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an Open Access Journal by MDPI Academic Open Access Publishing since 1996 an Open Access Journal by MDPI Editor-in-Chief Message from the Editor-in-Chief Prof. Dr. Kenji Suzuki AI (ISSN 2673-2688) is an international and interdisciplinary Artificial Intelligence in Biomedical Imaging Lab (AIBI scholarly open access journal on artificial intelligence. Lab), Laboratory for Future It publishes original research articles, reviews, Interdisciplinary Research of Science and Technology, communications, that offer substantial new insight Institute of Innovative Research, into any field of study that involves artificial intelligence Tokyo Institute of Technology, (AI), including machine and deep learning, knowledge Tokyo 152-8550, Japan reasoning and discovery, automated planning and scheduling, natural language processing and recognition, computer vision, robotics, and artificial general intelligence. There is no restriction on the length of papers. Our aim is to encourage scientists and engineers to publish their experimental and theoretical research in as much detail as possible. Full experimental details and/or study methods must be provided for research articles so that the results can be reproduced. Author Benefits Open Access Unlimited and free access for readers No Copyright Constraints Retain copyright of your work and free use of your article Thorough Peer-Review No Space Constraints, No Extra Space or Color Charges No restriction on the length of the papers, number of figures or colors Free Publishing without Article Processing Charge (APC) An article processing charge (APC) of 1000 CHF (Swiss Francs) applies to each paper accepted after peer review. Applications for waivers should be approved by the Editorial Office Aims and Scope AI (ISSN 2673-2688) is an international peer-reviewed open access journal devoted entirely to artificial intelligence (AI), including broad aspects of cognition and reasoning, perception and planning, machine learning, intelligent robotics, and applications of AI. AI is published quarterly online by MDPI. The editorial team is able to process manuscripts of high impact and quality without delays. You are all welcome to participate as a referee, author, or editorial board member. The scope of AI includes: AI theory AI techniques AI applications Intelligent machines/agents Knowledge representation Knowledge reasoning Machine learning Deep learning Computer vision Planning Robotics Natural language processing Artificial neural networks Evolutionary computing Editorial Office Probabilistic computing AI Editorial Office Genetic algorithms [email protected] Fuzzy logic MDPI, St. Alban-Anlage 66 4052 Basel, Switzerland Expert systems Tel: +41 61 683 77 34 Other Keywords: reinforcement learning; data science; Fax: +41 61 302 89 18 www.mdpi.com machine perception; machine recognition; automated mdpi.com/journal/ai reasoning and inference; case-based reasoning; reasoning under uncertainty; knowledge engineering; etc. MDPI is a member of Follow Us facebook.com/MDPIOpenAccessPublishing twitter.com/MDPIOpenAccess linkedin.com/company/mdpi weibo.com/mdpicn Wechat: MDPI-China blog.mdpi.com www.mdpi.com mdpi.com/journal/ai See www.mdpi.com for a full list of offices and contact information. MDPI is a company registered in Basel, Switzerland, No. CH-270.3.014.334-3, whose registered office is at St. Alban-Anlage 66, CH-4052 Basel, Switzerland. Basel, July 2021.

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APA Newsletter on Philosophy and Computers, Vol. 18, No. 2 (Spring
NEWSLETTER | The American Philosophical Association Philosophy and Computers SPRING 2019 VOLUME 18 | NUMBER 2 FEATURED ARTICLE Jack Copeland and Diane Proudfoot Turing’s Mystery Machine ARTICLES Igor Aleksander Systems with “Subjective Feelings”: The Logic of Conscious Machines Magnus Johnsson Conscious Machine Perception Stefan Lorenz Sorgner Transhumanism: The Best Minds of Our Generation Are Needed for Shaping Our Future PHILOSOPHICAL CARTOON Riccardo Manzotti What and Where Are Colors? COMMITTEE NOTES Marcello Guarini Note from the Chair Peter Boltuc Note from the Editor Adam Briggle, Sky Croeser, Shannon Vallor, D. E. Wittkower A New Direction in Supporting Scholarship on Philosophy and Computers: The Journal of Sociotechnical Critique CALL FOR PAPERS VOLUME 18 | NUMBER 2 SPRING 2019 © 2019 BY THE AMERICAN PHILOSOPHICAL ASSOCIATION ISSN 2155-9708 APA NEWSLETTER ON Philosophy and Computers PETER BOLTUC, EDITOR VOLUME 18 | NUMBER 2 | SPRING 2019 Polanyi’s? A machine that—although “quite a simple” one— FEATURED ARTICLE thwarted attempts to analyze it? Turing’s Mystery Machine A “SIMPLE MACHINE” Turing again mentioned a simple machine with an Jack Copeland and Diane Proudfoot undiscoverable program in his 1950 article “Computing UNIVERSITY OF CANTERBURY, CHRISTCHURCH, NZ Machinery and Intelligence” (published in Mind). He was arguing against the proposition that “given a discrete- state machine it should certainly be possible to discover ABSTRACT by observation sufficient about it to predict its future This is a detective story. The starting-point is a philosophical behaviour, and this within a reasonable time, say a thousand discussion in 1949, where Alan Turing mentioned a machine years.”3 This “does not seem to be the case,” he said, and whose program, he said, would in practice be “impossible he went on to describe a counterexample: to find.” Turing used his unbreakable machine example to defeat an argument against the possibility of artificial I have set up on the Manchester computer a small intelligence.
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Envisioning AI for K-12: What Should Every Child Know About AI?
The Thirty-Third AAAI Conference on Artiﬁcial Intelligence (AAAI-19) Envisioning AI for K-12: What Should Every Child Know about AI? David Touretzky,1 Christina Gardner-McCune,2 Fred Martin,3 Deborah Seehorn4 1Carnegie Mellon University, Pittsburgh, PA 15213 2University of Florida, Gainesville, FL, 32611 3University of Massachusetts Lowell, Lowell, MA, 01854 4CSTA Curriculum Committee, Cary, NC, 27513 [email protected]; [email protected]; [email protected]; [email protected] Allen 2018). We must consider the role we play as AI Abstract researchers and educators in helping people understand the The ubiquity of AI in society means the time is ripe to science behind our research, its limits, and its potential consider what educated 21st century digital citizens should societal impacts. On the graduate level, courses have been know about this subject. In May 2018, the Association for the keeping pace with advances in the field. In undergraduate Advancement of Artificial Intelligence (AAAI) and the education, we’ve sought to provide fun and inspiring ways Computer Science Teachers Association (CSTA) formed a to engage students in various aspects of AI, such as robotics, joint working group to develop national guidelines for modeling and simulation, game playing, and machine teaching AI to K-12 students. Inspired by CSTA's national standards for K-12 computing education, the AI for K-12 learning (Dodds, Hirsh, and Wagstaff 2018). In addition, guidelines will define what students in each grade band we’ve sought to write textbooks and design tools, nifty and should know about artificial intelligence, machine learning, model AI assignments, and curricula that help students learn and robotics.
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Self-Developing Proprioception-Based Robot Internal Models Tao Zhang, Fan Hu, Yian Deng, Mengxi Nie, Tianlin Liu, Xihong Wu, Dingsheng Luo
Self-developing Proprioception-Based Robot Internal Models Tao Zhang, Fan Hu, Yian Deng, Mengxi Nie, Tianlin Liu, Xihong Wu, Dingsheng Luo To cite this version: Tao Zhang, Fan Hu, Yian Deng, Mengxi Nie, Tianlin Liu, et al.. Self-developing Proprioception- Based Robot Internal Models. 2nd International Conference on Intelligence Science (ICIS), Nov 2018, Beijing, China. pp.321-332, 10.1007/978-3-030-01313-4_34. hal-02118797 HAL Id: hal-02118797 https://hal.inria.fr/hal-02118797 Submitted on 3 May 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Self-developing Proprioception-based Robot Internal Models Tao Zhang, Fan Hu, Yian Deng, Mengxi Nie, Tianlin Liu, Xihong Wu, and Dingsheng Luo* Key Lab of Machine Perception (Ministry of Education), Speech and Hearing Research Center, Department of Machine Intelligence, School of EECS, Peking University, Beijing 100871, China. *corresponding author {tao_zhang,fan_h,yiandeng,niemengxi,xhwu,dsluo}@pku.edu.cn Abstract. Research in cognitive science reveals that human central ner- vous system internally simulates dynamic behavior of the motor system using internal models (forward model and inverse model).
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Integrating Olfaction in a Robotic Telepresence Loop*
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Repositorio Institucional Universidad de Málaga Authors’ accepted manuscript: IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2017. Integrating Olfaction in a Robotic Telepresence Loop* Javier Monroy, Francisco Melendez-Fernandez, Andres Gongora and Javier Gonzalez-Jimenez Abstract— In this work we propose enhancing a typical land-mine detection, operations in dangerous areas due to robotic telepresence architecture by considering olfactory and the presence of hazardous chemicals, or explosive deacti- wind flow information in addition to the common audio vation, among others. Furthermore, traditional telepresence and video channels. The objective is to expand the range of applications where robotics telepresence can be applied, applications that rely only on videoconferencing, can indeed including those related to the detection of volatile chemical enhance the user feeling of being at the remote location by substances (e.g. land-mine detection, explosive deactivation, op- complementing these senses with olfaction [10], [11]. erations in noxious environments, etc.). Concretely, we analyze Telepresence demands the users’ senses to be provided how the sense of smell can be integrated in the telepresence with such stimuli as to give the feeling of immersion in the loop, covering the digitization of the gases and wind flow present in the remote environment, the transmission through remote site. This requires to capture, transmit, and finally the communication network, and their display at the user display each stimuli in a proper way: images/video for location. Experiments under different environmental conditions visual awareness, sound for auditory perception, or forces to are presented to validate the proposed telepresence system when simulate the feeling of touch.
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An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices
Wright State University CORE Scholar The Ohio Center of Excellence in Knowledge- Kno.e.sis Publications Enabled Computing (Kno.e.sis) 11-2012 An Efficient Bitect V or Approach to Semantics-Based Machine Perception in Resource-Constrained Devices Cory Andrew Henson Wright State University - Main Campus Krishnaprasad Thirunarayan Wright State University - Main Campus, [email protected] Amit P. Sheth Wright State University - Main Campus, [email protected] Follow this and additional works at: https://corescholar.libraries.wright.edu/knoesis Part of the Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, and the Science and Technology Studies Commons Repository Citation Henson, C. A., Thirunarayan, K., & Sheth, A. P. (2012). An Efficient Bitect V or Approach to Semantics-Based Machine Perception in Resource-Constrained Devices. Lecture Notes in Computer Science, 7649, 149-164. https://corescholar.libraries.wright.edu/knoesis/622 This Conference Proceeding is brought to you for free and open access by the The Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis) at CORE Scholar. It has been accepted for inclusion in Kno.e.sis Publications by an authorized administrator of CORE Scholar. For more information, please contact library- [email protected]. An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University, Dayton, Ohio, USA {cory, tkprasad, amit}@knoesis.org Abstract. The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data.
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Artificial Intelligence and Its Applications in Theory of Mind
المجلة اﻷكاديمية لﻷبحاث والنشر العلمي | اﻹصدار الرابع | تأريخ اﻹصدار: 9102-8-5 ISSN: 2706-6495 Artificial Intelligence and its Applications in Theory of Mind Hanan Mohammed al-qahtani King Fahd University Email: [email protected] Abstract The first use of the phrase “Artificial Intelligence” in 1956 is attributed to John McCarthy of the University of Massachusetts. It is a fairly new field which involves programming computers to play games, comprehend and respond to natural language, reproduce neural networks like those of humans and exhibit sensitivity (Hear, see, move, and react to the environment) like humans (this branch is termed robotics). First we must define Artificial Intelligence. A number of proposed definitions would be considered here. Keywords: Artificial Intelligence, Theory of Mind, AI www.ajrsp.com 1 المجلة اﻷكاديمية لﻷبحاث والنشر العلمي | اﻹصدار الرابع | تأريخ اﻹصدار: 9102-8-5 ISSN: 2706-6495 Introduction The first use of the phrase “Artificial Intelligence” in 1956 is attributed to John McCarthy of the University of Massachusetts. It is a fairly new field which involves programming computers to play games, comprehend and respond to natural language, reproduce neural networks like those of humans and exhibit sensitivity (Hear, see, move, and react to the environment) like humans (this branch is termed robotics). First we must define Artificial Intelligence. A number of proposed definitions would be considered here. It is the science and engineering of making intelligent machines, especially intelligent computer programs. The ability of a computer or other machine to perform actions thought to require intelligence… An intelligent machine would be more flexible than a computer and would engage in the kind of "thinking" that people actually do.
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Perception As an Inference Problem
Perception as an Inference Problem Bruno A. Olshausen Abstract. Although the idea of thinking of perception as in inference problem goes back to Helmholtz, it is only recently that we have seen the emergence of neural models of perception that embrace this idea. Here I describe why inferential computations are necessary for perception, and how they go beyond traditional computational approaches based on deductive processes such as feature detection and classification. Neural models of perceptual inference rely heavily upon recurrent computation in which information propagates both within and between levels of representation in a bi- directional manner. The inferential framework shifts us away from thinking of ‘receptive fields’ and ‘tuning’ of individual neurons, and instead toward how populations of neurons interact via horizontal and top-down feedback connections to perform collective computations. Introduction One of the vexing mysteries facing neuroscientists in the study of perception is the plethora of intermediate-level sensory areas that lie between low level and high level representations. Why in visual cortex do we have a V2, V3, and V4, each containing a complete map of visual space, in addition to V1? Why S2 in addition to S1 in somatosensory cortex? Why the multiple belt fields surrounding A1 in auditory cortex? A common explanation for this organization is that multiple stages of processing are needed to build progressively more complex or abstract representations of sensory input, beginning with neurons signaling patterns of activation among sensory receptors in lower levels and culminating with representations of entire objects or properties of the environment in higher areas. For example, numerous models of visual cortex propose that invariant representations of objects are built up through a hierarchical, feedforward processing architecture (Fukushima 1980; Riesenhuber & Poggio 1999; Wallis & Rolls 1997).
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Performance Vs. Competence in Human– Machine Comparisons
PERSPECTIVE Performance vs. competence in human– machine comparisons PERSPECTIVE Chaz Firestonea,1 Edited by David L. Donoho, Stanford University, Stanford, CA, and approved July 3, 2020 (received for review July 3, 2019) Does the human mind resemble the machines that can behave like it? Biologically inspired machine- learning systems approach “human-level” accuracy in an astounding variety of domains, and even predict human brain activity—raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and “unhumanlike” ways, threatening their status as models of our minds. How can we know when human–machine behavioral differences reflect deep disparities in their underlying capacities, vs. when such failures are only superficial or peripheral? This article draws on a foundational insight from cognitive science—the distinction between performance and competence—to encourage “species-fair” comparisons between humans and machines. The performance/ competence distinction urges us to consider whether the failure of a system to behave as ideally hypoth- esized, or the failure of one creature to behave like another, arises not because the system lacks the relevant knowledge or internal capacities (“competence”), but instead because of superficial constraints on demonstrating that knowledge (“performance”). I argue that this distinction has been neglected by research comparing human and machine behavior, and that it should be essential to any such comparison. Focusing on the domain of image classification, I identify three factors contributing to the species-fairness of human–machine comparisons, extracted from recent work that equates such constraints. Species-fair comparisons level the playing field between natural and artificial intelligence, so that we can separate more superficial differences from those that may be deep and enduring.
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Shared Challenges in Object Perception for Robots and Infants
Shared Challenges in Object Perception for Robots and Infants Paul Fitzpatrick∗ Amy Needham∗∗ Lorenzo Natale∗,∗∗∗ Giorgio Metta∗,† ∗LIRA-Lab, DIST ∗∗ Duke University ∗∗∗ MIT CSAIL University of Genova 9 Flowers Drive 32 Vassar St Viale F. Causa 13 Durham, NC 27798 Cambridge, MA 02139 16145 Genoa, Italy North Carolina, USA Massachusetts, USA † IIT, Italian Institute of Technology, Genoa, Italy. Abstract Robots and humans receive partial, fragmentary hints about the world’s state through their respective sensors. These hints – tiny patches of light intensity, frequency components of sound, etc. – are far removed from the world of objects we feel we perceive so effortlessly around us. The study of infant development and the construction of robots are both deeply concerned with how this apparent gap between the world and our experience of it is bridged. In this paper, we focus on some fundamental problems in perception that have attracted the attention of researchers in both robotics and infant development. Our goal is to identify points of contact already existing between the two fields, and also important questions identified in one field that could fruitfully be addressed in the other. We start with the problem of object segregation: how do infants and robots determine visually where one object ends and another begins? For object segregation, both fields have examined the idea of using “key events” where perception is in some way simplified and the infant or robot acquires knowledge that can be exploited at other times. We propose that the identification of the key events themselves constitutes a point of contact between the fields.
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A Bionic Model for Human-Like Machine Perception
Die approbierte Originalversion dieser Dissertation ist an der Hauptbibliothek der Technischen Universität Wien aufgestellt (http://www.ub.tuwien.ac.at). The approved original version of this thesis is available at the main library of the Vienna University of Technology (http://www.ub.tuwien.ac.at/englweb/). DISSERTATION A Bionic Model for Human-like Machine Perception Submitted at the Faculty of Electrical Engineering, Vienna University of Technology in partial fulfillment of the requirements for the degree of Doctor of Technical Sciences under supervision of Prof. Dr. Dietmar Dietrich Institute number: 384 Institute of Computer Technology and Prof. Dr. Peter Palensky Department of Electrical, Electronic and Computer Engineering University of Pretoria and Prof. Dr. Wolfgang Kastner Institute number: 183/1 Institute of Computer Aided Automation by Dipl.-Ing. Rosemarie Velik Matr.Nr.: 0125411 Lassendorf 43 A-9064 Pischeldorf Vienna, 11.4.2008 Abstract Machine perception is a research field that is still in its infancy and is confronted with many unsolved problems. In contrast, humans generally perceive their environment without problems. For the work at hand, these facts were the motivation to develop a bionic model for human-like machine perception, which is based on neuroscientific and neuropsychological research findings about the structural organization and function of the perceptual system of the human brain. Having systems available that are capable of a human-like perception of their environment would allow the automation of processes for which, today, human observers and their cognitive abilities are necessary. Potential applications are, among others, security and safety surveillance of public and private buildings and the automatic observation of the state of health of persons in retirement homes and hospitals.
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Somatotopic Representation of Tactile Duration Evidence from Tactile
Behavioural Brain Research 371 (2019) 111954 Contents lists available at ScienceDirect Behavioural Brain Research journal homepage: www.elsevier.com/locate/bbr Research report Somatotopic representation of tactile duration: evidence from tactile duration aftereffect T ⁎ ⁎ Baolin Lia, Lihan Chena,b, , Fang Fanga,b,c,d, a School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China b Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China c Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China d PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China ARTICLE INFO ABSTRACT Keywords: Accurate perception of sub-second tactile duration is critical for successful human-machine interaction and tactile duration human daily life. However, it remains debated where the cortical processing of tactile duration takes place. adaptation aftereffect Previous studies have shown that prolonged adaptation to a relatively long or short auditory or visual stimulus somatotopic distance leads to a repulsive duration aftereffect such that the durations of subsequent test stimuli within a certain range somatotopic processing appear to be contracted or expanded. Here, we demonstrated a robust repulsive tactile duration aftereffect with the method of single stimuli, where participants determined whether the duration of the test stimulus was shorter or longer than the internal mean formed before the adaptation (Experiment 1A). The tactile duration aftereffect was also observed when participants reproduced the duration of the test stimulus by holding down a button press (Experiment 1B). Importantly, the observed tactile duration aftereffect was tuned around the adapting duration (Experiment 1C).
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Pattern Recognition in Interdisciplinary Perception and Intelligence
Pattern Recognition in Interdisciplinary Perception and Intelligence This Special Issue came in our mind to celebrate the 50th anniversary of the field of Artificial Intelligence with the objective to show the interdisciplinary of the fields of Pattern Recognition (PR), Artificial Intelligence (AI) and Perception. Pattern Recognition is a field with a strong relation to Artificial Intelligence however oriented to solve basically engineering problems in a large number of applications, and other systems, for example Perception where is required processing sensory data for its interpretation. The name of Artificial Intelligence was coined in 1955 when J. McCarthy (Dartmouth College, New Hampshire), M. L. Minsky (Harvard University), N. Rochester (I.B.M. Corporation), and C.E. Shannon (Bell Telephone Laboratories) proposed to hold "Dartmouth summer research project on ARTIFICIAL INTELLIGENCE", known now as the Dartmouth Conference. The Dartmouth Conference was held during the summer of 1956, to discuss various aspects of learning and intelligence that could be simulated on machines. PR had been one of the important fields in AI [Minsky 1961] till the beginning of 70's when the first IJCPR (International Joint Conference on Pattern Recognition) was organised. The name of Pattern Recognition appeared later on with the objective of developing techniques in the area of classification, oriented to solve engineering problems. Both areas share objectives and applications that can be solved in different ways. Pattern Recognition is the research area studying the operation and design of systems that recognise patterns in data. It encloses sub disciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition); grammatical inference, parsing and matching (sometimes called syntactical and structural pattern recognition).
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