Intelligent Libraries: New Horizons with Artificial Intelligence Volume 3
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AI and Education
AI and educationducation Guidance for policymakerspolicy-makers UNESCO Education Sector The Global Education 2030 Agenda Education is UNESCO’s top priority because UNESCO, as the United Nations’ specialized it is a basic human right and the foundation agency for education, is entrusted to lead and on which to build peace and drive sustainable coordinate the Education 2030 Agenda, which is development. UNESCO is the United Nations’ part of a global movement to eradicate poverty specialized agency for education and the through 17 Sustainable Development Goals by Education Sector provides global and 2030. Education, essential to achieve all of these regional leadership in education, strengthens goals, has its own dedicated Goal 4, which aims to national education systems and responds “ensure inclusive and equitable quality education to contemporary global challenges through and promote lifelong learning opportunities for education with a special focus on gender all.” The Education 2030 Framework for Action equality and Africa. provides guidance for the implementation of this ambitious goal and commitments. Published in 2021 by the United Nations Educational, Scientific and Cultural Organization 7, place de Fontenoy, 75352 Paris 07 SP, France © UNESCO 2021 ISBN 978-92-3-100447-6 This publication is available in Open Access under the Attribution-ShareAlike 3.0 IGO (CC-BY-SA 3.0 IGO) license (http://creativecommons.org/licenses/by-sa/3.0/igo/). By using the content of this publication, the users accept to be bound by the terms of use of the UNESCO Open Access Repository (http://www.unesco.org/open-access/terms-use-ccbysa-en). The designations employed and the presentation of material throughout this publication do not imply the expression of any opinion whatsoever on the part of UNESCO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. -
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. -
Artificial Intelligence and Fundamental Physics Research Tommaso Dorigo, INFN – Padova
Artificial Intelligence and Fundamental Physics Research Tommaso Dorigo, INFN – Padova 5th USERN Congress, Workshop 2 November 8, 2020 - Tehran Contents • Artificial Intelligence: Where It Comes From, What It Is, Where It Is Going • Applications in Fundamental Physics Research • Quantum Computing: Physics Research for AI • The Future What is Intelligence ? Before we discuss artificial intelligence, we should agree on what intelligence is – and what isn’t. • The term «intelligence» comes from Latin “intelligo” to comprehend, to perceive …but that does not help much. We can consider the literature for help. We find that notable definitions differ significantly, also in relation to what can be general and what is specific of human beings: “The aggregate or global capacity of the individual to act purposefully, to think rationally, and to deal effectively with his environment” [Wechsler 1944] “The unique propensity of human beings to change or modify the structure of their cognitive functioning to adapt to the changing demands of a life situation” [Feuerstein 1990] But also, and more useful to us, are more abstract definitions such as: “Intelligence measures an agent's ability to achieve goals in a wide range of environments” [Legg 2007] “Intelligence is goal-directed adaptive behavior” [Sternberg 1982] What is Artificial Intelligence ? Curiously, a precise definition of Artificial Intelligence is not less challenging than that of Intelligence at large • AI: “Intelligence demonstrated by machines” – devices that have the ability to perceive -
Relative Study on Artificial Intelligence Interaction with Human Emotions
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 02 | Feb 2019 www.irjet.net p-ISSN: 2395-0072 Relative Study on Artificial Intelligence Interaction with Human Emotions Chandrika1, K Radha2 1M.Tech, I –Year, CSE, GITAM UNIVERSITY, RUDRARAM HYDERABAD, TELANAGANA, INDIA 2Asst.Professor, CSE, GITAM UNIVERSITY, RUDRARAM, HYDERABAD, TELANAGANA, INDIA ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract – Artificial Intelligence is a multidisciplinary field 1950 Alan Turing published “Computing Machinery and whose goal is to making software which thinks intelligently Intelligence” in which he proposes “the imitation game” similar to human activities and makes a lot easier to human to which will later become known as the “Turing Test.”[5] achieve their goal. There are some recent successes in Artificial Intelligence from computerized medical diagnosticians and It may be said that Alan Turing’s ideas for computational systems that automatically customize hardware to particular thinking lay the foundation for AI. user requirements. This Paper presents about, how does 1952 Arthur Samuel develops the first computer checkers- Artificial Intelligence interact with humans, Does Artificial playing program and the first computer program to learn on Intelligence can understand the human emotions, what can be its own. the impact of Artificial Intelligence on humans in upcoming years, and Does Artificial Intelligence can replace humans. August 31, 1955 The term “artificial intelligence” is coined in a proposal for a “2 month, 10 man study of artificial Key Words: Artificial Intelligence, computerized medical intelligence” submitted by John McCarthy (Dartmouth diagnosticians, Human Interaction, Human Emotions. College), Marvin Minsky (Harvard University), Nathaniel 1. What is Artificial Intelligence Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories). -
The Future of AI
Seeing the forest for the trees, and the forests beyond The future of AI A report by the Deloitte AI Institute The question of whether a computer can think is no more interesting than the question of whether a submarine can swim. —Edsger W. Dijkstra, computer science pioneer 2 Contents 01 Everything and 03 The machines 06 Guardrails nothing at all (or, a technological 25 4 history of the future) 8 07 Conclusion 02 Something old, 29 somehow new 04 Us and the machines 6 (or, an anthropological history of the future) 14 05 Us as the machines (or, a biological history of the future) 20 3 Everything and nothing at all 01 As futurists, my team and I secretly And so, when tech headlines begin to Like any gold rush, there’s hope beneath the spend the lion’s share of our time increasingly read as breathless brochures hype. To be sure, there is plenty of actual 02 studying the past. I like to say that for Artificial Intelligence, we grey-hairs can’t gold afoot insofar as we’re seeing a genuine, we’re closet historians. Specifically, help but be reminded of a certain upstart evidence-based phase shift from AI as “cherry- 03 we research the history of various technology category some 20 years ago called on-top” curiosity to “key ingredient” at leading technologies and how they’ve impacted, “the world wide web.” organizations. 61 percent of respondents to or failed to impact, the way the world a recent Deloitte Insights report say AI will 04 works and lives. -
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. -
AI Testing - Testing AI-Based Systems (AIT – TAI)
Certified Tester AI Testing - Testing AI-Based Systems (AIT – TAI) Foundation Level Syllabus Version 2019 Korean Software Testing Qualifications Board Chinese Software Testing Qualifications Board Certified Tester – Foundation Level INTRODUCTION The testing of traditional systems is well-understood, but AI-Based systems, which are becoming more prevalent and critical to our daily lives, introduce new challenges. This syllabus covers the key concepts of Artificial Intelligence (AI), how we decide acceptance criteria and how we test AI-Based systems. These systems are typically complex (e.g. deep neural nets), based on big data, poorly specified and non- deterministic, which creates many new challenges and opportunities for testing them. This syllabus describes the requirements for a 2-day course to be followed by a 1-hour, closed book, multiple choice exam. In this document an AI-Based system is a system that includes at least one AI component. Throughout this document the acronym AI is used to represent the term ‘Artificial Intelligence’. BUSINESS OUTCOMES At the end of this course candidates will be able to: Understand the current state of AI and expected advances in the near future; Interpret and provide guidance on the specification of acceptance criteria for AI-Based systems; Contribute to the development process for machine learning systems and suggest opportunities for influencing their quality; Understand the new challenges of testing AI-Based systems, such as their complexity and non- determinism; Contribute to the test strategy for an AI-Based system; Apply black box and white box test design techniques to generate test suites for AI-Based systems; Recognize the need for virtual test environments to support the release of complex AI-Based systems; Understand the current state of testing supported by AI. -
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). -
And Machines: a Classification for AI in Medicine (ADAM Framework)
ORIGINAL RESEARCH Future Healthcare Journal Vol 8 No 2 July 2021 DIGITAL TECHNOLOGY The automation of doctors and machines: A classification for AI in medicine (ADAM framework) Author: Fawz KazzaziA The advances in artificial intelligence (AI) provide an opportunity (AGI; also known as ‘strong’ or ‘true’ AI) refers to a machine with to expand the frontier of medicine to improve diagnosis, the ability to perform any intellectual task that a human could. efficiency and management. By extension of being able to When John Searle coined this term in 1980, it included attributes of perform any task that a human could, a machine that meets the consciousness, sentience and mind; however, the moral legitimacy requirements of artificial general intelligence (‘strong’ AI; AGI) of machines mimicking characteristics ordinarily attributed to ABSTRACT possesses the basic necessities to perform as, or at least qualify ‘personhood’ remains an active debate.3,4 to become, a doctor. In this emerging field, this article explores Yet, by extension of being able to perform any task that a human the distinctions between doctors and AGI, and the prerequisites could, a machine that meets the requirements of AGI possesses the for AGI performing as clinicians. In doing so, it necessitates the basic necessities to perform as, or at least to qualify to become, a requirement for a classification of medical AI and prepares for doctor. In this evolving fast-paced field, I explore the distinctions the development of AGI. With its imminent arrival, it is beneficial between doctors and AGI, and the emerging prerequisites for AGI to create a framework from which leading institutions can define performing as clinicians. -
1987-An Automated Reasoning Technique for Providing Moment-By
From: AAAI-87 Proceedings. Copyright ©1987, AAAI (www.aaai.org). All rights reserved. Artificial htelligence Department oneywelll Corporate Systems Development Divisiola 1000 Boome Avenue North Golden Valley, Minanessta 5542 7 This is because, in most applications, there will always be some actions that cannot be performed without human A system for continuously providing advice about intervention (e.g.? replacing broken parts, operating manual the operation of some other device or process, valves, etc.) Thus, the reasoning technique used by such rather than just problem diagnoses, must not only systems must be able to cope with the unpredictability of function in real time, but also cope with dynamic operator behavior. The system cannot be based on problem courses. The reasoning technique assumptions that the operator will always approve and underlying such a system must not assume that comply with recommended actions, respond to queries for faults have single causes, that queries to the user information not obtainable through instrumentation, or will be answered and advice to the user will be even be available at the time advice is issued. In many followed, nor that aspects of a problem, once application environments, it is also important that the resolved, will not reoccur. This paper presents a advisory system l-lot interact with the operator reasoning technique that can be used in unnecessarily. conjunction with an inference engine to model the This paper presents a reasoning technique we have state of a problem situation throughout the entire found suitable for providing the problem-monitoring and problem-handling process, from discovery to final the advice-giving functions of a real time, interactive resolution. -
Importance of Artificial Intelligence in India
Vol-7 Issue-1 2021 IJARIIE-ISSN(O)-2395-4396 Importance of Artificial Intelligence In India Mamta Kesri ABSTRACT As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler's Theorem says "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, intelligent routing in content delivery networks, and military simulations. Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving" KEYWORD- Artificial Intelligence, machine, learning, technology INTRODUCTIONS Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. -
Cognitive Expert Systems and Machine Learning: Artificial Intelligence Research at the University of Connecticut
AI Magazine Volume 8 Number 1 (1987) (© AAAI) RESEARCH IN PROGRESS Mallory Selfridge, Donald J. Dickerson, and Stanley F. Biggs Cognitive Expert Systems and Machine Learning: Artificial Intelligence Research at the University of Connecticut Research at the Artificial Intelligence Laboratory of the Uni- judge performance of corporations and learn about them by versity of Connecticut is currently focused on a number of reading real-world sources, and (3) fundamental research in projects addressing both fundamental and applied aspects of computer models of cognitive development. next-generation expert systems and machine learning. We CMACS: Learning Causal Models of Physical believe that these next-generation expert systems will have Mechanisms by Understanding Real-World Natural to be based on cognitive models of expert human reasoning Language Explanations and learning in order to perform with the ability of a human expert. Consequently, we term such next-generation expert The causal model acquisition system (CMACS) (Daniel1 systems cognitive expert systems. 1985; Klimczak 1986; Selfridge, Daniell, and Simmons Cognitive expert systems should display three charac- 1985) addresses how an expert system can learn causal teristics. First, because expert human reasoning and learning models of physical mechanisms by understanding real-world rely in part on qualitative causal models and large-scale natural language explanations of these mechanisms. Follow- event-based memory structures, cognitive expert systems ing research conducted by deKleer and Brown (1983; 1984)) should rely on similar knowledge. Second, because human CMACS represents physical mechanisms as combinations of experts are skilled at acquiring knowledge, often through natural language interaction, cognitive expert systems should learn through real-world natural language interac- tion.