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Today, scenario is one of development & growing field and from which more attention in industry sector. Artificial Intelligence (AI) is one of method from which can improve the industrialization. Using AI in industry can make the industry to depend on stronger, inexpensive and more precise mode of marketing. Artificial Intelligence (AI) is a cognitive science to enables human to explore many intelligent ways to model our sensing and reasoning processes. Industrial AI is a methodical order to allow engineers to methodically expand and organize AI algorithms with repeating and steady successes. In this paper, the key enablers for this transformative technology along with their significant advantages are discussed. [ABSTRACT FROM AUTHOR]

In recent years, a multitude of datasets of human-human conversations has been released for the main purpose of training conversational agents based on data-hungry artificial neural networks. In this paper, we argue that datasets of this sort represent a useful and underexplored source to validate, complement, and enhance cognitive studies on human behavior and language use. We present a method that leverages the recent development of powerful computational models to obtain the fine-grained annotation required to apply metrics and techniques from Cognitive Science to large datasets. Previous work in Cognitive Science has investigated the question-asking strategies of human participants by employing different variants of the so-called 20-question-game setting and proposing several evaluation methods. In our work, we focus on GuessWhat, a task proposed within the Computer Vision and Natural Language Processing communities that is similar in structure to the 20-question-game setting. Crucially, the GuessWhat dataset contains tens of thousands of dialogues based on real-world images, making it a suitable setting to investigate the question-asking strategies of human players on a large scale and in a natural setting. Our results demonstrate the effectiveness of computational tools to automatically code how the hypothesis space changes throughout the dialogue in complex visual scenes. On the one hand, we confirm findings from previous work on smaller and more controlled settings. On the other hand, our analyses allow us to highlight the presence of "uninformative" questions (in terms of Expected Information Gain) at specific rounds of the dialogue. We hypothesize that these questions fulfill pragmatic constraints that are exploited by human players to solve visual tasks in complex scenes successfully. Our work illustrates a method that brings together efforts and findings from different disciplines to gain a better understanding of human question-asking strategies on large-scale datasets, while at the same time posing new questions about the development of conversational systems.

Purpose: I take as a starting point the disparaging comments about the place of history and philosophy of education in initial teacher education (ITE) made by the chair of the Teacher Education Expert Panel established by the Australian Government in 2023, which I take to be the most recent attempt at resurrecting the tired debate over "the art versus science of teaching". I draw on an example from my own ITE classroom to tease possibilities for how historical research and research in the sciences of learning can be in dialogue and collaborate to deepen educational scholarship. Design/Methodology/Approach: The purpose of this essay is to reflect on the scholarly discussions and debates in the sciences of learning that historical researchers of education have largely been absent or excluded from. I argue that historical researchers have an important role to play as "critical friends" to the burgeoning fields of cognitive science and educational neuroscience. Findings: From my cursory and singular account, I highlight two key areas of education where historical research and the sciences of learning can be fruitfully in dialogue and collaboration: Firstly, on the cultural dimension that prefigures learners' approach to learning; and secondly, on the factors that enable or disable effective learning. Originality/Value: This essay sets an agenda for historical researchers in education to exercise "critical friendship" with the cognitive and brain sciences of learning, specifically by adding temporal considerations to the way biology, psychology, and sociality interact to produce different educational outcomes. This will be of "practical" service to ITE -- and to students in schools.

[This paper is part of the Focused Collection on Curriculum Development: Theory into Design.] This paper discusses the theoretical framework and curriculum materials that form the basis of the Investigative Science Learning Environment (ISLE) approach to learning and teaching physics. ISLE, as a philosophical approach to learning, has two core intentionalities: (i) We want students to learn physics by thinking like physicists; by engaging in knowledge-generating activities that mimic the actual practices of physics and using the reasoning tools that physicists use when constructing and applying knowledge. (ii) The way in which students learn physics should enhance their well being. These intentionalities form the basis upon which we build a bricolage of multiple theoretical perspectives. We will show how the ISLE approach and its implementation is shaped by (a) the epistemological commitments of physics, (b) the findings of cognitive science, (c) theories of learning communities, and (d) the perspective of universal design. We will present both qualitative and quantitative data that demonstrate the effectiveness of ISLE in helping students to achieve our intentionalities. We conclude with a call to curriculum developers and implementers to explicitly articulate their intentionalities and theoretical perspectives so that we may forge deeper connections between educational theories, curriculum development, and implementation.

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies. [ABSTRACT FROM AUTHOR]

The critical question for cognitive scientists is what does cognitive science do, if anything, for people? Cognitive science is primarily concerned with human cognition but has fallen short in continuously and critically assessing the "who" in human cognition. This complacency in a world where white supremacist and patriarchal structures leave cognitive science in the unfortunate position of potentially "supporting" those structures. We take it that many cognitive scientists operate on the assumption that the study of human cognition is both interesting and important. We want to invoke that importance to note that cognitive scientists must continue to work to show how the field is useful to all of humanity and reflects a humanity that is not white by default. We wonder how much the field has done, and can do, to show that it is useful not only in the sense that we might make connections with researchers in other fields, win grants and write papers, even of the highest quality, but useful in some material way to the billions of non-cognitive scientists across the globe.

Purpose: While many approaches in the field of unlearning aim at describing, understanding or explaining the "what" and/or "how" of unlearning, this paper aims to focus on the "where-to" and the goal of unlearning. In many cases, unlearning starts off with a specific result or goal in mind. This paper suggests that such an approach has to be challenged in the context of a highly complex and uncertain world and to introduce a mode of unlearning following a strategy of future-oriented open-endedness. Design/methodology/approach: This conceptual paper draws on (both theoretical/philosophical and empirical) interdisciplinary evidence from a wide variety of fields, such as organization studies, organizational (un)learning, systems theory, cognitive science and innovation studies. Findings: It turns out that open-endedness in unlearning processes plays a central role, especially if we are confronted with high levels of uncertainty and complexity. In such an environment, following a strategy of co-becoming with an unfolding environment and with an emergent goal seems to be more promising than aiming at a preconceived (un-)learning goal. Originality/value: The unlearning literature provides various approaches to what unlearning is and how it can be executed. However, understanding the actual goals and outcomes of unlearning and how these goals are identified and determined is a rather under-researched field. In many cases, they are preconceived in advance finding their realization in new forms of knowledge, assumptions, belief systems, values or routines. This paper challenges this strategy and addresses the gap of how it is possible to unlearn toward an uncertain future. This has an impact on the process of unlearning itself; it has to be reframed and understood as an open-ended strategy for identifying emerging future potentials, purposes and goals in a process of co-becoming with an unfolding future.

This paper comments on the theoretical formulations and usage of the construct of teacher noticing in a selection of the papers in this special issue of "ZDM Mathematics Education." The analysis of how the notion of teacher noticing is used in the papers suggests that it draws attention to several interdependencies involved that have not been attended to in the past. However, the contributions in this special issue have only partially accounted for the dynamic interactions in teacher noticing, suggesting that there is potential for enriching our understanding of the complexities involved in the realm of teacher noticing. The purpose of this commentary is to stimulate the current discussion on teacher noticing by providing insights from cognitive science and the applied science of human factors, which have the potential to challenge the current understanding of noticing. In doing so, the paper sets the stage for several related constructs from these research disciplines to raise awareness of aspects that recent conceptualizations of teacher noticing may have blinded rather than enlightened.

There's no doubt that research bearing directly on classroom practice is crucial. In this article Daniel Willingham maintains that it's useful for educators also to know the basic science around children's cognition, emotion, and motivation, because beliefs about what children are like inevitably influence teaching practice. The purpose of learning the science would be to broaden and deepen that knowledge, and to disabuse teachers of any misconceptions they might have. Still, not everything of concern to scientists is valuable to educators. The author makes the point that some statements concerning children's learning are perfectly sound scientifically but should not influence educational decisions. That includes some statements that seem like they ought to have a direct bearing on classrooms. In this article, Willingham describes three types of statements scientists make, only one of which he thinks ought to influence teaching practice, but he points out that even that type of statement, can be misapplied.

Artificial intelligence (AI) policy, innovation, and practice are moving ahead in rapid fashion. There is a growing mismatch between technological innovations in AI, which are advancing at a rapid rate, and normative and regulatory frameworks, which are lagging, particularly when it comes to protecting democratic values and human rights principles. National governments and multilateral institutions are attempting to catch up. At least 175 countries, firms and other organizations have produced documents listing ethical principles for AI. These efforts have proceeded in a somewhat fragmented manner, yet there are emerging signs of consolidation as the United States, Europe, and other countries begin to coalesce around shared principles. Europe, in particular, has raced ahead to draft comprehensive legislation, the Artificial Intelligence Act (AIA), to oversee these technologies and systems. What has motivated the European Union to pursue this approach? And how will this effort influence AI norms globally? This paper describes how Europe's AI norm-building process represents an effort to ensure EU priorities are reflected in the AI governance landscape. Europe's approach faces uncertainty. While it is likely that the AIA will meaningfully influence global AI norms, several factors may hinder its global diffusion and adoption. [ABSTRACT FROM AUTHOR]

The purpose of this article is to offer insights into current understanding of digital learning environments (DLEs) from a neuroscientific perspective. Cognitive neuroscience methods are increasingly applied in educational research to examine the neural underpinnings of learning. As such, neuroscientific evidence can play an important role in advancing current knowledge base from the existing self-reported data and behavioural measures in the field of educational technology. In this paper, we focus our review of neuroscience research on DLEs that can potentially transform the way we view learning and instruction. We discuss recent empirical studies done on DLEs using common cognitive neuroscience methods which included eye tracking, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). We offer recommendations for future applications of neuroscience methods in behavioural research within DLEs.