scholarly journals Interlinked. Machines and humans facing the 10101 century

2021 ◽  
pp. 116
Author(s):  
Carme Torras ◽  
Ramon López de Mántaras
Keyword(s):  
Artificial Intelligence ◽  
Common Sense ◽  
General Public ◽  
Cognitive Skills ◽  
State Of The Art ◽  
Common Sense Reasoning ◽  
Research Fields ◽  
Key Points ◽  
The Media ◽  
High Level

Robotics and artificial intelligence are two scientific research fields that receive considerable attention from the media and, consequently, from society. Unfortunately, many advances are reported to the general public in sensationalist (or even alarmist) terms, leading to false hopes or unjustified fears, and taking the focus from other key points. For instance, recent successes in artificial intelligence, amplified by the media, are the cause of a mistaken perception of this discipline’s state of the art. The reality is that artificial intelligence is still far from achieving many high-level cognitive skills; particularly, common sense reasoning.

Common-sense reasoning, theories of

2018 ◽  
Author(s):  
John Horty
Keyword(s):  
Artificial Intelligence ◽  
Twentieth Century ◽  
Common Sense ◽  
Scientific Reasoning ◽  
Logical Framework ◽  
Applied Logic ◽  
Common Sense Reasoning ◽  
New Information ◽  
Logical Study ◽  
Reasoning Agent

The task of formalizing common-sense reasoning within a logical framework can be viewed as an extension of the programme of formalizing mathematical and scientific reasoning that has occupied philosophers throughout much of the twentieth century. The most significant progress in applying logical techniques to the study of common-sense reasoning has been made, however, not by philosophers, but by researchers in artificial intelligence, and the logical study of common-sense reasoning is now a recognized sub-field of that discipline. The work involved in this area is similar to what one finds in philosophical logic, but it tends to be more detailed, since the ultimate goal is to encode the information that would actually be needed to drive a reasoning agent. Still, the formal study of common-sense reasoning is not just a matter of applied logic, but has led to theoretical advances within logic itself. The most important of these is the development of a new field of ‘non-monotonic’ logic, in which the conclusions supported by a set of premises might have to be withdrawn as the premise set is supplemented with new information.

Common-sense reasoning and everyday activities

1993 ◽  
Vol 1 (2) ◽  
pp. 307-340 ◽  
Author(s):  
Yvonne Rogers
Keyword(s):  
Common Sense ◽  
Theory And Practice ◽  
Cooking Methods ◽  
Common Sense Reasoning ◽  
Everyday Activities ◽  
Knowledge Based ◽  
Theoretical Approaches ◽  
High Level ◽  
The Relationship ◽  
Selection Of

This paper is concerned with the nature of common-sense reasoning and understanding in relation to practical behaviour. It examines the relationship between intuitive knowledge based on everyday experience and institutionalized theory and practice. An analysis of the types of knowledge that guide the selection of actions and understanding in the domain of cooking practice is presented. Verbal transcripts were elicited from participants, with varying levels of experience, of the cooking methods they followed and their underlying rationale. The results suggest that individuals utilize various high level knowledge primitives in combination with pragmatic utility principles in their reasoning. The findings are discussed in the light of recent theoretical approaches concerned with the relationship between knowledge and inference.

scholarly journals Periodismo de alta tecnología: signo de los tiempos digitales del tercer milenio

2021 ◽  
Author(s):  
Xosé López-García ◽  
Ángel Vizoso
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
High Technology ◽  
The State ◽  
Current Phase ◽  
Quality Information ◽  
High Quality ◽  
Daily Routines ◽  
High Quality Information ◽  
The Media

High technology is driving most of the innovation and debates in journalism today. Artificial intelligence and journalism are walking hand in hand in the current phase, defined by processes of digitization. Studies on the state of the art of such technology in the media reveal a clear tendency towards the use of more sophisticated tools. Furthermore, this research highlights how journalists are increasingly using such approaches in challenging situations. This shift has thus led to more debate on the threats and opportunities of the introduction of such technologies into a communication ecosystem that is already in need of models that can produce high-quality information. This study thus describes the state of the art on the integration of high technology into daily routines in the media. Resumen La denominada “alta tecnología” marca buena parte de la innovación y de los debates del periodismo actual. Inteligencia artificial y periodismo caminan de la mano en la fase actual de la digitalización de los procesos. Los estudios sobre el estado de las tecnologías en las redacciones de los medios muestran una clara tendencia de los periodistas a trabajar con herramientas más sofisticadas y a emplearlas en los desafíos que tienen a la hora de realizar su cometido profesional. La tendencia, que no parece tener marcha atrás, introduce renovados debates sobre las amenazas y oportunidades en un ecosistema comunicativo cada vez más complejo y más necesitado de respuestas para establecer modelos sostenibles que aseguren la existencia de información periodística de calidad. En este texto se realiza una aproximación al estado de la cuestión, se analizan experiencias y se sitúan algunos de los retos.

scholarly journals Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 167 ◽  
Author(s):  
Roberta Calegari ◽  
Giovanni Ciatto ◽  
Enrico Denti ◽  
Andrea Omicini
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
State Of The Art ◽  
Future Perspectives ◽  
Main Application ◽  
The Core ◽  
Research Fields ◽  
Research Perspectives ◽  
Key Features ◽  
Symbolic Ai

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future.

scholarly journals Deep Learning: A Review

2018 ◽  
Author(s):  
Rocio Vargas ◽  
Amir Mosavi ◽  
Ramon Ruiz
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
State Of The Art ◽  
The Novel ◽  
Nonlinear Transformations ◽  
Large Databases ◽  
The Common ◽  
Novel Concept ◽  
High Level ◽  
Novel Applications

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architec- tures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contri- butions and the novel applications of deep learning. The following review chron- ologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applica- tions. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

scholarly journals Zveřejnit či nechat skryté: Jak čtenáři Blesku zdůvodňují politickou relevanci informací o soukromí politiků

2014 ◽  
Vol 16 (1) ◽  
pp. 52-74
Author(s):  
Martin Volek
Keyword(s):  
General Public ◽  
Private Life ◽  
Public Office ◽  
Public And Private ◽  
The Public ◽  
Private Lives ◽  
The Media ◽  
Public Scrutiny ◽  
The Right ◽  
High Level

Politicians as public persons are under increasing review by both the media and the general public. This review focuses not only on acts in public office, both official and unofficial, but also on the behavior of politicians in private life that could, in a real or imagined way, influence their performance in public office. This paper presents the perceptions of the boundaries between the public and private lives of politicians by readers of the most popular Czech tabloid, Blesk. The qualitative analysis based on in-depth interviews and a focus group with readers presents arguments that readers use to categorize information about politicians into that that belongs in the public domain and should be therefore published, and that that belongs to the private sphere and should not be published. In the readers’ views, citizens have the right to know about a politician’s private life, such as information that reflects a politician’s character and about possible influences from their private life on their performance in public office. The readers also consider how it feels for a politician to be a private person in public office under public scrutiny. These readers then often advocate the right of a politician to have his privacy respected, since these readers themselves would not be pleased to be under such a high level of public scrutiny regarding their own lives. It seems that the readers’ arguments are largely based on their personal history. We finally suggest that research on political participation would be enriched by including the perspective of everyday life experiences of the general public.

Reflections on Successful Research in Artificial Intelligence: An Introduction

AI Magazine ◽  
2019 ◽  
Vol 40 (4) ◽  
pp. 3-5
Author(s):  
Ching-Hua Chen ◽  
James Hendler ◽  
Sabbir Rashid ◽  
Oshani Seneviratne ◽  
Daby Sow ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
General Public ◽  
Full Length ◽  
Editorial Team ◽  
Special Topic

This editorial introduces the special topic articles on reflections on successful research in artificial intelligence. Consisting of a combination of interviews and full-length articles, the special topic articles examine the meaning of success and metrics of success from a variety of perspectives. Our editorial team is especially excited about this topic, because we are in an era when several of the aspirations of early artificial intelligence researchers and futurists seem to be within reach of the general public. This has spurred us to reflect on, and re-examine, our social and scientific motivations for promoting the use of artificial intelligence in governments, enterprises, and in our lives.

Europe

2020 ◽  
pp. 649-666
Author(s):  
Andrea Renda
Keyword(s):  
Artificial Intelligence ◽  
Digital Technology ◽  
Policy Framework ◽  
Expert Group ◽  
Ethical Guidelines ◽  
Ethical Implications ◽  
Strong Focus ◽  
Ethics Guidelines ◽  
Definition Of ◽  
High Level

This chapter assesses Europe’s efforts in developing a full-fledged strategy on the human and ethical implications of artificial intelligence (AI). The strong focus on ethics in the European Union’s AI strategy should be seen in the context of an overall strategy that aims at protecting citizens and civil society from abuses of digital technology but also as part of a competitiveness-oriented strategy aimed at raising the standards for access to Europe’s wealthy Single Market. In this context, one of the most peculiar steps in the European Union’s strategy was the creation of an independent High-Level Expert Group on AI (AI HLEG), accompanied by the launch of an AI Alliance, which quickly attracted several hundred participants. The AI HLEG, a multistakeholder group including fifty-two experts, was tasked with the definition of Ethics Guidelines as well as with the formulation of “Policy and Investment Recommendations.” With the advice of the AI HLEG, the European Commission put forward ethical guidelines for Trustworthy AI—which are now paving the way for a comprehensive, risk-based policy framework.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

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