DOCSLIB.ORG

How Machines Are Affecting People and Places

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
How Machines Are Affecting People and Places EXECUTIVE SUMMARY and How machines are affecting people and places MARK MURO ROBERT MAXIM JACOB WHITON With contributions from Ian Hathaway January 2019 EXECUTIVE SUMMARY The power and prospect of automation and artificial intelligence (AI) initially alarmed technology experts for fear that machine advancements would destroy jobs. Then came a correction, with a wave of reassurances. Now, the discourse appears to be arriving at a more complicated, mixed understanding that suggests that automation will bring neither apocalypse nor utopia, but instead both benefits and stresses alike. Such is the ambiguous and sometimes-disembodied nature of the “future of work” discussion. Which is where the present analysis aims to help. Intended to clear up misconceptions on the subject of automation, the following report employs government and private data, including from the McKinsey Global Institute, to develop both backward- and forward-looking analyses of the impacts of automation over the years 1980 to 2016 and 2016 to 2030 across some 800 occupations. In doing so, the report assesses past and coming trends as they affect both people and communities and suggests a comprehensive response framework for national and state-local policymakers. 2 Metropolitan Policy Program at Brookings In terms of current trends, the report finds that: • Approximately 25 percent of U.S. employment (36 million jobs in 2016) will 1. Automation and AI will affect tasks in face high exposure to automation in the virtually all occupational groups in the future coming decades (with greater than 70 but the effects will be of varied intensity—and percent of current task content at risk of drastic for only some. The effects in this sense substitution). will be broad but variable: • At the same time, some 36 percent of U.S. • Almost no occupation will be unaffected employment (52 million jobs in 2016) will by the adoption of currently available experience medium exposure to automation technologies. by 2030, while another 39 percent (57 million jobs) will experience low exposure. FIGURE 5 Most jobs are not highly susceptible to automation Shares of employment by automation potential 25% 36 million jobs Potential for automation olme of tass within the o that 39% are ssceptile to atomation 57 million jobs High of more Medium ow 36% 52 million jobs Source: Brookings analysis of BLS, Census, EMSI, and McKinsey data Automation and Artificial Intelligence | Executive summary 3 2. The impacts of automation and AI in the educational requirements, to low-paying coming decades will vary especially across personal care and domestic service work occupations, places, and demographic groups. characterized by non-routine activities or the Several patterns are discernable: need for interpersonal social and emotional intelligence. • “Routine,” predictable physical and cognitive tasks will be the most vulnerable Near-future automation potential will be to automation in the coming years. highest for roles that now pay the lowest wages. Likewise, the average automation Among the most vulnerable jobs are potential of occupations requiring a those in office administration, production, bachelor’s degree runs to just 24 percent, transportation, and food preparation. less than half the 55 percent task exposure Such jobs are deemed “high risk,” with faced by roles requiring less than a bachelor’s over 70 percent of their tasks potentially degree. Given this, better-educated, higher- automatable, even though they represent paid earners for the most part will continue only one-quarter of all jobs. The remaining, to face lower automation threats based on more secure jobs include a broader array of current task content—though that could occupations ranging from complex, “creative” change as AI begins to put pressure on some professional and technical roles with high higher-wage “non-routine” jobs. FIGURE 8 Smaller, more rural places will face heightened automation risks County distribution by community size type, 2016 90th 75th 54% Percentile 50th 25th l 52% 10th a i t n e t 50% o p n o i 48% t a m o t 46% u a e g a 44% r e v A 42% 40% Nonmetro areas Metro areas Nonmetro Metro Small Medium arge Small Medium arge <2.5K >20K <250K mil mil Source: Brookings analysis of BLS, Census, EMSI, Moody’s, and McKinsey data 4 Metropolitan Policy Program at Brookings FIGURE 6 The lowest wage jobs are the most exposed to automation Automation potential. United States, 2016 80% 70% 60% 50% 40% 30% 20% 10% 0% 0 10 20 30 40 50 60 70 80 90 100 Occupational wage percentile, 2016 Note: Figures have been smoothed using a LOWESS regression Source: Brookings analysis of BLS, Census, EMSI, and McKinsey data • Automation risk varies across U.S. variations in task exposure to automation. regions, states, and cities, but it will be Along these lines, the state-by-state variation most disruptive in Heartland states. While of automation potential is relatively narrow, automation will take place everywhere, ranging from 48.7 and 48.4 percent of the its inroads will be felt differently across employment-weighted task load in Indiana the country. Local risks vary with the local and Kentucky to 42.9 and 42.4 percent in industry, task, and skill mix, which in turn Massachusetts and New York, as depicted in determines local susceptibility to task Map 2. automation. Yet, the map of state automation exposure Large regions and whole states—which is distinctive. Overall, the 19 states that differ less from one another in their overall the Walton Family Foundation labels as industrial compositions than do smaller the American Heartland have an average locales like metropolitan areas or cities—will employment-weighted automation potential see noticeable but not, in most cases, radical of 47 percent of current tasks, compared with 45 percent in the rest of the country. Much Automation and Artificial Intelligence | Executive summary 5 MAP 2 Average automation potential by state Routine-intensive occupations 2016 Employment share, 1980 142.48.1% - 244%0% 244%0% - 2 -5 %45% 245%5% - 3 -0 %46% 346%0% - 3 -5 %47% 347%5% - 3 -8 .48%9% Source: Brookings analysis of BLS, Census, EMSI, Moody’s, and McKinsey data Automation potential Automation potential 2016 42.4% - 44% 44% - 45% 45% - 46% 46% - 47% 47% - 48.7% 2016 of this exposure reflects Heartland states’ average. By contrast, small university towns longstanding and continued specialization in like Charlottesville, Va. and Ithaca, N.Y., or 42.4% - 44% 44% - 45% 45% - 46% 46% - 47% 47% - 48.7% Automation potential 2016manufacturing and agricultural industries. state capitals like Bismarck, N.D. and Santa 42.4% - 44% 44% - 45% 45% - 46% 46% - 47% 47% - 48.7% Fe, N.M., appear relatively well-insulated. • At the community level, the data reveal sharper variation, with smaller, more rural As to the 100 largest metropolitan areas, it communities significantly more exposed is also clear that while the risk of current- to automation-driven task replacement— task automation will be widely distributed, it and smaller metros more vulnerable than won’t be evenly spread. Among this subset larger ones. The average worker in a small of key metro areas, educational attainment metro area with a population of less than will prove decisive in shaping how local 250,000, for example, works in a job where labor markets may be affected by AI-age 48 percent of current tasks are potentially technological developments. automatable. But that can rise or decline. In small, industrial metros like Kokomo, Ind. Among the large metro areas, employment- and Hickory, N.C. the automatable share weighted task risk in 2030 ranges from 50 of work reaches as high as 55 percent on percent and 49 percent in less well-educated 6 Metropolitan Policy Program at Brookings MAP 4 Routine-intensive occupations Average automation potential by metropolitan area Employment share, 1980 2016 139.1%8.1% - 2-0 44%% 244%0% - 2 -5 %46% 246%5% - 3 -0 %48% 348%0% - 3 5- %50% 350%5% - 3 8- .56.0%9% Source: Brookings analysisAu oftom BLS,ation pCensus,otential EMSI, Moody’s, and McKinsey data Automation potential 2016 39.1% - 44% 44% - 46% 46% - 48% 48% - 50% 50% - 56.0% 2016 locations like Toledo, Ohio and Greensboro- • Men, young workers, and underrepresented 39.1% - 44% 44% - 46% 46% - 48% 48% - 50% 50% - 56.0% AutoHighmation pPoint,otential N.C., to just 40 percent and 39 communities work in more automatable 2016 percent39.1% - 44% in4 4high% - 46% education46% - 48% attainment48% - 50% 50 %metros - 56.0% occupations. In this respect, the sharp like San Jose, Calif. and Washington, D.C. segmentation of the labor market by gender, age, and racial-ethnic identity ensures Following Washington, D.C. and San Jose that AI-era automation is going to affect among the larger metros with the lowest demographic groups unevenly. current-task automation risk comes a “who’s who” of well-educated and technology- Male workers appear noticeably oriented centers including New York; more vulnerable to potential future Durham-Chapel Hill, N.C.; and Boston— automation than women do, given all with average current-task risks below their overrepresentation in production, 43 percent. These metro areas relatively transportation, and construction-installation protected by their specializations in durable occupations—job areas that have above- professional, business, and financial services average projected automation exposure. occupations, combined with relatively large By contrast, women comprise upward of 70 education and health enterprises. percent of the labor force in relatively safe Automation and Artificial Intelligence | Executive summary 7 occupations, such as health care, personal automation potentials of 47 percent, 45 services, and education occupations. percent, and 44 percent for their jobs, respectively, figures well above those likely Automation exposure will vary even more for their white (40 percent) and Asian (39 sharply across age groups, meanwhile, with percent) counterparts.
Load more

Recommended publications
  • Assembly Automation with Evolutionary Nanorobots and Sensor-Based Control Applied to Nanomedicine
    IEEE Transactions on Nanotechnology, Vol. 2, no. 2, June 2003 82 Assembly Automation with Evolutionary Nanorobots and Sensor-Based Control applied to Nanomedicine Adriano Cavalcanti, Member, IEEE Germany only the Federal Ministry of Education and Research Abstract—The author presents a new approach within has announced 50 million Euros to be invested in the years advanced graphics simulations for the problem of nano-assembly 2002-2006 in research and development on nanotechnology automation and its application for medicine. The problem under [21]. More specifically the firm DisplaySearch predicts rapid study concentrates its main focus on nanorobot control design for assembly manipulation and the use of evolutionary competitive market growth from US$ 84 million today to $ 1.6 Billion in agents as a suitable way to warranty the robustness on the 2007 [20]. A first series of commercially nanoproducts has proposed model. Thereby the presented paper summarizes as well been announced also as foreseeable for 2007, and to reach this distinct aspects of some techniques required to achieve a goal of build organic electronics, firms are forming successful nano-planning system design and its simulation collaborations and alliances that bring together new visualization in real time. nanoproducts through the joint effort from companies such as IBM, Motorola, Philips Electronics, PARC, Xerox, Hewlett Index Terms—Biomedical computing, control systems, genetic algorithms, mobile robots, nanotechnology, virtual reality. Packard, Dow Chemical, Bell Laboratories, Intel Corp., just to quote a few ones [13], [20]. Building patterns and manipulating atoms with the use of I.INTRODUCTION Scanning Probe Microscope (SPM) such as Atomic Force he presented paper describe the design and simulation of Microscopy and Scanning Tunneling Microscopy has been Ta mobile nanorobot in atomic scales to perform used with satisfactory success as a promising approach for the biomolecular assembly manipulation for nanomedicine [12].
    [Show full text]
  • Artificial Intelligence, Automation, and Work
    Artificial Intelligence, Automation, and Work The Economics of Artifi cial Intelligence National Bureau of Economic Research Conference Report The Economics of Artifi cial Intelligence: An Agenda Edited by Ajay Agrawal, Joshua Gans, and Avi Goldfarb The University of Chicago Press Chicago and London The University of Chicago Press, Chicago 60637 The University of Chicago Press, Ltd., London © 2019 by the National Bureau of Economic Research, Inc. All rights reserved. No part of this book may be used or reproduced in any manner whatsoever without written permission, except in the case of brief quotations in critical articles and reviews. For more information, contact the University of Chicago Press, 1427 E. 60th St., Chicago, IL 60637. Published 2019 Printed in the United States of America 28 27 26 25 24 23 22 21 20 19 1 2 3 4 5 ISBN-13: 978-0-226-61333-8 (cloth) ISBN-13: 978-0-226-61347-5 (e-book) DOI: https:// doi .org / 10 .7208 / chicago / 9780226613475 .001 .0001 Library of Congress Cataloging-in-Publication Data Names: Agrawal, Ajay, editor. | Gans, Joshua, 1968– editor. | Goldfarb, Avi, editor. Title: The economics of artifi cial intelligence : an agenda / Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors. Other titles: National Bureau of Economic Research conference report. Description: Chicago ; London : The University of Chicago Press, 2019. | Series: National Bureau of Economic Research conference report | Includes bibliographical references and index. Identifi ers: LCCN 2018037552 | ISBN 9780226613338 (cloth : alk. paper) | ISBN 9780226613475 (ebook) Subjects: LCSH: Artifi cial intelligence—Economic aspects. Classifi cation: LCC TA347.A78 E365 2019 | DDC 338.4/ 70063—dc23 LC record available at https:// lccn .loc .gov / 2018037552 ♾ This paper meets the requirements of ANSI/ NISO Z39.48-1992 (Permanence of Paper).
    [Show full text]
  • Detecting Automation of Twitter Accounts: Are You a Human, Bot, Or Cyborg?
    IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL. 9, NO. X, XXXXXXX 2012 1 Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? Zi Chu, Steven Gianvecchio, Haining Wang, Senior Member, IEEE, and Sushil Jajodia, Senior Member, IEEE Abstract—Twitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear to be a double-edged sword to Twitter. Legitimate bots generate a large amount of benign tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. More interestingly, in the middle between human and bot, there has emerged cyborg referred to either bot-assisted human or human-assisted bot. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human, bot, and cyborg accounts on Twitter. We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts: 1) an entropy-based component, 2) a spam detection component, 3) an account properties component, and 4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg. Our experimental evaluation demonstrates the efficacy of the proposed classification system.
    [Show full text]
  • Design and Implementation of Home Automation System
    DESIGN AND IMPLEMENTATION OF HOME AUTOMATION SYSTEM USING RASPBERRY PI A Project Presented to the Faculty of California State Polytechnic University, Pomona In Partial Fulfilment of the Requirements for the Degree Master of Science in Computer Science By Irwin Soni 2018 SIGNATURE PAGE PROJECT: DESIGN AND IMPLEMENTATION OF HOME AUTOMATION SYSTEM USING RASPBERRY PI AUTHOR: Irwin Soni DATE SUBMITTED: Spring 2018 Computer Science Department Dr. Yu Sun Project Committee Chair Computer Science Dr. Sampath Jayarathna Computer Science ii ACKNOWLEDGEMENT First and foremost, I would like to thank God for blessing me with such amazing people who have been there to support me in all that I have achieved. To my parents, who have filled my life with immense love, happiness and shaping me into the person I have become today. I would like to thank Dr. Yu Sun, my project advisor, for his selfless support and enlightening guidance. Working with Dr. Sun was such a valuable experience of learning computer science and life at the same time. I would also like to thank Dr. Sampath Jayarathna for his review, suggestions and encouragement. I would also like to thank my classmates for their companionship and friendship. iii ABSTRACT Home automation system achieved great popularity in the last decades as it increases the comfort and quality of life. Smartphone applications are used to control and monitor the home appliances using different types of communication techniques. As mobile devices continue to grow in popularity and functionality, the demand for advanced ubiquitous mobile applications in our daily lives also increases. The paper deals with the design and implementation of a flexible and low-cost Home Automation System for various mobile devices that leverages mobile technology to provide essential functionalities to our homes and associated control operations.
    [Show full text]
  • Robots, Automation, and Employment: Where We Are
    WORKING PAPER SERIES ROBOTS, AUTOMATION, AND EMPLOYMENT: WHERE WE ARE Lukas Wolters PhD Candidate MIT Political Science [email protected] MIT Work of the Future Working Paper 05-2020 Date: May 26, 2020 400 Main Street, E19-733, Cambridge, MA 02139 Robots, Automation, and Employment: Where We Are∗ Lukas Woltersy May 26, 2020 Introduction “Robots will destroy our jobs – and we’re not ready for it” titled The Guardian in early 2017.1 Headlines like this have become more and more common over the past couple of years, with newspapers and media outlets reporting that “the robots are coming! And they are going to take all our jobs,“2 asserting that “sometime in the next 40 years, robots are going to take your job,”3 and that “robots may steal as many as 800 million jobs in the next 13 years,”4 and proclaiming gloomy headlines such as “automation threatening 25% of jobs in the US,”5 and “robots to replace up to 20 million factory jobs by 2030.”6 While idea that technology can render human labor obsolete is not new, and concerns about technological unemployment go back at least to Keynes (1930; p. 3) who in 1930 wrote about potential unemployment “due to our discovery of means of economizing the use of labor outrunning the pace at which we can find new uses for labor,” the recent proliferation of reports warning about the potential effect of new technologies, particularly advances in machine learning and robotics, on employment stands out in terms of the number of jobs allegedly under threat of replacement by machines and obsolescence.
    [Show full text]
  • History of Robotics: Timeline
    History of Robotics: Timeline This history of robotics is intertwined with the histories of technology, science and the basic principle of progress. Technology used in computing, electricity, even pneumatics and hydraulics can all be considered a part of the history of robotics. The timeline presented is therefore far from complete. Robotics currently represents one of mankind’s greatest accomplishments and is the single greatest attempt of mankind to produce an artificial, sentient being. It is only in recent years that manufacturers are making robotics increasingly available and attainable to the general public. The focus of this timeline is to provide the reader with a general overview of robotics (with a focus more on mobile robots) and to give an appreciation for the inventors and innovators in this field who have helped robotics to become what it is today. RobotShop Distribution Inc., 2008 www.robotshop.ca www.robotshop.us Greek Times Some historians affirm that Talos, a giant creature written about in ancient greek literature, was a creature (either a man or a bull) made of bronze, given by Zeus to Europa. [6] According to one version of the myths he was created in Sardinia by Hephaestus on Zeus' command, who gave him to the Cretan king Minos. In another version Talos came to Crete with Zeus to watch over his love Europa, and Minos received him as a gift from her. There are suppositions that his name Talos in the old Cretan language meant the "Sun" and that Zeus was known in Crete by the similar name of Zeus Tallaios.
    [Show full text]
  • Home Automation Planning Guide for Individuals with Autism
    HOME AUTOMATION PLANNING GUIDE FOR INDIVIDUALS WITH AUTISM WRITTEN BY A.J. PARON-WILDES WHY HOME AUTOMATION? When caring for an individual with autism, many needs must be addressed, and even the best caregiver can miss something or not see an incident coming. Home automation can offer protective solutions that support and elevate human care. The most important thing to remember when designing for individuals with autism is that they do not experience the designed environment in the same way others do. They can have increased sensitivities that evoke strong reactions to environmental stimuli. Color, lighting, sounds, and smells all can trigger extreme responses. Having an understanding that many people with autism have either hypo or hyper sensory issues will help you design an environment that can foster increased independence and confidence. Below I outline four main areas of consideration when starting a new design project or retrofitting a home to aid a person with autism. SAFETY The number-one priority for families with an autistic child is safety. Many of us do not understand the inherit dangers of our built environment and the impact it can have on those with autism. Daily challenges can range from possible elopement—kids walking out the front door—to an inadequate understanding of systems and appliances—how does the stove turn on and off? Then, as children get older, safety concerns shift. Instead of preventing the child from leaving the house, you monitor if and when they get in: Did they get off the bus and make it in the house independently? Can you see if a stranger is at the front door? Many children with autism have a hard time with speech, or have no speech, so communicating on the phone is not effective.
    [Show full text]
  • Industrial Robot
    1 Introduction 25 1.2 Industrial robots - definition and classification 1.2.1 Definition (ISO 8373:2012) and delimitation The annual surveys carried out by IFR focus on the collection of yearly statistics on the production, imports, exports and domestic installations/shipments of industrial robots (at least three or more axes) as described in the ISO definition given below. Figures 1.1 shows examples of robot types which are covered by this definition and hence included in the surveys. A robot which has its own control system and is not controlled by the machine should be included in the statistics, although it may be dedicated for a special machine. Other dedicated industrial robots should not be included in the statistics. If countries declare that they included dedicated industrial robots, or are suspected of doing so, this will be clearly indicated in the statistical tables. It will imply that data for those countries is not directly comparable with those of countries that strictly adhere to the definition of multipurpose industrial robots. Wafer handlers have their own control system and should be included in the statistics of industrial robots. Wafers handlers can be articulated, cartesian, cylindrical or SCARA robots. Irrespective from the type of robots they are reported in the application “cleanroom for semiconductors”. Flat panel handlers also should be included. Mainly they are articulated robots. Irrespective from the type of robots they are reported in the application “cleanroom for FPD”. Examples of dedicated industrial robots that should not be included in the international survey are: Equipment dedicated for loading/unloading of machine tools (see figure 1.3).
    [Show full text]
  • Security Automation Best Practices
    SECURITY AUTOMATION BEST PRACTICES A Guide to Making Your Security Team Successful with Automation TABLE OF CONTENTS Introduction 3 What Is Security Automation? 3 Security Automation: A Tough Nut to Crack 4 Prepare Your Security Organization for Success 6 Make a Choice: Build or buy? 8 Add Automation When the Time Is Right 10 Know Which Tasks Are Ideal for Automation 12 Testing Automation’s Capabilities 14 Implementing Security Automation 15 About Rapid7 16 Appendix 17 | Rapid7.com Security Automation Best Practices - 2 INTRODUCTION The best security postures are those that are built on efficiency and time-to-response. While processes make it possible to get a job done faster, creating ones that solve practical problems and result in measurable efficiency gains can be a time-consuming task, and without the expertise required to create and build them, they simply don’t get done. This is where security automation comes in. WHAT IS SECURITY AUTOMATION? Security automation streamlines a series of repetitive, manual tasks into cohesive and automated workflows. By plugging a set of tasks into an automated system (such as those involved in phishing investigations), security processes become: • More efficient • Less prone to human error With increased efficiency, better and faster decisions can be made, which in turn can improve your organization’s entire security posture. Even better, with repetitive and manual tasks taken care of by automation, security personnel can instead focus on more strategic work, which boosts their job satisfaction and ensures you’re retaining good talent. | Rapid7.com Security Automation Best Practices - 3 SECURITY AUTOMATION: A TOUGH NUT TO CRACK Historically, security automation has been difficult to implement, which is why many companies have yet to take advantage of it.
    [Show full text]
  • SICX1020 THEORY of ROBOTICS and AUTOMATION (Common to EIE, E&C, ECE, ETCE, EEE & BIOMED)
    SICX1020 THEORY OF ROBOTICS AND AUTOMATION (Common to EIE, E&C, ECE, ETCE, EEE & BIOMED) UNIT I BASIC CONCEPTS: History of Robot (Origin): 1968 Shakev, first mobile robot with vision capacity made at SRI. 1970 The Stanford Arm designed eith electrical actuators and controlled by a computer 1973 Cincinnati Milacron‘s (T3) electrically actuated mini computer controlled by industrial robot. 1976 Viking II lands on Mars and an arm scoops Martian soil for analysis. 1978 Unimation Inc. develops the PUMA robot- even now seen in university labs 1981 Robot Manipulators by R. Paul, one of the first textbooks on robotics. 1982 First educational robots by Microbot and Rhino. 1983 Adept Technology, maker of SCARA robot, started. 1995 Intuitive Surgical formed to design and market surgical robots. 1997 Sojourner robot sends back pictures of Mars; the Honda P3 humanoid robot, started in unveiled 2000 Honda demonstrates Asimo humanoid robot capable of walking. 2001 Sony releases second generation Aibo robot dog. 2004 Spirit and Opportunity explore Mars surface and detect evidence of past existence of water. 2007 Humanoid robot Aiko capable of ―feeling‖ pain. 2009 Micro-robots and emerging field of nano-robots marrying biology with engineering. An advance in robotics has closely followed the explosive development of computers and electronics. Initial robot usage was primarily in industrial application such as part/material handling, welding and painting and few in handling of hazardous material. Most initial robots operated in teach-playback mode, and replaced ‗repetitive‘ and ‗back-breaking‘ tasks. Growth and usage of robots slowed significantly in late 1980‘s and early 1990‘s due to ―lack of intelligence‖ and ―ability to adapt‖ to changing environment – Robots were essentially blind, deaf and dumb!.Last 15 years or so, sophisticated sensors and programming allow robots to act much more intelligently, autonomously and react to changes in environments faster.
    [Show full text]
  • Artificial Intelligence Engineering for Cyborg Technology Implementation
    Short Communication Robot Autom Eng J Volume 3 Issue 1 - May 2018 Copyright © All rights are reserved by Sadique Shaikh DOI: 10.19080/RAEJ.2018.03.555604 Artificial Intelligence Engineering for Cyborg Technology Implementation Sadique Shaikh* Institute of Management & Science (IMS), India Submission: April 01, 2018; Published: May 17, 2018 *Corresponding author: Sadique Shaikh Md, Institute of Management & Science (IMS), M.S, India, Email: Keywords: Artificial intelligence; Cyborg technology; Neuron command operating devices; Cyborg intelligence; Brain computer interfaces; CyborgAbbreviations: analysis design; Bionic organs; Medical robotics domains; Human biology Intelligence CAD: Cyborg Analysis Design; BCI: Brain Computer Interfaces; NCOD: Neuron Command Operating Devices; CI: Cyborg Introduction Modeling Cyborg analysis design (CAD) model A s hu m a n s l ive longer t her e i s a g r ow i ng ne e d for av a i l abi l it y of organs for transplant however shortage in donations necessitates the development of artificial alternatives with AI often called “Bionic”. Advances in medicine have led to the and heart-lung machines that are common implanted using availability of artificial blood, replacement joints, heart valves, AI for Bionic organs. One of the primary and utilitarian goals of artificial intelligence research is to develop machines with human-like intelligence. Great progress has been made since the start of AI as a field of study. One dominating research paradigm in AI has been based on the assumption that various aspects of human intelligence can be described and understood well enough to the extent that it can be simulated by computer programs through smart representational frameworks and generic reasoning mechanisms.
    [Show full text]
  • Programmable Cloud Platform for Iot(Smart Home)
    Programmable Cloud Platform for IoT(Smart Home) DashOS.net Co-founder, Braden Napier, Wayne Zhao [email protected] (408)421-9840 Market-Smart Home monitor and control • Target Dealer installed smart home market (instead of DIY market ) • We provides programmable cloud platform for home monitor and control. • Partner with installation dealers and hardware vendors to reach customers Problem and Our Solution Our Solution:Program home Problem of current smart automation rules on cloud, receive home control push notification, push to react Program home automation on Too hard to use! Smart Home Cloud Platform Trigger push 1.Find the app notification to 2. open app phone 3.find the button 4.click close light Find App, open app, find button, Click button Why cant I just walk there and turn off the light! Click “OK” Action! Example 1 Program Home Camera Detect automation in cloud: If motion Receive Push Choose action to motion in front camera, notification in perform send push notification phone Example 2 Program geo- Send push fencing on cloud: notification: confirm open If within 100 feet Open garage garage door of home, send door? push notification Our Solution-Smart Home Cloud OS User programs home automation rules on cloud platform DashOS.net Devices talk to Cloud using DashOS API (REST, Websocket, MQTT) • Partner with Home controller hardware vendor such as URC • Provide open API for home device to connect to DashOS cloud. (REST, Websocket, MQTT interface) User use push • Program the home automation in cloud platform. notification reaction to • User can use smart phone to monitor and control home device from control home anywhere devices Our Solution • We provide cloud platform for users to program home automation rules in the cloud.
    [Show full text]