MIS4TEL Conference 2023

Technological advances, especially in the miniaturization of robotic devices foreshadow the emergence of large-scale ensembles of small-size resource-constrained robots that distributively cooperate to achieve complex tasks. These ensembles are formed by independent, intelligent, and communicating units which act as a whole forming a programmable material i.e., a material able to autonomously change its shape.

In my talk, I will present our research effort in building a modular robot composed of mm-scale units. We use micro-technology to scale down the size of each element, and we study geometry, structure, actuation, power, electronics, and integration. We develop multi-agent algorithms to scale up in the number of managed robots to perform synchronization, leader election, self-assembly and self-reconfiguration. As multi-agent systems are by essence decentralized, they are the best candidate to manage these distributed robotic systems.

Julien Bourgeois is a professor of computer science at the University of Bourgogne Franche-Comté (UBFC) in France. He is part of the computer science department at the FEMTO-ST institute, CNRS. His research interests include distributed intelligent MEMS (DiMEMS) and Programmable Matter. He has worked for more than 15 years on these topics and has co-authored more than 180 international publications. He was an invited professor at Carnegie Mellon University (US) from 2012 to 2013, at Emory University (US) in 2011 and at Hong Kong Polytechnic University in 2010, 2011 and 2015. As a PI, he led different funded research projects (Smart Surface, Smart Blocks, Computation and coordination for DiMEMS, Programmable Matter). He is currently leading the Programmable Matter Consortium. He organized and chaired many conferences (dMEMS 2010, 2012, HotP2P/IPDPS 2010, Euromicro PDP 2008 and 2010, IEEE GreenCom 2012, IEEE iThings 2012, IEEE CPSCom 2012, GPC 2012, IEEE HPCC 2014, IEEE ICESS 2014, CSS 2014, IEEE CSE 2016, IEEE EUC 2015, IEEE ATC 2017, IEEE CBDCom 2017, DARS 2022).

Large language models have stretched our beliefs on what kind of machine intelligence is possible. However, there are still practical needs in several fields of computing to align artificial intelligence (AI) with human intelligence.

In this talk, I will discuss and problematize:

• 1) What is intelligence?
• 2) Where are the prevailing differences in artificial vs. human intelligence?
• 3) How artificial agents could be evolved to overcome these differences and understand their users better?

I will give insights on how cognitive mimetics can inspire AI solutions and review on-going research on modeling human intelligence as computational rationality with reinforcement learning agents in our cognitive science research group at University of Jyväskylä.

Tuomo Kujala is Associate Professor of cognitive science at the Faculty of Information Technology in University of Jyväskylä, and Adjunct Professor of cognitive science in University of Helsinki. He obtained his PhD in cognitive science from University of Jyväskylä in 2010. His research interests focus on computational modeling of attention, inattention and multitasking performance.

TBC

Ana Paiva is a Full Professor in the Department of Computer Science and Engineering, IST (“Instituto Superior Técnico”) from the University of Lisbon and coordinator of GAIPS – “Intelligent and Social Agents Group” at INESC-ID, now named “Research Group on AI for People and the Society”. She is also the Katherine Hampson Bessell Fellow at the Radcliffe Institute for Advanced Study, Harvard University. She investigate the creation of AI and complex systems using an agent-based approach, with a special focus on social agents. Hermain research interests are in the fields of Autonomous Agents and Multi-Agent Systems, Affective Computing, Virtual Agents and Human-Robot Interaction.

Generative AI systems such as ChatGPT are already disrupting education. They can write essays for students, summarise scientific texts, produce lesson plans, engage in conversations, and draft academic papers. New hybrid systems such as Auto-GPT extend the capabilities of generative AI, to interpret goals, form plans, enact tasks and access external tools such as web browsers and databases.

In this presentation I will introduce the capabilities and limitations of current generative AI and discuss implications for education. The talk will cover: how humans and AI can learn together, future roles for AI in education, new educational generative AI systems, and ethics of learning with AI. Rather than seeing AI as a challenge to traditional education, we should prepare students for a future where AI is a tool for creativity, to be operated with great care and awareness of its limitations.

Mike Sharples is Emeritus Professor of Educational Technology at The Open University, UK. His expertise involves human-centred design and evaluation of new technologies and environments for learning. He holds a PhD from the Department of Artificial Intelligence at the University of Edinburgh on the topic of “Cognition, Computers and Creative Writing”. He is an Associate Editor of the International Journal of Artificial Intelligence in Education. He founded the Innovating Pedagogy report series and is author of over 300 papers in the areas of educational technology, learning sciences, science education, human-centred design of personal technologies, artificial intelligence and cognitive science. His recent books are Practical Pedagogy: 40 New Ways to Teach and Learn, and Story Machines: How Computers Have Become Creative Writers, both published by Routledge.