Introducing Chatbots in Libraries
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Turing Test Round One Loebner Prize Competition: November 8
Summer 1 9 9 1 r Museum I VVS Turing Test Round One Loebner Prize Competition: November 8 The first round of the classic Turing Test of While the subject has been debated for machine intelligence will be held November 8, decades, artificial intelligence experts will get 1991 , at The Computer Museum. New York their first shot at a Turing Test real-time this philanthropist Dr. Hugh Loebner, President of fall at the Museum. Instead of being open Crown Industries, Inc., has offered a $100,000 ended- a challenge that no computer can prize for the first machine to pass the test. The approach at this time-each "conversation" historic contest is being will be limited to a particular subject to give ".. .ang computer that actuallg passes the unrestricted administered by the Cambridge the computer a better chance. Using a format (MA) Center for Behavioral akin to a public chess match, the judges will Turing Test will he. in euerg theoreticallg interesting Studies and the Museum. hold conversations on several computer In 1950, the brilliant British terminals in the Museum's auditorium. The sense. athinking thing. "[Daniel t Dennett] mathematician Alan Turing responses of each terminal will be controlled issued the ultimate challenge to either by "human confederates" or computers. computer science. He proposed an experiment The audience will be able to see the conversa to determine if a machine could think. His test tions on large screens near each terminal. requires a computer to emulate human behavior Afterwards, the tally of scores comparing (via a computer terminal) so well that it fools human and computer performances will be human judges into thinking its responses are announced. -
Lessons from a Restricted Turing Test
Lessons from a Restricted Turing Test Stuart M. Shieber Aiken Computation Laboratory Division of Applied Sciences Harvard University March 28, 1994 (Revision 6) Abstract We report on the recent Loebner prize competition inspired by Turing’s test of intelligent behavior. The presentation covers the structure of the competition and the outcome of its first instantiation in an actual event, and an analysis of the purpose, design, and appropriateness of such a competition. We argue that the competition has no clear purpose, that its design prevents any useful outcome, and that such a competition is inappropriate given the current level of technology. We then speculate as to suitable alternatives to the Loebner prize. arXiv:cmp-lg/9404002v1 4 Apr 1994 This paper is to appear in Communications of the Association for Comput- ing Machinery, and is available from the Center for Research in Computing Technology, Harvard University, as Technical Report TR-19-92 and from the Computation and Language e-print server as cmp-lg/9404002. The Turing Test and the Loebner Prize The English logician and mathematician Alan Turing, in an attempt to develop a working definition of intelligence free of the difficulties and philosophical pitfalls of defining exactly what constitutes the mental process of intelligent reasoning, devised a test, instead, of intelligent behavior. The idea, codified in his cel- ebrated 1950 paper “Computing Machinery and Intelligence” (Turing, 1950), was specified as an “imitation game” in which a judge attempts to distinguish which of two agents is a human and which a computer imitating human re- sponses by engaging each in a wide-ranging conversation of any topic and tenor. -
I V Anthropomorphic Attachments in U.S. Literature, Robotics, And
Anthropomorphic Attachments in U.S. Literature, Robotics, and Artificial Intelligence by Jennifer S. Rhee Program in Literature Duke University Date:_______________________ Approved: ___________________________ Kenneth Surin, Supervisor ___________________________ Mark Hansen ___________________________ Michael Hardt ___________________________ Katherine Hayles ___________________________ Timothy Lenoir Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Literature in the Graduate School of Duke University 2010 i v ABSTRACT Anthropomorphic Attachments in U.S. Literature, Robotics, and Artificial Intelligence by Jennifer S. Rhee Program in Literature Duke University Date:_______________________ Approved: ___________________________ Kenneth Surin, Supervisor ___________________________ Mark Hansen ___________________________ Michael Hardt ___________________________ Katherine Hayles ___________________________ Timothy Lenoir An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Literature in the Graduate School of Duke University 2010 Copyright by Jennifer S. Rhee 2010 Abstract “Anthropomorphic Attachments” undertakes an examination of the human as a highly nebulous, fluid, multiple, and often contradictory concept, one that cannot be approached directly or in isolation, but only in its constitutive relationality with the world. Rather than trying to find a way outside of the dualism between human and not- human, -
Why Python for Chatbots
Schedule: 1. History of chatbots and Artificial Intelligence 2. The growing role of Chatbots in 2020 3. A hands on look at the A.I Chatbot learning sequence 4. Q & A Session Schedule: 1. History of chatbots and Artificial Intelligence 2. The growing role of Chatbots in 2020 3. A hands on look at the A.I Chatbot learning sequence 4. Q & A Session Image credit: Archivio GBB/Contrasto/Redux History •1940 – The Bombe •1948 – Turing Machine •1950 – Touring Test •1980 Zork •1990 – Loebner Prize Conversational Bots •Today – Siri, Alexa Google Assistant Image credit: Photo 172987631 © Pop Nukoonrat - Dreamstime.com 1940 Modern computer history begins with Language Analysis: “The Bombe” Breaking the code of the German Enigma Machine ENIGMA MACHINE THE BOMBE Enigma Machine image: Photographer: Timothy A. Clary/AFP The Bombe image: from movie set for The Imitation Game, The Weinstein Company 1948 – Alan Turing comes up with the concept of Turing Machine Image CC-BY-SA: Wikipedia, wvbailey 1948 – Alan Turing comes up with the concept of Turing Machine youtube.com/watch?v=dNRDvLACg5Q 1950 Imitation Game Image credit: Archivio GBB/Contrasto/Redux Zork 1980 Zork 1980 Text parsing Loebner Prize: Turing Test Competition bit.ly/loebnerP Conversational Chatbots you can try with your students bit.ly/MITsuku bit.ly/CLVbot What modern chatbots do •Convert speech to text •Categorise user input into categories they know •Analyse the emotion emotion in user input •Select from a range of available responses •Synthesize human language responses Image sources: Biglytics -
'Realness' in Chatbots: Establishing Quantifiable Criteria
'Realness' in Chatbots: Establishing Quantifiable Criteria Kellie Morrissey and Jurek Kirakowski School of Applied Psychology, University College Cork, Ireland {k.morrissey,jzk}@ucc.ie Abstract. The aim of this research is to generate measurable evaluation criteria acceptable to chatbot users. Results of two studies are summarised. In the first, fourteen participants were asked to do a critical incident analysis of their tran- scriptions with an ELIZA-type chatbot. Results were content analysed, and yielded seven overall themes. In the second, these themes were made into statements of an attitude-like nature, and 20 participants chatted with five win- ning entrants in the 2011 Chatterbox Challenge and five which failed to place. Latent variable analysis reduced the themes to four, resulting in four subscales with strong reliability which discriminated well between the two categories of chatbots. Content analysis of freeform comments led to a proposal of four dimensions along which people judge the naturalness of a conversation with chatbots. Keywords: Chatbot, user-agent, intelligent assistant, naturalness, convincing, usability, evaluation, quantitative, questionnaire, Turing, Chatterbox. 1 Evaluating for Naturalness Conversational agents, or chatbots, are systems that are capable of performing actions on behalf of computer users; in essence, reducing the cognitive workload on users engaging with computer systems. There are two key strategies used. The first is the use of a set of well-learnt communicative conventions: natural language -
Artificial Intelligence: a Roadmap for California
Artificial Intelligence: A Roadmap for California Report #245, November 2018 Milton Marks Commission on California State Government Organization and Economy Little Hoover Commission Dedicated to Promoting Economy and Efficiency Pedro Nava Chairman in California State Government Sean Varner Vice Chairman/ Subcommitee Member The Little Hoover Commission, formally known as the Milton Marks “Little Hoover” David Beier Commission on California State Government Organization and Economy, is an Subcommitee Chair independent state oversight agency created in 1962. By statute, the Commission Iveta Brigis is bipartisan and composed of nine public members, two senators and two Subcommitee Member assemblymembers. Cynthia Buiza In creating the Commission, the Legislature declared its purpose: Anthony Cannella Senator [T]o secure assistance for the Governor and itself in promoting Chad Mayes economy, efficiency and improved services in the transaction of Assemblymember the public business in the various departments, agencies and Don Perata instrumentalities of the executive branch of the state government, Bill Quirk and in making the operation of all state departments, agencies and Assemblymember instrumentalities, and all expenditures of public funds, more directly Richard Roth responsive to the wishes of the people as expressed by their elected Senator representatives. Cathy Schwamberger The Commission fulfills this charge by holding public hearings, consulting with experts Janna Sidley and listening to stakeholders. During the course of its studies, the Commission may Former Commissioners Who create subcommittees and conduct site visits. Served During The Study Joshua LaFarga The findings and recommendations of the Commission are submitted to the Governor and the Legislature for their consideration. Recommendations often take the form of Helen Iris Torres legislation, which the Commission supports through the legislative process. -
Presentación De Powerpoint
ARTIFICIAL INTELLIGENCE www.aceleralia.com | 2020 Aceleralia es una marca registrada de 2 Digits Growth® Todos los derechos reservados ARTIFICIAL INTELLIGENCE Problem solving Reasoning Problem solving, particularly in artificial intelligence, may be characterized as a systematic search through a To reason is to draw inferences appropriate to the range of possible actions in order to reach some situation. Inferences are classified as either deductive predefined goal or solution. Problem-solving methods or inductive. An example of the former is, “Fred must divide into special purpose and general purpose. A be in either the museum or the café. He is not in the special-purpose method is tailor-made for a particular café; therefore he is in the museum,” and of the latter, problem and often exploits very specific features of the “Previous accidents of this sort were caused by situation in which the problem is embedded. In instrument failure; therefore this accident was caused contrast, a general-purpose method is applicable to a by instrument failure.” wide variety of problems. The most significant difference between these forms One general-purpose technique used in AI is means- of reasoning is that in the deductive case the truth of end analysis—a step-by-step, or incremental, reduction the premises guarantees the truth of the conclusion, of the difference between the current state and the whereas in the inductive case the truth of the premise final goal. lends support to the conclusion without giving absolute assurance. Inductive reasoning is common in science, The program selects actions from a list of means—in where data are collected and tentative models are the case of a simple robot this might consist of PICKUP, developed to describe and predict future behavior— PUTDOWN, MOVEFORWARD, MOVEBACK, until the appearance of anomalous data forces the MOVELEFT, and MOVERIGHT—until the goal is model to be revised. -
AI for Autonomous Ships – Challenges in Design and Validation
VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD AI for Autonomous Ships – Challenges in Design and Validation ISSAV 2018 Eetu Heikkilä Autonomous ships - activities in VTT . Autonomous ship systems . Unmanned engine room . Situation awareness . Autonomous autopilot . Connectivity . Human factors of remote and autonomous systems . Safety assessment 22/03/2018 2 Contents . AI technologies for autonomous shipping . Design & validation challenges . Methodological . Technical . Acknowledgements: Olli Saarela, Heli Helaakoski, Heikki Ailisto, Jussi Martio 22/03/2018 3 AI technologies Definitions - Autonomy Level Name 1 Human operated Autonomy is the ability of a system to 2 Human assisted achieve operational goals in complex domains by making decisions and executing 3 Human delegated actions on behalf of or in cooperation with 4 Supervised humans. (NFA, 2012) 5 Mixed initiative . Target to increase productivity, cost 6 Fully autonomous efficiency, and safety . Not only by reducing human work, but also by enabling new business logic 22/03/2018 5 Definitions - Artificial Intelligence . The Turing test: Can a person tell which of the other parties is a machine . AI is a moving target . When a computer program is able to perform a task, people start to consider the task “merely” computational, not requiring actual intelligence after all. ”AI is all the software we don’t yet know how to write.” . More practically: AI is a collection of technologies facilitating “smart” operation of machines and systems. Conclusions and actions fitting the prevailing -
Overview of Artificial Intelligence
Updated October 24, 2017 Overview of Artificial Intelligence The use of artificial intelligence (AI) is growing across a Currently, AI technologies are highly tailored to particular wide range of sectors. Stakeholders and policymakers have applications or tasks, known as narrow (or weak) AI. In called for careful consideration of strategies to influence its contrast, general (or strong) AI refers to intelligent behavior growth, reap predicted benefits, and mitigate potential risks. across a range of cognitive tasks, a capability which is This document provides an overview of AI technologies unlikely to occur for decades, according to most analysts. and applications, recent private and public sector activities, Some researchers also use the term “augmented and selected issues of interest to Congress. intelligence” to capture AI’s various applications in physical and connected systems, such as robotics and the AI Technologies and Applications Internet of Things, and to focus on using AI technologies to Though definitions vary, AI can generally be thought of as enhance human activities rather than to replace them. computerized systems that work and react in ways commonly thought to require intelligence, such as solving In describing the course of AI development, the Defense complex problems in real-world situations. According to Advanced Research Projects Agency (DARPA) has the Association for the Advancement of Artificial identified three “waves.” The first wave focused on Intelligence (AAAI), researchers broadly seek to handcrafted knowledge that allowed for system-enabled understand “the mechanisms underlying thought and reasoning in limited situations, though lacking the ability to intelligent behavior and their embodiment in machines.” learn or address uncertainty, as with many rule-based systems from the 1980s. -
Is the Artificial Intelligent? a Perspective on AI-Based Natural Language Processors
Pobrane z czasopisma New Horizons in English Studies http://newhorizons.umcs.pl Data: 16/03/2020 15:10:20 New Horizons in English Studies 4/2019 LANGUAGE • Wojciech Błachnio Maria Curie-SkłodowSka univerSity, LubLin [email protected] Is the Artificial Intelligent? A Perspective on AI-based Natural Language Processors Abstract. The issue of the relation between AI and human mind has been riddling the scientific world for ages. Having been an innate and exclusive faculty of the human mind, language is now manifested in a countless number of ways, transcending beyond the human-only production. There are applica- tions that can not only understand what is meant by an utterance, but also engage in a quasi-humane discourse. The manner of their operating is perfectly organised and can be accounted for by incorporat- ing linguistic theories. The main theory used in this article is Fluid Construction Grammar, which has been developed by Luc Steels. It is concerned with parsing and the segmentation of any utterance – two processes that are pivotalUMCS in AI’s understanding and production of language. This theory, in addition to five main facets of languages (phonological, morphological, semantic, syntactic and pragmatic), provides valuable insight into discrepancies between the natural and artificial perceptions of language. Though there are similarities between them, the article shall conclude with what makes two adjacent capabilities different. The aim of this paper is to display the mechanisms of AI natural language proces- sors with the aid of contemporary linguistic theories, and present possible issues which may ensue from using artificial language-recognising systems. -
Artificial Intelligence in Health Care Islam Haythem Salama Osama Jumaa Alzway Supervisor : Dr
Artificial intelligence in Health Care Islam Haythem Salama Osama Jumaa Alzway Supervisor : Dr. Abdelmonem Abdelnabi Objectives 1. General View of Artificial Intelligence 2. Artificial Intelligence in Health Care 3. Artificial Intelligence in Dentistry General View of AI - Islam Salama So , What is the AI ? The theory and development of a computer systems that is able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision- making, translation between languages ,and concept making . AI Revelation AI Revelation Human Conscious Levels 1.Information's 2.Believe 3.Ideas 4.Thinking 5.Conscious Classification of AI : 1. Strong AI 2. Weak AI Strong AI Artificial general intelligence a multitask system or machine with skillful and flexible as humans do and with ability to apply intelligence to any problem, rather than just one specific problem IBM Watson • -Watson is a question answering computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project. • -Won 2011 Jeopardy Google Deep Mind • Company in UK now under alphabet group • mimics the short-term memory of the human brain • Won in Go Game “ Alfa Go “ 2016 Weak AI “Narrow AI” is artificial intelligence that is focused on one narrow task. Weak AI is defined in contrast to either. All currently existing systems considered artificial intelligence of any sort are weak AI at most. e.g: Weak AI “Narrow AI” Intelligence levels of machines 1. Optimal AI = good at his application e.g: scaning method can scans every letter. 2. Super Strong = better than other humans. 3. Strong = at his app not all people but some of them = face rec. -
AISB Quarterly an Anarchy of Methods: Current Trends in How Intelligence Is Abstracted in AI by John Lehman (U
AISB QUARTERLY the newsletter of the society for the study of artificial intelligence and simulation of behaviour No. 141 July, 2015 Do you feel artistic? Exhibit your artwork on our front covers! Email us at [email protected]! Artwork by Alwyn Husselmann, PhD (Massey Univ., New Zealand) Visualisation is an important tool for gaining insight into how algorithms behave. There have been many techniques developed, including some to visualise 3D voxel sets [1], program space in genetic programming [2] and vector fields [3], amongst a large number of methods in other domains. A qualitative understanding of an algorithm is useful not only in diagnosing implementations, but also in improving performance. In parametric optimisation, algorithms such as the Firefly Algorithm [4] are quite sim- ple to visualise provided they are being used on a problem with less than four dimensions. Search algorithms in this class are known as metaheuristics, as they have an ability to optimise unknown functions of an arbitrary number of variables without gradient infor- mation. Observations of particle movement are particularly useful for calibrating the internal parameters of the algorithm. Pictured on the cover is a visualisation of a Firefly Algorithm optimising the three- dimensional Rosenbrock Function [5]. Each coloured sphere represents a potential min- imum candidate. The optimum is near the centre of the cube, at coordinate (1, 1, 1). Colour is used here to indicate the output of the Rosenbrock function, whereas the 3D coordinate of each particle is representative of the actual values used as input to the function. The clustering seen amongst the particles in the image is due to the local neighbourhood searches occurring.