A Paradigm Shift in Artificial Intelligence: Why Social Intelligence Matters in the Design and Development of Robots with Human-Like Intelligence

2007 ◽  
pp. 288-302 ◽  
Author(s):  
Kerstin Dautenhahn
Keyword(s):  
Artificial Intelligence ◽  
Paradigm Shift ◽  
Social Intelligence ◽  
Design And Development

scholarly journals Connotation Analysis and Paradigm Shift of Teaching Design under Artificial Intelligence Technology

2021 ◽  
Vol 16 (05) ◽  
pp. 73
Author(s):  
Jian Huang ◽  
Gang Shen ◽  
Xiping Ren
Keyword(s):  
Artificial Intelligence ◽  
Paradigm Shift ◽  
Learning Ability ◽  
Teaching Activities ◽  
Learning Efficiency ◽  
Teaching Objectives ◽  
Teaching Design ◽  
Teachers And Students ◽  
Artificial Intelligence Technology ◽  
Teaching Efficiency

The influence of artificial intelligence technology on teaching design is explored to improve teaching efficiency. First, artificial intelligence is introduced and its impacts on teaching design are analyzed. Second, the connotation of the paradigm of teaching design and the paradigm shift for teaching design are explored using the paradigm shift analysis framework. Finally, the changes in teaching design under artificial intelligence are analyzed, and the impacts of artificial intelligence on teaching activities are investigated. The results show that the application of artificial intelligence technology has led to different levels of change in the six elements of teaching design, including teaching objectives, service objects (teachers and students), teaching content, teaching media, teaching environment, and teaching evaluation. The connotation and paradigm shift of the teaching design are introduced from the four elements based on the artificial intelligence technology. It is found that artificial intelligence technology can enhance the learning ability and cognitive ability of students to a certain extent while improving the teaching efficiency and learning efficiency. The investigation proves that the teaching design based on artificial intelligence technology can be applied to teaching activities, thereby improving the learning efficiency of students and the teaching efficiency of teachers.

Supporting Social Interactions In Human-Ai Teams: Profiling Human Teammates From Sparse Data

2021 ◽  
Vol 65 (1) ◽  
pp. 665-669
Author(s):  
Rhyse Bendell ◽  
Jessica Williams ◽  
Stephen M. Fiore ◽  
Florian Jentsch
Keyword(s):  
Artificial Intelligence ◽  
Social Cognition ◽  
Social Interactions ◽  
Team Performance ◽  
Intelligent Agents ◽  
Social Intelligence ◽  
Social Cognitive ◽  
Social Cues ◽  
Cognitive Mechanisms ◽  
Human Agent

Artificial intelligence has been developed to perform all manner of tasks but has not gained capabilities to support social cognition. We suggest that teams comprised of both humans and artificially intelligent agents cannot achieve optimal team performance unless all teammates have the capacity to employ social-cognitive mechanisms. These form the foundation for generating inferences about their counterparts and enable execution of informed, appropriate behaviors. Social intelligence and its utilization are known to be vital components of human-human teaming processes due to their importance in guiding the recognition, interpretation, and use of the signals that humans naturally use to shape their exchanges. Although modern sensors and algorithms could allow AI to observe most social cues, signals, and other indicators, the approximation of human-to-human social interaction -based upon aggregation and modeling of such cues is currently beyond the capacity of potential AI teammates. Partially, this is because humans are notoriously variable. We describe an approach for measuring social-cognitive features to produce the raw information needed to create human agent profiles that can be operated upon by artificial intelligences.

scholarly journals From Homo Sapiens to Robo Sapiens: The Evolution of Intelligence

Information ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 2 ◽  
Author(s):  
Anat Ringel Raveh ◽  
Boaz Tamir
Keyword(s):  
Artificial Intelligence ◽  
Human Behavior ◽  
Social Intelligence ◽  
Deep Neural Network ◽  
Homo Sapiens ◽  
Human Cognition ◽  
Network Technology ◽  
Research Fields ◽  
Recent Developments ◽  
Neural Network Technology

In this paper, we present a review of recent developments in artificial intelligence (AI) towards the possibility of an artificial intelligence equal that of human intelligence. AI technology has always shown a stepwise increase in its capacity and complexity. The last step took place several years ago with the increased progress in deep neural network technology. Each such step goes hand in hand with our understanding of ourselves and our understanding of human cognition. Indeed, AI was always about the question of understanding human nature. AI percolates into our lives, changing our environment. We believe that the next few steps in AI technology, and in our understanding of human behavior, will bring about much more powerful machines that are flexible enough to resemble human behavior. In this context, there are two research fields: Artificial Social Intelligence (ASI) and General Artificial Intelligence (AGI). The authors also allude to one of the main challenges for AI, embodied cognition, and explain how it can be viewed as an opportunity for further progress in AI research.

scholarly journals Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

2019 ◽  
Vol 35 (1) ◽  
pp. 28-35 ◽  
Author(s):  
Joseph E Burns ◽  
Jianhua Yao ◽  
Ronald M Summers
Keyword(s):  
Artificial Intelligence ◽  
Paradigm Shift ◽  
Musculoskeletal Imaging

scholarly journals Escaping the McNamara Fallacy: Towards more Impactful Recommender Systems Research

AI Magazine ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 79-95
Author(s):  
Dietmar Jannach ◽  
Christine Bauer
Keyword(s):  
Artificial Intelligence ◽  
Recommender Systems ◽  
Real World ◽  
Paradigm Shift ◽  
Prediction Accuracy ◽  
Research Community ◽  
Research Culture ◽  
Research Gaps ◽  
Systems Research ◽  
Research Questions

Recommender systems are among today’s most successful application areas of artificial intelligence. However, in the recommender systems research community, we have fallen prey to a McNamara fallacy to a worrying extent: In the majority of our research efforts, we rely almost exclusively on computational measures such as prediction accuracy, which are easier to make than applying other evaluation methods. However, it remains unclear whether small improvements in terms of such computational measures matter greatly and whether they lead us to better systems in practice. A paradigm shift in terms of our research culture and goals is therefore needed. We can no longer focus exclusively on abstract computational measures but must direct our attention to research questions that are more relevant and have more impact in the real world. In this work, we review the various ways of how recommender systems may create value; how they, positively or negatively, impact consumers, businesses, and the society; and how we can measure the resulting effects. Through our analyses, we identify a number of research gaps and propose ways of broadening and improving our methodology in a way that leads us to more impactful research in our field.

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