scholarly journals Knowledge Geometry in Phenomenon Perception and Artificial Intelligence

2020 ◽  
Vol 26 (5) ◽  
pp. 604-623
Author(s):  
João Gabriel Lopes De Oliveira ◽  
Editorial office Pedro Moreira Menezes Da Costa ◽  
Flavio De Mello
Keyword(s):  
Artificial Intelligence ◽  
Geometric Theory ◽  
Ground Truth ◽  
Machine Intelligence ◽  
Case Based Reasoning ◽  
Main Concern ◽  
Formal Representation ◽  
Matching Process ◽  
Transportation Finance ◽  
Casual Observer

Artificial Intelligence (AI) pervades industry, entertainment, transportation, finance, and health. It seems to be in a kind of golden age, but today AI is based on the strength of techniques that bear little relation to the thought mechanism. Contemporary techniques of machine learning, deep learning and case-based reasoning seem to be occupied with delivering functional and optimized solutions, leaving aside the core reasons of why such solutions work. This paper, in turn, proposes a theoretical study of perception, a key issue for knowledge acquisition and intelligence construction. Its main concern is the formal representation of a perceived phenomenon by a casual observer and its relationship with machine intelligence. This work is based on recently proposed geometric theory, and represents an approach that is able to describe the inuence of scope, development paradigms, matching process and ground truth on phenomenon perception. As a result, it enumerates the perception variables and describes the implications for AI.

scholarly journals Estimating the deep replicability of scientific findings using human and artificial intelligence

2020 ◽  
Vol 117 (20) ◽  
pp. 10762-10768
Author(s):  
Yang Yang ◽  
Wu Youyou ◽  
Brian Uzzi
Keyword(s):  
Artificial Intelligence ◽  
Ground Truth ◽  
Machine Intelligence ◽  
Prediction Markets ◽  
Self Assessment ◽  
Ground Truth Data ◽  
Out Of Sample ◽  
Scientific Papers ◽  
Assessment Techniques ◽  
Better Than

Replicability tests of scientific papers show that the majority of papers fail replication. Moreover, failed papers circulate through the literature as quickly as replicating papers. This dynamic weakens the literature, raises research costs, and demonstrates the need for new approaches for estimating a study’s replicability. Here, we trained an artificial intelligence model to estimate a paper’s replicability using ground truth data on studies that had passed or failed manual replication tests, and then tested the model’s generalizability on an extensive set of out-of-sample studies. The model predicts replicability better than the base rate of reviewers and comparably as well as prediction markets, the best present-day method for predicting replicability. In out-of-sample tests on manually replicated papers from diverse disciplines and methods, the model had strong accuracy levels of 0.65 to 0.78. Exploring the reasons behind the model’s predictions, we found no evidence for bias based on topics, journals, disciplines, base rates of failure, persuasion words, or novelty words like “remarkable” or “unexpected.” We did find that the model’s accuracy is higher when trained on a paper’s text rather than its reported statistics and that n-grams, higher order word combinations that humans have difficulty processing, correlate with replication. We discuss how combining human and machine intelligence can raise confidence in research, provide research self-assessment techniques, and create methods that are scalable and efficient enough to review the ever-growing numbers of publications—a task that entails extensive human resources to accomplish with prediction markets and manual replication alone.

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

The Future of Human Workers

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Mental Capacity ◽  
Machine Intelligence ◽  
The Internet ◽  
Traditional Model ◽  
Global Workforce ◽  
Repetitive Tasks ◽  
The Internet Of Things

New technologies like artificial intelligence, robotics, machine intelligence, and the Internet of Things are seeing repetitive tasks move away from humans to machines. Humans cannot become machines, but machines can become more human-like. The traditional model of educating workers for the workforce is fast becoming irrelevant. There is a massive need for the retooling of human workers. Humans need to be trained to remain focused in a society which is constantly getting bombarded with information. The two basic elements of physical and mental capacity are slowly being taken over by machines and artificial intelligence. This changes the fundamental role of the global workforce.

The Future of Work and the Changing Workplace

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Political Issue ◽  
Digital Innovation ◽  
Future Of Work ◽  
The Past ◽  
The World ◽  
Public Debates ◽  
The Future ◽  
Market Opportunities

The world of work has been impacted by technology. Work is different than it was in the past due to digital innovation. Labor market opportunities are becoming polarized between high-end and low-end skilled jobs. Migration and its effects on employment have become a sensitive political issue. From Buffalo to Beijing public debates are raging about the future of work. Developments like artificial intelligence and machine intelligence are contributing to productivity, efficiency, safety, and convenience but are also having an impact on jobs, skills, wages, and the nature of work. The “undiscovered country” of the workplace today is the combination of the changing landscape of work itself and the availability of ill-fitting tools, platforms, and knowledge to train for the requirements, skills, and structure of this new age.

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

2005 ◽  
Author(s):  
Guanghsu A. Chang ◽  
Cheng-Chung Su ◽  
John W. Priest
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Feature Weight ◽  
Similar Part ◽  
Case Base ◽  
Case Based

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

scholarly journals AI for augmenting human judgement in Business Processes Management

2021 ◽  
Vol 201 (3) ◽  
pp. 507-518
Author(s):  
Łukasz Osuszek ◽  
Stanisław Stanek
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Business Processes ◽  
Workflow Management ◽  
Organizational Memory ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Case Based Reasoning ◽  
Historical Case

The paper outlines the recent trends in the evolution of Business Process Management (BPM) – especially the application of AI for decision support. AI has great potential to augment human judgement. Indeed, Machine Learning might be considered as a supplementary and complimentary solution to enhance and support human productivity throughout all aspects of personal and professional life. The idea of merging technologies for organizational learning and workflow management was first put forward by Wargitsch. Herein, completed business cases stored in an organizational memory are used to configure new workflows, while the selection of an appropriate historical case is supported by a case-based reasoning component. This informational environment has been recognized in the world as being effective and has become quite common because of the significant increase in the use of artificial intelligence tools. This article discusses also how automated planning techniques (one of the oldest areas in AI) can be used to enable a new level of automation and processing support. The authors of the article decided to analyse this topic and discuss the scientific state of the art and the application of AI in BPM systems for decision-making support. It should be noted that readily available software exists for the needs of the development of such systems in the field of artificial intelligence. The paper also includes a unique case study with production system of Decision Support, using controlled machine learning algorithms to predictive analytical models.

scholarly journals Erosión ética en el trato con la inteligencia artificial

2020 ◽  
pp. 43-58
Author(s):  
Desireé Torres Lozano
Keyword(s):  
Artificial Intelligence ◽  
San Francisco ◽  
Mirror Neurons ◽  
Machine Intelligence ◽  
Publishing Company ◽  
Frankfurt Am Main ◽  
Modern Approach ◽  
Oxford University ◽  
Computing Machinery ◽  
West Bohemia

ResumenEl presente artículo tiene como finalidad definir la IA y poner en discusión su injerencia social, así como las consecuencias éticas que esto conlleva, ya que la construcción del hombre contemporáneo debe tener en cuenta el trato con estos sistemas. Definiremos qué es la inteligencia, cómo es que se le ha llamado inteligencia a los procesos de las máquinas y podremos establecer un diálogo entre la influencia ética que conlleva el trato con las mismas. Palabras clave Inteligencia artificial; Ética; Sistemas; Tecnología; Hombre Referencias Aristóteles, De Anima, Madrid: Gredos, 2000. ___, Ética a Nicómaco, Madrid: Gredos, 2000. ___, Política, Madrid, Gredos, 2003. Aspe, V. Nuevos sentidos mimesis en la Poética de Aristóteles, en Tópicos, Revista de filosofía, México: Tópicos, 2005. Bellman, Richard, An Introduction To Artificial Intelligence, San Francisco: Boyd and Fraser Publishing Company, 1978. Büchner et al, Discovering Internet Marketing Intelligence through Web Log Mining, Antrin, Mine it, Newtownabbey: University of Ulster Shore Road, 1998. Corominas, Pascual, Diccionario Crítico Etimológico Castellano e Hispánico, Madrid, Gredos, 2002. Descartes, Meditaciones Metafísicas, Gredos, Madrid, 2000. Elaine Rich, Kevin Knight, Artificial Intelligence, New Delhi: McGraw-Hill, 1991. Bude, Gesellschaft der Angst, Hamburgo: Hamburger Edition HIS, 2014. Heidegger, Platon: Sophistes, Frankfurt: Vittorio Klostermann, 1992. ___, Über den Humanismus, Frankfurt: Vittorio Klostermann, 1949. ___, Was heisst denken?, Frankfurt Am Main: Vittorio Klostermann, 2002. Hickock, Gregory, The Myth of Mirror Neurons. The Real Neuroscience of communication and cognition, Nueva York: W. W. Norton & ­Company, 2014. J. Haugeland, Artificial Intelligence: The very idea, Cambridge: MIT Press, 1985. Kirk, G.S. y Raven, J. E., Los filósofos presocráticos, Madrid: Gredos, 1970. Kurzweil Raymond, The Age of Intelligent Machines, Cambridge: MIT Press, 1990. Mariarosaria Taddeo, Luciano Floridi, How AI can be a force for good, en Science, Vol. 361, Issue 6404, Oxford: Oxford University, 2018. Nils Johan Nilsson, Artificial Intelligence: A new synthesis, USA: Morgan Kaufmann, 1998. Platón, Cratilo, Madrid, Gredos, 2004. Poole David et al, Computational Intelligence, a Logical Approach, Oxford: Oxford University, 1998. Press, Gill, A Very Short History Of Artificial Intelligence (AI), USA: Forbes, 2016. Russell, Norvig, Artificial Intelligence, A Modern Approach, New Jersey, Pearson, 2010. Armstrong, S., & K. Sotala, ​How we​’re predicting AI​ or failing to,​ Beyond Artificial Intelligence, Machine Intelligence Research Institute, Pilsen: University of West Bohemia,2015. Turing Alan, MIND, Computing Machinery and Intelligence, Cambridge: A Quarterly Review of Psychology and Philosophy, 1950. Winston Patrick Henry, Artificial intelligence, USA: Addison Wesley, Publishing Company, 1992.

scholarly journals Editorial. AI, Arts & Design: Questioning Learning Machines

Artnodes ◽  
2020 ◽  
Author(s):  
Ruth West ◽  
Andrés Burbano
Keyword(s):  
Artificial Intelligence ◽  
Historical Development ◽  
Machine Intelligence ◽  
Special Issue ◽  
Control Structures ◽  
Learning Machines ◽  
The Arts ◽  
Production Of Culture ◽  
The Relationship ◽  
Algorithmic Bias

Explorations of the relationship between Artificial Intelligence (AI), the arts, and design have existed throughout the historical development of AI. We are currently witnessing exponential growth in the application of Machine Learning (ML) and AI in all domains of art (visual, sonic, performing, spatial, transmedia, audiovisual, and narrative) in parallel with activity in the field that is so rapid that publication can not keep pace. In dialogue with our contemplation about this development in the arts, authors in this issue answer with questions of their own. Through questioning authorship and ethics, autonomy and automation, exploring the contribution of art to AI, algorithmic bias, control structures, machine intelligence in public art, formalization of aesthetics, the production of culture, socio-technical dimensions, relationships to games and aesthetics, and democratization of machine-based creative tools the contributors provide a multifaceted view into crucial dimensions of the present and future of creative AI. In this Artnodes special issue, we pose the question: Does generative and machine creativity in the arts and design represent an evolution of “artistic intelligence,” or is it a metamorphosis of creative practice yielding fundamentally distinct forms and modes of authorship?

Case-Based Reasoning and Some Typical Applications

2014 ◽  
pp. 229-267 ◽  
Author(s):  
Durga Prasad Roy ◽  
Baisakhi Chakraborty
Keyword(s):  
Problem Solving ◽  
Machine Intelligence ◽  
Case Based Reasoning ◽  
Engineering System ◽  
Yale University ◽  
Control Pattern ◽  
Clustering And Classification ◽  
How People Learn ◽  
Case Based

Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977–1993. CBR may be defined as a model of reasoning that incorporates problem solving, understanding, and learning, and integrates all of them with memory processes. It focuses on the human problem solving approach such as how people learn new skills and generates solutions about new situations based on their past experience. Similar mechanisms to humans who intelligently adapt their experience for learning, CBR replicates the processes by considering experiences as a set of old cases and problems to be solved as new cases. To arrive at the conclusions, it uses four types of processes, which are retrieve, reuse, revise, and retain. These processes involve some basic tasks such as clustering and classification of cases, case selection and generation, case indexing and learning, measuring case similarity, case retrieval and inference, reasoning, rule adaptation, and mining to generate the solutions. This chapter provides the basic idea of case-based reasoning and a few typical applications. The chapter, which is unique in character, will be useful to researchers in computer science, electrical engineering, system science, and information technology. Researchers and practitioners in industry and R&D laboratories working in such fields as system design, control, pattern recognition, data mining, vision, and machine intelligence will benefit.

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