiquitous selfie. The steps, including this week's introduction of a chatbot for banks called "MasterCard KAI" that uses artificial intelligence to respond to customer queries via texts or through apps like Facebook Messenger, are vital parts of CEO Ajay Banga's strategy of leverage technological development to expand the $113 billion company beyond traditional card-based transactions. To create the chatbot, MasterCard partnered with startup Kasisto, developing a "conversational artificial intelligence platform" that banks and merchants can use to let customers make transactions, monitor their spending habits, check account balances and ask questions, the company said at the Money 20/20 conference in Las Vegas. It will be released in the U.S. early next year. "This bot enables entirely new experiences, bringing Mastercard benefits and offers to consumers with human-like conversations that are personal and contextual," Zor Gorelov, Kasisto CEO and co-founder, said in a statement.

Don't know where to start? If you are looking for something more, you could look here for an overview of MOOCs and online lectures from freely-available university lectures. Of course, nothing substitutes rigorous formal education, but let's say that isn't in the cards for whatever reason. Not all machine learning positions require a PhD; it really depends where on the machine learning spectrum one wants to fit in. Check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year.

Conservatives are girding for an extended clash on two fronts in the months ahead: one with a possible Clinton administration and one with Republicans who rejected Donald J. Trump. Megyn Kelly's divergent approach at Fox News took a different turn in her exchange with Newt Gingrich and again raised the question of the channel's future. A lot of healthy people are defying predictions by the Affordable Care Act architects and refusing to enroll, throwing off the calculations behind the system. The startling double-digital declines in TV viewership raise questions about whether the football and soccer leagues have reached their peak. Mr. Beatty's "Rules Don't Apply" is the first film he has written, directed and starred in since "Bulworth" in 1998.

Watson, can you grow into a multibillion-dollar business and become the engine of IBM's resurgence? IBM is betting its future that the answer is yes. Its campaign to commercialize Watson, the company's version of artificial intelligence technology, stands out, even during the current A.I. frenzy in the tech industry. IBM has invested billions of dollars in its Watson business unit, created at the start of 2014, which now employs an estimated 10,000 workers. Its big-ticket marketing push includes clever television ads that feature Watson trading quips with famous people like Serena Williams and Bob Dylan.

We describe a novel optimization method for finite sums (such as empirical risk minimization problems) building on the recently introduced SAGA method. Our method achieves an accelerated convergence rate on strongly convex smooth problems. Our method has only one parameter (a step size), and is radically simpler than other accelerated methods for finite sums. Additionally it can be applied when the terms are non-smooth, yielding a method applicable in many areas where operator splitting methods would traditionally be applied.

We present an intriguing discovery related to Random Fourier Features: in Gaussian kernel approximation, replacing the random Gaussian matrix by a properly scaled random orthogonal matrix significantly decreases kernel approximation error. We call this technique Orthogonal Random Features (ORF), and provide theoretical and empirical justification for this behavior. Motivated by this discovery, we further propose Structured Orthogonal Random Features (SORF), which uses a class of structured discrete orthogonal matrices to speed up the computation. The method reduces the time cost from $\mathcal{O}(d^2)$ to $\mathcal{O}(d \log d)$, where $d$ is the data dimensionality, with almost no compromise in kernel approximation quality compared to ORF. Experiments on several datasets verify the effectiveness of ORF and SORF over the existing methods. We also provide discussions on using the same type of discrete orthogonal structure for a broader range of applications.

The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network's own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps. We apply Professor Forcing to language modeling, vocal synthesis on raw waveforms, handwriting generation, and image generation. Empirically we find that Professor Forcing acts as a regularizer, improving test likelihood on character level Penn Treebank and sequential MNIST. We also find that the model qualitatively improves samples, especially when sampling for a large number of time steps. This is supported by human evaluation of sample quality. Trade-offs between Professor Forcing and Scheduled Sampling are discussed. We produce T-SNEs showing that Professor Forcing successfully makes the dynamics of the network during training and sampling more similar.

We propose a geometric algorithm for topic learning and inference that is built on the convex geometry of topics arising from the Latent Dirichlet Allocation (LDA) model and its nonparametric extensions. To this end we study the optimization of a geometric loss function, which is a surrogate to the LDA's likelihood. Our method involves a fast optimization based weighted clustering procedure augmented with geometric corrections, which overcomes the computational and statistical inefficiencies encountered by other techniques based on Gibbs sampling and variational inference, while achieving the accuracy comparable to that of a Gibbs sampler. The topic estimates produced by our method are shown to be statistically consistent under some conditions. The algorithm is evaluated with extensive experiments on simulated and real data.

Non-negative Matrix Factorization (NMF) is a popular tool for data exploration. Bayesian NMF promises to also characterize uncertainty in the factorization. Unfortunately, current inference approaches such as MCMC mix slowly and tend to get stuck on single modes. We introduce a novel approach using rapidly-exploring random trees (RRTs) to asymptotically cover regions of high posterior density. These are placed in a principled Bayesian framework via an online extension to nonparametric variational inference. On experiments on real and synthetic data, we obtain greater coverage of the posterior and higher ELBO values than standard NMF inference approaches.