HDFC Bank Ltd., announced the winners of the third edition of its Digital Innovation Summit 2018 (#DIS2018). The winners are from artificial intelligence, machine learning, credit risk assessment, and rural fin-tech domains. While four winners are from Bengaluru, one is based in Ra'anana in Israel. The FinTech start-up from Israel was one of the 24 overseas entries. This year, the Bank has joined hands with Startup India in its endeavor to harness the start-up and fintech ecosystem and get access to innovative ideas for potential application and deployment.

SINGAPORE: Expect to see more, not less, of artificial intelligence (AI) in the coming year. At least, that is what tech giants such as Google, Microsoft, Salesforce, Alibaba and HP Enterprise (HPE) are predicting as the New Year beckons. From fighting dengue and breaking language barriers to creating an "artificial companion" for Singapore's elderly, Channel NewsAsia finds out what these companies are brewing in their labs. HOW WILL AI BE DEVELOPED IN THE COMING YEAR? One of the themes that kept cropping up was the efforts being made by these big tech companies to "democratise" AI.

Much as mass electrification enabled the rise of the United States and other advanced economies, so AI is poised to reshape the global order. Forecasts suggest that AI will add a massive $15.7 trillion to the global economy by 2030. Prospects for sustaining global competitiveness are now directly tied to the industrialization of AI. AI and machine learning are predicted to reshape manufacturing, energy management, urban transportation, agricultural production, labor markets, and financial management. Governments that can successfully cultivate a culture of disruptive innovation will be strategically positioned to lead in the twenty-first century.

It might sound far-fetched, but those are just a couple of the feats a machine learning algorithm designed by researchers at the Korea Advanced Institute of Science and Technology and the Pohang University of Science and Technology can accomplish after ingesting large datasets of images. It's described in a new paper ("InstaGAN: Instance-Aware Image-to-Image Translation") published on the preprint server Arxiv.org this week. Image-to-image translation systems -- that is, systems that learn the mapping from input image to output image -- aren't anything new, to be clear. Only earlier this month, Google AI researchers developed a model that can realistically insert an object in a photo by predicting its scale, occlusions, pose, shape, and more. But as the creators of InstaGAN wrote in the paper, even state-of-the-art methods aren't perfect.

Recently, phase processing is attracting increasinginterest in speech enhancement community. Some researchersintegrate phase estimations module into speech enhancementmodels by using complex-valued short-time Fourier transform(STFT) spectrogram based training targets, e.g. Complex RatioMask (cRM) [1]. However, masking on spectrogram would violentits consistency constraints. In this work, we prove that theinconsistent problem enlarges the solution space of the speechenhancement model and causes unintended artifacts. ConsistencySpectrogram Masking (CSM) is proposed to estimate the complexspectrogram of a signal with the consistency constraint in asimple but not trivial way. The experiments comparing ourCSM based end-to-end model with other methods are conductedto confirm that the CSM accelerate the model training andhave significant improvements in speech quality. From ourexperimental results, we assured that our method could enha

Two important characteristics of multi-objective evolutionary algorithms are distribution and convergency. As a classic multi-objective genetic algorithm, NSGA-II is widely used in multi-objective optimization fields. However, in NSGA-II, the random population initialization and the strategy of population maintenance based on distance cannot maintain the distribution or convergency of the population well. To dispose these two deficiencies, this paper proposes an improved algorithm, OTNSGA-II II, which has a better performance on distribution and convergency. The new algorithm adopts orthogonal experiment, which selects individuals in manner of a new discontinuing non-dominated sorting and crowding distance, to produce the initial population. And a new pruning strategy based on clustering is proposed to self-adaptively prunes individuals with similar features and poor performance in non-dominated sorting and crowding distance, or to individuals are far away from the Pareto Front according to the degree of intra-class aggregation of clustering results. The new pruning strategy makes population to converge to the Pareto Front more easily and maintain the distribution of population. OTNSGA-II and NSGA-II are compared on various types of test functions to verify the improvement of OTNSGA-II in terms of distribution and convergency.

In this article, we provide and overview of what we consider to be some of the most pressing research questions facing the fields of artificial intelligence (AI) and computational intelligence (CI); with the latter focusing on algorithms that are inspired by various natural phenomena. We demarcate these questions using five unique Rs - namely, (i) rationalizability, (ii) resilience, (iii) reproducibility, (iv) realism, and (v) responsibility. Notably, just as air serves as the basic element of biological life, the term AIR5 - cumulatively referring to the five aforementioned Rs - is introduced herein to mark some of the basic elements of artificial life (supporting the sustained growth of AI and CI). A brief summary of each of the Rs is presented, highlighting their relevance as pillars of future research in this arena.

A description of the environment cognition process by intelligent systems with a fixed set of system goals is suggested. Such a system is represented by the set of its goals only without any models of the system elements or the environment. The set has a lattice structure and a monoid structure; thus, the structure of linear logic is defined on the set. The cognition process of some environment by the system is described on this basis. The environment is represented as a configuration space of possible system positions which are estimated by an information amount (by corresponding sets). This information is supplied to the system by the environment. Thus, it is possible to define the category of Conway games with a payoff on the configuration space and to choose an optimal system's play (i.e., a trajectory). The choice is determined by the requirement of maximal information increasing and takes into account the structure of the system goal set: the linear logic on the set is used to determine the priority of possible different parallel processes. The survey may be useful to describe the behavior of robots and simple biological systems, e.g., ants.

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.

Classification is a fundamental problem in machine learning and data mining. During the past decades, numerous classification methods have been presented based on different principles. However, most existing classifiers cast the classification problem as an optimization problem and do not address the issue of statistical significance. In this paper, we formulate the binary classification problem as a two-sample testing problem. More precisely, our classification model is a generic framework that is composed of two steps. In the first step, the distance between the test instance and each training instance is calculated to derive two distance sets. In the second step, the two-sample test is performed under the null hypothesis that the two sets of distances are drawn from the same cumulative distribution. After these two steps, we have two p-values for each test instance and the test instance is assigned to the class associated with the smaller p-value. Essentially, the presented classification method can be regarded as an instance-based classifier based on hypothesis testing. The experimental results on 40 real data sets show that our method is able to achieve the same level performance as the state-of-the-art classifiers and has significantly better performance than existing testing-based classifiers. Furthermore, we can handle outlying instances and control the false discovery rate of test instances assigned to each class under the same framework.