scholarly journals Cognitive Mimetics for Designing Intelligent Technologies

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Tuomo Kujala ◽  
Pertti Saariluoma

Design mimetics is an important method of creation in technology design. Here, we review design mimetics as a plausible approach to address the problem of how to design generally intelligent technology. We argue that design mimetics can be conceptually divided into three levels based on the source of imitation. Biomimetics focuses on the structural similarities between systems in nature and technical solutions for solving design problems. In robotics, the sensory-motor systems of humans and animals are a source of design solutions. At the highest level, we introduce the concept of cognitive mimetics, in which the source for imitation is human information processing. We review and discuss some historical examples of cognitive mimetics, its potential uses, methods, levels, and current applications, and how to test its success. We conclude by a practical example showing how cognitive mimetics can be a highly valuable complimentary approach for pattern matching and machine learning based design of artificial intelligence (AI) for solving specific human-AI interaction design problems.

2021 ◽  
Vol 11 (2) ◽  
pp. 870
Author(s):  
Galena Pisoni ◽  
Natalia Díaz-Rodríguez ◽  
Hannie Gijlers ◽  
Linda Tonolli

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed.


Author(s):  
А.И. Гайкович ◽  
С.И. Лукин ◽  
О.Я. Тимофеев

Процесс создания проекта судна или корабля рассматривается как преобразование информации, содержащейся в техническом задании на проектирование, нормативных документах и знаниях проектанта, в информацию, объем которой позволяет реализовать проект. Проектирование может быть представлено как поиск решения в пространстве задач. Построение цепочки последовательно решаемых задач составляет методику проектирования. Проектные задачи могут быть разбиты на две группы. Первая группа ‒ это полностью формализуемые задачи, для решения которых есть известные алгоритмы. Например, построение теоретического чертежа по известным главным размерениям и коэффициентам формы. Ко второй группе задач можно отнести трудно формализуемые или неформализуемые задачи. Например, к задачам этого типа можно отнести разработку общего расположения корабля. Важнейшим инструментом проектирования современного корабля или судна является система ав­томатизированного проектирования (САПР). Решение САПР задач первой группы не представляет проблемы. Введение в состав САПР задач второй группы подразумевает разработку специального ма­тематического аппарата, базой для которого, которым является искусственный интеллект, использующий теорию нечетких множеств. Однако, настройка искусственных нейронных сетей, создание шкал для функций принадлежности элементов нечетких множеств и функций предпочтений лица принимающего решения, требует участие человека. Таким образом, указанные элементы искусственного интеллекта фиксируют качества проек­танта как специалиста и создают его виртуальный портрет. The process of design a project of a ship is considered as the transformation of information contained in the design specification, regulatory documents and the designer's knowledge into information, the volume of which allows the project to be implemented. Designing can be represented as a search for a solution in the space of problems. The construction of a chain of sequentially solved tasks constitutes the design methodology. Design problems can be divided into two groups. The first group is completely formalizable tasks, for the solution of which there are known algorithms. For example, the construction of ship's surface by known main dimensions and shape coefficients. Tasks of the second group may in­clude those which are difficult to formalize or non-formalizable. For example, tasks of this type can include develop­ment of general arrangement of a ship. The most important design tool of a modern ship or vessel is a computer-aided design system (CAD). The solu­tion of CAD problems of the first group is not a problem. Introduction of tasks of the second group into CAD implies development of a special mathematical apparatus, the basis for which is artificial intelligence, which uses the theory of fuzzy sets. However, the adjustment of artificial neural networks, the creation of scales for membership functions of fuzzy sets elements and functions of preferences of decision maker, requires human participation. Thus, the above elements of artificial intelligence fix the qualities of the designer as a specialist and create his virtual portrait.


BIOPHILIA ◽  
2016 ◽  
Vol 2016 (2) ◽  
pp. 26-26 ◽  
Author(s):  
R. Saggini ◽  
G. Barassi ◽  
E. Ancona ◽  
S.M. Carmignano ◽  
A. Sablone ◽  
...  

2018 ◽  
Author(s):  
Bryan C. Daniels ◽  
William S. Ryu ◽  
Ilya Nemenman

AbstractThe roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process phenomenologically, and we use the Sir Isaac dynamical inference platform to infer the model in a fully automated fashion directly from experimental data. The inferred model requires incorporation of an unobserved dynamical variable, and is biologically interpretable. The model makes accurate predictions about the dynamics of the worm behavior, and it can be used to characterize the functional logic of the dynamical system underlying the escape response. This work illustrates the power of modern artificial intelligence to aid in discovery of accurate and interpretable models of complex natural systems.


Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2882
Author(s):  
Thi Thu Em Vo ◽  
Hyeyoung Ko ◽  
Jun-Ho Huh ◽  
Yonghoon Kim

Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. This review provides and overview of the development of smart aquaculture and intelligent technology. We summarized and collected 100 articles about machine learning in smart aquaculture from nearly 10 years about the methodology, results as well as the recent technology that should be used for development of smart aquaculture. We hope that this review will give readers interested in this field useful information.


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