Session 1: Humans, AI and Robots!
Wie werden intelligente Roboter und Menschen künftig zusammenarbeiten – nicht nur in der Industrie, sondern auch im privaten Bereich, in der Medizintechnik und im Katastrophenschutz? Die Referenten dieser Session zeigen Beispiele auf und stellen sich auch Fragen der Akzeptanz und der Ethik.
Folgende Wissenschaftler beschreiben neue Aufgabenfelder für intelligente Roboter außerhalb der industriellen Produktion
Roboter mit KI werden einige der drängenden Probleme der Menschheit lösen, so Prof. Dr.-Ing. Toshio Fukuda von der Nagoya University, Japan. Als Programmdirektor eines der Moonshot R&D Programme der japanischen Regierung zu intelligenten Robotern belegt er dies mit Beispielen aus seiner Forschungsarbeit.
Die für ihre Arbeiten im Bereich Mensch-Roboter-Interaktion ausgezeichnete Schweizerin Prof. Dr. Aude Billard, President-Elect des Weltver-bandes IEEE Robotis and Automation Society (RAS), stellt Methoden des maschinellen Lernens für schnelle und reaktive Robotersteuerung vor – eine wesentliche Voraussetzung für den Einsatz von Robotern außerhalb der industriellen Welt.
Prof. Dr. Robin R. Murphy, Texas A&M University, USA, konzentriert sich in ihrem Vortrag auf ihr besonderes Forschungsgebiet: die Entwicklung von KI-gestützten Robotern für den Katastrophenschutz. Sie weiß, wovon Sie spricht: Ihre Roboter kamen bereits bei 30 Katastrophen in fünf Ländern zum Einsatz.
Prof. Dr. Virginia Dignum leitet die Forschungsgruppe zu sozialen und ethischen Auswirkungen von KI an der Universität Umeå/ Schweden. Sie fordert eine verantwortungsvolle KI und propagiert einen multidisziplinären Ansatz mit Blick auf die gesellschaftlichen Auswirkungen. Notwendige Regularien werden in diesem Licht zur Voraussetzung für Innovationen: zum Nutzen der Menschheit.
Die Speaker und Vorträge dieser Session
Robotics and AI can challenge for the Mega-Trend problems such as aging society, climate change, energy and food issues in the world in the future, 2050 and beyond. Intelligent robotic technology can be used for the wide variety of human life, such that aged people can live independently and comfortably with less assistance from others in near future, and that robot/AI can help human find new solutions and discovery in many wider applications, including the design and manufacturing in automation and many others.
Thus today’s asymmetry nature in function between human and robot will be dramatically changed to the more symmetric relation between both: easy to use and dependent each other equally in the future. Those symmetric AI robot will help people find new scientific and technological solution and discovery in many fields in future, so that there will be evolutionary changes of our society in not only the manufacturing but also design for device and system in automation and even the structure of our society itself.
To make such ambitious goal realizable in future, it is necessary for AI robots to have the capabilities of co-evolution and self-organization. I will show a new initiative on AI and Robot, Moon Shot Programs aiming on challenging to the Mega-Trend problems.
Thus robotics/AI will greatly change the structure and architecture of the world itself in future, 2050 and beyond.
Toshio Fukuda received Dr. Eng. from the University of Tokyo, Tokyo, Japan, in 1977.
Currently, He is Professor Emeritus Nagoya University(2013), visiting professor of Nagoya University(2013-), Professor Meijo University( 2013-2022), Professor Waseda University(2019-). His major is bio-robotics, especially Micro and Nano Robotics.
Dr. Fukuda is IEEE President (2020), the IEEE Director of Division X, Systems and Control (2017-2018), IEEE Region 10 Director (2013-2014) and served President of IEEE Robotics and Automation Society (1998-1999), Director of the IEEE Division X, Systems and Control (2001- 2002), Co-founding Editor-in-Chief of IEEE / ASME Transactions on Mechatronics (2000-2002) and Editor-in-Chief of ROBOMECH Journal, Springer (2013-), Editor-in-Chief, Journal of Cyborg and Bionic Systems(2018-). He was Founding President of IEEE Nanotechnology Council (2002-2003, 2005).He was elected as a member of Science Council of Japan (2008-2013). He organized many conferences, such as IEEE/RSJ Conference on Intelligent Robots and Systems(IROS, 1988), System Integration International(SII, 2008), Cyborg and Bionic Systems(CBS, 2017) as the founding Chair and others.
Dr. Fukuda received IEEE Robotics and Automation Pioneer Award (2004), IEEE Robotics and Automation Technical Field Award (2010), Honorary Doctor of Aalto University School of Science and Technology (2010), member of the Japan Academy of Engineering(2013), Friendship Award of State Administration of Foreign Experts affairs of the PR China (2014), Medal of Honor on Purple Ribbon (2015), Foreign member of Chinese Academy of Science (2017), Chunichi Culture Award(2019), The Order of the Sacred Treasure, Gold Rays with Neck Ribbon (2022).
IEEE Fellow (1995), SICE Fellow (1995), JSME Fellow (2001), RSJ Fellow (2004).
AI Robot to solve the Mega-Trend problems
Robotics and AI can challenge for the Mega-Trend problems such as aging society, climate change, energy and food issues in the world in the future, 2050 and beyond. Intelligent robotic technology can be used for the wide variety of human life, such that aged people can live independently and comfortably with less assistance from others in near future, and that robot/AI can help human find new solutions and discovery in many wider applications, including the design and manufacturing in automation and many others.
Thus today’s asymmetry nature in function between human and robot will be dramatically changed to the more symmetric relation between both: easy to use and dependent each other equally in the future. Those symmetric AI robot will help people find new scientific and technological solution and discovery in many fields in future, so that there will be evolutionary changes of our society in not only the manufacturing but also design for device and system in automation and even the structure of our society itself.
To make such ambitious goal realizable in future, it is necessary for AI robots to have the capabilities of co-evolution and self-organization. I will show a new initiative on AI and Robot, Moon Shot Programs aiming on challenging to the Mega-Trend problems.
Thus robotics/AI will greatly change the structure and architecture of the world itself in future, 2050 and beyond.
Hightech-Summit Session 1: Humans, AI and Robots!
Today, many would like to deploy robots everywhere: in the streets, as cars, wheelchairs, and other mobility devices; in our homes, to cook, clean, and entertain us; on the body, to replace a lost limb or to augment its capabilities. For these robots to become reality, they need to depart from their ancestors in one dramatic way: They must escape from the comfortable, secluded, and largely predictable industrial world. In the past decades, robotics has made leaps forward in the design of increasingly complex robotic platforms to meet these challenges. In this endeavor, it has benefited from advances in optimization for solving high-dimensional constrained problems. These methods are powerful for planning in slow-paced tasks and when the environment is known. Advances in machine learning to analyze vast amounts of data often have offered powerful solutions for real-time control, but they often fall short of providing explicit guarantees on the learned model. The alternative is to develop machine learning methods that retain theoretical guarantees traditional from control theory.
A key issue faced by robotics today is to endow robots with the necessary reactivity to adapt their path at time-critical situations. Online reactivity is not just a matter of ensuring that there is a good-enough central processing unit on board the robot. It requires inherently robust control laws that can provide multiple solutions. Methods that combine machine learning and control theory do not necessitate large datasets and allow robots to learn control laws from only a handful of examples, while generalizing to the entire state space.
Prof. Aude Billard is Head of the LASA laboratory in the School of Engineering at the Swiss Institute of Technology Lausanne (EPFL) and holds a B.Sc and M.Sc. in Physics from EPFL (1995) and a Ph.D. in Artificial Intelligence (1998) from the University of Edinburgh. Aude Billard’s research spans the fields of machine learning and robotics with a particular emphasis on learning from sparse data and performing fast and robust retrieval. This work finds application to robotics, human-robot / human-computer interaction and computational neuroscience. Aude Billard leads the Swiss National Thematic Network Innovation Booster on Robotics, a half a million fund in support of industrial-academic partnerships, and is the current president-elect of the IEEE Robotics and Automation Society.
Machine Learning Methods for Fast and Reactive Robot Control with Theoretical Guarantees
Today, many would like to deploy robots everywhere: in the streets, as cars, wheelchairs, and other mobility devices; in our homes, to cook, clean, and entertain us; on the body, to replace a lost limb or to augment its capabilities. For these robots to become reality, they need to depart from their ancestors in one dramatic way: They must escape from the comfortable, secluded, and largely predictable industrial world. In the past decades, robotics has made leaps forward in the design of increasingly complex robotic platforms to meet these challenges. In this endeavor, it has benefited from advances in optimization for solving high-dimensional constrained problems. These methods are powerful for planning in slow-paced tasks and when the environment is known. Advances in machine learning to analyze vast amounts of data often have offered powerful solutions for real-time control, but they often fall short of providing explicit guarantees on the learned model. The alternative is to develop machine learning methods that retain theoretical guarantees traditional from control theory.
A key issue faced by robotics today is to endow robots with the necessary reactivity to adapt their path at time-critical situations. Online reactivity is not just a matter of ensuring that there is a good-enough central processing unit on board the robot. It requires inherently robust control laws that can provide multiple solutions. Methods that combine machine learning and control theory do not necessitate large datasets and allow robots to learn control laws from only a handful of examples, while generalizing to the entire state space.
Hightech-Summit Session 1: Humans, AI and Robots!
Small ground, aerial, or marine robots has been used for disaster response since 2001, but why aren’t they used more often? Why hasn’t AI revolutionized search and rescue? The barriers stem from the collision between the unique constraints of emergency management with the narrow focus of start-up culture and investment. Fortunately, robots, disasters, and high tech can mix if technologists turn to systems thinking.
Dr. Robin R. Murphy is the Raytheon Professor of Computer Science and Engineering at Texas A&M University and a director of the Center for Robot-Assisted Search and Rescue. Her research focuses on artificial intelligence, robotics, and human-robot interaction for emergency management. She has deployed ground, aerial, and marine robots to over 30 disasters in five countries including the 9/11 World Trade Center, Fukushima, Hurricane Harvey, and the Surfside collapse. She is an ACM and IEEE Fellow, a TED speaker, and the author of over 200 papers and four books including the award-winning Disaster Robotics. Her contributions to robotics have been recognized with numerous awards including the ACM Eugene L. Lawler Award for Humanitarian Contributions and the Motohiro Kisoi Rescue Engineering Awards.
Robots, Disasters, and High Tech
Small ground, aerial, or marine robots has been used for disaster response since 2001, but why aren’t they used more often? Why hasn’t AI revolutionized search and rescue? The barriers stem from the collision between the unique constraints of emergency management with the narrow focus of start-up culture and investment. Fortunately, robots, disasters, and high tech can mix if technologists turn to systems thinking.
Hightech-Summit Session 1: Humans, AI and Robots!
Responsible Artificial Intelligence (AI) is not an option but the only possible way to go. It involves understanding AI's nature, design choices, societal role, and ethical considerations. It extends human capabilities but requires addressing challenges in education, jobs, and biases. Recognizing the societal role of AI is vital, understanding that it is not an autonomous entity but rather dependent on human responsibility and deci-sion-making. Recent AI developments, including foundational models, transformer models, generative models, and large language models (LLMs), raise questions about whether they are changing the paradigm of AI, and about the responsibility of those that are developing and deploying AI systems.
In this talk, I will further discuss the need for a relational perspective on AI that emphasize acceptance, trust, cooperation, and the common good. Taking responsibility involves regulation, governance, and awareness. Ethics and dilemmas are ongoing considerations, but require understanding that trade-offs must be made and that decision processes are always contextual. Taking responsibility requires designing AI systems with values in mind, implementing regulations, governance, monitoring, agreements, and norms.
Rather than viewing regulation as a constraint, it should be seen as a stepping stone for innovation, ensuring public acceptance, driving trans-formation, and promoting business differentiation.
Virginia Dignum is Professor of Responsible Artificial Intelligence at Umeå University, Sweden and director of WASP-HS, the Wallenberg Program on Humanities and Society for AI, Autonomous Systems and Software, the largest Swedish national research program on fundamental multidisciplinary research on the societal and human impact of AI. She is a member of the Royal Swedish Academy of Engineering Sciences (IVA), and a Fellow of the European Artificial Intelligence Association (EURAI). She is member of the Global Partnership on AI (GPAI), World Economic Forum’s Global Artificial Intelligence Council, Executive Committee of the IEEE Initiative on Ethically Aligned Design, of ALLAI, the Dutch AI Alliance, EU’s High Level Expert Group on Artificial Intelligence, and leader of UNICEF's guidance for AI and children, and member of UNESCO expert group on the implementation of AI recommendations. She is author of “Responsible Artificial Intelligence: developing and using AI in a responsible way”.
Responsible AI: why care?
Responsible Artificial Intelligence (AI) is not an option but the only possible way to go. It involves understanding AI's nature, design choices, societal role, and ethical considerations. It extends human capabilities but requires addressing challenges in education, jobs, and biases. Recognizing the societal role of AI is vital, understanding that it is not an autonomous entity but rather dependent on human responsibility and decision-making. Recent AI developments, including foundational models, transformer models, generative models, and large language models (LLMs), raise questions about whether they are changing the paradigm of AI, and about the responsibility of those that are developing and deploying AI systems.
In this talk, I will further discuss the need for a relational perspective on AI that emphasize acceptance, trust, cooperation, and the common good. Taking responsibility involves regulation, governance, and awareness. Ethics and dilemmas are ongoing considerations, but require understanding that trade-offs must be made and that decision processes are always contextual. Taking responsibility requires designing AI systems with values in mind, implementing regulations, governance, monitoring, agreements, and norms.
Rather than viewing regulation as a constraint, it should be seen as a stepping stone for innovation, ensuring public acceptance, driving transformation, and promoting business differentiation.
Hightech-Summit Session 1: Humans, AI and Robots!
Session-Chair
„Humans, AI and Robots!” wird von Prof. Dr. Stefan Leutenegger, Ordinarius am Lehrstuhl für Machine Learning for Robotics (TU München), als Session Chair moderiert.
Technologien haben das Potenzial, Menschen zu unterstützen und unsere Lebensqualität zu verbessern. Es ist jedoch unerlässlich, Technologien so zu gestalten, dass der Nutzen für die Vielen und nicht für die Wenigen im Vordergrund steht. Fragen der sozialen Gerechtigkeit und Gleichberechtigung müssen in den Mittelpunkt der Technologieentwicklung rücken, insbesondere in Bereichen wie KI und Robotik. Dazu müssen wir die interdisziplinäre Zusammenarbeit zwischen den Sozialwissenschaften und der KI-Forschung fördern und soziale, ethische und politische Fragestellungen bereits bei der Technologieentwicklung integrieren.