Reliabilism and the Testimony of Robots

2020 ◽  
Vol 24 (3) ◽  
pp. 332-356
Author(s):  
Billy Wheeler ◽  
Keyword(s):  
Artificial Intelligence ◽  
Testimonial Knowledge ◽  
Sources Of Knowledge ◽  
The Difference ◽  
Legal Consequences

We are becoming increasingly dependent on robots and other forms of artificial intelligence for our beliefs. But how should the knowledge gained from the “say-so” of a robot be classified? Should it be understood as testimonial knowledge, similar to knowledge gained in conversation with another person? Or should it be understood as a form of instrument-based knowledge, such as that gained from a calculator or a sundial? There is more at stake here than terminology, for how we treat objects as sources of knowledge often has important social and legal consequences. In this paper, I argue that at least some robots are capable of testimony. I make my argument by exploring the differences between instruments and testifiers on a well-known account of knowledge: reliabilism. On this approach, I claim that the difference between instruments and testifiers as sources of knowledge is that only the latter are capable of deception. As some robots can be designed to deceive, so they too should be recognized as testimonial sources of knowledge.

scholarly journals On the Juridical Relevance of the Phenomenological Notion of Person in Max Scheler and Edith Stein

2021 ◽  
Author(s):  
Francesco Galofaro
Keyword(s):  
Artificial Intelligence ◽  
Edmund Husserl ◽  
Phenomenological Analysis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Edith Stein ◽  
Max Scheler ◽  
Human Decision ◽  
The Difference ◽  
Social Units

AbstractThe paper presents a semiotic interpretation of the phenomenological debate on the notion of person, focusing in particular on Edmund Husserl, Max Scheler, and Edith Stein. The semiotic interpretation lets us identify the categories that orient the debate: collective/individual and subject/object. As we will see, the phenomenological analysis of the relation between person and social units such as the community, the association, and the mass shows similarities to contemporary socio-semiotic models. The difference between community, association, and mass provides an explanation for the establishment of legal systems. The notion of person we inherit from phenomenology can also be useful in facing juridical problems raised by the use of non-human decision-makers such as machine learning algorithms and artificial intelligence applications.

scholarly journals Study of Multimedia Delivery over Software Defined Networks

2016 ◽  
Vol 7 (4) ◽  
pp. 37 ◽  
Author(s):  
Jose Miguel Jimenez ◽  
Oscar Romero ◽  
Albert Rego ◽  
Avinash Dilendra ◽  
Jaime Lloret
Keyword(s):  
Artificial Intelligence ◽  
Personal Computer ◽  
Network Protocols ◽  
Performance Comparison ◽  
Distributed Artificial Intelligence ◽  
Software Defined Networks ◽  
Multimedia Delivery ◽  
Real Network ◽  
Multimedia Streams ◽  
The Difference

Software Defined Networks (SDN) have become a new way to make dynamic topologies. They have great potential in both the creation and development of new network protocols and the inclusion of distributed artificial intelligence in the network. There are few emulators, like Mininet, that allow emulating a SDN in a single personal computer, but there is lack of works showing its performance and how it performs compared with real cases. This paper shows a performance comparison between Mininet and a real network when multimedia streams are being delivered. We are going to compare them in terms of consumed bandwidth (throughput), delay and jitter. Our study shows that there are some important differences when these parameters are compared. We hope that this research will be the basis to show the difference with real deployments when Mininet is used.

PHOTOGRAPHICALLY BASED KNOWLEDGE

Episteme ◽  
2013 ◽  
Vol 10 (3) ◽  
pp. 283-297 ◽  
Author(s):  
Dan Cavedon-Taylor
Keyword(s):  
Pictorial Representation ◽  
Perceptual Knowledge ◽  
Testimonial Knowledge ◽  
Epistemic Utility ◽  
Sources Of Knowledge ◽  
Novel Approach ◽  
Aesthetic Pleasure ◽  
Foundational Issue ◽  
Do So

AbstractPictures are a quintessential source of aesthetic pleasure. This makes it easy to forget that they are epistemically valuable no less than they are aesthetically so. Pictures are representations. As such, they may furnish us with knowledge of the objects they represent. In this article I provide an account of why photographs are of greater epistemic utility than handmade pictures. To do so, I use a novel approach: I seek to illuminate the epistemic utility of photographs by situating both photographs and handmade pictures among the sources of knowledge. This method yields an account of photography's epistemic utility that better connects the issue with related issues in epistemology and is relatively superior to other accounts. Moreover, it answers a foundational issue in the epistemology of pictorial representation: ‘What kinds of knowledge do pictures furnish?’ I argue that photographs have greater epistemic utility than handmade pictures because photographs are sources of perceptual knowledge, while handmade pictures are sources of testimonial knowledge.

scholarly journals Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA

2013 ◽  
Vol 7 (2) ◽  
pp. 189
Author(s):  
Silviani E Rumagit ◽  
Azhari SN
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Statistical Method ◽  
Moving Average ◽  
Electric Energy ◽  
Backpropagation Neural Network ◽  
The Difference ◽  
The Government ◽  
Increasing Demand

AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan.            ARIMA merupakan salah satu metode statistik yang banyak digunakan untuk prediksi dimana ARIMA mengikuti model autoregressive (AR) moving average (MA). Syarat dari ARIMA adalah data harus stasioner, data yang tidak stasioner harus distasionerkan dengan differencing. Selain metode statistik, prediksi juga dapat dilakukan dengan teknik kecerdasan buatan, dimana dalam penelitian ini jaringan syaraf tiruan backpropagation dipilih untuk melakukan prediksi. Dari hasil pengujian yang dilakukan selisih MSE ARIMA, JST dan penggabungan ARIMA, jaringan syaraf tiruan tidak berbeda secara signifikan. Kata Kunci— ARIMA, jaringan syaraf tiruan, kelompok tarif.  AbstractBackground this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.            ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly different. Keyword—ARIMA, neural network, tarif groups

scholarly journals Hakikat Ilmu Pengetahuan dalam Perspektif Modern dan Islam

2017 ◽  
Vol 7 (1) ◽  
pp. 81
Author(s):  
Via Linda Siswati
Keyword(s):  
Correspondence Theory ◽  
Human Experience ◽  
Coherence Theory ◽  
Natural Phenomena ◽  
Human Knowledge ◽  
Truth Theory ◽  
Sources Of Knowledge ◽  
The Difference ◽  
Basic Characteristics ◽  
Structure Of Knowledge

Philosophy of science is a branch of philosophy that reflects, radically and integral about the nature of knowledge itself. This writing aims to understand: (1) understanding of knowledge and science in etymology and terminology. (2) the difference of science, knowledge and religion in epistimology. (3) the extent of science in Islam. (4) the basic characteristics of science. (5) truth theory. (6) sources of knowledge. (7) the boundaries of science (8) the structure of knowledge. The results of this paper are: (1) science is from Arabic, 'alima. The meaning of this word is knowledge. And science in terminology is the whole conscious effort to investigate, find, and improve human understanding from various aspects of reality in human nature and we know (2) The location of the difference is the science is a summary of a collection of knowledge or the result of knowledge and facts, The order of faith or order of belief in the existence of something absolute outside man, in accordance and in line with the order of faith and order of worship. (3) The principal features of science are as follows: (a) Systematic, (b) Authenticity, (c) Rationality, (d) Objectivity, (e) Verifiability, (f) Communality. (4) The theory in a theory of truth there are 3 namely: Correspondence Theory, Coherence Theory, Theory of Pragmatism. (4) The source of human knowledge uses two ways of obtaining correct knowledge, first through rationality and secondly through experience. (5) Science limits its exploration to human experience, hence science begins on exploration of human experience and ceases to human experience, and that is the limit of knowledge. (6) Science is essentially a collection of knowledge that explains the various natural phenomena that allow humans to perform a series of actions to master these symptoms based on existing explanations.

scholarly journals The next step for Artificial Intelligence in a globally connected economy

2021 ◽  
Vol 129 ◽  
pp. 04001
Author(s):  
Dumitru Alexandru Bodislav ◽  
Florina Bran ◽  
Carol Cristina Gombos ◽  
Amza Mair
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Factor ◽  
Value Added ◽  
Point Of View ◽  
Business Analytics ◽  
Economic Point ◽  
Technological Breakthrough ◽  
Big Picture ◽  
The Difference

Research background: This research paper represents an overview of what artificial intelligence is, what are its roots, and what is the next big thing regarding the domain. In this paper we try to highlight how the domain is growing and what is the difference between the ideology, the business factor and the human factor. We try to create a big picture on the entire phenomenon by creating a parallel between machine learning, artificial intelligence and the influence of technological breakthrough from a hardware perspective. Purpose of the article: The paper is built as a tool in understanding technology, globalization and the pathway to success and scientific glory for what can be seen as the industry of artificial intelligence. The tools presented in the research have the purpose to create an easier path to how we can develop this domain by accelerating theoretical processing and business analytics that come together to form the next level of machine learning/artificial intelligence; research and development, everything being filtered from an economic point of view. Methods: The used research method is based on fundamental analysis of the artificial intelligence domain and its purpose in the complexity of globalization and economic development. Findings & Value added: The paper tries to offer a tool for building a better understanding of the next decade in the domain of artificial intelligence.

6. Heuristic computing

2016 ◽  
pp. 104-118
Author(s):  
Subrata Dasgupta
Keyword(s):  
Artificial Intelligence ◽  
Common Sense ◽  
Heuristic Search ◽  
Heuristic Algorithms ◽  
Heuristic Rules ◽  
Sense Experience ◽  
Everyday Activities ◽  
The Difference

Many ordinary problems and everyday activities are not conducive to algorithmic solutions. Yet, people do perform these tasks and solve such problems, so what other computational means are available to perform such tasks? The answer is to resort to a mode of computing that deploys heuristics—rules, precepts, principles, hypotheses based on common sense, experience, judgement, analogies, informed guesses, etc., which offer promise but are not guaranteed to solve problems. Heuristic computing encompasses both heuristic search and heuristic algorithms. ‘Heuristic computing’ explains a meta-heuristic called ‘satisficing’; the difference between exact and heuristic algorithms; how heuristics is used in artificial intelligence; weak and strong methods; and how to interpret heuristic rules.

5. Jurisdiction

2019 ◽  
pp. 82-97
Author(s):  
Anders Henriksen
Keyword(s):  
Legal Concept ◽  
Legal Principles ◽  
The Difference ◽  
Legal Consequences ◽  
Principle Of Territoriality

This chapter discusses the international legal concept of jurisdiction as well as the content of the relevant legal principles. The term jurisdiction relates to the authority of a state to exert its influence and power—in practice make, apply and enforce its rules—and create an impact or consequence on individuals or property. The chapter explains the difference between, respectively, the jurisdiction to prescribe and the jurisdiction to enforce and the main elements thereof. It analyses the different principles of prescriptive jurisdiction (the principle of territoriality, nationality, universality, protection and so-called passive personality) and discusses the issue of concurring jurisdictions as well as jurisdiction on ships and aircraft. It also discusses the prohibition on enforcing jurisdiction on the territory of another state as well as the legal consequences of violating that prohibition.

Reform and innovation of artificial intelligence technology for information service in university physical education

2020 ◽  
pp. 1-11
Author(s):  
Jianye Zhang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Physical Education ◽  
Information Service ◽  
Management Decision ◽  
Negative Effects ◽  
Education Management ◽  
Depth Analysis ◽  
Artificial Intelligence Technology ◽  
The Difference

This article analyzes the reform of information services in university physical education based on artificial intelligence technology and conducts in-depth and innovative research on it. In-depth analysis of the relationship between big data and the development and application of information technology such as the Internet, Internet of Things, cloud computing, to clarify the difference and connection between big data, informatization and intelligence. Artificial intelligence will bring opportunities for changes in data collection, management decision-making, governance models, education and teaching, scientific research services, evaluation and evaluation of physical education in our university. At the same time, big data education management in colleges and universities faces many challenges such as the balance of privacy and freedom, data hegemony, data junk, data standards, and data security, and they have many negative effects. In accordance with the requirements of educational modernization, centering on the goal of intelligent and humanized education management, it aims existing issues in college physical education management.

scholarly journals Roundtable Discussion III: The Development and Uses of Artificial Intelligence in Medicine: A Work in Progress

2020 ◽  
Vol 4 ◽  
pp. 247028971989870 ◽  
Author(s):  
Marianne J. Legato ◽  
Francoise Simon ◽  
James E. Young ◽  
Tatsuya Nomura ◽  
Ibis Sánchez-Serrano
Keyword(s):  
Artificial Intelligence ◽  
Arabic Numeral ◽  
Official Journal ◽  
Roundtable Discussion ◽  
Desktop Computer ◽  
Modern Computer ◽  
Quantum Leap ◽  
The Difference ◽  
Problem Solve ◽  
Classical Computer

Humans have devised machines to replace computation by individuals since ancient times: The abacus predated the written Hindu–Arabic numeral system by centuries. We owe a quantum leap in the development of machines to help problem solve to the British mathematician Charles Babbage who built what he called the Difference Engine in the mid-19th century. But the Turing formula created in 1936 is the foundation for the modern computer; it produced printed symbols on paper tape that listed a series of logical instructions. Three decades later, Olivetti manufactured the first mass-marketed desktop computer (1964), and by 1981, IBM had developed the first personal computer. Computing machines have become more and more powerful, culminating recently in Google’s claim that it had achieved quantum supremacy in developing a system that can complete a task in 200 seconds that it would take the most powerful type of classical computer available 10 000 years to achieve. In short, we are in a period of human history in which we are creating more and more powerful and complex machines potentially capable of duplicating human intelligence and indeed surpassing/expanding its power. We are solidly in the age of artificial intelligence (AI). Increasing interest in the development of AI and its application to human health at all levels makes a roundtable discussion by experts a valuable project for publication in our journal, Gender and the Genome, the official journal of the Foundation for Gender-Specific Medicine and the International Society of Gender Medicine.

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