The Dark Ages of AI: a Panel Discussion at AAAI-84
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Artificial Intelligence in Today's Challenging World
International Journal of Advance Research, Ideas and Innovations in Technology ISSN: 2454-132X Impact Factor: 6.078 (Volume 7, Issue 3 - V7I3-1809) Available online at: https://www.ijariit.com Artificial intelligence in today’s challenging world Abhishek Negi Mohit Semwal [email protected] [email protected] Uttaranchal Institute of Management, Uttaranchal Institute of Management, Dehradun, Uttarakhand Dehradun, Uttarakhand ABSTRACT Artificial Intelligence refers to any machine that shows feature similar to human mind just like learning and problem solving. AI can take actions that have best chance of solving specific issues in an efficient way. Machine learning is a subset of Artificial Intelligence and refers to the idea of automatically learn and adapt new facts without getting any help from humans. Artificial Intelligence is developing day by day and performing Multitasking and making human work easy and helping human in a fast and efficient ways and gives better solution for complex problems. This paper will describe how AI is helping today’s world and also see what problem is also creating by AI in today’s world. This paper also describe the AI technologies uses in different areas in today world. We also discuss about how AI is useful in many fields but also damaging our environment and the damage is hard to fix and we also get the humans are the main problem. We have to change us to fix a problem. Keywords: Artificial Intelligence, Machine Learning, Humans, Database, Expert System. 1. AI HELPING HUMAN AI assisted robotic surgery is the best example for AI in health sector. It gives positive results are indeed promising. -
The Greatest Business Decisions of All Time: How Apple, Ford, IBM, Zappos, and Others Made Radical Choices That Changed the Cour
The Greatest BUSINESS DECISIONS of All Time HOW APPLE, FORD, IBM, ZAPPOS, AND OTHERS MADE RADICAL CHOICES THAT CHANGED THE COURSE OF BUSINESS. By Verne Harnish and the Editors of Fortune Foreword by Jim Collins . ACKNOWLEDGMENTS When you delve into the great decisions chronicled in these pages, you’ll find that in most instances it was the people involved that really mattered. The same holds true for producing this book. First, we want to thank Fortune managing editor Andy Serwer, who, displaying the vision and entrepreneurial spirit we’ve long admired him for, green-lighted this project in the same meeting in which we pitched it and then provided support all along the way. Fortune art director Emily Kehe, working with Time Inc.’s talented Anne-Michelle Gallero, applied their usual elegant sense of style to the design. Carol Gwinn, our copyeditor par excellence, used her superb language skills to save ourselves from ourselves. Steve Koepp and Joy Butts at Time Home Entertainment Inc., the book’s publisher, worked creatively behind the scenes to make this project a reality, and for that we’re truly grateful. And we extend our thanks and admiration to Jim Collins for providing such an insightful foreword to the book. Last, a big bow to the writers and editors on Fortune’s staff who used their in-depth knowledge of business and their nonpareil writing skills to make this book what I hope you’ll find to be a wonderful, informative read. TO DECISION-MAKERS WHO KEEP MAKING THE TOUGH CALLS . TABLE OF CONTENTS Foreword BY JIM COLLINS Introduction By VERNE HARNISH Chapter 1 Apple Brings Back Steve Jobs By ADAM LASHINSKY Chapter 2 How Free Shipping Saved Zappos By JENNIFER REINGOLD Chapter 3 Why Samsung Lets Its Stars Goof Off BY NICHOLAS VARCHAVER Chapter 4 At Johnson & Johnson, the Shareholder Comes Last BY TIMOTHY K. -
Global Markets Institute a Survivor's Guide to Disruption
GLOBAL MARKETS A SURVIVOR’S GUIDE INSTITUTE July 2019 TO DISRUPTION Steve Strongin Amanda Hindlian Sandra Lawson Sonya Banerjee Dan Duggan, Ph.D. [email protected] [email protected] [email protected] [email protected] [email protected] The Goldman Sachs Group, Inc. Table of Contents Chapter 1: Survivor’s guide - the short form 3 Chapter 2: Disruption’s evolutionary roots 9 Chapter 3: Perfecting Platforms 19 Chapter 4: Niche after niche - Organizers 32 Chapter 5: The competitive value of data 44 Chapter 6: Concluding thoughts 56 Appendix A: Considering communities 59 Bibliography 61 Disclosure Appendix 62 The Global Markets Institute is the research think tank within Goldman Sachs Global Investment Research. For other important disclosures, see the Disclosure Appendix. 2 Survivor’s guide - the short form Chapter 1: Survivor’s guide - the short form We examine how companies can reshape themselves to better compete in today’s Everything-as-a-Service (EaaS) economy1. In this new economy, firms can use services provided by other businesses to grow faster, while using less capital and fewer people than would otherwise be possible. Industries are reorganizing in response to these dynamics, and companies must adapt or risk falling behind. EaaS can be thought of as an extreme form of outsourcing. In the past, firms would selectively outsource business functions to reduce costs, for example by outsourcing ancillary functions like operating a cafeteria within an office or by outsourcing labor-intensive but simple manufacturing processes. Over time, however, the high degree of standardization that has emerged across manufacturing, communications, data systems and user interfaces, among other areas, has made it possible to outsource virtually any business function. -
Non-Independent Investment Research Disclaimer
CONTENT 03 2020 theme – Engines of Disruption 04 Chips go cold in AI winter 05 Stagflation rewards value over growth 06 ECB folds and hikes rates 07 In energy, green is not the new black 08 South Africa gets electrocuted by ESKOM debt 09 Trump announces America First Tax 10 Sweden breaks bad 11 Dems win clean sweep in 2020 election 12 Hungary leaves the EU 13 Asia launches digital reserve currency 2020 rent at twice the price of owning and maybe even slightly normalise viewed as plain fun, the principles Outrageous Predictions 2020 – a tax on the poor if ever there rates, while governments step into behind the Outrageous Predictions was one, and a driver of inequality. the breach with infrastructure and resonate very strongly with our This risks leaving an entire climate policy-linked spending. clients and the Saxo Group. Not - Engines of Disruption generation without the savings just in terms of what could happen STEEN JAKOBSEN, CHIEF INVESTMENT OFFICER needed to own their own house, The list of Outrageous Predictions to the portfolios and wealth typically the only major asset that this year all play to the theme of allocation, but also as an input to many medium- and lower-income disruption, because our current our respective areas of business, households will ever obtain. Thus paradigm is simply at the end of careers and life in general. Looking into the future is something of a fool’s game, but it remains a Already, there are a number of we are denying the very economic the road. Not because we want it to This is the spirit of Outrageous useful exercise — helping prepare for what lies ahead by considering cracks in the system that show mechanism that made the older end, but simply because extending Predictions: to create the very the full possible range of economic and political outcomes. -
Final Exam Review History of Science 150
Final Exam Review History of Science 150 1. Format of the Exam 90 minutes, on canvas 12:25pm December 18. You are welcome to bring notes to the exam, so you could start by filling out this sheet with notes from lectures and the readings! Like the mid-term, the final exam will have two kinds of questions. 1) Multiple choice questions examining your knowledge of key concepts, terms, historical developments, and contexts 2) Short answer questions in which ask you to draw on things you’ve learned in the course (from lecture, readings, videos) to craft a short argument in a brief essay expressing your informed issue on a historical question 2. Sample Questions Multiple Choice: Mina Rees was involved in (and wrote about) which of the following computing projects? A) Silicon Valley start-ups in the dot-com period B) Charles Babbage’s Difference Engine C) Works Projects Administration Tables Project D) Federal funding for computing research after WWII Short Answer: (Your answers should be between 100-200 words, and keep to specifics (events, machines, developments, people) that demonstrate your knowledge of materials covered from the course) A) What are two historical factors important to the development of Silicon Valley’s technology industry after World War II? B) In what ways did the field of programming change (in terms of its status and workers) between World War II and the late 1960s? 3. Topics to Review: Below, is a list of ideas to review for the final exam, which covers material through the entire course. You should review in particular, lecture notes, O’Mara’s The Code and other course readings provided on Canvas. -
Narrative Intelligence
From: AAAI Technical Report FS-99-01. Compilation copyright © 1999, AAAI (www.aaai.org). All rights reserved. Narrative Intelligence Michael Mateas Phoebe Sengers Computer Science Department Media Arts Research Studies Carnegie Mellon University Institut fuer Medienkommunikation 5000 Forbes Avenue GMD Pittsburgh, PA 15213 Schloss Birlinghoven [email protected] D-53754 Sankt Augustin Germany [email protected] to sentences around it, to prior experience, and to some larger context, the group's work quickly became focused Introduction on understanding narratives. In a series of programs, they developed a theory of the knowledge structures necessary People are narrative animals. As children, our caretakers to understand textual narratives. The story-understanding immerse us in stories: fairy tales, made-up stories, favorite system SAM (Cullingford 1981) used scripts to capture the stories, "Read me a story!" Even when barely verbal, we notion of a stereotyped situations or contexts. The scripts begin to tell our own proto-stories. "Phoebe! Pizza! captured the typical causal connections holding in a Phoebe! Pizza!" was the excited story of a 2-year-old stereotyped situation. The story-understanding system friend Addie when one of us happened to arrive PAM (Wilensky 1981) and the story-generation system simultaneously with the pizza delivery man. This story TAIL-SPIN (Meehan 1977) both incorporated a notion of means, approximately, "Can you believe it? Phoebe and the goals held by characters in a narrative and the various pizza came into the house at the same time!" As children, means they have to accomplish these goals. Other work in narrative frameworks become an important part of the way this group included a model of ideologically-biased we learn to approach the world (Nelson 1989). -
The Competitive Value of Data
GLOBAL THE COMPETITIVE MARKETS INSTITUTE May 2019 VALUE OF DATA Steve Strongin Amanda Hindlian Sandra Lawson Sonya Banerjee [email protected] [email protected] [email protected] [email protected] The Goldman Sachs Group, Inc. Goldman Sachs Global Markets Institute Table of Contents Executive summary 3 The learning curve 5 Data-driven learning strategies 10 The four-part test 15 Disclosure Appendix 16 9 May 2019 2 Goldman Sachs Global Markets Institute Executive summary Data is now the lifeblood of many firms, particularly in the modern economy in which companies tend to focus on their narrow area of expertise while outsourcing the rest1. From organizing and optimizing complex multi-vendor production processes to customer acquisition, service and retention – these modern firms are almost entirely dependent on data. Naturally, trying to use data to establish a competitive edge has therefore become big business. Anecdotes about data-driven successes abound, but experience suggests that it is actually quite difficult for businesses to use data to build a sustainable competitive advantage. In fact, pinpointing examples of companies that have successfully used data to maintain a competitive edge is a challenging task. This begs the following two questions: 1) why haven’t more companies been able to build a sustainable competitive edge using data, and 2) when can data serve this purpose? We address these two questions by building a conceptual framework that we refer to as the “learning curve.” The learning curve helps us assess the factors that underpin when a company can use data to create a competitive edge – and perhaps more importantly, when it cannot. -
A Linguistic and Conceptual Study of American Public Discourse
Beyond the Issues: A Linguistic and Conceptual Study of American Public Discourse by Pamela Sue Morgan B.A. (University of Tulsa) 1974 B.A. (University of Arizona) 1976 M.A. (University of California, Santa Barbara) 1981 M.A. (University of California, Berkeley) 1993 Ph.D. (University of California, Santa Barbara) 1985 A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Linguistics in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor George Lakoff, Co-Chair Professor Eve Sweetser, Co-Chair Professor Robin Lakoff Professor David Collier Spring 1998 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Beyond the Issues: A Linguistic and Conceptual Study of American Public Discourse © 1998 by Pamela Sue Morgan Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The dissertation of Pamela Sue Morgan is approved: Date 18 /f?8 Co-Chair Date ^ /h^. Date o =oJ2 ^ ^ ' ? £ s' Date University of California, Berkeley Spring 1998 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Abstract Beyond the Issues: A Linguistic and Conceptual Study of American Public Discourse by Pamela Sue Morgan Doctor of Philosophy in Linguistics University of California, Berkeley Professor George Lakoff, Co-Chair Professor Eve Sweetser, Co-Chair Cultural cognitive models (CCMs) are learned and shared by members of cultural communities and serve as shortcuts to the presentation and understanding of communicative events, including public discourse. They are made up of "frames," here defined as prototypical representations of recurrent cultural experiences or historical references that contain culturally-agreed-upon sets of participants, event scenarios, and evaluations. -
Global-Bibliography.Pdf
Bibliography The following abbreviations are used for frequently cited conferences and journals: AAAI Proceedings of the AAAI Conference on Artificial Intelligence AAMAS Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems ACL Proceedings of the Annual Meeting of the Association for Computational Linguistics AIJ Artificial Intelligence (Journal) AIMag AI Magazine AIPS Proceedings of the International Conference on AI Planning Systems AISTATS Proceedings of the International Conference on Artificial Intelligence and Statistics BBS Behavioral and Brain Sciences CACM Communications of the Association for Computing Machinery COGSCI Proceedings of the Annual Conference of the Cognitive Science Society COLING Proceedings of the International Conference on Computational Linguistics COLT Proceedings of the Annual ACM Workshop on Computational Learning Theory CP Proceedings of the International Conference on Principles and Practice of Constraint Programming CVPR Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition EC Proceedings of the ACM Conference on Electronic Commerce ECAI Proceedings of the European Conference on Artificial Intelligence ECCV Proceedings of the European Conference on Computer Vision ECML Proceedings of the The European Conference on Machine Learning ECP Proceedings of the European Conference on Planning EMNLP Proceedings of the Conference on Empirical Methods in Natural Language Processing FGCS Proceedings of the International Conference on Fifth Generation Computer -
Lisp: Final Thoughts
20 Lisp: Final Thoughts Both Lisp and Prolog are based on formal mathematical models of computation: Prolog on logic and theorem proving, Lisp on the theory of recursive functions. This sets these languages apart from more traditional languages whose architecture is just an abstraction across the architecture of the underlying computing (von Neumann) hardware. By deriving their syntax and semantics from mathematical notations, Lisp and Prolog inherit both expressive power and clarity. Although Prolog, the newer of the two languages, has remained close to its theoretical roots, Lisp has been extended until it is no longer a purely functional programming language. The primary culprit for this diaspora was the Lisp community itself. The pure lisp core of the language is primarily an assembly language for building more complex data structures and search algorithms. Thus it was natural that each group of researchers or developers would “assemble” the Lisp environment that best suited their needs. After several decades of this the various dialects of Lisp were basically incompatible. The 1980s saw the desire to replace these multiple dialects with a core Common Lisp, which also included an object system, CLOS. Common Lisp is the Lisp language used in Part III. But the primary power of Lisp is the fact, as pointed out many times in Part III, that the data and commands of this language have a uniform structure. This supports the building of what we call meta-interpreters, or similarly, the use of meta-linguistic abstraction. This, simply put, is the ability of the program designer to build interpreters within Lisp (or Prolog) to interpret other suitably designed structures in the language. -
Blockbusters
BLOCKBUSTERS HIT-MAKING, RISK-TAKING, AND THE BIG BUSINESS OF ENTERTAINMENT ANITA ELBERSE HENRY HOLT AND COMPANY NEW YORK 14 BLOCKBUSTERS sector's most successful impresarios are leading a revolution, trans forming the business from one that is all about selling bottles— high-priced alcohol delivered to "table customers" seated at hot spots in the club—to one that is just as much about selling tickets Chapter One to heavily marketed events featuring superstar DJs. But I'll also point to other examples, from Apple and its big bets in consumer electronics, to Victoria's Secret with its angelic-superstar-studded fashion shows, and to Burberry's success in taking the trench coat digital. As these will show, many of the lessons to be learned about blockbusters not only apply across the entertainment industry— they even extend to the business world at large. BETTING ON BLOCKBUSTERS n June 2012, less than two weeks after the news of his appoint ment as chairman of Walt Disney Pictures had Hollywood in- siders buzzing, Alan Horn walked onto the Disney studio lot. The well-liked sixty-nine-year-old executive ("I try to be a nice person almost all the time, but next to Alan Horn I look like a com plete jerk," actor Steve Carell had joked during Horn's good-bye party at Warner Bros.) was excited about joining Disney, which he described as "one of the most iconic and beloved entertainment companies in the world." But he also knew he had his work cut out for him, as Disney Pictures had posted disappointing box-office results in recent years. -
Origins of the American Association for Artificial Intelligence
AI Magazine Volume 26 Number 4 (2006)(2005) (© AAAI) 25th Anniversary Issue The Origins of the American Association for Artificial Intelligence (AAAI) Raj Reddy ■ This article provides a historical background on how AAAI came into existence. It provides a ratio- nale for why we needed our own society. It pro- vides a list of the founding members of the com- munity that came together to establish AAAI. Starting a new society comes with a whole range of issues and problems: What will it be called? How will it be financed? Who will run the society? What kind of activities will it engage in? and so on. This article provides a brief description of the consider- ations that went into making the final choices. It also provides a description of the historic first AAAI conference and the people that made it happen. The Background and the Context hile the 1950s and 1960s were an ac- tive period for research in AI, there Wwere no organized mechanisms for the members of the community to get together and share ideas and accomplishments. By the early 1960s there were several active research groups in AI, including those at Carnegie Mel- lon University (CMU), the Massachusetts Insti- tute of Technology (MIT), Stanford University, Stanford Research Institute (later SRI Interna- tional), and a little later the University of Southern California Information Sciences Insti- tute (USC-ISI). My own involvement in AI began in 1963, when I joined Stanford as a graduate student working with John McCarthy. After completing my Ph.D. in 1966, I joined the faculty at Stan- ford as an assistant professor and stayed there until 1969 when I left to join Allen Newell and Herb Simon at Carnegie Mellon University Raj Reddy.