scholarly journals Connectors of smart design and smart systems

2021 ◽  
pp. 1-19
Author(s):  
Imre Horváth
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Real Life ◽  
Exploratory Research ◽  
Smart Systems ◽  
The Third ◽  
Recent Developments ◽  
Creative Problem ◽  
Application Specific ◽  
System Knowledge

AbstractThough they can be traced back to different roots, both smart design and smart systems have to do with the recent developments of artificial intelligence. There are two major questions related to them: (i) What way are smart design and smart systems enabled by artificial narrow, general, or super intelligence? and (ii) How can smart design be used in the realization of smart systems? and How can smart systems contribute to smart designing? A difficulty is that there are no exact definitions for these novel concepts in the literature. The endeavor to analyze the current situation and to answer the above questions stimulated an exploratory research whose first findings are summarized in this paper. Its first part elaborates on a plausible interpretation of the concept of smartness and provides an overview of the characteristics of smart design as a creative problem solving methodology supported by artificial intelligence. The second part exposes the paradigmatic features and system engineering issues of smart systems, which are equipped with application-specific synthetic system knowledge and reasoning mechanisms. The third part presents and elaborates on a conceptual model of AI-based couplings of smart design and smart systems. The couplings may manifest in various concrete forms in real life that are referred to as “connectors” in this paper. The principal types of connectors are exemplified and discussed. It has been found that smart design tends to manifest as a methodology of blue-printing smart systems and that smart systems will be intellectualized the enablers of implementation of smart design. Understanding the affordances of and creating proper connectors between smart design and smart systems need further explorative research.

scholarly journals “Regulatory Sandboxes” in Russia: New Horizons and Challenges

2020 ◽  
Vol 1 (2) ◽  
pp. 17-27
Author(s):  
Damir R. Salikhov
Keyword(s):  
Artificial Intelligence ◽  
Real Life ◽  
Federal Law ◽  
Digital Innovation ◽  
New Horizons ◽  
The Third ◽  
Special Mechanism ◽  
Life Model ◽  
The Government ◽  
Universal Mechanism

“Regulatory sandboxes” are regarded as a special mechanism for setting up experimental regulation in the area of digital innovation (especially in financial technologies), creating a special regime for a limited number of participants and for a limited time.Russiahas its own method of experimental regulation, which is not typical but may be helpful for other jurisdictions. There are three approaches to legal experiments (including digital innovations) inRussia. The first approach is accepting special regulation on different issues. There are recent examples of special laws (e.g. Federal Law on the experiment with artificial intelligence technologies inMoscow). An alternative to this option is establishing experimental regulation by an act of the Government if legislation does not prohibit it (e.g. labeling with means of identification). The second approach deals only with Fintech innovations and provides a special mechanism to pilot models of innovative financial technologies. The participants of such a “sandbox” may create a close-to-life model in order to estimate the effects and risks. If the model works fine, the regulation may be amended. The third approach works with creating a universal mechanism of real-life experiments in the sphere of digital innovations based on the special Federal Law and the specific decision of the Government of theRussian Federationor the Bank of Russia in the financial sphere. The author compares the three approaches and their implementation within the framework of Russian legislation and practice and concludes that this experience may be used by developing countries with inflexible regulation, in order to facilitate the development of digital innovations.

A Compelling Case for Calculators

1987 ◽  
Vol 34 (6) ◽  
pp. 16-19
Author(s):  
Bernard R. Yvon
Keyword(s):  
High Schools ◽  
Problem Solving ◽  
Final Section ◽  
Junior High ◽  
Mathematics Classroom ◽  
Junior High Schools ◽  
The Third ◽  
Creative Problem ◽  
The Many

Calculator can do a great deal for the mathematics classroom. The first part of this article will present six bonuses I have found that students and teacher experienced when using calculators in elementary, middle, and junior high schools. Next is a section on problem solving and creative problem making as well. Practical help for the teacher who wants to try calculators in the classroom appears in the third section, along with a teacher's checklist. The final section offers advice on personalizing the use of calculators for students and recognizing some of their limitations. I hope the reader will agree that the many pluses present a compelling case for calculator use in today's classroom.

Real-life creative problem solving in teams: fNIRS based hyperscanning study

NeuroImage ◽  
2019 ◽  
Vol 203 ◽  
pp. 116161 ◽  
Author(s):  
Naama Mayseless ◽  
Grace Hawthorne ◽  
Allan L. Reiss
Keyword(s):  
Problem Solving ◽  
Real Life ◽  
Creative Problem Solving ◽  
Creative Problem

A Review on the Importance of Machine Learning and Artificial Intelligence in Real Life Problem Solving

2020 ◽  
Vol 17 (9) ◽  
pp. 4336-4339
Author(s):  
D. S. V. Suma Priya ◽  
D. Esther Rani ◽  
A. Pavan Shankar Sai ◽  
A. Konda Babu ◽  
Durgesh Nandan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Problem Solving ◽  
Concept Learning ◽  
Real Life ◽  
Learning System ◽  
Level System ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Real Life Problem

This paper clearly explains the concept, importance and main aim of machine learning and construction of the machine learning system. There are several ideas regarding this machine learning which are formed by a number of strategies. This effort leads to introduce many machine learning methods such as learning by commands, concept, learning by comparison, and learning by some algorithms. This article provides information about the main purpose of machine learning and its development. Machine learning is the primary aspect that promotes any system to have intelligence. One of its main applications is artificial intelligence. Machine learning is highly suited for complex level system representation. There are a number of machine learning concepts that leads to the integration of number of networks.

On Reasonable Inquiry and Analysis Domains of Sympérasmology

2021 ◽  
pp. 1-39
Author(s):  
Imre Horváth
Keyword(s):  
Scientific Knowledge ◽  
Social Life ◽  
Empirical Studies ◽  
Public Debate ◽  
Personal Knowledge ◽  
Exploratory Research ◽  
Smart Systems ◽  
Contemporary Epistemology ◽  
System Knowledge ◽  
Research Studies

Sympérasmology was proposed as the theory of synthetic system knowledge (SSK), which is seen as the fuel for the engine of systelligence. There are two main reasons why the proposal is made: (i) rapidly growing, SSK represents a third category of knowledge beside common personal knowledge and testified scientific knowledge, and (ii) though important, neither modern gnoseology nor contemporary epistemology studies its nature, principles, progression, and impacts. The need for rational and empirical studies of SSK is also underpinned by the on-going intelligence revolution, in which knowledge is deemed to be a productive power, a cognitive enabler of smart systems, and a strong transformer of social life. Sympérasmology is still in an embryonic state. Notwithstanding, a map of possible inquiry and analysis domains is released for a public debate in this paper. These domains can be sorted into four categories: (i) rudiments, (ii) principles, (iii) faculties, and (iv) implications. This paper explains these categories and the related domains of interest, and discusses some relevant aspects of study. Without striving for exhaustiveness, it elaborates on many relevant discussion topics and issues. The paper emphasizes that a precise specification of the scope and objectives of sympérasmology needs a stream of exploratory research studies as well as further insightful philosophical discussions.

scholarly journals Fostering Student Engagement: Creative Problem-Solving in Small Group Facilitations

2015 ◽  
Vol 8 ◽  
pp. 153 ◽  
Author(s):  
Patricia L Samson
Keyword(s):  
Student Engagement ◽  
Problem Solving ◽  
Active Learning ◽  
Small Group ◽  
Learning Strategies ◽  
Teaching Method ◽  
Real Life ◽  
Teaching Methodology ◽  
Creative Problem Solving ◽  
Creative Problem

Creative Problem-Solving (CPS) can be a transformative teaching methodology that supports a dialogical learning atmosphere that can transcend the traditional classroom and inspire excellence in students by linking real life experiences with the curriculum. It supports a sense of inquiry that incorporates both experiential learning and the development of critical thinking skills. Incorporating active learning strategies in a way that transcends the classroom and sparks interest and passion for students is an important pedagogical ingredient for educators. The key question driving this study is how can CPS as a teaching method be used to motivate students and engage them in a process of active learning within the context of a social policy course? This study examines student engagement and motivation in a problem-centred approach to teaching and learning, and provides a concrete example of a CPS exercise couched in small group facilitations to support peer learning.

scholarly journals Sympérasmology: A Proposal for the Theory of Synthetic System Knowledge

Designs ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 47
Author(s):  
Imre Horváth
Keyword(s):  
Artificial Intelligence ◽  
Social Life ◽  
Cognitive Engineering ◽  
Smart Systems ◽  
Open Questions ◽  
The Social ◽  
Productive Asset ◽  
Engineered Systems ◽  
Generic Theory ◽  
System Knowledge

Owing to the results of research in system science, artificial intelligence, and cognitive engineering, engineered systems are becoming more and more powered by knowledge. Complementing common-sense and scientific knowledge, system knowledge is maturing into a crucial productive asset. However, an overall theory of the knowledge of intellectualized systems does not exist. Some researchers suggest that time has come to establish a philosophically underpinned theoretical framework. This motion is seconded by the on-going intelligence revolution, in which artificial intelligence becomes a productive power, enabler of smart systems, and a strong transformer of the social life. The goal of this paper is to propose a skeleton of the needed generic theory of system knowledge (and a possible new branch of philosophical studies). The major assumption is that a significant part of the synthetic system knowledge (SSK) is “sympérasma”, that is, knowledge conjectured, inferred, constructed, or otherwise derived during the operation of systems. This part will become even more dominant in the future. Starting out from the above term, the paper suggests calling this theory “sympérasmology”. Some specific domains of “sympérasmological” studies, such as (i) manifestations of SSK, (ii) mechanisms of generating SSK, (iii) dependability of SSK, (iv) operational power of SSK, (v) composability of SSK, and (vi) advancement of SSK, are identified. It is understood that the essence and status of SSK cannot be investigated without considering the related cognitive processes and technological enablers. The paper presents a number of open questions relevant for follow-up discussions.

SPATIAL KNOWLEDGE REPRESENTATION

1989 ◽  
Vol 03 (01) ◽  
pp. 67-84 ◽  
Author(s):  
SARGUR N. SRIHARI ◽  
ZHIGANG XIANG
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Intelligent Systems ◽  
Image Interpretation ◽  
Spatial Knowledge ◽  
Robot Motion ◽  
Language Understanding ◽  
The Third ◽  
Analogical Representation ◽  
Propositional Representation

The use of spatial knowledge is necessary in a variety of artificial intelligence and expert systems applications. The need is not only in tasks with spatial goals such as image interpretation and robot motion, but also in tasks not involving spatial goals, e.g. diagnosis and language understanding. The paper discusses methods of representing spatial knowledge, with particular focus on the broad categories known as analogical and propositional representations. The problem of neurological localization is considered in some detail as an example of intelligent problem-solving that requires the use of spatial knowledge. Several solutions for the problem are presented: the first uses an analogical representation only, the second uses a propositional representation and the third uses an integrated representation. Conclusions about the different representations for building intelligent systems are drawn.

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