Zhang, Daniel, Mishra, Saurabh, Brynjolfsson, Erik, Etchemendy, John, Ganguli, Deep, Grosz, Barbara, Lyons, Terah, Manyika, James, Niebles, Juan Carlos, Sellitto, Michael, Shoham, Yoav, Clark, Jack, Perrault, Raymond
Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.
Language identification (“LI”) is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature. We describe the features and methods using a unified notation, to make the relationships between methods clearer. We discuss evaluation methods, applications of LI, as well as off-the-shelfLI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI.
Thanks to its generality, RL has been widely studied in many areas, such as control theory, game theory, operations research, multi-agent systems, machine learning, artificial intelligence, and statistics . In recent years, combining with deep learning, RL has demonstrated its great potential in addressing challenging practical control and optimization problems [17, 21]. Among all possible algorithms, the temporal difference (TD) learning has arguably become one of the most popular RL algorithms so far, which is further dominated by the celebrated TD(0) algorithm . TD learning provides an iterative process to update an estimate of the so-termed value function v π(s) with respect to a given policy π based on temporally successive samples. Dealing with a finite state space, the classical version of the TD(0) algorithm adopts a tabular representation for v π(s), which stores entry-wise value estimates on a per state basis. J. Sun and Q. Yang are with the College of Control Science and Engineering, and the State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China. G. Wang and G. B. Giannakis are with the Digital Technology Center and the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA. Z. Yang is with the Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China.
A humanoid robot, named Aiko Chihira by its creators at Toshiba and Osaka University, at a 2015 trial in Tokyo's Mitsukoshi department store. Toshiba says it will incorporate speech recognition and synthesis into the robot by 2020. Machines that speak are nothing new. Siri has been answering questions from iPhone users since 2011, and text-to-voice programs have been around even longer. People with speaking disabilities--most famously, Stephen Hawking--have used computers to generate speech for decades.
Farhad Bulbul is with the Department of Mathematics, Jessore University of Science and Technology, Bangladesh (email: email@example.com). Saiful Islam is with the Department of Mathematics, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Bangladesh. Dr. Hazrat Ali is with the Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Pakistan (email: firstname.lastname@example.org). Abstract-- In this paper, we present an approach for identification of actions within depth action videos. First, we process the video to get motion history images (MHIs) and static history images (SHIs) corresponding to an action video based on the use of 3D Motion Trail Model (3DMTM). We then characterize the action video by extracting the Gradient Local Auto-Correlations (GLAC) features from the SHIs and the MHIs. The two sets of features i.e., GLAC features from MHIs and GLAC features from SHIs are concatenated to obtain a representation vector for action. Finally, we perform the classification on all the action samples by using the l2-regularized Collaborative Representation Classifier (l2-CRC) to recognize different human actions in an effective way. We perform evaluation of the proposed method on three action datasets, MSR-Action3D, DHA and UTD-MHAD. Through experimental results, we observe that the proposed method performs superior to other approaches. I. INTRODUCTION Research in human action recognition (HAR) is considered as one of the most interesting domains of computer vision. The action recognition system is being extensively applied in human security system, medical science, social awareness, and entertainment , , , .. Indeed, to develop an applicable action recognition system, researchers still need to win against the odds due to diversity in human body sizes, appearances, postures, motions, clothing, camera motions, viewing angles, and illumination. In the early stage, the human action recognition system was developed by researchers based on RGB data , , , .