Students from Gujarat Technological University (GTU) will receive training in Artificial Intelligence (AI) as a part of a project initiated by the Royal Academy of Engineering, UK under Newton Bhabha Fund. GTU is one of the collaborators in the nationwide initiative on enhancing AI skills and research under the fund. Speaking about the same, Dr Navin Sheth, Vice-Chancellor, GTU, said, "One of the objectives of the project is to make a research group, which will work in a specific domain like Healthcare, Agriculture, Space Research, CyberSecurity, Education, Video Processing, Audio and Natural Language Processing, Business, Banking, Crime, Social Media Analytics, Entertainment, Brain-Computer Interface and Network Simulation. Under this project, faculty members will get training on deep learning and AI technologies without any cost and sabbaticals will be offered to faculty members." AICTE has recommended all of its 10,000 approved institutions to associate with the project.

The Indian banking sector is beginning to adopt AI (artificial intelligence) quite aggressively for both the back-office and customer facing purposes. According to a data provided by RBI, among state run banks in India, PNB topped in the number of loan fraud cases across India with 389 cases over the last five financial years. After the infamous bank fraud that the country witnessed this year, Punjab National Bank (PNB) on May 6, announced its plans to rely on AI for reconciliation of accounts and incorporate analytics for improving the audit systems as it seeks to clean up the process and counter fraud in the near future. This decision was taken after the biggest bank fraud in which two junior officers at a single branch had illegally steered USD 1.77 billion (Euro 1.43 billion) in fraudulent loans to companies, controlled by Nirav Modi and his uncle Mehul Choksi. PNB managing director Sunil Mehta said in a statement that, "The'business remodelling' brought alive by changes at PNB is essential to ensure that the bank continues to grow and compete with its peers better," and elaborated on several steps that would reduce human intervention.

The Chart of the Week is a weekly Visual Capitalist feature on Fridays. Industrial robots have come a long way since George Devol invented "Unimate" in 1961. After pitching his idea to Joseph Engelberger at a cocktail party, the two soon saw their new creation become the first mass-produced robotic arm to be used in factory automation. Today, this robot class is raising the bar of global manufacturing to new heights, striking a seamless mix of strength, speed, and precision. As a result, demand for industrial robots keeps growing at a robust 14% per year, setting the stage for 3.1 million industrial robots in operation globally by 2020.

When it comes to drone technology, the focus in India is mainly on either amateur photography drones or military applications of drones in India. But between these two ends of the spectrum, lies the whole gamut of drone technology. The drone startup sector is nascent in India and is still to make its mark. But its prospects are promising and under the right policies and an able vision, drones can contribute a lot to Indian economy as well as help in solving many social and ecological problems. The fast-adoption of drone technology would give added advantage to India particularly in the sector of agriculture.

The demand of metals by modern technology has been shifting from common base metals to a variety of minor metals, such as cobalt or indium. The industrial importance and limited geological availability of some minor metals have led to them being considered more "critical," and there is a growing interest in such critical metals and their producing companies. In this research, we create a novel framework, Dynamic Advisor-Based Ensemble (dynABE), to predict the stock trend of major critical metal producers. Specifically, dynABE first utilizes domain knowledge to group the features into different "advisors," each advisor dealing with a particular economic sector. Then through ensembles of weak classifiers, each advisor produces a prediction result, and all the advisors are combined again in a biased online update fashion to dynamically make the final prediction. Based on a misclassification error of 32% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296%, or an excess return of 130% within one year. In addition, the feature set selected by dynABE also suggests potentially influential factors to metal criticality, because stock prices of major producers influence metal production. Therefore, not only does this research propose a novel framework for specialized stock trend prediction, it also provides domain insights into dynamic features that potentially influence metal criticality.

We introduce Regularized Kernel and Neural Sobolev Descent for transporting a source distribution to a target distribution along smooth paths of minimum kinetic energy (defined by the Sobolev discrepancy), related to dynamic optimal transport. In the kernel version, we give a simple algorithm to perform the descent along gradients of the Sobolev critic, and show that it converges asymptotically to the target distribution in the MMD sense. In the neural version, we parametrize the Sobolev critic with a neural network with input gradient norm constrained in expectation. We show in theory and experiments that regularization has an important role in favoring smooth transitions between distributions, avoiding large discrete jumps. Our analysis could provide a new perspective on the impact of critic updates (early stopping) on the paths to equilibrium in the GAN setting.

We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.

Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction with an application to complex urban scenes. In contrast with previous work, the prediction model only requires video sequences and optical flow sequences for training and testing. Our method uses the rich spatial-temporal features in video sequences. The method takes advantage of the motion information extracting from optical flow maps between neighbor images as well as previous images. Empirical evaluations on the KITTI dataset and the Cityscapes dataset demonstrate the effectiveness of our method.

Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this trade-off is often undesirable as any increase in prediction error could have devastating consequences. In this work, we argue that the fairness of predictions should be evaluated in context of the data, and that unfairness induced by inadequate samples sizes or unmeasured predictive variables should be addressed through data collection, rather than by constraining the model. We decompose cost-based metrics of discrimination into bias, variance, and noise, and propose actions aimed at estimating and reducing each term. Finally, we perform case-studies on prediction of income, mortality, and review ratings, confirming the value of this analysis. We find that data collection is often a means to reduce discrimination without sacrificing accuracy.

HE FORECASTING of power demand is of crucial importance for the development of modern power systems. The stable and efficient management, scheduling and dispatch in power systems rely heavily on precise forecasting of future loads on various time horizons. In particular, shortterm load forecasting (STLF) focuses on the forecasting of loads from several minutes up to one week into the future [1]. A reliable STLF helps utilities and energy providers deal with the challenges posed by the higher penetration of renewable energies and the development of electricity markets with increasingly complex pricing strategies in future smart grids. Various STLF methods have been proposed by researchers over the years. Some of the models used for STLF include linear or nonparametric regression [2], [3], support vector regression (SVR) [1], [4], autoregressive models [5], fuzzylogic approach [6], etc. Reviews and evaluations of existing methods can be found in [7]-[10]. Building STLF systems with artificial neural networks (ANN) has long been one of the mainstream solutions to this task. As early as 2001, a review paper by Hippert et al. surveyed and examined a collection of papers that had been published between 1991 and 1999, and arrived at the conclusions that most of the proposed models