Intrinsically motivated learning systems based on biologically-inspired novelty detection

2015 ◽  
Vol 68 ◽  
pp. 12-20 ◽  
Author(s):  
Y. Gatsoulis ◽  
T.M. McGinnity
Keyword(s):  
Novelty Detection ◽  
Learning Systems ◽  
Biologically Inspired

Biologically-inspired vs. CNN texture representations in novelty detection

2021 ◽  
Author(s):  
Michael Grunwald ◽  
Matthias Hermann ◽  
Fabian Freiberg ◽  
Matthias O. Franz
Keyword(s):  
Novelty Detection ◽  
Biologically Inspired

scholarly journals Performance vs. competence in human–machine comparisons

2020 ◽  
Vol 117 (43) ◽  
pp. 26562-26571 ◽  
Author(s):  
Chaz Firestone
Keyword(s):  
Systems Approach ◽  
Brain Activity ◽  
Human Mind ◽  
Learning Systems ◽  
Behavioral Differences ◽  
Biologically Inspired ◽  
Playing Field ◽  
Intelligent Machines ◽  
The World ◽  
Exciting Possibility

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 hypothesized, 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.

scholarly journals Learning Entropy as a Learning-Based Information Concept

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 166 ◽  
Author(s):  
Ivo Bukovsky ◽  
Witold Kinsner ◽  
Noriyasu Homma
Keyword(s):  
Novelty Detection ◽  
Cognitive Learning ◽  
Learning Systems ◽  
Complex Dynamic ◽  
Model Accuracy ◽  
Multi Scale ◽  
Probabilistic Information ◽  
Complex Dynamic Systems ◽  
Novel Concept ◽  
Information Concept

Recently, a novel concept of a non-probabilistic novelty detection measure, based on a multi-scale quantification of unusually large learning efforts of machine learning systems, was introduced as learning entropy (LE). The key finding with LE is that the learning effort of learning systems is quantifiable as a novelty measure for each individually observed data point of otherwise complex dynamic systems, while the model accuracy is not a necessary requirement for novelty detection. This brief paper extends the explanation of LE from the point of an informatics approach towards a cognitive (learning-based) information measure emphasizing the distinction from Shannon’s concept of probabilistic information. Fundamental derivations of learning entropy and of its practical estimations are recalled and further extended. The potentials, limitations, and, thus, the current challenges of LE are discussed.

Assessment of Cognitive Load in Multimedia Learning Using Dual-Task Methodology

2002 ◽  
Vol 49 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Roland Brünken ◽  
Susan Steinbacher ◽  
Jan L. Plass ◽  
Detlev Leutner
Keyword(s):  
Cognitive Load ◽  
Dual Task ◽  
Cognitive Load Theory ◽  
Multimedia Learning ◽  
Learning Systems ◽  
Learning Materials ◽  
New Approach ◽  
Direct Assessment ◽  
Load Theory ◽  
Audiovisual Presentation

Abstract. In two pilot experiments, a new approach for the direct assessment of cognitive load during multimedia learning was tested that uses dual-task methodology. Using this approach, we obtained the same pattern of cognitive load as predicted by cognitive load theory when applied to multimedia learning: The audiovisual presentation of text-based and picture-based learning materials induced less cognitive load than the visual-only presentation of the same material. The findings confirm the utility of dual-task methodology as a promising approach for the assessment of cognitive load induced by complex multimedia learning systems.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

THE USE OF ICT IN ISLAMIC EDUCATION (PAI) LEARNING AT SCHOOL: A Case Study on Integrated Islamic High School (SMA-IT) Ummul Quro Bogor

2020 ◽  
Author(s):  
Farida Hanun
Keyword(s):  
High School ◽  
Learning Process ◽  
Research Method ◽  
Qualitative Approach ◽  
Learning Systems ◽  
Government Support ◽  
Islamic Education ◽  
Inhibiting Factors ◽  
The Impact

This study aims to obtain a description related to the learning of PAI by using ICT and how the impact of the use of ICT on PAI learning systems in the classroom. The research method uses a qualitative approach in the integrated Islamic high school Ummul Quro Bogor, West Java. The results showed that a) there were four stages of using ICT in the learning process, namely; emerging, applying, integrating dan transforming. PAI teachers are already at the integrating stage. In other words, ICT has been integrated into the PAI learning curriculum. b) supporting factors for the use of ICT are the existence of ICT support facilities, the availability of qualified educators, the commitment of the school to implement ICT in every PAI learning. c) Inhibiting factors in the use of ICT are aspects of financing ICT facilities require a large budget. Some elderly teachers have difficulty using ICT in the learning process. Besides, the internet network is unstable. d) The impact of the use of ICT is very significant on PAI learning process. e) the existence of ICT devices not only as a support but already as an important component in the education system. The research led to the recommendation of the need for government support in the form of concern for ICT in terms of policies, facilities, workforce, budget, and organizing training in the use of ICT for PAI teachers to improve their professionalism. Therefore, further research is suggested regarding the effectiveness of the use of ICT in the learning process of PAI.

Sign in / Sign up

Export Citation Format

Share Document