Global Big Data Conference's vendor agnostic Global Artificial Intelligence(AI) Conference is held on January 19th, January 20th, & January 21st 2017 on all industry verticals(Finance, Retail/E-Commerce/M-Commerce, Healthcare/Pharma/BioTech, Energy, Education, Insurance, Manufacturing, Telco, Auto, Hi-Tech, Media, Agriculture, Chemical, Government, Transportation etc..). It will be the largest vendor agnostic conference in AI space. The Conference allows practitioners to discuss AI through effective use of various techniques. Large amount of data created by various mobile platforms, social media interactions, e-commerce transactions, and IoT provide an opportunity for businesses to effectively tailor their services by effective use of AI. Proper use of Artificial Intelligence can be a major competitive advantage for any business considering vast amount of data being generated.

This tutorial introduces machine learning and data science concepts for developers. On the web, we already have many excellent resources for learning data science, however, the sheer amount of material can, in itself, be daunting. This is based on my insights from the Enterprise AI course and also the Data Science for IoT course which I teach at Oxford University. We explain concepts simply but in context. Many tutorials explain one specific aspect but do not show how it fits into the wider picture.

Artificial Intelligence (AI) is an important and evolving concept that is having significant impact within the Customer Experience industry -- and it's a topic that is being talked about on a seemingly daily basis at this point. But is AI really ready for primetime in customer care? I spoke with Michael Johnston, Lead Inventive Scientist at Interactions, about frequently asked questions about AI and Machine Learning as they apply to customer care. Artificial Intelligence refers to the capability of a machine to imitate intelligent human behavior. Put another way, AI technologies are algorithms that attempt to mimic things that humans do.

IMAGE: This is the Architecture of the Adversarial Autoencoder (AAE). The study was published in Oncotarget on 22nd of December, 2016. The study represents the proof of concept for applying Generative Adversarial Networks (GANs) to drug discovery. The authors significantly extended this model to generate new leads according to multiple requested characteristics and plan to launch a comprehensive GAN-based drug discovery engine producing promising therapeutic treatments to significantly accelerate pharmaceutical R&D and improve the success rates in clinical trials. Since 2010 deep learning systems demonstrated unprecedented results in image, voice and text recognition, in many cases surpassing human accuracy and enabling autonomous driving, automated creation of pleasant art and even composition of pleasant music.

Understanding efficiency in high dimensional linear models is a longstanding problem of interest. Classical work with smaller dimensional problems dating back to Huber and Bickel has illustrated the benefits of efficient loss functions. When the number of parameters $p$ is of the same order as the sample size $n$, $p \approx n$, an efficiency pattern different from the one of Huber was recently established. In this work, we consider the effects of model selection on the estimation efficiency of penalized methods. In particular, we explore whether sparsity, results in new efficiency patterns when $p > n$. In the interest of deriving the asymptotic mean squared error for regularized M-estimators, we use the powerful framework of approximate message passing. We propose a novel, robust and sparse approximate message passing algorithm (RAMP), that is adaptive to the error distribution. Our algorithm includes many non-quadratic and non-differentiable loss functions. We derive its asymptotic mean squared error and show its convergence, while allowing $p, n, s \to \infty$, with $n/p \in (0,1)$ and $n/s \in (1,\infty)$. We identify new patterns of relative efficiency regarding a number of penalized $M$ estimators, when $p$ is much larger than $n$. We show that the classical information bound is no longer reachable, even for light--tailed error distributions. We show that the penalized least absolute deviation estimator dominates the penalized least square estimator, in cases of heavy--tailed distributions. We observe this pattern for all choices of the number of non-zero parameters $s$, both $s \leq n$ and $s \approx n$. In non-penalized problems where $s =p \approx n$, the opposite regime holds. Therefore, we discover that the presence of model selection significantly changes the efficiency patterns.

In this paper, we introduce a package for semi-supervised learning research in the R programming language called RSSL. We cover the purpose of the package, the methods it includes and comment on their use and implementation. We then show, using several code examples, how the package can be used to replicate well-known results from the semi-supervised learning literature.

Word2vec is a widely used algorithm for extracting low-dimensional vector representations of words. State-of-the-art algorithms including those by Mikolov et al. [5, 6] have been parallelized for multi-core CPU architectures, but are based on vector-vector operations with "Hogwild" updates that are memory-bandwidth intensive and do not efficiently use computational resources. In this paper, we propose "HogBatch" by improving reuse of various data structures in the algorithm through the use of minibatching and negative sample sharing, hence allowing us to express the problem using matrix multiply operations. We also explore different techniques to distribute word2vec computation across nodes in a compute cluster, and demonstrate good strong scalability up to 32 nodes. The new algorithm is particularly suitable for modern multi-core/many-core architectures, especially Intel's latest Knights Landing processors, and allows us to scale up the computation near linearly across cores and nodes, and process hundreds of millions of words per second, which is the fastest word2vec implementation to the best of our knowledge.

A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, in which a prominent eigenvector is planted into a random matrix. These distributions form natural statistical models for principal component analysis (PCA) problems throughout the sciences. Baik, Ben Arous and P\'ech\'e showed that the spiked Wishart ensemble exhibits a sharp phase transition asymptotically: when the signal strength is above a critical threshold, it is possible to detect the presence of a spike based on the top eigenvalue, and below the threshold the top eigenvalue provides no information. Such results form the basis of our understanding of when PCA can detect a low-rank signal in the presence of noise. However, not all the information about the spike is necessarily contained in the spectrum. We study the fundamental limitations of statistical methods, including non-spectral ones. Our results include: I) For the Gaussian Wigner ensemble, we show that PCA achieves the optimal detection threshold for a variety of benign priors for the spike. We extend previous work on the spherically symmetric and i.i.d. Rademacher priors through an elementary, unified analysis. II) For any non-Gaussian Wigner ensemble, we show that PCA is always suboptimal for detection. However, a variant of PCA achieves the optimal threshold (for benign priors) by pre-transforming the matrix entries according to a carefully designed function. This approach has been stated before, and we give a rigorous and general analysis. III) For both the Gaussian Wishart ensemble and various synchronization problems over groups, we show that inefficient procedures can work below the threshold where PCA succeeds, whereas no known efficient algorithm achieves this. This conjectural gap between what is statistically possible and what can be done efficiently remains open.

Differential privacy formalises privacy-preserving mechanisms that provide access to a database. We pose the question of whether Bayesian inference itself can be used directly to provide private access to data, with no modification. The answer is affirmative: under certain conditions on the prior, sampling from the posterior distribution can be used to achieve a desired level of privacy and utility. To do so, we generalise differential privacy to arbitrary dataset metrics, outcome spaces and distribution families. This allows us to also deal with non-i.i.d or non-tabular datasets. We prove bounds on the sensitivity of the posterior to the data, which gives a measure of robustness. We also show how to use posterior sampling to provide differentially private responses to queries, within a decision-theoretic framework. Finally, we provide bounds on the utility and on the distinguishability of datasets. The latter are complemented by a novel use of Le Cam's method to obtain lower bounds. All our general results hold for arbitrary database metrics, including those for the common definition of differential privacy. For specific choices of the metric, we give a number of examples satisfying our assumptions.

In [3] we presented a prototype of a system that enables users to explore arguments for a given topic. This involves these steps: 1. Argument identification. In the first step, arguments concerning a given topic are identified in a given text and attacking and supporting relationships between the propositions are established. The result is an argumentation graph. In the future we hope to use argumentation mining techniques to automate this step. At this time, this is done manually by marking up some text.