Technology of Business has garnered opinions from dozens of companies on what they think will be the dominant global tech trends in 2018. Artificial intelligence (AI) dominates the landscape, closely followed, as ever, by cyber-security. But is AI an enemy or an ally? Whether helping to identify diseases and develop new drugs, or powering driverless cars and air traffic management systems, the consensus is that AI will start to deliver in 2018, justifying last year's sometimes hysterical hype. It will make its presence felt almost everywhere.

Levi's launched a new denim smart jacket with Google's Project Jacquard technology. The Trucker Jacket allows users to interact with an Android device with a gesture or touch to their sleeve. Jacquard is a Google project built to experiment with fiber electronics. The Trucker Jacket looks like an average jean jacket but it contains a small electronic tag which connects to a section of conductive fibers, all built into the sleeve. The tag connects by Bluetooth to an Android smartphone, and the conductive fibers act as a touchpad. The Jacquard app lets users determine what the four different gestures, brushing in, brushing out, double-tapping, and covering the area, do.

The Sun is the most important source of energy in the solar system. It is important for life to thrive on Earth but at the same time, can cause disruptions. Solar Flares - a sudden flash near sunspots occasionally accompanied by coronal mass ejection can cause interference in communication systems and even power grids. The Sun is an important factor that can impact the weather in space and on Earth, is constantly monitored by an array of telescopes and satellites. Scientists have figured out a more reliable method to study the spherical ball of plasma.

Thanks for your note on Facebook, which I reprint below, followed by some thoughts of my own. I appreciate your taking the time to consider these issues. I concur that you and I agree more than we disagree, and as you do, I share your implicit hope that field might benefit from an articulation of both our agreements and our disagreements. "is that a simple hybrid in which the output of the deep net are discretized and then passed to a GOFAI symbolic processing system will not work. Many reasons: (1) you need learning in the system 2 component as well as in the system 1 part, (2) you need to represent uncertainty there as well…" "… it's probably not realistic to encode by hand every-thing that machines need to know. Machines are going to need to learn lots of things on their own. We might want to hand-code the fact that sharp hard blades can cut soft material, but then an AI should be able to build on that knowledge and learn how knives, cheese graters, lawn mowers, and blenders work, without having each of these mechanisms coded by hand" " formal logic of the sort we have been talking about does only one thing well: it allows us to take knowledge of which we are certain and apply rules that are always valid to deduce new knowledge of which we are also certain. If we are entirely sure that Ida owns an iPhone, and we are sure that Apple makes Iphones, then we can be sure that Ida owns something made by Apple. But what in life is absolutely certain? As Bertrand Russell once wrote, "All human knowledge is uncertain, inexact, and partial." Yet somehow we humans manage. When machines can finally do the same, representing and reasoning about that sort of knowledge -- uncertain, inexact, and partial -- with the fluidity of human beings, the age of flexible and powerful, broad AI will finally be in sight."

Many banks and financial firms are investing in AI and seeing positive return from applying AI throughout their operations. AI-based systems are helping to make more informed, safer and profitable decisions. However, with any technology that's used in a heavily regulated industry there are challenges and pushbacks to adoption. Kumar Srivastava, VP of Product and Strategy of BNY Mellon recently shared with the AI Today podcast insights into AI adoption at the bank. BNY Mellon has a Silicon Valley based Innovation Center that aims to help bring AI innovations to the bank.

"Any nation that leads in Artificial intelligence, will be the ruler of the world"--Vladimir Putin Since time immemorial mercenaries were used to wage wars on foreign lands by Kings. Mercenaries were so effective that a King from Hungary, in the 15th century, had a standing mercenary army. It is a pragmatic strategy to deploy a trained manpower in prolonged high-casualty war zone, that is highly motivated, effective, and keeps the cost of the war and mission low. Behemoth countries like America and Russia have now decided to adopt autonomous weapon system (AWS) that work on artificial intelligence (AI), to spearhead their aggressive policy abroad. AI is the mercenary of 21st century!!! India has so far been reticent in relying on artificial intelligence due to the trepidation of losing control over the game play of events.

Abstract--Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in many real-world applications such as monitoring urban traffic and air quality . Making predictions on these time series has become a critical challenge due to not only the large-scale and high-dimensional nature but also the considerable amount of missing data. In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series--in particular spatiotemporal data--in the presence of missing values. By integrating low-rank matrix/tensor factorization and vector autoregressive (VAR) process into a single probabilistic graphical model, this framework can characterize both global and local consistencies in large-scale time series data. The graphical model allows us to effectively perform probabilistic predictions and produce uncertainty estimates without imputing those missing values. We develop efficient Gibbs sampling algorithms for model inference and test the proposed BTF framework on several real-world spatiotemporal data sets for both missing data imputation and short-term/long-term rolling prediction tasks. The numerical experiments demonstrate the superiority of the proposed BTF approaches over many state-of-the-art techniques. With recent advances in sensing technologies, large-scale and multidimensional time series data--in particular spatiotemporal data--are collected on a continuous basis from various types of sensors and applications. Making predictions on these time series, such as forecasting urban traffic states and regional air quality, serves as a foundation to many real-world applications and benefits many scientific fields [1], [2]. For example, predicting the demand and states (e.g., speed, flow) of urban traffic is essential to a wide range of intelligent transportation systems (ITS) applications, such trip planning, travel time estimation, route planning, traffic signal control, to name but a few [3]. However, given the complex spatiotemporal dependencies in these data sets, making efficient and reliable predictions for real-time applications has been a longstanding and fundamental research challenge. Despite the vast body of literature on time series analysis from many scientific areas, three emerging issues in modern sensing technologies are constantly challenging the classical modeling frameworks. First, modern time series data are often large-scale, collected from a large number of subjects/locations/sensors simultaneously .

We introduce the Mutual Information Machine (MIM), an autoencoder model for learning joint distributions over observations and latent states. The model formulation reflects two key design principles: 1) symmetry, to encourage the encoder and decoder to learn consistent factorizations of the same underlying distribution; and 2) mutual information, to encourage the learning of useful representations for downstream tasks. The objective comprises the Jensen-Shannon divergence between the encoding and decoding joint distributions, plus a mutual information term. We show that this objective can be bounded by a tractable cross-entropy loss between the true model and a parameterized approximation, and relate this to maximum likelihood estimation and variational autoencoders. Experiments show that MIM is capable of learning a latent representation with high mutual information, and good unsupervised clustering, while providing data log likelihood comparable to VAE (with a sufficiently expressive architecture).

Nowadays, the algorithm is acknowledged to be easy to use and to perform very well in general, even in problems involving many predictor variables (see for instance Biau and Scornet (2016) or the introduction to Scornet, Biau and Vert (2015)) ― so well, indeed, that several authors have posed and studied the question of their consistency (see Scornet, Biau and Vert (2015) and the earlier references provided by them). Consistent nonparametric statistical predictors have been known for a long time (e.g. Nadaraya (1964), Watson (1964), Stone (1977), Devroye and Wagner (1980)), but they converge very slowly and their computer implementations tend to be slow, especially when they involve many variables. In view of their comparative accuracy and high speed of implementation, random forests would become even more attractive if they were shown to be consistent under general data ‐ generating mechanisms. Besides, consistency is almost indispensable in applications of statistical prediction to the estimation of'causal effects' based on observational data (e.g.

Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables. However, they can be difficult to use in practice due to poorly understood bias/variance tradeoffs. We theoretically show that, under some conditions, estimators such as MINE exhibit variance that could grow exponentially with the true amount of underlying MI. We also empirically demonstrate that existing estimators fail to satisfy basic self-consistency properties of MI, such as data processing and additivity under independence. Based on a unified perspective of variational approaches, we develop a new estimator that focuses on variance reduction. Empirical results on standard benchmark tasks demonstrate that our proposed estimator exhibits improved bias-variance trade-offs on standard benchmark tasks.