Selected Grand Challenges in Cognitive Science
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Result Build a Self-Driving Network
SELF-DRIVING NETWORKS “LOOK, MA: NO HANDS” Kireeti Kompella CTO, Engineering Juniper Networks ITU FG Net2030 Geneva, Oct 2019 © 2019 Juniper Networks 1 Juniper Public VISION © 2019 Juniper Networks Juniper Public THE DARPA GRAND CHALLENGE BUILD A FULLY AUTONOMOUS GROUND VEHICLE IMPACT • Programmers, not drivers • No cops, lawyers, witnesses GOAL • Quadruple highway capacity Drive a pre-defined 240km course in the Mojave Desert along freeway I-15 • Glitches, insurance? • Ethical Self-Driving Cars? PRIZE POSSIBILITIES $1 Million RESULT 2004: Fail (best was less than 12km!) 2005: 5/23 completed it 2007: “URBAN CHALLENGE” Drive a 96km urban course following traffic regulations & dealing with other cars 6 cars completed this © 2019 Juniper Networks 3 Juniper Public GRAND RESULT: THE SELF-DRIVING CAR: (2009, 2014) No steering wheel, no pedals— a completely autonomous car Not just an incremental improvement This is a DISRUPTIVE change in automotive technology! © 2019 Juniper Networks 4 Juniper Public THE NETWORKING GRAND CHALLENGE BUILD A SELF-DRIVING NETWORK IMPACT • New skill sets required GOAL • New focus • BGP/IGP policies AI policy Self-Discover—Self-Configure—Self-Monitor—Self-Correct—Auto-Detect • Service config service design Customers—Auto-Provision—Self-Diagnose—Self-Optimize—Self-Report • Reactive proactive • Firewall rules anomaly detection RESULT Free up people to work at a higher-level: new service design and “mash-ups” POSSIBILITIES Agile, even anticipatory service creation Fast, intelligent response to security breaches CHALLENGE -
An Off-Road Autonomous Vehicle for DARPA's Grand Challenge
2005 Journal of Field Robotics Special Issue on the DARPA Grand Challenge MITRE Meteor: An Off-Road Autonomous Vehicle for DARPA’s Grand Challenge Robert Grabowski, Richard Weatherly, Robert Bolling, David Seidel, Michael Shadid, and Ann Jones. The MITRE Corporation 7525 Colshire Drive McLean VA, 22102 [email protected] Abstract The MITRE Meteor team fielded an autonomous vehicle that competed in DARPA’s 2005 Grand Challenge race. This paper describes the team’s approach to building its robotic vehicle, the vehicle and components that let the vehicle see and act, and the computer software that made the vehicle autonomous. It presents how the team prepared for the race and how their vehicle performed. 1 Introduction In 2004, the Defense Advanced Research Projects Agency (DARPA) challenged developers of autonomous ground vehicles to build machines that could complete a 132-mile, off-road course. Figure 1: The MITRE Meteor starting the finals of the 2005 DARPA Grand Challenge. 2005 Journal of Field Robotics Special Issue on the DARPA Grand Challenge 195 teams applied – only 23 qualified to compete. Qualification included demonstrations to DARPA and a ten-day National Qualifying Event (NQE) in California. The race took place on October 8 and 9, 2005 in the Mojave Desert over a course containing gravel roads, dirt paths, switchbacks, open desert, dry lakebeds, mountain passes, and tunnels. The MITRE Corporation decided to compete in the Grand Challenge in September 2004 by sponsoring the Meteor team. They believed that MITRE’s work programs and military sponsors would benefit from an understanding of the technologies that contribute to the DARPA Grand Challenge. -
NLP Commercialisation in the Last 25 Years
Natural Language Engineering (2019), 25, pp. 419–426 doi:10.1017/S1351324919000135 Anniversary INDUSTRY WATCH NLP commercialisation in the last 25 years Robert Dale∗ Language Technology Group ∗Corresponding author. Email: [email protected] Abstract The Journal of Natural Language Engineering is now in its 25th year. The editorial preface to the first issue emphasised that the focus of the journal was to be on the practical application of natural language processing (NLP) technologies: the time was ripe for a serious publication that helped encourage research ideas to find their way into real products. The commercialisation of NLP technologies had already started by that point, but things have advanced tremendously over the last quarter-century. So, to celebrate the journal’s anniversary, we look at how commercial NLP products have developed over the last 25 years. 1. Some context For many researchers, work in natural language processing (NLP) has a dual appeal. On the one hand, the computational modelling of language understanding or language production has often been seen as means of exploring theoretical questions in both linguistics and psycholinguistics; the general argument being that, if you can build a computational model of some phenomenon, then you have likely moved some way towards an understanding of that phenomenon. On the other hand, the scope for practical applications of NLP technologies has always been enticing: the idea that we could build truly useful computational artifacts that work with human language goes right back to the origins of the field in the early machine translation experiments of the 1950s. However, it was in the early 1990s that commercial applications of NLP really started to flourish, pushed forward in particular by targeted research in both the USA, much of it funded by the Defense Advanced Research Projects Agency (DARPA) via programs like the Message Understanding Conferences (MUC), and Europe, via a number of large-scale forward-looking EU-funded research programs. -
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Accepted March 22, 2020 Digital Object Identifier 10.1109/ACCESS.2020.2983149 A Survey of Autonomous Driving: Common Practices and Emerging Technologies EKIM YURTSEVER1, (Member, IEEE), JACOB LAMBERT 1, ALEXANDER CARBALLO 1, (Member, IEEE), AND KAZUYA TAKEDA 1, 2, (Senior Member, IEEE) 1Nagoya University, Furo-cho, Nagoya, 464-8603, Japan 2Tier4 Inc. Nagoya, Japan Corresponding author: Ekim Yurtsever (e-mail: [email protected]). ABSTRACT Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high- level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state- of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development. INDEX TERMS Autonomous Vehicles, Control, Robotics, Automation, Intelligent Vehicles, Intelligent Transportation Systems I. INTRODUCTION necessary here. CCORDING to a recent technical report by the Eureka Project PROMETHEUS [11] was carried out in A National Highway Traffic Safety Administration Europe between 1987-1995, and it was one of the earliest (NHTSA), 94% of road accidents are caused by human major automated driving studies. The project led to the errors [1]. Against this backdrop, Automated Driving Sys- development of VITA II by Daimler-Benz, which succeeded tems (ADSs) are being developed with the promise of in automatically driving on highways [12]. -
Intellibot: a Domain-Specific Chatbot for the Insurance Industry
IntelliBot: A Domain-specific Chatbot for the Insurance Industry MOHAMMAD NURUZZAMAN A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy UNSW Canberra at Australia Defence Force Academy (ADFA) School of Business 20 October 2020 ORIGINALITY STATEMENT ‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institute, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.’ Signed Date To my beloved parents Acknowledgement Writing a thesis is a great process to review not only my academic work but also the journey I took as a PhD student. I have spent four lovely years at UNSW Canberra in the Australian Defence Force Academy (ADFA). Throughout my journey in graduate school, I have been fortunate to come across so many brilliant researchers and genuine friends. It is the people who I met shaped who I am today. This thesis would not have been possible without them. My gratitude goes out to all of them. -
Grammar Checker for Hindi and Other Indian Languages
International Journal of Scientific & Engineering Research Volume 11, Issue 6, June-2020 1783 ISSN 2229-5518 Grammar Checker for Hindi and Other Indian Languages Anjani Kumar Ray, Vijay Kumar Kaul Center for Information and Language Engineering Mahatma Gandhi Antarrashtriya Hindi Vishwavidyalaya, Wardha (India) Abstract: Grammar checking is one of the sentence is grammatically well-formed. In most widely used techniques within absence of the potential syntactic parsing natural language processing (NLP) approach, incorrect or not-so-well applications. Grammar checkers check the grammatically formed sentences are grammatical structure of sentences based analyzed or produced. The preset paper is on morphological and syntactic an exploratory attempt to devise the hybrid processing. These two steps are important models to identify the grammatical parts of any natural language processing relations and connections of the words to systems. Morphological processing is the phrases to sentences to the extent of step where both lexical words (parts-of- discourse. Language Industry demands speech) and non-word tokens (punctuation such economic programmes doing justice marks, made-up words, acronyms, etc.) are and meeting the expectations of language analyzed into IJSERtheir components. In engineering. syntactic processing, linear sequences of words are transformed into structures that Keywords: Grammar Checking, Language show grammatical relationships among the Engineering, Syntax Processing, POS words in the sentence (Rich and Knight Tagging, Chunking, morphological 1991) and between two or more sentences Analysis joined together to make a compound or complex sentence. There are three main Introduction: Grammar Checker is an approaches/models which are widely used NLP application that helps the user to for grammar checking in a language; write correct sentence in the concerned language. -
A Hierarchical Control System for Autonomous Driving Towards Urban Challenges
applied sciences Article A Hierarchical Control System for Autonomous Driving towards Urban Challenges Nam Dinh Van , Muhammad Sualeh , Dohyeong Kim and Gon-Woo Kim *,† Intelligent Robotics Laboratory, Department of Control and Robot Engineering, Chungbuk National University, Cheongju-si 28644, Korea; [email protected] (N.D.V.); [email protected] (M.S.); [email protected] (D.K.) * Correspondence: [email protected] † Current Address: Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea. Received: 23 April 2020; Accepted: 18 May 2020; Published: 20 May 2020 Abstract: In recent years, the self-driving car technologies have been developed with many successful stories in both academia and industry. The challenge for autonomous vehicles is the requirement of operating accurately and robustly in the urban environment. This paper focuses on how to efficiently solve the hierarchical control system of a self-driving car into practice. This technique is composed of decision making, local path planning and control. An ego vehicle is navigated by global path planning with the aid of a High Definition map. Firstly, we propose the decision making for motion planning by applying a two-stage Finite State Machine to manipulate mission planning and control states. Furthermore, we implement a real-time hybrid A* algorithm with an occupancy grid map to find an efficient route for obstacle avoidance. Secondly, the local path planning is conducted to generate a safe and comfortable trajectory in unstructured scenarios. Herein, we solve an optimization problem with nonlinear constraints to optimize the sum of jerks for a smooth drive. In addition, controllers are designed by using the pure pursuit algorithm and the scheduled feedforward PI controller for lateral and longitudinal direction, respectively. -
Chatbot in English Classrooms Encourage Negotiations of Meaning
Chatbot in English Classrooms Encourage Negotiations of Meaning Bachelor’s Thesis of Kelvin Louis, 8th Semester [email protected] Nicola Cocquio, 8th Semester [email protected] University of Applied Sciences and Arts Northwestern Switzerland (FHNW) School of Engineering Computer Science Supervisors Manfred Vogel [email protected] Ivo Nussbaumer [email protected] Brugg-Windisch, 19.03.2019 Abstract Chatbots have become more prevalent in recent years, due to increasing demand as well as improvements in the fields of natural language processing (NLP) and machine learning. Within the field of education research, past studies have rightfully questioned the usefulness of chatbots as means of acquiring a foreign language. A review of the relevant literature shows that the applied chatbots were rule-based and limited to chitchatting in an open-domain. In this thesis we propose an alternate approach to using chatbots in English as a second language (ESL) classrooms. We evaluated the current state of technology to develop a machine learning-based chatbot capable of detecting errors in the students’ language. The chatbot’s domain is confined to interacting with the students in a room reservation roleplay exercise. Prerecorded transcripts of ESL student interactions were used to derive wordings of intents and utterances which served to train and test the chatbot’s machine learning models. Since misspellings are the most common errors in ESL students’ language, a language error detection was introduced into the chatbot’s architecture, providing additional feedback to the students and thereby mitigating repetitive errors. To test the performance of our solution, usability tests and a survey were conducted. -
Self-Driving Car Using Artificial Intelligence Prof
International Journal of Future Generation Communication and Networking Vol. 13, No. 3s, (2020), pp. 612–615 Self-Driving Car Using Artificial Intelligence Prof. S.R. Wategaonkar #1, Sujoy Bhattacharya*2, Shubham Bhitre*3 Prem Kumar Singh *4, Khushboo Bedse *5 #Professor and Project Faculty member, Department of Electronic and Telecommunication, Bharati Vidyapeeth’s College of Engineering, Navi Mumbai, Maharashtra, India. *Project Student, Department of Electronic and Telecommunication, Bharati Vidyapeeth’s College of Engineering, Navi Mumbai, Maharashtra, India. [email protected] [email protected] [email protected] [email protected] [email protected] Abstract Self-driving cars have the prospective to transform urban mobility by providing safe, convenient and congestion free transportability. This autonomous vehicle as an application of Artificial Intelligence (AI) have several difficulties like traffic light detection, lane end detection, pedestrian, signs etc. This problem can be overcome by using technologies such as Machine Learning (ML), Deep Learning (DL) and Image Processing. In this paper, author’s propose deep neural network for lane and traffic light detection. The model is trained and assessed using the dataset, which contains the front view image frames and the steering angle data captured when driving on the road. Keeping cars inside the lane is a very important feature of self-driving cars. It learns how to keep the vehicle in lane from human driving data. The paper presents Artificial Intelligence based autonomous navigation and obstacle avoidance of self-driving cars, applied with Deep Learning to a simulated cars in an urban environment. Keywords—Self-Driving car, Autonomous driving, Object Detection, Lane End Detection, Deep Learning, Traffic light Detection. -
Stanley: the Robot That Won the DARPA Grand Challenge
Stanley: The Robot that Won the DARPA Grand Challenge ••••••••••••••••• •••••••••••••• Sebastian Thrun, Mike Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip Fong, John Gale, Morgan Halpenny, Gabriel Hoffmann, Kenny Lau, Celia Oakley, Mark Palatucci, Vaughan Pratt, and Pascal Stang Stanford Artificial Intelligence Laboratory Stanford University Stanford, California 94305 Sven Strohband, Cedric Dupont, Lars-Erik Jendrossek, Christian Koelen, Charles Markey, Carlo Rummel, Joe van Niekerk, Eric Jensen, and Philippe Alessandrini Volkswagen of America, Inc. Electronics Research Laboratory 4009 Miranda Avenue, Suite 100 Palo Alto, California 94304 Gary Bradski, Bob Davies, Scott Ettinger, Adrian Kaehler, and Ara Nefian Intel Research 2200 Mission College Boulevard Santa Clara, California 95052 Pamela Mahoney Mohr Davidow Ventures 3000 Sand Hill Road, Bldg. 3, Suite 290 Menlo Park, California 94025 Received 13 April 2006; accepted 27 June 2006 Journal of Field Robotics 23(9), 661–692 (2006) © 2006 Wiley Periodicals, Inc. Published online in Wiley InterScience (www.interscience.wiley.com). • DOI: 10.1002/rob.20147 662 • Journal of Field Robotics—2006 This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without manual intervention. The robot’s software system relied predominately on state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This paper describes the major components of this architecture, and discusses the results of the Grand Chal- lenge race. © 2006 Wiley Periodicals, Inc. 1. INTRODUCTION sult of an intense development effort led by Stanford University, and involving experts from Volkswagen The Grand Challenge was launched by the Defense of America, Mohr Davidow Ventures, Intel Research, ͑ ͒ Advanced Research Projects Agency DARPA in and a number of other entities. -
Basic Version of Multilingual Semantic Text Analysis
V4Design Visual and textual content re-purposing FOR(4) architecture, Design and virtual reality games H2020-779962 D3.3 Basic version of multilingual semantic text analysis Dissemination level: Public Contractual date of delivery: Month 18, 30 June 2019 Actual date of delivery: Month 18, 30 June 2019 Workpackage: WP3 Visual and Textual content analysis Task: T3.2 Entity identification and linking, word sense disambiguation and lexical modelling T3.3 Dependency-based semantic parsing T3.4 Conceptual relation extraction Type: Report Approval Status: Approved Version: 1.2 Number of pages: 64 Filename: D3.3_V4Design_BasicAnalysisTechniques_v1.2.pdf Abstract In this deliverable, we report the advances on the Language Analysis components achieved during the first half of the V4Design project. The components include in particular a multilingual candidate concept detection tool, multilingual dependency parsers, semantic analysers, lexical resources, and a projection of the extracted dependency-based linguistic representations into ontological ones. The information in this document reflects only the author’s views and the European Community is not liable for any use that may be made of the information contained therein. The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. Page 1 co-funded by the European Union Page 2 D3.3 – V1.2 History Version Date Reason Revised by 0.1 05/04/2019 Creation -
Introducing Driverless Cars to UK Roads
Introducing Driverless Cars to UK Roads WORK PACKAGE 5.1 Deliverable D1 Understanding the Socioeconomic Adoption Scenarios for Autonomous Vehicles: A Literature Review Ben Clark Graham Parkhurst Miriam Ricci June 2016 Preferred Citation: Clark, B., Parkhurst, G. and Ricci, M. (2016) Understanding the Socioeconomic Adoption Scenarios for Autonomous Vehicles: A Literature Review. Project Report. University of the West of England, Bristol. Available from: http://eprints.uwe.ac.uk/29134 Centre for Transport & Society Department of Geography and Environmental Management University of the West of England Bristol BS16 1QY UK Email enquiries to [email protected] VENTURER: Introducing driverless cars to UK roads Contents 1 INTRODUCTION .............................................................................................................................................. 2 2 A HISTORY OF AUTONOMOUS VEHICLES ................................................................................................ 2 3 THEORETICAL PERSPECTIVES ON THE ADOPTION OF AVS ............................................................... 4 3.1 THE MULTI-LEVEL PERSPECTIVE AND SOCIO-TECHNICAL TRANSITIONS ............................................................ 4 3.2 THE TECHNOLOGY ACCEPTANCE MODEL ........................................................................................................ 8 3.3 SUMMARY ...................................................................................................................................................