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Prominence of Expert System and Case Study- DENDRAL Namita Mirjankar, Shruti Ghatnatti Karnataka, India [email protected],[email protected]
International Journal of Advanced Networking & Applications (IJANA) ISSN: 0975-0282 Prominence of Expert System and Case Study- DENDRAL Namita Mirjankar, Shruti Ghatnatti Karnataka, India [email protected],[email protected] Abstract—Among many applications of Artificial Intelligence, Expert System is the one that exploits human knowledge to solve problems which ordinarily would require human insight. Expert systems are designed to carry the insight and knowledge found in the experts in a particular field and take decisions based on the accumulated knowledge of the knowledge base along with an arrangement of standards and principles of inference engine, and at the same time, justify those decisions with the help of explanation facility. Inference engine is continuously updated as new conclusions are drawn from each new certainty in the knowledge base which triggers extra guidelines, heuristics and rules in the inference engine. This paper explains the basic architecture of Expert System , its first ever success DENDRAL which became a stepping stone in the Artificial Intelligence field, as well as the difficulties faced by the Expert Systems Keywords—Artificial Intelligence; Expert System architecture; knowledge base; inference engine; DENDRAL I INTRODUCTION the main components that remain same are: User Interface, Knowledge Base and Inference Engine. For more than two thousand years, rationalists all over the world have been striving to comprehend and resolve two unavoidable issues of the universe: how does a human mind work, and can non-people have minds? In any case, these inquiries are still unanswered. As humans, we all are blessed with the ability to learn and comprehend, to think about different issues and to decide; but can we design machines to do all these things? Some philosophers are open to the idea that machines will perform all the tasks a human can do. -
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. -
Awards and Distinguished Papers
Awards and Distinguished Papers IJCAI-15 Award for Research Excellence search agenda in their area and will have a first-rate profile of influential re- search results. e Research Excellence award is given to a scientist who has carried out a e award is named for John McCarthy (1927-2011), who is widely rec- program of research of consistently high quality throughout an entire career ognized as one of the founders of the field of artificial intelligence. As well as yielding several substantial results. Past recipients of this honor are the most giving the discipline its name, McCarthy made fundamental contributions illustrious group of scientists from the field of artificial intelligence: John of lasting importance to computer science in general and artificial intelli- McCarthy (1985), Allen Newell (1989), Marvin Minsky (1991), Raymond gence in particular, including time-sharing operating systems, the LISP pro- Reiter (1993), Herbert Simon (1995), Aravind Joshi (1997), Judea Pearl (1999), Donald Michie (2001), Nils Nilsson (2003), Geoffrey E. Hinton gramming languages, knowledge representation, commonsense reasoning, (2005), Alan Bundy (2007), Victor Lesser (2009), Robert Anthony Kowalski and the logicist paradigm in artificial intelligence. e award was estab- (2011), and Hector Levesque (2013). lished with the full support and encouragement of the McCarthy family. e winner of the 2015 Award for Research Excellence is Barbara Grosz, e winner of the 2015 inaugural John McCarthy Award is Bart Selman, Higgins Professor of Natural Sciences at the School of Engineering and Nat- professor at the Department of Computer Science, Cornell University. Pro- ural Sciences, Harvard University. Professor Grosz is recognized for her pio- fessor Selman is recognized for expanding our understanding of problem neering research in natural language processing and in theories and applica- complexity and developing new algorithms for efficient inference. -
“History of Artificial Intelligence (AI)”
The Privacy Foundation “History Of Artificial Intelligence (AI)” 1308 Catalan poet and theologian Ramon Llull publishes Ars generalis ultima (The Ultimate General Art), further perfecting his method of using paper-based mechanical means to create new knowledge from combinations of concepts. 1666 Mathematician and philosopher Gottfried Leibniz publishes Dissertatio de arte combinatoria (On the Combinatorial Art), following Ramon Llull in proposing an alphabet of human thought and arguing that all ideas are nothing but combinations of a relatively small number of simple concepts. 1763 Thomas Bayes develops a framework for reasoning about the probability of events. Bayesian inference will become a leading approach in machine learning. 1898 At an electrical exhibition in the recently completed Madison Square Garden, Nikola Tesla makes a demonstration of the world’s first radio-controlled vessel. The boat was equipped with, as Tesla described, “a borrowed mind.” 1914 The Spanish engineer Leonardo Torres y Quevedo demonstrates the first chess-playing machine, capable of king and rook against king endgames without any human intervention. 1921 Czech writer Karel Čapek introduces the word "robot" in his play R.U.R. (Rossum's Universal Robots). The word "robot" comes from the word "robota" (work). 1925 Houdina Radio Control releases a radio-controlled driverless car, travelling the streets of New York City. 1927 The science-fiction film Metropolis is released. It features a robot double of a peasant girl, Maria, which unleashes chaos in Berlin of 2026—it was the first robot depicted on film, inspiring the Art Deco look of C-3PO in Star Wars. 1929 Makoto Nishimura designs Gakutensoku, Japanese for "learning from the laws of nature," the first robot built in Japan. -
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. -
UC Berkeley Previously Published Works
UC Berkeley UC Berkeley Previously Published Works Title Building the Second Mind, 1961-1980: From the Ascendancy of ARPA-IPTO to the Advent of Commercial Expert Systems Permalink https://escholarship.org/uc/item/7ck3q4f0 ISBN 978-0-989453-4-6 Author Skinner, Rebecca Elizabeth Publication Date 2013-12-31 eScholarship.org Powered by the California Digital Library University of California Building the Second Mind, 1961-1980: From the Ascendancy of ARPA to the Advent of Commercial Expert Systems copyright 2013 Rebecca E. Skinner ISBN 978 09894543-4-6 Forward Part I. Introduction Preface Chapter 1. Introduction: The Status Quo of AI in 1961 Part II. Twin Bolts of Lightning Chapter 2. The Integrated Circuit Chapter 3. The Advanced Research Projects Agency and the Foundation of the IPTO Chapter 4. Hardware, Systems and Applications in the 1960s Part II. The Belle Epoque of the 1960s Chapter 5. MIT: Work in AI in the Early and Mid-1960s Chapter 6. CMU: From the General Problem Solver to the Physical Symbol System and Production Systems Chapter 7. Stanford University and SRI Part III. The Challenges of 1970 Chapter 8. The Mansfield Amendment, “The Heilmeier Era”, and the Crisis in Research Funding Chapter 9. The AI Culture Wars: the War Inside AI and Academia Chapter 10. The AI Culture Wars: Popular Culture Part IV. Big Ideas and Hardware Improvements in the 1970s invert these and put the hardware chapter first Chapter 11. AI at MIT in the 1970s: The Semantic Fallout of NLR and Vision Chapter 12. Hardware, Software, and Applications in the 1970s Chapter 13. -
The Profile of Edward Feigenbaum Begins of Leadership That He Has
v#' ■/ '■J1 A PROFILE OF EDWARD A. FEIGENBAUM, OFFERED IN CONNECTION WITH THE MCI COMMUNICATIONS INFORMATION TECHNOLOGY LEADERSHIP AWARD FOR INNOVATION The profile of Edward Feigenbaum begins with his Curriculum Vitae, which is attached. Those facts indicate the dimensions of leadership that he has offered to innovation in information technology and the applications of computers. Briefly summarized, the dimensions of his leadership are: a. scientific and technological: pioneering in applied artificial intelligence, an information technology that has created important new applications of computers and that offers promise of major future gains in productivity and quality of work in the new economy of "knowledge workers/ the so-called post-industrial knowledge-based society. b. literary: i.e. "entrepreneuring" with the word, writing books to disseminate an understanding of the new developments to both lay and professional audiences. This also includes appearances on television and radio broadcasts, public lectures, and the making of an award-winning film. c. corporate: i.e. entrepreneuring in the usual sense of establishing companies to transfer the fruits of university basic research to industry and business. This also includes service on boards of large corporations. d. professional: serving his university, the national government, and his science in a variety of leadership positions. This also includes his role in helping to found the American Association for Artificial Intelligence and serving as its second president. HONORS For his scientific innovation and leadership, Feigenbaum has been accorded many honors. Among the most prominent are his election to the National Academy of Engineering and to the American Academy of Arts and Sciences; his honorary degree from Aston University in England; his election to fellow status in the American Association for the Advancement of Science and the Society of Medical Informatics; and his Lee Kuan Yew Professorship, as well as his election to the Productivity Hall of Fame, in the Republic of Singapore. -
Mccarthy As Scientist and Engineer, with Personal Recollections
Articles McCarthy as Scientist and Engineer, with Personal Recollections Edward Feigenbaum n John McCarthy, professor emeritus of com - n the late 1950s and early 1960s, there were very few people puter science at Stanford University, died on actually doing AI research — mostly the handful of founders October 24, 2011. McCarthy, a past president (John McCarthy, Marvin Minsky, and Oliver Selfridge in of AAAI and an AAAI Fellow, helped design the I Boston, Allen Newell and Herbert Simon in Pittsburgh) plus foundation of today’s internet-based computing their students, and that included me. Everyone knew everyone and is widely credited with coining the term, artificial intelligence. This remembrance by else, and saw them at the few conference panels that were held. Edward Feigenbaum, also a past president of At one of those conferences, I met John. We renewed contact AAAI and a professor emeritus of computer sci - upon his rearrival at Stanford, and that was to have major con - ence at Stanford University, was delivered at the sequences for my professional life. I was a faculty member at the celebration of John McCarthy’s accomplish - University of California, Berkeley, teaching the first AI courses ments, held at Stanford on 25 March 2012. at that university, and John was doing the same at Stanford. As – AI Magazine Stanford moved toward a computer science department under the leadership of George Forsythe, John suggested to George, and then supported, the idea of hiring me into the founding fac - ulty of the department. Since we were both Advanced Research Project Agency (ARPA) contract awardees, we quickly formed a close bond concerning ARPA-sponsored AI research and gradu - ate student teaching. -
Oral History of Edward Feigenbaum
Oral History of Edward Feigenbaum Interviewed by: Don Knuth Recorded: April 4, and May 2, 2007 Mountain View, California CHM Reference number: X3897.2007 © 2007 Computer History Museum Oral History of Edward Feigenbaum (Knuth) Don Knuth: Hello, everyone. My name is Don Knuth. Today [April 4, 2007] I have the great privilege of interviewing Ed Feigenbaum for oral history where we hope to reach people generations from now and also people of today. I’m going to try to ask a zillion questions about things that I wish I could have asked people who unfortunately are dead now so that students and general scientific people, people with general interest in the world, historians and so on in the future will have some idea as to what Ed was really like even though none of us are going to be around forever. Now this is the second part of a two- phase study. Ed grilled me a couple weeks ago and now it’s my turn to do him. I hope I can do half as well as he did for me. I’d like to get right into it But first of all sort of for organization, my plan -- who knows if it’ll carry it out -- is to do a little bit first that’s chronological, in order to set the scenes for his whole life. Then most of my questions are probably going to be cross-cutting that go through the years. But first of all, Ed, do you have some remarks you would like to start with? Or shall I get right into the— Edward Feigenbaum: No, Don. -
The History of Expert Systems
Articles Learning from Artificial Intelligence’s Previous Awakenings: The History of Expert Systems David C. Brock n Much of the contemporary moment’s f it is indeed true that we cannot fully understand our enthusiasms for and commercial inter- present without knowledge of our past, there is perhaps ests in artificial intelligence, specificial- Ino better time than the present to attend to the history of ly machine learning, are prefigured in artificial intelligence. Late 2017 saw Sundar Pichai, the CEO the experience of the artificial intelli- of Google, Inc., opine that “AI is one of the most important gence community concerned with expert systems in the 1970s and 1980s. This things that humanity is working on. It’s more profound than, essay is based on an invited panel on I don’t know, electricity or fire” (Schleifer 2018). Pichai’s the history of expert systems at the notable enthusiasm for, and optimism about, the power of AAAI-17 conference, featuring Ed multilayer neural networks coupled to large data stores is Feigenbaum, Bruce Buchanan, Randall widely shared in technical communities and well beyond. Davis, and Eric Horvitz. It argues that Indeed, the general zeal for such artificial intelligence sys- artifical intelligence communities today tems of the past decade across the academy, business, gov- have much to learn from the way that ernment, and the popular imagination was reflected in a earlier communities grappled with the New York Times Magazine , issues of intelligibility and instrumen- recent article “The Great AI Awak- tality in the study of intelligence. ening” (Lewis-Kraus 2016). Imaginings of our near-future promoted by the World Economic Forum under the banner of a Fourth Industrial Revolution place this “machine learn- ing” at the center of profound changes in economic activity and social life, indeed in the very meaning of what it means to be human (Schwab 2016). -
Interview with Edward Feigenbaum
An Interview with EDWARD FEIGENBAUM OH 157 Conducted by William Aspray on 3 March 1989 Palo Alto, CA Charles Babbage Institute Center for the History of Information Processing University of Minnesota, Minneapolis Copyright, Charles Babbage Institute 1 Edward Feigenbaum Interview 3 March 1989 Abstract Feigenbaum begins the interview with a description of his initial recruitment by ARPA in 1964 to work on a time- sharing system at Berkeley and his subsequent move to Stanford in 1965 to continue to do ARPA-sponsored research in artificial intelligence. The bulk of the interview is concerned with his work on AI at Stanford from 1965 to the early 1970s and his impression of the general working relationship between the IPT Office at ARPA and the researchers at Stanford. He discusses how this relationship changed over time under the various IPT directorships and the resulting impact it had on their AI research. The interview also includes a general comparison of ARPA with other funding sources available to AI researchers, particularly in terms of their respective funding amounts, criteria for allocation, and management style. This interview was recorded as part of a research project on the influence of the Defense Advanced Research Projects Agency (DARPA) on the development of computer science in the United States. 2 EDWARD FEIGENBAUM INTERVIEW DATE: 3 March 1989 INTERVIEWER: William Aspray LOCATION: Palo Alto, CA ASPRAY: I'd like your comments about how you first became involved in getting DARPA support (ARPA at that time), and what your interactions were with the program officers at the time. FEIGENBAUM: The first thing to say is that for me the period of coverage that you're interested in starts at 1965, so you're talking about 1965 through the early 1970s. -
Alan Mathison Turing and the Turing Award Winners
Alan Turing and the Turing Award Winners A Short Journey Through the History of Computer TítuloScience do capítulo Luis Lamb, 22 June 2012 Slides by Luis C. Lamb Alan Mathison Turing A.M. Turing, 1951 Turing by Stephen Kettle, 2007 by Slides by Luis C. Lamb Assumptions • I assume knowlege of Computing as a Science. • I shall not talk about computing before Turing: Leibniz, Babbage, Boole, Gödel... • I shall not detail theorems or algorithms. • I shall apologize for omissions at the end of this presentation. • Comprehensive information about Turing can be found at http://www.mathcomp.leeds.ac.uk/turing2012/ • The full version of this talk is available upon request. Slides by Luis C. Lamb Alan Mathison Turing § Born 23 June 1912: 2 Warrington Crescent, Maida Vale, London W9 Google maps Slides by Luis C. Lamb Alan Mathison Turing: short biography • 1922: Attends Hazlehurst Preparatory School • ’26: Sherborne School Dorset • ’31: King’s College Cambridge, Maths (graduates in ‘34). • ’35: Elected to Fellowship of King’s College Cambridge • ’36: Publishes “On Computable Numbers, with an Application to the Entscheindungsproblem”, Journal of the London Math. Soc. • ’38: PhD Princeton (viva on 21 June) : “Systems of Logic Based on Ordinals”, supervised by Alonzo Church. • Letter to Philipp Hall: “I hope Hitler will not have invaded England before I come back.” • ’39 Joins Bletchley Park: designs the “Bombe”. • ’40: First Bombes are fully operational • ’41: Breaks the German Naval Enigma. • ’42-44: Several contibutions to war effort on codebreaking; secure speech devices; computing. • ’45: Automatic Computing Engine (ACE) Computer. Slides by Luis C.