Speakers


Maoguo Gong
Inner Mongolia Normal University, Xidian University

Maoguo Gong is the Director of Key Laboratory of Collaborative Intelligence Systems, Ministry of Education of China. He is the Vice President of Inner Mongolia Normal University and the Leading Professor of Xidian University.
His research interests are broadly in the area of computational intelligence, with applications to optimization, learning, data mining and image understanding. He has published over two hundred papers in journals and conferences, and holds over thirty granted patents as the first inventor. His papers have been cited over 23000 times in Google Scholar, possessing 76 H-index and 345 I10-index. He is Highly Cited Researcher (Clarivate), and China's Highly Cited Scholar (Elsevier). He is leading or has completed over thirty projects as the Principle Investigator, funded by the National Natural Science Foundation of China, the National Key Research and Development Program of China, and others. He is also the recipient of the National Program for Support of Top-notch Young Professionals, the Excellent Young Scientist Foundation, the National Program for Support of the Leading Innovative Talents, and the National Natural Science Award of China.
He is a Fellow of IEEE, the Director of Chinese Association for Artificial Intelligence-Youth Branch, Associate Editor or Editorial Board Member for over five journals including the IEEE Transactions on Neural Networks and Learning Systems, and the IEEE Transactions on Emerging Topics in Computational Intelligence. He has received the Outstanding Associate Editor of IEEE Transactions on Evolutionary Computation in 2020.

Title: Collaborative Intelligence Systems for AI application in mega scenarios
Abstract: The development of information technology has propelled technological reform in artificial intelligence (AI). To address the needs of diversified and complex applications in mega scenarios, AI has been increasingly trending towards intelligent, collaborative, and systematized development across different levels and tasks. To deploy AI models in diverse and complex mega application scenarios in the future, it is necessary to build collaborative intelligence systems by integrating multiple participants. This talk reviews research on systematic, intelligent, and collaborative AI technology from the perspective of individual collaboration, decision variables collaboration, multi-task and multi-party collaboration, and macro-level collaboration such as terrestrial-satellite collaboration, space-air-ground-sea collaboration, vehicle-road-cloud collaboration.


Angelo Cangelosi

University of Manchester, UK 


Angelo Cangelosi is Professor of Machine Learning and Robotics at the University of Manchester (UK) and co-director and founder of the Manchester Centre for Robotics and AI. He was selected for the award of the European Research Council (ERC) Advanced grant (funded by UKRI). His research interests are in cognitive and developmental robotics, neural networks, language grounding, human robot-interaction and trust, and robot companions for health and social care. Overall, he has secured over £38m of research grants as coordinator/PI, including the ERC Advanced eTALK, the UKRI TAS Trust Node and CRADLE Prosperity, the US AFRL project THRIVE++, and numerous Horizon and MSCAs grants. Cangelosi has produced more than 300 scientific publications. He is Editor-in-Chief of the journals Interaction Studies and IET Cognitive Computation and Systems, and in 2015 was Editor-in-Chief of IEEE Transactions on Autonomous Development. He has chaired numerous international conferences, including ICANN2022 Bristol, and ICDL2021 Beijing. His book “Developmental Robotics: From Babies to Robots” (MIT Press) was published in January 2015, and translated in Chinese and Japanese. His latest book “Cognitive Robotics” (MIT Press), coedited with Minoru Asada, was recently published in 2022.


Title: Cognitive Robotics: From Babies to Robots and AI
Abstract: This talk introduces the concept of Cognitive Robotics, i.e. the field that combines insights and methods from AI, as well as cognitive and biological sciences, to robotics (cf. Cangelosi & Asada 2022 for book open access). This is a highly interdisciplinary approach that sees AI computer scientists and roboticists collaborating closely with psychologists and neuroscientists. We will use the case study of language learning to demonstrate this highly interdisciplinary field, presenting developmental psychology studies on children’s language acquisition and robots’ experiment on language learning.

Growing theoretical and experimental psychology research on action and language processing and on number learning and gestures in children and adults clearly demonstrates the role of embodiment in cognition and language processing. In psychology and neuroscience, this evidence constitutes the basis of embodied cognition, also known as grounded cognition. In robotics and AI, these studies have important implications for the design of linguistic capabilities, in particular language understanding, in robots and machines for human-robot collaboration. This focus on language acquisition and development uses Developmental Robotics methods, as part of the wider Cognitive Robotics approach. During the talk we will present examples of developmental robotics models and experimental results with the baby robot iCub and with the Pepper robot. One study focuses on the embodiment biases in early word acquisition and grammar learning. The same developmental robotics method is used for experiments on pointing gestures and finger counting to allow robots to learning abstract concepts such as numbers. We will then present a novel developmental robotics model, and human-robot interaction experiments, on Theory of Mind and its relationship to trust. This considers both people’s Theory of Mind of robots’ capabilities, and robot’s own ‘Artificial Theory of Mind’ of people’s intention. Results show that trust and collaboration is enhanced when we can understand the intention of the other agents and when robots can explain to people their decision making strategies.  

The implications for the use of such cognitive robotics approaches for embodied cognition in AI and cognitive sciences, and for robot companion applications will also be discussed. The talk will also consider philosophy of science issues on embodiment and on machine’s understanding of language, the ethical issues of trustworthy AI and robots, and the limits of current big-data large language models.



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