Le but premier de l'école n'est ni de transmettre des connaissances, ni de dresser l'enfant à l'objectivité en restant passif, mais de favoriser son développement et son intelligence en lui ...fournissant les moyens de penser, de réfléchir, bref de s'étonner en classe. La pédagogie de l'étonnement est parmi les pédagogies actives dont le pionnier est Louis Legrand. Etonner l'apprenant, c'est le déstabiliser et le mettre dans une situation dans laquelle ses connaissances et croyances seront mises en cause, l'engageant ainsi dans la recherche de nouvelles connaissances. Notre étude est de nature descriptive qui porte sur l'enseignement des mathématiques à des élèves de CM1 et CM2 au Maroc. Les résultats montrent que les problèmes proposés aux apprenants représentent plutôt des exercices d'application des règles « apprises » et d'une imitation stricto sensu de l'enseignant sans faire appel à une recherche de sens dû au manque de sollicitation de toute curiosité et de stimulation de leur intelligence.
The primary purpose of the school is not transmission of knowledge, nor to train student to objectivity and remain passive, but to promote his development and intelligence by providing him means to think, to reflect: to be astonished in class. The pedagogy of astonishment is among the active pedagogies whose pioneer is Louis Legrand. To astonish the student is to destabilize and put him in a situation where his knowledge and beliefs will be questioned, so he will be engaged to search for new knowledge. Our study is descriptive in nature, it focuses on the teaching of mathematics to students of level 4 and 5 at the primary school in Morocco. The results show that the problems proposed to learners are rather exercises in applying the 'learnt' rules, of an imitation of the teacher without appealing to a search for meaning due to the lack of solicitation of their curiosity and stimulation of their intelligence.
Swarming is a collective behavior of living organisms observed in nature. School of fishes, flock of birds and colony of ants are some typical examples. Thousands of individual units create ...fascinating swarm patterns through local interaction between themselves in a decentralized way. Through swarming, they achieve advantages like parallelism, flexibility, and robustness. Parallelism allows sharing of tasks, flexibility permits responding to their environments, and robustness helps ensuring tasks to execute properly. Inspired by such unique swarming system, a new concept "swarm robotics" has been emerged in the field of robotics. Although using different kind of mechanical robots, swarming has been performed until now, construction of large numbers of individual robots capable of programmable selfassembly is yet to be achieved. The creation of molecular robots composed of molecular actuators, processors, and sensors could be a means to address the challenge. Chemists and biologists have employed various self-propelled molecular actuators to demonstrate swarming. Among them, biomolecular motor systems have been a promising one due to their small size.
Marino & Merskin (2019) demonstrate that sheep are more cognitively complex than typically thought. We should be cautious in interpreting the implications of these results for welfare considerations ...to avoid perpetuating mistaken beliefs about the moral value of intelligence as opposed to sentience. There are, however, still important ways in which this work can help improve sheeps' lives.
•We review concepts related to the explainability of AI methods (XAI).•We comprehensive analyze the XAI literature organized in two taxonomies.•We identify future research directions of the XAI ...field.•We discuss potential implications of XAI and privacy in data fusion contexts.•We identify Responsible AI as a concept promoting XAI and other AI principles in practical settings.
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.
Current developments in Artificial Intelligence (AI) led to a resurgence of Explainable AI (XAI). New methods are being researched to obtain information from AI systems in order to generate ...explanations for their output. However, there is an overall lack of valid and reliable evaluations of the effects on users' experience of, and behavior in response to explanations. New XAI methods are often based on an intuitive notion what an effective explanation should be. Rule- and example-based contrastive explanations are two exemplary explanation styles. In this study we evaluate the effects of these two explanation styles on system understanding, persuasive power and task performance in the context of decision support in diabetes self-management. Furthermore, we provide three sets of recommendations based on our experience designing this evaluation to help improve future evaluations. Our results show that rule-based explanations have a small positive effect on system understanding, whereas both rule- and example-based explanations seem to persuade users in following the advice even when incorrect. Neither explanation improves task performance compared to no explanation. This can be explained by the fact that both explanation styles only provide details relevant for a single decision, not the underlying rational or causality. These results show the importance of user evaluations in assessing the current assumptions and intuitions on effective explanations.
Using Marxist critique, this book explores manifestations of Artificial Intelligence (AI) in Higher Education and demonstrates how it contributes to the functioning and existence of the capitalist ...university. Challenging the idea that AI is a break from previous capitalist technologies, the book offers nuanced examination of the impacts of AI on the control and regulation of academic work and labour, on digital learning and remote teaching, and on the value of learning and knowledge. Applying a Marxist perspective, Preston argues that commodity fetishism, surveillance, and increasing productivity ushered in by the growth of AI, further alienates and exploits academic labour and commodifies learning and research. The text puts forward a solid theoretical framework and methodology for thinking about AI to inform critical and revolutionary pedagogies. Offering an impactful and timely analysis, this book provides a critical engagement and application of key Marxist concepts in the study of AI’s role in Higher Education. It will be of interest to those working or researching in Higher Education.
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