Mumbai: Sebastian Thrun is a man of many parts. The president and co-founder of e-learning company Udacity, is not only an innovator and computer scientist but also CEO of Kitty Hawk Corporation that makes flying cars and chairman of Cresta.ai--a Germany-born Thrun was earlier a Google VP and Fellow. At Google, he founded Google X and Google's self-driving car team. He is currently also an Adjunct Professor at Stanford University and at Georgia Tech.

Momentum is a popular technique to accelerate the convergence in practical training, and its impact on convergence guarantee has been well-studied for first-order algorithms. However, such a successful acceleration technique has not yet been proposed for second-order algorithms in nonconvex optimization.In this paper, we apply the momentum scheme to cubic regularized (CR) Newton's method and explore the potential for acceleration. Our numerical experiments on various nonconvex optimization problems demonstrate that the momentum scheme can substantially facilitate the convergence of cubic regularization, and perform even better than the Nesterov's acceleration scheme for CR. Theoretically, we prove that CR under momentum achieves the best possible convergence rate to a second-order stationary point for nonconvex optimization. Moreover, we study the proposed algorithm for solving problems satisfying an error bound condition and establish a local quadratic convergence rate. Then, particularly for finite-sum problems, we show that the proposed algorithm can allow computational inexactness that reduces the overall sample complexity without degrading the convergence rate.

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use.

Many scientifically well-motivated statistical models in natural, engineering and environmental sciences are specified through a generative process, but in some cases it may not be possible to write down a likelihood for these models analytically. Approximate Bayesian computation (ABC) methods, which allow Bayesian inference in these situations, are typically computationally intensive. Recently, computationally attractive empirical likelihood based ABC methods have been suggested in the literature. These methods heavily rely on the availability of a set of suitable analytically tractable estimating equations. We propose an easy-to-use empirical likelihood ABC method, where the only inputs required are a choice of summary statistic, it's observed value, and the ability to simulate summary statistics for any parameter value under the model. It is shown that the posterior obtained using the proposed method is consistent, and its performance is explored using various examples.

In machine learning and data mining, Cluster analysis is one of the most widely used unsupervised learning technique. Philosophy of this algorithm is to find similar data items and group them together based on any distance function in multidimensional space. These methods are suitable for finding groups of data that behave in a coherent fashion. The perspective may vary for clustering i.e. the way we want to find similarity, some methods are based on distance such as K-Means technique and some are probability based, like GMM. Understanding prominent segment of data is always challenging as multidimension space does not allow us to have a look and feel of the distance or any visual context on the health of the clustering. While explaining data using clusters, the major problem is to tell how many cluster are good enough to explain the data. Generally basic descriptive statistics are used to estimate cluster behaviour like scree plot, dendrogram etc. We propose a novel method to understand the cluster behaviour which can be used not only to find right number of clusters but can also be used to access the difference of health between different clustering methods on same data. Our technique would also help to also eliminate the noisy variables and optimize the clustering result. keywords - Clustering, Metric, K-means, hierarchical clustering, silhoutte, clustering index, measures

Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it still lacks of quantitative ways of measuring talent competencies as well as the job's talent requirements. To this end, in this paper, we propose a novel end-to-end data-driven model based on Convolutional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job. To be specific, PJFNN is a bipartite neural network which can effectively learn the joint representation of Person-Job fitness from historical job applications. In particular, due to the design of a hierarchical representation structure, PJFNN can not only estimate whether a candidate fits a job, but also identify which specific requirement items in the job posting are satisfied by the candidate by measuring the distances between corresponding latent representations. Finally, the extensive experiments on a large-scale real-world dataset clearly validate the performance of PJFNN in terms of Person-Job Fit prediction. Also, we provide effective data visualization to show some job and talent benchmark insights obtained by PJFNN.

Sparse model selection is ubiquitous from linear regression to graphical models where regularization paths, as a family of estimators upon the regularization parameter varying, are computed when the regularization parameter is unknown or decided data-adaptively. Traditional computational methods rely on solving a set of optimization problems where the regularization parameters are fixed on a grid that might be inefficient. In this paper, we introduce a simple iterative regularization path, which follows the dynamics of a sparse Mirror Descent algorithm or a generalization of Linearized Bregman Iterations with nonlinear loss. Its performance is competitive to \texttt{glmnet} with a further bias reduction. A path consistency theory is presented that under the Restricted Strong Convexity (RSC) and the Irrepresentable Condition (IRR), the path will first evolve in a subspace with no false positives and reach an estimator that is sign-consistent or of minimax optimal $\ell_2$ error rate. Early stopping regularization is required to prevent overfitting. Application examples are given in sparse logistic regression and Ising models for NIPS coauthorship.

I'm still driving my own car and visit a human doctor when I feel sick. I still haven't surrendered my job to a lifeless robot, and I don't think Alexa or Siri is my best friend. And no, we haven't manufactured our AI-powered robot overlords yet. Nonetheless, just like last year, this year saw some interesting developments in the field of artificial intelligence. As I watched the landscape, I can describe the developments as a shift from hype and craze to reality checks and more focus on the social and political repercussions of this fast moving domain.

Sony has established a set of ethical guidelines for handling artificial intelligence technologies, following the lead of Google and Microsoft. "Sony has to show a clear standpoint on the technology," said Hiroaki Kitano, chief executive at Sony Computer Science Laboratories. AI has become crucial to businesses in many industries, especially for the development of new products and services. But using the technology to process vast amounts of data, often including customers' personal information, raises a number of ethical concerns. Google in June disclosed its guidelines on AI, which detail policies such as not pursuing "technologies that cause or are likely to cause overall harm," such as weapons.

As businesses struggle to combat increasingly sophisticated cybersecurity attacks, the severity of which is exacerbated by both the vanishing IT perimeters in today's mobile and IoT era, coupled with an acute shortage of skilled security professionals, IT security teams need both a new approach and powerful new tools to protect data and other high-value assets. Increasingly, they are looking to artificial intelligence (AI) as a key weapon to win the battle against stealthy threats inside their IT infrastructures, according to a new global research study conducted by the Ponemon Institute on behalf of Aruba, a Hewlett Packard Enterprise company. The Ponemon Institute study, entitled "Closing the IT Security Gap with Automation & AI in the Era of IoT," surveyed 4,000 security and IT* professionals across the Americas, Europe and Asia to understand what makes security deficiencies so hard to fix, and what types of technologies and processes are needed to stay a step ahead of bad actors within the new threat landscape. The research revealed that in the quest to protect data and other high-value assets, security systems incorporating machine learning and other AI-based technologies are essential for detecting and stopping attacks that target users and IoT devices. The majority of respondents from India agree that security products with AI functionality will help to: * Reduce false alerts (69 percent) * Increase their team's effectiveness (65 percent) * Provide greater investigation efficiencies (56 percent) * Advance their ability to more quickly discover and respond to stealthy attacks that have evaded perimeter defense systems (66 percent).