Institutes gear up India for age of artificial intelligence Shuriah Niazi 11 May 2019 The Indian workforce, academics and students are gearing up for the advent of the age of artificial intelligence (AI). According to the National Association of Software and Services Companies (NASSCOM), the country will require nearly 238,000 AI professionals in the next three years and the AI industry will be worth US$16 billion by 2025. AI is a field that has a comparatively long history but continues to actively grow and evolve. NASSCOM last month launched the programme titled'AI Foundation for Faculty Development' to hone faculty's skills in AI at Bengaluru (formerly Bangalore) in the southern Indian state of Karnataka. In addition, some top institutions like the Indian Institute of Technology (IIT) Madras, IIT Hyderabad and Indian Institute of Science in Bengaluru are training students and faculty in AI.

The global artificial-intelligence (AI) market could grow at a compound annual growth rate of 55.6% between 2018 and 2025, according to Allied Market Research, as a wide range of industries use AI applications to streamline and improve their businesses. However, AI is such a hot buzzword in tech circles that it can be tough to identify the industry's worthy long-term investments. Today, a trio of our Motley Fool investors will highlight three promising AI plays -- Tencent (NASDAQOTH: TCEHY), Microsoft (NASDAQ: MSFT), and NVIDIA (NASDAQ: NVDA). Leo Sun (Tencent): Tencent, one of China's top tech companies, owns the country's most popular mobile messaging platform in WeChat, as well as one of its most popular payment systems in WeChat Pay. Tencent Video is one of China's leading streaming video platforms, and Tencent Music (NYSE: TME), which was spun off in an IPO last year, is the country's leading music streaming platform.

This paper proposes a novel landscape smoothing method for the symmetric Traveling Salesman Problem (TSP). We first define the homotopic convex (HC) transformation of a TSP as a convex combination of a well-constructed simple TSP and the original TSP. We observe that controlled by the coefficient of the convex combination, (i) the landscape of the HC transformed TSP is smoothed in terms that its number of local optima is reduced compared to the original TSP; (ii) the fitness distance correlation of the HC transformed TSP is increased. We then propose an iterative algorithmic framework in which the proposed HC transformation is combined with a heuristic TSP solver. It works as an escaping scheme from local optima for improving the global search ability of the combined heuristic. A case study with the 3-Opt local search as the heuristic solver shows that the resultant algorithm significantly outperforms iterated local search and two other smoothing-based TSP heuristic solvers on most of commonly-used test instances.

In this work we focus on fine-tuning a pre-trained BERT model and applying it to patent classification. When applied to large datasets of over two millions patents, our approach outperforms the state of the art by an approach using CNN with word embeddings. In addition, we focus on patent claims without other parts in patent documents. Our contributions include: (1) a new state-of-the-art method based on pre-trained BERT model and fine-tuning for patent classification, (2) a large dataset USPTO-3M at the CPC subclass level with SQL statements that can be used by future researchers, (3) showing that patent claims alone are sufficient for classification task, in contrast to conventional wisdom.

Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal responses. The FASTER platform that we describe in this work, deployed at nation scale and handling 1.5 billion public transport trips a year, offers such a full stack of techniques for this large-scale, real-time problem. FASTER provides fine-grained situational awareness and real-time decision support with the objective of improving the public transport commuter experience. The methods employed range from statistical machine learning to agent-based simulation and mixed-integer optimization. In this work we present an overview of the challenges and methods involved, with details of the commuter movement prediction module, as well as a discussion of open problems.

We propose moment-based variational inference as a flexible framework for approximate smoothing of latent Markov jump processes. The main ingredient of our approach is to partition the set of all transitions of the latent process into classes. This allows to express the Kullback-Leibler divergence between the approximate and the exact posterior process in terms of a set of moment functions that arise naturally from the chosen partition. To illustrate possible choices of the partition, we consider special classes of jump processes that frequently occur in applications. We then extend the results to parameter inference and demonstrate the method on several examples.

We describe a method to infer dense depth from camera motion and sparse depth as estimated using a visual-inertial odometry system. Unlike other scenarios using point clouds from lidar or structured light sensors, we have few hundreds to few thousand points, insufficient to inform the topology of the scene. Our method first constructs a piecewise planar scaffolding of the scene, and then uses it to infer dense depth using the image along with the sparse points. We use a predictive cross-modal criterion, akin to `self-supervision,' measuring photometric consistency across time, forward-backward pose consistency, and geometric compatibility with the sparse point cloud. We also launch the first visual-inertial + depth dataset, which we hope will foster additional exploration into combining the complementary strengths of visual and inertial sensors. To compare our method to prior work, we adopt the unsupervised KITTI depth completion benchmark, and show state-of-the-art performance on it.

We propose a novel procedure which adds "content-addressability" to any given unconditional implicit model e.g., a generative adversarial network (GAN). The procedure allows users to control the generative process by specifying a set (arbitrary size) of desired examples based on which similar samples are generated from the model. The proposed approach, based on kernel mean matching, is applicable to any generative models which transform latent vectors to samples, and does not require retraining of the model. Experiments on various high-dimensional image generation problems (CelebA-HQ, LSUN bedroom, bridge, tower) show that our approach is able to generate images which are consistent with the input set, while retaining the image quality of the original model. To our knowledge, this is the first work that attempts to construct, at test time, a content-addressable generative model from a trained marginal model.

This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS). The use of low-resolution ADCs could largely reduce hardware complexity and circuit power consumption, however, makes the channel station information almost blind to the BS, hence causing difficulty in solving the equalization problem. In this paper, we consider a supervised learning architecture, where the goal is to learn a representative function that can predict the targets (constellation points) from the inputs (outputs of the low-resolution ADCs) based on the labeled training data (pilot signals). Specially, our main contributions are two-fold: 1) First, we design a new activation function, whose outputs are close to the constellation points when the parameters are finally optimized, to help us fully exploit the stochastic gradient descent method for the discrete optimization problem. 2) Second, an unsupervised loss is designed and then added to the optimization objective, aiming to enhance the representation ability (so-called generalization). The experimental results reveal that the proposed equalizer is robust to different channel taps (i.e., Gaussian, and Poisson), significantly outperforms the linearized MMSE equalizer, and shows potential for pilot saving.

In this paper, we introduce the algorithms of Orthogonal Deep Neural Networks (OrthDNNs) to connect with recent interest of spectrally regularized deep learning methods. OrthDNNs are theoretically motivated by generalization analysis of modern DNNs, with the aim to find solution properties of network weights that guarantee better generalization. To this end, we first prove that DNNs are of local isometry on data distributions of practical interest; by using a new covering of the sample space and introducing the local isometry property of DNNs into generalization analysis, we establish a new generalization error bound that is both scale- and range-sensitive to singular value spectrum of each of networks' weight matrices. We prove that the optimal bound w.r.t. the degree of isometry is attained when each weight matrix has a spectrum of equal singular values, among which orthogonal weight matrix or a non-square one with orthonormal rows or columns is the most straightforward choice, suggesting the algorithms of OrthDNNs. We present both algorithms of strict and approximate OrthDNNs, and for the later ones we propose a simple yet effective algorithm called Singular Value Bounding (SVB), which performs as well as strict OrthDNNs, but at a much lower computational cost. We also propose Bounded Batch Normalization (BBN) to make compatible use of batch normalization with OrthDNNs. We conduct extensive comparative studies by using modern architectures on benchmark image classification. Experiments show the efficacy of OrthDNNs.