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Gigabit Telerobotics: Applying Advanced Information Infrastructure
University of Pennsylvania ScholarlyCommons Technical Reports (CIS) Department of Computer & Information Science January 1993 Gigabit Telerobotics: Applying Advanced Information Infrastructure Ruzena Bajcsy University of Pennsylvania David J. Farber University of Pennsylvania Richard P. Paul University of Pennsylvania Jonathan M. Smith University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/cis_reports Recommended Citation Ruzena Bajcsy, David J. Farber, Richard P. Paul, and Jonathan M. Smith, "Gigabit Telerobotics: Applying Advanced Information Infrastructure", . January 1993. University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-93-11. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/cis_reports/422 For more information, please contact [email protected]. Gigabit Telerobotics: Applying Advanced Information Infrastructure Abstract Advanced manufacturing concepts such as "Virtual Factories" use an information infrastructure to tie together changing groups of specialized facilities into agile manufacturing systems. A necessary element of such systems is the ability to teleoperate machines, for example telerobotic systems with full- capability sensory feedback loops. We have identified three network advances needed for splitting robotic control from robotic function: increased bandwidth, decreased error rates, and support for isochronous traffic. Theseeatur f es are available in the Gigabit networks under -
The Ethics of Machine Learning
SINTEZA 2019 INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATION TECHNOLOGY AND DATA RELATED RESEARCH DATA SCIENCE & DIGITAL BROADCASTING SYSTEMS THE ETHICS OF MACHINE LEARNING Danica Simić* Abstract: Data science and machine learning are advancing at a fast pace, which is why Nebojša Bačanin Džakula tech industry needs to keep up with their latest trends. This paper illustrates how artificial intelligence and automation in particular can be used to en- Singidunum University, hance our lives, by improving our productivity and assisting us at our work. Belgrade, Serbia However, wrong comprehension and use of the aforementioned techniques could have catastrophic consequences. The paper will introduce readers to the terms of artificial intelligence, data science and machine learning and review one of the most recent language models developed by non-profit organization OpenAI. The review highlights both pros and cons of the language model, predicting measures humanity can take to present artificial intelligence and automatization as models of the future. Keywords: Data Science, Machine Learning, GPT-2, Artificial Intelligence, OpenAI. 1. INTRODUCTION Th e rapid development of technology and artifi cial intelligence has al- lowed us to automate a lot of things on the internet to make our lives eas- ier. With machine learning algorithms, technology can vastly contribute to diff erent disciplines, such as marketing, medicine, sports, astronomy and physics and more. But, what if the advantages of machine learning and artifi cial intelligence in particular also carry some risks which could have severe consequences for humanity. 2. MACHINE LEARNING Defi nition Machine learning is a fi eld of data science which uses algorithms to improve the analytical model building [1] Machine learning uses artifi cial Correspondence: intelligence (AI) to learn from great data sets and identify patterns which Danica Simić could support decision making parties, with minimal human intervention. -
Object Recognition Using Vision and Touch
OBJECT RECOGNITION USING VISION AND TOUCH Peter Allen and Ruzena Bajcsy Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 number of false recognitions. However, the model primitives must ABSTRACT be computable from the sensed data. More complex object models A system is described that integrates vision and are being built [8] but they are of limited power unless the sensing tactile sensing in a robotics environment to perform systems can uncover the structural primitives and relationships they object recognition tasks. It uses multiple sensor systems contain. The approach used here allows complex and rich models (active touch and passive stereo vision) to compute of objects that extends the kinds of generic objects that can be three dimensional primitives that can be matched recognized by the system. This is due to the rich nature of the sur• against a model data base of complex curved surface face and feature primitives the sensors compute. Surfaces are the objects containing holes and cavities. The low level actual parts of an object that we see; the primitive computed is sensing elements provide local surface and feature exactly this. Holes and cavities are important visual and tactile matches which arc constrained by relational criteria features; these are also computed by integrating touch and vision. embedded in the models. Once a model has been Further, the system described here is viewpoint independent. The invoked, a verification procedure establishes confidence problems caused by visual occlusion are overcome by the ability to measures for a correct recognition. The three dimen* use active touch sensing in visually occluded areas. -
Active Perception
Active Perception RUZENA BAJCSY, MEMBER, IEEE Invited Paper Active Perception (Active Vision specifically) is defined as a study II. WHATIS ACTIVESENSING? of Modeling and Control strategies for perception. By modeling we mean models of sensors, processing modules and their interac- In the robotics and computer vision literature, the term tion. We distinguish local models from global models by their “active sensor” generally refers to a sensor that transmits extent of application in space and time. The local models repre- (generally electromagnetic radiation, e.g., radar, sonar, sent procedures and parameters such as optical distortions of the lens, focal lens, spatial resolution, band-pass filter, etc. The global ultrasound, microwaves and collimated light) into the envi- models on the other hand characterize the overall performance ronment and receives and measures the reflected signals. and make predictions on how the individual modules interact. The We believe that the use of active sensors is not a necessary control strategies are formulated as a search of such sequence of condition on active sensing, and that sensing can be per- steps that would minimize a loss function while one is seeking the formed with passive sensors (that only receive, and do not most information. Examples are shown as the existence proof of the proposed theory on obtaining range from focus and sterolver- emit, information), employed actively. Here we use the term gence on 2-0 segmentation of an image and 3-0 shape parametri- active not to denote a time-of-flight sensor, but to denote za tion. a passive sensor employed in an active fashion, purpose- fully changing the sensor’s state parameters according to sensing strategies. -
Winter and Spring), and T C
COMPUTER SCIENCE DEPARTMENT REPORT TO COMPUTER SCIENCE ADVISORY COMMITTEE October 1973 FACULTY AND STAFF Don Knuth has returned from a year's leave spent in Norway. George Dantzig is on sabbatical leave for the current academic year. Roger Schank is on leave and Harold Stone is here but on research leave for the academic year. John T. Gill of the Electrical Engineering Department has been named to the list of affiliated faculty. Two of our faculty received distinguished awards in the past year Dantzig was awarded the Honorary Degree of Doctor of Science by Technion- Israel Institute of Technology. Knuth was made a Fellow of the American Academy of Science. Visiting faculty members for the current year are Peter Franaszek, Terry Winograd, C.A.R. Hoare (Autumn quarter), Joseph Oliger (Winter quarter), Robert M. Keller (Winter and Spring), and T C . Hu (Spring quarter). The department's resources are augmented by about 19 professional people who are research associates, research computer scientists, etc. Also several professionals from the local area serve as lecturers helping with the teaching program of the department. OTHER VISITORS In addition to the visiting faculty listed above, there are a number of other visitors who have come here to participate in the research acti- vities of the department. Among these are Mario and Luigia Aiello from Italy, Gheorghe Dodescu from Romania, Geoffrey Dromey from Australia, lan Duff from England, Kjell Overholt from Norway and F. yon Henke from Germany. STUDENTS AND ENROLLMENTS We have approximately 115 graduate student majors in our Department but there is no undergraduate major. -
Supervised Machine Learning
Tikaram Acharya Supervised Machine Learning Accuracy Comparison of Different Algorithms on Sample Data Metropolia University of Applied Sciences Bachelor of Engineering Information Technology Bachelor’s Thesis 11 January 2021 Author Tikaram Acharya Title Supervised Machine Learning: Accuracy Comparison of Differ- ent Algorithms on Sample Data Number of Pages 39 pages Date 11 January 2021 Degree Bachelor of Engineering Degree Programme Information Technology Professional Major Software Engineering Instructors Janne Salonen, Head of School The purpose of the final year project was to discuss the theoretical concepts of the analyt- ical features of supervised machine learning. The concepts were illustrated with sample data selected from the open-source platform. The primary objectives of the thesis include explaining the concept of machine learning, presenting supportive libraries, and demon- strating their application or utilization on real data. Finally, the aim was to use machine learning to determine whether a person would pay back their loan or not. The problem was taken as a classification problem. To accomplish the goals, data was collected from an open data source community called Kaggle. The data was downloaded and processed with Python. The complete practical part or machine learning workflow was done in Python and Python’s libraries such as NumPy, Pandas, Matplotlib, Seaborn, and SciPy. For the given problem on sample data, five predictive models were constructed and tested with machine learning with different techniques and combinations. The results were highly accurately matched with the classification problem after evaluating their f1-score, confu- sion matrix, and Jaccard similarity score. Indeed, it is clear that the project achieved the main defined objectives, and the necessary architecture has been set up to apply various analytical abilities into the project. -
Seinfeld's Recent Comments on Zoom…. Energy
Stanford Data Science Departmental 2/4/2021 Seminar Biomedical AI: Its Roots, Evolution, and Early Days at Stanford Edward H. Shortliffe, MD, PhD Chair Emeritus and Adjunct Professor Department of Biomedical Informatics Columbia University Department of Biomedical Data Science Seminar Stanford Medicine Stanford, California 2:30pm – 4:50pm – February 4, 2021 Seinfeld’s recent comments on Zoom…. Energy, attitude and personality cannot be “remoted” through even the best fiber optic lines Jerry Seinfeld: So You Think New York Is ‘Dead’ (It’s not.) Aug. 24, 2020 © E.H. Shortliffe 1 Stanford Data Science Departmental 2/4/2021 Seminar Disclosures • No conflicts of interest with content of presentation • Offering a historical perspective on medical AI, with an emphasis on US activities in the early days and specific work I know personally • My research career was intense until I became a medical school dean in 2007 • Current research involvement is largely as a textbook author and two decades as editor-in-chief of a peer- reviewed journal (Journal of Biomedical Informatics [Elsevier]) Goals for Today’s Presentation • Show that today’s state of the art in medical AI is part of a 50-year scientific trajectory that is still evolving • Provide some advice to today’s biomedical AI researchers, drawing on that experience • Summarize where we are today in the evolution, with suggestions for future emphasis More details on slides than I can include in talk itself – Full deck available after the talk © E.H. Shortliffe 2 Stanford Data Science Departmental -
(Autumn Q. .Ter), Josep! Iger ■ La Robert M
1 COMPUTER SCIENC3 Di P-RTMEN. TO COMPUTER SCjPiMCE ADV ...PRY CO- ' -tooer 197? pPuiflY AND STAFF Don Knuth has returned from a year's le "way. G- ; ritzig is on sabbatical, leave for che current aco . /-ear. Roger Sc'J Ls on leave and Harold Stone is here but or. reseax . t^ave for ;he acadein year. John T. Gill of the Electrical Engineering I tment has been nam* 1 1 the list of affiliated faculty. Two of our faculty received distinguished a .-" "■"': h 3t yes Dantzig was awarded the Honorary Degree of Doctor i/, lechnion- ] srael Institute of Technology. Knuth was made a ."' . th Ameri< '■ :demy of Science. Visiting faculty members for the current yeaj ?■ isz. ['erry Winograd, C.A.R. Hoare (Autumn q. .ter), Josep! Iger ■ La Robert M. Keller (Winter and Spring;, and T C. IP 9 lg .uarterp The department's resources are augmented by p.rofe3^.ior >eople 'ho are r -search associates, research commute., scientists, etc. Also several professionals fr..-- the local area serve as curers helj ./ith the teaching program of ti i 'partment. OTHER VISITORS In addition to the visiting faculty listed ..re re a : ■Z other visitors who have come here to participate ses. .. Ac tes of the department. Among these are Mario ar. Aie: L; fr, ■rghe Dodescu from Romania, Geoffrey Dromey from ] Dv: ( ■ il'ml, i'.,i'-ll 'v -riu IM. iVi'iu Norway aiP ("' "■-a FT< i-j ■ . i>. ■■■i'lNlS AND ENROL! PENIS We have approximately 115 graduc p- student m; , separtm< .. there is no undergraduate major. -
IJCAI Organization
IJCAI Organization Trustees Former Trustees Serving on the President: Francesca Rossi (University of Padova, Italy) Executive Committee Craig Knoblock (University of Southern California, USA) Hiroaki Kitano (Sony Computer Science Laboratories, Inc., Japan) Sebastian run (Stanford University, USA) Ramon López de Mántaras (Spanish Scientific Research Council, Spain) Michael Wooldridge (University of Oxford, UK) Craig Boutilier (University of Toronto, Canada) Qiang Yang (Hong Kong University of Science and Technology, Fausto Giunchiglia (University of Trento, Italy) Hong Kong) Anthony G. Cohn (University of Leeds, UK) Gerhard Brewka (University of Leipzig, Germany) Hector Levesque (University of Toronto, Canada) Subbarao Kambhampati (Arizona State University, USA) Luigia Carlucci Aiello (Universita' di Roma, Italy) Toby Walsh (University of New South Wales, Australia) Michael P. Georgeff (Georgeff International Inc, Australia) Fahiem Bacchus (University of Toronto, Canada) C. Raymond Perrault (SRI International, USA) Carles Sierra (Artificial Intelligence Research Institute of the Spanish Wolfgang Wahlster (German Research Center for AI, Germany) Research Council, Spain) Barbara J. Grosz (Harvard University, USA) Trustees Elect Wolfgang Bibel (Darmstadt University of Technology, Germany) Alan Bundy (University of Edinburgh, UK) Jeffrey Rosenschein (e Hebrew University of Jerusalem, Israel) Alan Mackworth (University of British Columbia, Canada) Jérôme Lang (University Paris Dauphine, France) Patrick J. Hayes (IHMC-UWF, USA) D. Raj Reddy (Carnegie -
Robots Being Exhibited.No Link 1
Association for the Advancement of Artificial Intelligence 445 Burgess Drive Menlo Park, CA 94025 (650) 328-3123 www.aaai.org For press inquiries only, contact: Sara Hedberg (425) 444-7272 2008 Robot Workshop and Exhibition Backgrounder July 14-16, 2008 Hyatt Regency McCormick Place, Chicago, Illinois Second Level, Hyatt Regency Conference Center, CC23 In conjunction with the AAAI-08 Conference This year AAAI is sponsoring two Robot Workshops and Exhibitions – one on Robots and Creativity (pages 1-9) and one on Mobility and Manipulation (pages 10-14). Below are schedules, descriptions of the robots to be presented, and brief biographies of the researchers involved. Exhibits will be showcased on July 15 and 16. This gives audiences opportunities to see and experience what was discussed in the Workshop. Workshop on Robotics and Creativity July 14, 2008, 8:20a.m.-12:30 p.m. This workshop explores the roles that creativity plays in robotics. This includes research where robots employ cognitive models and computation to display creativity. Also explored are partnerships among artists, scientists and engineers to generate a creative synergy and stimulate breakthroughs. Format A distinguished panel will kick off the workshop by providing a broad overview of the area, emerging paradigms and exciting opportunities. Those invited to exhibit at AAAI, will give short 10-minute briefs. These talks provide examples and personal thoughts of creativity and robotics. The workshop concludes with a 60-min Discussion Forum to capture thoughts and generate -
Artificial Intelligence Machine Learning and Deep Learning
ARTIFICIAL INTELLIGENCE MACHINE LEARNING AND DEEP LEARNING LICENSE, DISCLAIMER OF LIABILITY, AND LIMITED WARRANTY By purchasing or using this book and its companion files (the “Work”), you agree that this license grants permission to use the contents contained herein, but does not give you the right of ownership to any of the textual content in the book or ownership to any of the information, files, or products contained in it. This license does not permit uploading of the Work onto the Internet or on a network (of any kind) without the written consent of the Publisher. Duplication or dissemination of any text, code, simu- lations, images, etc. contained herein is limited to and subject to licensing terms for the respective products, and permission must be obtained from the Publisher or the owner of the content, etc., in order to reproduce or network any portion of the textual material (in any media) that is contained in the Work. MERCURY LEARNING AND INFORMATION (“MLI” or “the Publisher”) and anyone involved in the creation, writing, production, accompanying algorithms, code, or computer programs (“the software”), and any accompanying Web site or software of the Work, cannot and do not warrant the performance or results that might be obtained by using the contents of the Work. The author, developers, and the Publisher have used their best efforts to insure the accuracy and functionality of the textual material and/or pro- grams contained in this package; we, however, make no warranty of any kind, express or implied, regarding the performance of these contents or programs. The Work is sold “as is” without warranty (except for defective materials used in manufacturing the book or due to faulty workmanship). -
Internati Nal W Men's
_________________________________ WOMEN IN THE DIGITAL SPACE United Nations _________________________________ Educational, Scientific and Cultural Organization 8 MARCH INTERNATI NAL W MEN’S DAY ______________________________ “Mary Allen Wilkes, programmer, with a LINC at M.I.T. in the early 1960’s. _______________________________ © Joseph ”C. Towler, Jr. Published in 2019 by the United Nations Educational, Scientific and Cultural Organization Division for Gender Equality, Cabinet of the Director-General 7, place de Fontenoy 75352 Paris 07 SP France TABLE OF CONTENTS Message of the Director-General p3 Foreword by the Director for Gender Equality p5 Debate p7 “Women Online: Challenges for Gender Equality Women in the Online Space“ Launch of EQUALS Policy Paper p8 “I’D BLUSH IF I COULD: Closing gender divides in digital skills through education” #Wiki4Women p9 Exhibition p10 “Remarkable Women in Technology” MESSAGE OF THE DIRECTOR-GENERAL This year’s International Women’s Day, we celebrate women’s contributions to society – particularly in the digital space – and reflect on how we can ensure women fully enjoy their rights. Digital technologies are affecting the ways in which we work, learn, teach and live together. Unfortunately, women are not necessarily fully benefitting from this technological revolution. A recent report by the Broadband Commission, co- authored by UNESCO, concluded that the gender digital divide is actually increasing: in 2016, there were over 250 million fewer women online than men that year. Women are not only less connected, but benefit less from digital literacy and skills training, are less likely to be hired by tech companies, and often earn less than their male colleagues.