生成AI：民主化、透明性、持続可能性への道筋

Dr. Yasuhiro KATAGIRI (Chair) / Prof. Dr. Judith SIMON / Prof. Céline HUDELOT / Dr. Arisa EMA

Abstract:
Recent rapid advances in Generative AI not only exhibit great potential but also have profound impacts on many aspects of our daily and professional lives. This technology will bring about irreversible changes to our social, political, and economic systems. While it presents transformative changes across diverse domains, it also poses serious challenges such as bias, misinformation, and lack of ethical consideration.It is important to discuss and reach a consensus regarding the directions in which research and development of Generative AI should proceed. This discussion should involve not only the vendors/researchers responsible for its development but also those who generate the data essential for its development, the organizations that will deploy and benefit from it, and individuals who will be affected by it. In this session, the future of Generative AI and the direction of its democratization will be discussed from various perspectives: technology developers, affected users, and organizations that benefit from the technology.

Dr. Anton ZIMMERMANN (Chair) / Prof. Dr. Andreas DENGEL / Prof. Florence D’ALCHÉ-BUC / Dr. Yasuhiro KATAGIRI

Abstract:
Transparency – or rather, the lack thereof – is a key issue which Generative AI is faces today. To date, the reasons why an AI generates a specific output are still perceived as opaque. Despite this well-known phenomenon, there is a tendency towards overreliance on the accuracy of AI.

The panelists will provide insights into their research with which they intend to shed light on the decision-making process of AI. Their keynotes will cover various areas: They will present concepts which serve to enhance interpretability and call attention to the need to distinguish different AI architectures. The keynotes will also touch upon new developments in the field of neuro-symbolic approaches that will enable new types of dialogue between humans and knowledge graphs. To illustrate the practical application and importance of transparency, one of the panels key areas will be the use of AI in modern medicine.

Dr. Hiroyuki SAWADA (Chair) / Dr. Michael SCHRAPP / Dr. Koichi TAKAHASHI / Guillaume GERONDEAU

Abstract:
In this session, three speakers give presentations about applications of AI in industry from different viewpoints. Dr. Schrapp talks about real-world case studies of generative AI in manufacturing and industry. Key applications include predictive maintenance, worker guidance and process optimization. Dr. Takahashi introduces research projects in the field of life science in which AI with tacit knowledge improves research and development efficiency. This approach is also applicable to other fields. Mr. Geróndeau gives a presentation about applications of generative AI in automotive industry, including generative design in which a human designer can produce design solutions efficiently with the support of the generative AI. After that, we discuss how we collaborate with AI to enhance and augment our abilities and create values in industry.

Dr. Josephine GALIPON (Chair) / Dr. Ana ILIEVSKA / Dr. Jean-Claude CRIVELLO / Dr. Kentaro TORISAWA

Abstract:
This session brings together leading researchers from Germany, France, and Japan to explore the intersection of artificial intelligence, material sciences, and humanistic study. Dr. Ana Ilievska (University of Bonn) will discuss the evolving role of humanistic studies in the age of Generative AI (GenAI), addressing how emerging technologies are reshaping traditional fields of inquiry. Dr. Jean-Claude Crivello (CNRS) will focus on generative approaches within material sciences, examining how AI is unlocking new possibilities for material innovation. Dr. Kentaro Torisawa (NICT) will present on the synergistic potential of combining multiple AI systems, aiming to overcome the limitations of single-model outputs. Together, these presentations offer insights into the future of AI’s role in academic and scientific advancements.

Prof. Dr. Jiré Emine GÖZEN (Chair) / Prof. Dr. Maren BENNEWITZ / Dr. Jean-Baptiste MOURET / Dr. Tatsuya HARADA

Abstract:
As robots move from industrial settings into roles that directly support humans, they are increasingly viewed as embodied AI—companions rather than mere tools. This panel will explore the challenges and advancements in human-machine interaction within real-world applications.

Maren Bennewitz will present two frameworks for service robots that enhance human-robot interaction by adapting to user preferences in navigation and object handling. Jean-Baptiste Mouret will examine the integration of generative AI, particularly the connection between text and motion in humanoid robots, through data-driven and prompt-based approaches. Tatsuya Harada will introduce an active multimodal model for intelligent robots. Together, these presentations highlight the pathways for the evolution of embodied AI, transforming robots into sophisticated agents capable of effectively supporting and collaborating with humans across a range of contexts. Thus, these presentations demonstrate how robots can become sophisticated agents, effectively supporting and collaborating with humans in different contexts.

Dr. Florence HO (Chair) / Johannes Leon KIRNBERGER / Dr. Marc DURANTON / Dr. Rio YOKOTA

Abstract:
With the introduction of Generative AI in our lives, sustainability is a crucial matter to address to mitigate effects such as environmental impacts and so on.

This session will examine the potential enabling effects and challenges of Generative AI in regards to sustainability through three presentations.

Firstly, we will study the potential of Generative AI to be a game-changer for achieving our sustainability goals. We will explore where Generative AI could be used to accelerate climate action, and also possible risks as AI diffuses and scales.

Secondly, we will highlight the importance of energy-efficient AI accelerators, edge processing to reduce data transfer, and agentic AI for optimizing computational resources and for avoiding to create from scratch new large foundation models.

Thirdly, as training large language models (LLM) requires unprecedented amount of computational resources, leading to a very large carbon footprint, we will present an approach to start from an already trained open LLM, and continually train it on Japanese with minimal computational resources.

Professor for Humanoid Robots, University of Bonn
Website / LinkedIn

Maren Bennewitz is professor for Computer Science at the University of Bonn and head of the Humanoid Robots Lab. She is additionally affiliated with the Lamarr Institute for Machine Learning and Artificial Intelligence and has been PI in several national and European projects. She is a member of the executive board and steering committee of the Cluster of Excellence PhenoRob – Robotics and Phenotyping for Sustainable Crop Production and founding member as well as steering committee member of the Center for Robotics, University of Bonn.

Researcher at CNRS
Website

Jean-Claude Crivello is currently a senior researcher at the LINK lab (CNRS / NIMS / Saint-Gobain), an international French-Japanese CNRS unit located in Tsukuba. Additionally, he serves as the deputy director of the French research group on Material Science by Artificial Intelligence (GDR “”IA-MAT””).

His research focuses on solid-phase chemistry through thermodynamic modeling and electronic structure calculations, with an emphasis on materials such as hydrogen-absorbing intermetallic compounds. He predicts phase equilibrium and investigates crystal lattice vibrational properties for mechanical stability. Utilizing cluster expansion theory, he also analyzes disordered solutions. Recently, he has been exploring methods such as high-throughput DFT and machine learning algorithms to predict innovative materials as example for thermoelectricity and energy storage.

Professor in the Image, Data and Signal Department, LTCI, Télécom Paris, IP Paris
Website / LinkedIn

Florence d’Alché-Buc is professor at Télécom Paris in the department Image, Data and Signal that she led during more than 2 years. She has served as program co-chair of NeurIPS 2019 and was the scientific director of Digicosme, the Paris-Saclay excellence center in Computer Science from 2017 to 2019. She has held the Télécom Paris chair on Data Science and AI for Industry from 2018 to 2023 and is a member of the Ellis board. Her research spans various areas of Machine Learning including structured output prediction and frugal and trustworthy AI. Her leitmotiv is to develop mathematically grounded AI to solve key challenges in biochemistry, energy and industry.

Executive Director at DFKI GmbH, Kaiserslautern
Website / LinkedIn

Andreas Dengel is Professor at the Department of Computer Science at RPTU Kaiserslautern-Landau and Managing Director of the German Research Centre for Artificial Intelligence (DFKI) in Kaiserslautern as well as Head of the Smart Data & Knowledge Services research department at DFKI. Since 2009, he has also been a professor (kyakuin) at the Department of Computer Science and Intelligent Systems at Osaka Metropolitan University. He has received many awards for his work and scientific endeavours, including being selected by a jury on behalf of the Federal Ministry of Education and Research (BMBF) in 2019 as one of the most influential scientists in 50 years of AI history in Germany for his research in the field of document analysis. He is the recipient of the Order of Merit of the State of Rhineland-Palatinate and was honoured in 2021 in the name of His Majesty Emperor Naruhito with the oldest Japanese order, the ‘Order of the Rising Sun on the Necklace with Golden Rays’.

Senior Fellow, CEA (Digital Systems and Integrated Circuits Division)
Website / LinkedIn

Dr. Marc Duranton is a member of the Research and Technology Department of CEA, where he is involved in realizations for Artificial Intelligence, Edge to Cloud and Cyber Physical Systems. He previously worked at Philips Semiconductors and NXP where, among others, he led the development of the family of L-Neuro chips, digital processors for artificial neural networks. He is participating in the definition of the SRAs of ECS/KDT and of ETP4HPC, and is in charge of the roadmap activity of HiPEAC on High Performance, Edge And Cloud computing.

Associate Professor, The University of Tokyo
Website

Arisa Ema is an Associate Professor at the University of Tokyo and Visiting Researcher at RIKEN Center for Advanced Intelligence Project in Japan. She is a researcher in Science and Technology Studies, and her primary interest is to investigate the benefits and risks of artificial intelligence. She is also a member of the Japanese government’s AI Strategy Council launched in May 2023. Internationally, she is an expert member of the working group on the Future of Work, GPAI (Global Partnership on AI). She is an elected member of the UN Secretary General’s Advisory Body on Artificial Intelligence.

Associate Professor, Yamagata University
Website

Josephine Galipon’s main expertise is molecular biology, and in particular RNA biology, focusing on the transcriptional and translational regulation of gene expression in response to cellular stress. Her team is merging molecular biology with other fields such as material science (RNA hydrogels), environmental science (portable RNA lab), and 3D imaging / 3D printing applied to biomimetics and biorobotics research (shark skin).

Vice President, Transportation and Mobility Asia, Dassault Systèmes
Website / LinkedIn

Guillaume Gerondeau, VP Transportation and Mobility Asia and co-MD of Software République, an open innovation ecosystem for future mobility, is a senior executive and strategy consultant, speaker and lecturer focusing on innovation.
He was head of global product strategy and product planning at Nissan and Toyota Motor Europe and of brand strategy and product marketing at Toyota globally. He has managed a long-term research budget, was leader of innovation management, led the reorganization of the R&D resource management at Renault. He advised many companies on all continents on innovation, digital transformation and automotive. He is an expert on future mobility.

Professor for Media and Cultural Theory / Vice-President International Affairs and University Development
Website / LinkedIn

Jiré Emine Gözen is a professor of media and cultural theory at the University of Europe for Applied Sciences in Berlin. She has been vice president of international affairs and university development since October 2023 and Chair of the German Society for Media Studies since September 2020.

Her research focuses on media cultural studies, media aesthetics, and visual culture, emphasizing AI, future technologies, posthumanism, and the visual construction of subversive masculinities. Influenced by critical theory, gender and queer studies, and new materialist perspectives, she explores the epistemologies underpinning human-machine relations. Having lived and worked in Japan at institutions such as the Mori Art Museum and 3331 Arts Chiyoda, she brings a global perspective to her research, engaging with marginalized perspectives and their entanglements with hegemonic understandings.

Professor, the University of Tokyo
Website

Tatsuya Harada is a professor in RCAST at the University of Tokyo (UTokyo). His research interests include visual recognition, machine learning, and intelligent robot. He received his Ph.D. from UTokyo in 2001. He is also a team leader at RIKEN AIP and a vice director of Research Center for Medical Bigdata at NII. He received awards for Science and Technology, the Commendation for Science and Technology by the MEXT, Japan 2022, Honorable Mention Open source software at ACM Multimedia 2017 and Grand Challenge Special Prize on the Best Application of a Theoretical Framework at ACM Multimedia 2011.

Senior Researcher, NEC

Florence Ho is a senior researcher at NEC since 2023, where she conducts research on optimization methods for solving various industrial problems and applications. Before joining NEC, she was a researcher at AIST (National Institute of Advanced Industrial Science and Technology). Her research interests include multi-agent systems, optimization, metaheuristics, traffic management.

Professor at CentraleSupélec, ParisSaclay University. Head of the MICS Laboratory.
Website / LinkedIn

Céline Hudelot is Professor in Computer Science at CentraleSupélec. She heads the MICS laboratory (Mathematics and IT for Complex Systems). She is interested in both data-driven and knowledge-driven Artificial Intelligence for the semantic interpretation of non-structured data, with a particular interest in explainable AI (XAI). She also works on foundation models for the medical domain and LLMs.

Senior Research Fellow, Center for Science and Thought, University of Bonn
Website / LinkedIn

Dr. Ana Ilievska is Senior Research Fellow on the joint project “Desirable Digitalisation” between the Universities of Cambridge and Bonn, funded by Stiftung Mercator. Before coming to Bonn, she taught at Stanford University and has studied at the University of Chicago, Yale, Lisbon, Tübingen, and in Sicily. Her teaching and research focus on the critical role of the humanities in the age of digital technologies. Dr. Ilievska’s publications include various peer-reviewed articles, public scholarship, podcast appearances, and translations on literature and ChatGPT, the question of critical thinking and AI, and the ethos of Silicon Valley. Most recently, she translated Maurizio Ferraris’s book Webfare: A Manifesto for Digital Well-Being (transcript 2024) and hosted an international conference on Humanism and Artificial Intelligence (May 2024).

Director, Artificial Intelligence Research Center, AIST
Website

Yasuhiro Katagiri received his Ph.D in Information Engineering from the University of Tokyo in 1981. He worked in NTT Basic Research Labs and ATR Research Labs. He was director of ATR Media Information Science Laboratories. He is currently president of Future University Hakodate. He is a fellow of the Japanese Society of Cognitive Science, and president of The Japanese Association of Sociolinguistic Sciences.

AI Policy Advisor, Organisation for Economic Co-operation and Development (OECD)
Website / LinkedIn

Johannes Leon Kirnberger is a policy advisor for AI and sustainability at the AI Unit of the OECD Division of Science, Technology and Innovation (STI). He previously led the program on climate action and biodiversity preservation at the Global Partnership on AI (GPAI) and the International Centre of Expertise in Montreal on AI (CEIMIA). Johannes is a member of the UNEP Expert Group on Digital Tech for Circular Economy, advises the Intergovernmental Panel on Climate Change (IPCC) on the green and digital “twin transition” and serves as guest lecturer at the Technical University of Munich (TUM) on climate change and AI policy. He holds a Bachelor of Science in Management from ESCP Business School, a Master of International Public Management from Sciences Po, and a Master in International Affairs, Energy and Environment from Columbia University.

Senior researcher, Inria
Website / LinkedIn

Jean-Baptiste Mouret is a senior researcher (“directeur de recherche”) at Inria, the national French institute dedicated to computer science and mathematics. His research work intertwines robotics and machine learning to make robots more adaptive. Before joining Inria, he was an assistant professor at Sorbonne Université, where he earned his Ph.D. in computer science in 2008. Mouret was the principal investigator of the ERC-funded project ResiBots (2015-2020), which aimed to develop robots that adapt to damage, and his work was notably featured on the cover of Nature (Cully et al., 2015).

Career Researcher, National Institute of Advanced Industrial Science and Technology (AIST)
Website

Dr. Hiroyuki Sawada is a Career Researcher at the Industrial Cyber-Physical Systems Research Center of the National Institute of Advanced Industrial Science and Technology (AIST) in Japan. He received his B.E. and M.E. from the University of Tokyo in 1987 and 1989 respectively. He received his Ph.D. from the University of Strathclyde, Scotland, in 2001. He joined Mechanical Engineering Laboratory (MEL) in 1989. From 1990 to 1992, he left MEL and worked at the Institute for New Generation Computer Technology (ICOT). His research interests include end-user development in the manufacturing industry for digital transformation.

Global Head of Industrial AI, Siemens Digital Industries
Website / LinkedIn

Dr. Michael Schrapp is the Global Head of Industrial AI Innovations at Siemens Digital Industries. He holds a PhD in Physics from the Technical University Munich, a Master in Theoretical Physics from the Technical University Munich & Utrecht University, and a Master in Stochastic Engineering in Business and Finance from the University of Applied Sciences Munich. Prior to his current position, he was the Director of Data Analytics & Customer Services at Siemens Healthineers.

Professor for Ethics and Information Technologies, University of Hamburg
Website

Judith Simon is Full Professor for Ethics in Information Technologies at the Universität Hamburg. She is interested in ethical, epistemological and political questions arising in the context of digital technologies, in particular in regards to big data and artificial intelligence. Judith Simon is a member of the German Ethics Council, where she was the spokesperson for the report on the ethics of AI. She currently serves on various other committees of scientific policy advice and has been a member of the Data Ethics Commission of the German Federal Government (2018-2019). She is the editor of the Routledge Handbook of Trust and Philosophy (2020).

RIKEN
Website / LinkedIn

Koichi Takahashi, Ph.D., is a principal investigator at RIKEN Center for Biosystems Dynamics Research. He received his degree from Keio University in 2004. He was involved in the development of LabDroid Maholo when he was a Chief Information Officer (CIO) at Robotic Biology Institute, Inc. He is currently Project Leader of the JST-MIRAI ‘Robotic Biology’ Project. He is a project professor at Keio University Graduate School of Media and Governance, and an invited professor at Osaka University Graduate School of Frontier Biosciences. He is also the Chair at AI Alignment Network.

NICT Fellow/National Institute of Information and Communications Technology (NICT)
Website

Kentaro Torisawa graduated from the University of Tokyo in 1992. After receiving an MSc in Computer Science from the Graduate School of Science of the University of Tokyo, he became an assistant professor at the university in 1995. In 2001, he was appointed associate professor at the Japan Advanced Institute of Science and Technology. Since 2008, he is working at NICT and has been an NICT fellow since 2020. He has been awarded many awards/grants, including the JSPS prize and Twitter Data Grants.

Professor, Institute of Science Tokyo, Supercomputing Research Center
Website

Rio Yokota is a Professor at the Global Scientific Information and Computing Center, Tokyo Institute of Technology. His research interests lie at the intersection of high performance computing, linear algebra, and machine learning. He is the developer of numerous libraries for fast multipole methods (ExaFMM), hierarchical low-rank algorithms (Hatrix), and information matrices in deep learning (ASDL) that scale to the full system on the largest supercomputers today. He has been optimizing algorithms on GPUs since 2006, and was part of a team that received the Gordon Bell prize in 2009 using the first GPU supercomputer. Rio is a member of ACM, IEEE, and SIAM.

Postdoctoral Researcher
Website / LinkedIn

Legal studies at Heidelberg University (Germany), first state exam, 2016; Ph.D. in private law, 2020; second state exam (German judicial/bar exam), 2021; studies of economics (University of Hagen, Germany); postdoctoral researcher since 2021 and, starting in summer 2024, leader of a junior research group funded by the German Research Foundation at Heidelberg University (Institute for comparative law, conflict of laws and international business law). Current fields of research include private law, law and technology as well as the interplay of public and private international law as governance instruments.

Prof. Florian BOUDIN
Associate Professor at Nantes University, CNRS research leave at the JFLI / National Institute of Informatics, JFLI, CNRS, NII, Nantes Université

Poster Title:
Automatic instruction generation using LLMs for scientific paragraph revision

Poster Abstract:
Large Language Models (LLMs) are mainly used for instruction following tasks. However, when gathering real-world data for training or fine-tuning those models, we are often able to collect the desired input-output pairs but lack the corresponding instructions necessary to transition from one to the other. Manually annotating these instructions is both costly and time-consuming, and cross-annotation can be required as several instructions could be valid for a single data-point.
This poster explores the potential of automatically generating these instructions using LLMs. We focus on generating instructions for the specific task of revising paragraphs from scientific articles. Our work includes identifying the most suitable metrics for automatic evaluation and assessing the initial performance of three models: Llama-2-13b-chat, Mistral-7B-Instruct, and Llama-3-8B-Instruct on the instruction generation task.

Biography:
Florian Boudin is an Associate Professor (HDR) of Computer Science at Nantes University, where he co-leads the TALN research group at the Laboratory of Digital Sciences of Nantes (LS2N). His research focuses on Natural Language Processing (NLP) and Information Retrieval (IR), particularly on applications involving scholarly documents. He is currently on a research leave at the Japanese-French Laboratory for Informatics (JFLI) in Tokyo.

The work he will be presenting is by his PhD student, Léane Jourdan, and was carried out in collaboration with colleagues from the Nantes University (Nicolas Hernandez and Richard Dufour) and the National Institute of Informatics (Akiko Aizawa).

Dr. Andreas STIEGLER / Sabrina MOLDENHAUER
Creative Technologist / Art Director
Strichpunkt Design

Poster Title:
Branding and Artificial Intelligence – Practical Applications

Poster Abstract:
In this poster presentation followed by a roundtable discussion on Nov 13, we offer a tour through some real-world applications of Artificial Intelligence in the Branding and Design world. We will venture through different categories of AI used here (generative approaches and beyond) and illustrate them through concrete examples from the actual applications in production. These will be presented in client-cases, such as the Honda Research Institute or DHL. Herein, we will also highlight some of the intercultural differences when deploying Artificial Intelligence in the rather delicate and subjective field of Design. Each presented case will be a practical application showcasing how Artificial Intelligence is already put from the research labs into production today.

Biography:
As Creative Technologist at Strichpunkt, Dr. Andreas Stiegler is engaged in the fusion of know-how from branding and design with research in AI and practical applications such as Game Development. With a PhD in Game-AI, he is dedicated not only to technological integration, but also to creative topics. In addition, Dr. Andreas Stiegler is a lecturer for Artificial Intelligence and Game Development at the Hochschule der Medien in Stuttgart.
Sabrina Moldenhauer is an Art Director at Strichpunkt with a interest in interactive design and characters, for instance as found in Game Development.

Jonas KARGE
PhD Student, Konrad Zuse Schools of Excellence in Artificial Intelligence / TU Dresden

Poster Title:
Condorcet Generates a Democratic Choice

Poster Abstract:
At the heart of social choice and democracy theory is the Condorcet Jury Theorem (CJT), which provides probabilistic guarantees for identifying the optimal choice among multiple alternatives, albeit under strict assumptions.
The main goal of our work is to overcome the limitations of the Condorcet-Jury Theorem by exploring its generalizations under relaxed assumptions. In particular, we prove the first CJT that simultaneously relaxes all of its original assumptions. Most importantly, we do not restrict the electorate to independent agents of equal competence.
In addition to introducing the mathematical framework of our generalization, we illustrate how its probabilistic guarantees can be used in the context of a newly proposed research direction at the intersection of generative AI and social choice theory, called Generative Social Choice.

Biography:
Jonas Karge is a PhD student in the Computational Logic Group and the School of Embedded Composite Artificial Intelligence (SECAI) at TU Dresden. He holds a bachelor’s degree in philosophy and French from the University of Tübingen and a master’s degree in logic from Leipzig University. His research focuses on multi-agent systems, knowledge representation and reasoning as well as formal epistemology. More specifically, he is interested in providing probabilistic guarantees for identifying the correct decision among many choices in multi-agent settings, and in aggregating multiple opinions under severe uncertainty.

Dr. Jun KATO
Senior Researcher, National Institute of Advanced Industrial Science and Technology (AIST)

Poster Title:
Creativity support research “in the wild” for the development of human-centered AI

Poster Abstract:
Research on creativity support tools (CSTs) has long been conducted in the field of human-computer interaction (HCI), but there is growing criticism that applied computer science, including HCI, has typically been conducted and evaluated in a limited cultural context, such as the fast-paced academic community and the WEIRD (Western, educated, industrialized, rich, and developed) part of the world. This poster presents recent efforts in creativity support research “in the wild”, taking a longitudinal and constructive approach with the help of creators outside the research community to build computational tools for creative activities. Examples include TextAlive and its API for creating lyric-driven visual art [ACM CHI 2015, 2023] and Griffith for creating animation storyboards [ACM CHI 2024], both of which contribute to the discussion of how to develop human-centered AI that nurtures the creativity of people with diverse technical and cultural backgrounds.

Biography:
Dr. Jun Kato is a senior researcher at the National Institute of Advanced Industrial Science and Technology (AIST) and the technical advisor at Arch, Inc. He worked for Microsoft and Adobe Research, received his Ph.D. degree from The University of Tokyo in 2014, and has regularly received academic recognition such as Honorable Mentions at ACM CHI (2013, 2015, 2023) and IPSJ/ACM Award (2021). He has researched Human-Computer Interaction (HCI), particularly creativity support for programmers, music video creators, anime directors, DIYers in the maker culture, etc. His work has often gone beyond research papers and been released for public use.

Juliana VRADY / Andrey VRADY
Media Artist, Via Vrady Media Art Lab

Poster Title:
Emotion Blender. Tokyo.

Poster Abstract:
The artwork shows the dialogue between people and technology, how A.I. perceives human beings and how, through the lens of the artists’ computational code, humans and their sentiments might look as art.

Biography:
Juliana and Andrey VRADY are multimedia artists based in Germany. Andrey has been an artistic director of leading print and advertising companies for many years. He is keen on undertaking experiments with photography, making digital collages and finding new aesthetics in the new media. Juliana, his other half, comes from the film industry. The duo has achieved several milestones in interactive works and is still developing further. Their latest two media art installations, running at MOCA Bangkok and as part of Campus Germany (EXPO 2020) in Dubai, were inspired by the dialogue between people and technology.

Dr. Jean-Claude CRIVELLO
Senior Reseacher, LINK (CNRS / NIMS / Saint-Gobain), CNRS

Poster Title:
Generative Model-Based Exploration of Chemical Space for Crystals

Poster Abstract:
This poster explores machine learning approaches to discovering new crystal structures. It addresses three main points:
(i) Utilizing existing crystal structure representations and material data to train a denoising generative model for generating crystal structure candidates.
(ii) Enhancing these models by adding symmetry and compositional constraints to improve generalization capabilities.
(iii) Investigating the impact of symmetry restrictions on the quality of structures and proposing a method for selecting appropriate symmetry constraints during generation.
The proposed methods are then applied to specific chemical systems of interest.
Ref : https://pubs.acs.org/doi/10.1021/acs.jcim.3c00969?goto=supporting-info

Biography:
Dr. Jean-Claude Crivello studies the stability of solid phases and electronic structures via density functional theory (DFT) calculations, focusing on transformations like hydrogen absorption in intermetallic compounds. He couples DFT with thermodynamic modeling (Calphad method) to predict phase equilibrium in multi-component systems. He also examines crystal lattice vibrational properties through phonon calculations to understand mechanical stability and thermodynamic properties. Techniques like cluster expansion theory address disordered solutions. Recently, he has explored high-throughput DFT for screening new compounds and machine learning algorithms for predicting innovative materials for thermoelectricity and energy storage.
https://orcid.org/0000-0002-4849-2556

Dr. Kristiina JOKINEN
Senior Researcher, AIST Tokyo Waterfront

Poster Title:
Grounding Words to the Real World: Construction of Shared Context in Generative AI-based Dialogue Models.

Poster Abstract:
Grounding is a collaborative mechanism for establishing mutual knowledge among dialogue participants. It is a necessary skill for language-capable AI-agents, and the recent progress in generative AI has brought the issues into focus. To avoid generation of irrelevant or false information, AI-agents need to ground their utterances into real-world events, and to avoid the statistical parrot effect, they need to construct shared understanding of the context with the partner. Grounding and construction of shared context bridges the gap between words and real-world understanding, thus making AI systems more reliable and effective.
In this poster, I will focus on context-aware AI-agents: how grounding is linked to conversational reasoning and collaboration in Generative AI-based agents, and discuss implications, challenges and opportunities to develop adequately accepted and trusted AI-agents that support friendly interaction and also sustain technical innovation and societal impact in smart environments.

Biography:
Dr. Kristiina Jokinen is a Senior Researcher at AI Research Center AIST Tokyo Waterfront, and Adjunct Professor at University of Helsinki. She is a member of the pan-European AI network of excellence ELLIS, Advisory Board for Japanese AIE (AI in Engineering) Programme, and Steering Committee for International dialogue workshop series IWSDS. Her research concerns human-robot interaction, GenAI-based dialogue modelling and multimodality. She has published widely on these topics and is Editor of the TiiS Journal Interactive Intelligent Systems. She has led numerous national and international research projects and recently led dialogue research in the EU-Japan collaboration project with partners from Germany, France, Italy, and Japan.

Dr. Tamon NAKANO
Research Staff, Institute of AI Safety and Security, DLR – German Aerospace Center

Poster Title:
Hybrid Reservoir Computing – combine knowledge-based information and Reservoir Computing

Poster Abstract:
Reservoir Computing (RC) is a subset of Recurrent Neural Networks (RNNs), excels in handling time-evolutionary, non-linear, and chaotic phenomena in the form of time series, such as stock market. RC comprises a random RNN with fixed weights (referred to as the Reservoir) and a trainable output layer, enabling it to retain the memory of past inputs. The fixed weights significantly enhance training efficiency. In recent years, RC has gained popularity due to its ability to train effectively on small datasets and its low computational cost, making it suitable for edge computing applications. We are developing a hybrid approach to improve RC predictions by integrating knowledge-based information, such as an imperfect physical model, with the reservoir to enhance its predictive capabilities. This combination leverages both data-driven and model-driven methods for more accurate predictions. In this poster, we present our work on this hybrid approach, including preliminary results and future research directions.

Biography:
Tamon Nakano, an AI researcher at DLR (German Aerospace Center), initially specialized in Mechanical Engineering and Computational Fluid Dynamics. Transitioning to AI-related fields lately, particularly AI in mechanical engineering, he now focuses on developing Reservoir Computing for non-linear system predictions. Originally from Japan, with past residences in France, he now lives in Germany since 2022, fluent in JP/FR/DE/EN languages. His current pursuit centers on connecting of these three nations in AI. The three nations have different perspectives and have different visions. He wishes to play the role to make a bridge between the three.

Prof. Dr. Shoya ISHIMARU
Project Proressor, Osaka Metropolitan University

Poster Title:
Integrating Eye-Tracking and Generative AI into an Interactive Digital Textbook

Poster Abstract:
This poster explores the integration of eye-tracking technology and generative AI to create an adaptive digital textbook that dynamically responds to learners’ internal states, such as comprehension and interest levels. By employing eye-tracking methods, we accurately gauge where and how long a learner focuses while reading. In addition, machine learning algorithms analyze these data to infer the learner’s cognitive and affective states. On the basis of these inferences, generative AI customizes the content, offering tailored explanations, examples, and interactive elements to enhance learning experiences. This poster presentation is a part of the outcomes of our trilateral collaborative project between Japan, Germany, and France, summarizing experimental results and future challenges.

Biography:
Shoya Ishimaru is a Project Professor at Osaka Metropolitan University and the CEO of Affectify Inc. He formally worked as a Junior Professor (PI) at the University of Kaiserslautern, a Senior Researcher at the German Research Center for Artificial Intelligence (DFKI), and a Co-Founder CRO at Alphaben. He received his PhD in Engineering at the University of Kaiserslautern, Germany, and the title of MITOU Super Creator from the Ministry of Economy, Trade, and Industry in Japan. His research interest is to invent new technologies augmenting human intellect.

Peter NEIGEL
PhD Student, Graduate School of Informatics, Osaka Metropolitan University

Poster Title:
LLM Assistance for the Interpretation of Physiological Data for Consumers

Poster Abstract:
Wearable health-tracing devices have allowed consumers to gather a wide array of physiological data about themselves, such as heart rate (variability), body temperature or breathing rate, as well as summaries derived from these, such as readiness and sleep scores. But many users report that they don’t know what to infer from that data or how to interact with it, which is an essential step in improving health outcomes. In this poster, we present a pilot attempt in training personalized LLM agents for the interpretation of wearable device recorded physiological data. Looking at the whole array of data, the agent summarizes a users measurements into understandable language, gives information about which behaviours can result in a change in specific measures and gives recommendations about which activity is suited for todays physical state of the user. The agent takes into account preferences of the user and thus personalizes its answers.

Biography:
After graduating with a degree in Physics from the University of Heidelberg, Germany, Peter Neigel joined the Augmented Vision group at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He then travelled to Japan for a research internship with Prof. Koichi Kise at the Osaka Metropolitan University before joining the Intelligent Media Processing group as a PhD student. His current research is about wearable health data analysis and stress detection.

Gauvain ROBERT
Research Assistant, Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST)

Poster Title:
Obstacle avoidance based on diffusion model in narrow indoor environments

Poster Abstract:
We developed a model for navigating narrow indoor environments, enhancing the NoMaD framework, a newly introduced Foundation Model for Navigation with cross-embodiment capabilities using diffusion to generate navigable waypoints. NoMaD struggles in narrow environments, and our hypothesis is that NoMaD’s pretraining implicitly accounts for the robot’s embodiment. By conditioning our model on the robot’s specific embodiment, we can generate trajectories that consider physical constraints, such as a small robot passing through a doorway that would block a larger one. We will show that our approach can reduce collision rates in indoor environments compared to NoMaD. Additionally, we will highlight the benefits of our navigation approach based on generative models over previous deterministic approaches.

Biography:
Gauvain Robert is a research assistant at AIST, focusing on navigation systems, planning, and 3D reconstruction of environments. His current research interests lies in the cross-embodiment capabilities of navigation systems to enhance their efficiency and accuracy in complex, constrained environments.

Yuichiro IWASHITA
Student, Graduate School of Informatics, Osaka Metropolitan University

Poster Title:
Quantitative Knowledge Retrieval from Large Language Models

Poster Abstract:
Large Language Models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information retrieval is less well understood. In this poster we explore the feasibility of LLMs as a mechanism for quantitative knowledge retrieval to aid data analysis tasks such as elicitation of prior distributions for Bayesian models and imputation of missing data. We present a prompt engineering framework, treating an LLM as an interface to a latent space of scientific literature, comparing responses in different contexts and domains against more established approaches. Implications and challenges of using LLMs as “experts” are discussed.

Biography:
Yuichiro Iwashita is a master’s student at the Graduate School of Informatics, Osaka Metropolitan University. He received the B.E. from the College of Engineering, Osaka Prefecture University in 2023. His research focuses on augmenting human intelligence and behavior change. Since 2022, he has also been working on applying AI to solve problems in the medical field. From 2023 to 2024, he is an internship student at the German Research Center for Artificial Intelligence GmbH (DFKI).

Wukai ZHOU
Master student, Toyo University

Poster Title:
Secure Data Acquisition and Privacy-Preserving AI Modeling Platform for Research

Poster Abstract:
In today’s environment where large-scale language models are widely available, the need for data is increasing. However, data required for application in healthcare, education, and other fields may often contain a large amount of personal information. Although the development of artificial intelligence cannot be stopped, techniques to prevent the leakage of private information still need to be developed. Therefore, this study aims to propose solutions for data acquisition processes and AI modeling workflow to help researchers acquire data without compromising the privacy of data providers. The poster introduces a general website platform that will allow researchers to access personal information data and conduct research while ensuring that the data cannot be downloaded and used for purposes other than research.

Biography:
Wukai Zhou completed his Bachelor’s degree in Information Management and Information Systems at Shanghai Business School in China. Currently, he is working on his Master’s degree at Toyo University, researching data annotation techniques to allow AI to read and understand qualitative data. His research interests are in various aspects related to AI technologies, including the relationship between AI and people and research methods to improve existing AI data acquisition and modeling.

Dr. Rohan P. SINGH
Postdoctoral researcher, CNRS-AIST JRL, AIST, Tsukuba, Japan

Poster Title:
Sim2real: Robust Humanoid Walking on Compliant and Uneven Terrain with Deep Reinforcement Learning

Poster Abstract:
For the deployment of legged robots in real-world environments, it is essential to develop robust locomotion control methods for challenging terrains that may exhibit unexpected deformability and irregularity. In this poster, we explore the application of sim2real deep reinforcement learning (RL) for the design of locomotion controllers for large-sized humanoid robots on compliant and uneven terrains. Our key contribution is to show that a simple training curriculum for exposing the RL agent to randomized terrains in simulation can achieve robust walking on the real humanoid robot using only proprioceptive feedback. We train an end-to-end omnidirectional locomotion policy using the proposed approach and show extensive real robot demonstrations on the HRP-5P humanoid over several difficult terrains inside and outside the lab environment.

Additionally, we propose a new control policy to enable modification of the observed clock signal, leading to adaptive gait frequencies depending on the terrain and command velocity. In simulation experiments, we show the effectiveness of this policy specifically for walking over challenging terrains by controlling swing and stance durations.

Biography:
Rohan P. Singh is a postdoctoral researcher at the CNRS-AIST JRL (Joint Robotics Lab). He received his Ph.D. degree from the University of Tsukuba, Japan in April 2024 and his Master’s degree in April 2021. Earlier, he worked as a Robotics Engineer at JRL (then known as the Humanoid Robotics Group) from 2017 to 2019. His current research objective focuses on developing reinforcement learning-based locomotion controllers for humanoid robots.

Dr. Junko OKUYAMA
Associate professor, Health Service Center, Tokyo University of Agriculture an Technology

Poster Title:
Support for premenstrual syndrome (PMS) in female university students through psychological state AI analysis

Poster Abstract:
Purpose: Premenstrual Syndrome (PMS) and dysmenorrhea can easily affect exercise and performance and require specialized knowledge to deal with them. As a method to improve premenstrual syndrome (PMS) without resorting to a specialized department, we investigated support using a smartphone application, me-fullness app that improves mood through AI analysis.
Subjects: Female students at the Department of Physical Education, International Pacific University, Okayama, Japan, were the subjects of this study.
Methods: The app-using group used the app for one month from November 9, 2023. The app-using group was surveyed for premenstrual syndrome before and after use of the app.
Results : The app-using group showed significant improvement in PMS by Wilcoxon’s signed rank test (P<0.05).
Conclusion : The results suggest that the use of a me-fullness app reduces PMS and improves performance in female athletes.

Biography:
Junko Okuyama, MD received her PhD in medicine from Tohoku University. Her research focuses on post-disaster psychological trauma, new methods for improving the psychological state of disaster victims, and the relationship between the environment and psychological state. And she became an associate professor at Health Service Center of Tokyo University of Agriculture and Technology in April 2024. Her latest published paper is “Establishment of a post-disaster healthcare information booklet for the Turkey–Syrian earthquake, based on past disasters. Scientific reports, 14, 1558” in 2024 (https://doi.org/10.1038/s41598-024-52121).

Prof. Dr. Philippe GRECIANO
Vice-President, Franco-German University (FGU)

Poster Title:
The Franco-German University as new AI partner with Japan

Poster Abstract:
The poster will present the Franco-German University (FGU) which is developing numerous AI projects in Europe and around the world. It is a privileged partner for organizing Franco-German and Japanese events in this field of excellence.

Through its range of programs and scientific events, the FGU is training many AI specialists with the aim of overcoming current global challenges, including the protection of democracy, security and major social and environmental changes. Its network comprises more than 200 universities worldwide. It is developing several international activities with scientific and economic players and civil society.

Biography:
Philippe Gréciano is Full professor, Jean Monnet Chair in Franco-German relations, European integration and globalization. He is vice-president of the Franco-German University( FGU) and strongly committed to international scientific and economic cooperation. He develops cooperation with Japan and offers opportunities for professors and researchers to work together from a Franco-German and European perspective.

Dr. Jean-Claude CRIVELLO / Prof. Florence D’ALCHÉ-BUC
Researcher at CNRS / Professor in the Image, Data and Signal Department, LTCI, Télécom Paris, IP Paris

Roundtable Title:
AI for science discoveries.

Roundtable Abstract:
AI has the potential to be a game-changer for scientists across a wide range of disciplines. By leveraging machine learning and deep learning techniques, AI can suggest new materials or chemical formulas, autonomously conduct experiments, and even generate large datasets for further analysis. In this roundtable, we will delve into how AI can fundamentally reshape the process of scientific discovery in various fields, from drug development and materials science to environmental research and physics. The discussion will focus on specific examples where AI has already shown promise, and we’ll explore the potential for further advancements. We will also examine how researchers can harness this powerful tool responsibly, ensuring that AI complements human expertise rather than replacing it. This conversation will provide insights into how the scientific community can fully embrace this new era of AI-enhanced research and innovation.

Dr. Andreas STIEGLER / Sabrina MOLDENHAUER
Creative Technologist / Art Director

Roundtable Title:
AI in Branding and Design

Roundtable Abstract:
As Artificial Intelligence is appearing more and more in our everyday lives, it shapes Human-Machine Interaction to a significant degree. This is particularly observable in the creative industry, where working with images and texts is already changing. In this roundtable, we will tell tales right from the trenches. We will illustrate how we already use AI for some of the most prominent brands out there, what works well – and what does not – and take a holistic view on cybernetic societies, the close link between social robotics research and virtual characters, and try to arrive at a prediction on where we are heading.

Prof. Dr. Jiré Emine GÖZEN / Dr. Sunjin OH
Professor for Media and Cultural Theory; Vice-President International Affairs and University Development / Project Assistant Professor, B’AI Global Forum, The University of Tokyo

Roundtable Title:
Concepts for Desirable AI Futures

Roundtable Abstract:
Discussions about AI’s impact often oscillate between dystopian fears and utopian promises, both rooted in dominant “sociotechnical imaginaries” that shape societal visions of technology and the future. In this roundtable, we will critically examine these prevailing narratives and explore alternative perspectives on desirable AI futures.

By reviewing common AI narratives, we’ll engage with diverse approaches such as Indigenous AI and feminist AI. Our aim is to collaboratively develop frameworks for envisioning more inclusive and equitable AI futures.

Dr. Jun KATO
Senior Researcher, National Institute of Advanced Industrial Science and Technology (AIST)

Roundtable Title:
Creativity and Cultures in the Post-AI Era

Roundtable Abstract:
This roundtable will explore the role of AI in creativity support, possibly starting with the creative industries as an example, but extending to knowledge work and other sectors of the post-AI society. It aims to foster open-ended discussions from both humanity-centered and technical perspectives to critically engage with the socio-cultural and technical implications of AI technologies. Potential topics include, but are not limited to, how interactive AI-enabled tools can enhance the creative process, the ethical implications of AI systems using creators’ content and returning value to them, and the challenge of developing culturally-aware AI systems within a capitalist framework.

Dr. Kristiina JOKINEN / Prof. Yukiko NAKANO
Senior Researcher, AIST Tokyo Waterfront / Seikei University

Roundtable Title:
Language-capable AI Agents – Does fluency improve usability?

Roundtable Abstract:
Language-capable robots are more widespread, and LLMs make chatty conversations a standard interaction mode, while global changes require rethinking a sustainable future. This roundtable concerns a fundamental human feature, communication by language, and the challenges related to fluency and reliability of dialogues with robots in real-world tasks. Can robots become fluent communicators, and is that desirable? Can becoming fluent enable social robots to be more helpful and useful, to join in teamwork and collaborate in everyday tasks? Can human-robot interaction leverage new aspects of AI for sustainable development? What are the risks and benefits of robots achieving human-like communication abilities?

Dr. Anton ZIMMERMANN
Postdoctoral Researcher

Roundtable Title:
Private Law Frameworks for AI

Roundtable Abstract:
The roundtable explores the potential of interdisciplinary cooperation in the field of AI and private law. Legislation on the involvement of AI in private law (contracts, torts) is scarce. Virtually all legal systems are geared towards human interactions: Only humans and their organizations can form contracts, and only they can be held liable for debts. This creates frictions when AI – and its potential for autonomy – comes into play. The field calls for interdisciplinary cooperation: Lawyers require knowledge of other fields to identify legal hurdles, and representatives from other fields need legal knowledge to assess permissibility and liability risks of potential AI applications.

Dr. Marc DURANTON
Senior Fellow, CEA (Digital Systems and Integrated Circuits Division)

Roundtable Title:
The power of AI swarm agents

Roundtable Abstract:
‘Agentic AI’ is increasingly recognized as a powerful approach for maximizing the capabilities of generative AI. By viewing AI as a ‘system’ of specialized agents, each with distinct roles and access to various tools, agent-based methods unlock new possibilities. These agents can work collaboratively or autonomously, making complex decisions, and dynamically adapting to new data, environments, or challenges. In this roundtable, we will explore how these AI ‘swarms’—composed of multiple agents working in tandem—can revolutionize various industries by offering enhanced problem-solving, creativity, and efficiency. We’ll delve into various applications and examine how this approach can push the boundaries of what AI can achieve, while addressing the technical and ethical considerations that come with deploying autonomous systems.

Prof. Céline HUDELOT
Professor at CentraleSupélec, ParisSaclay University. Head of the MICS Laboratory.

Roundtable Title:
Training a multilingual LLM with a collaborative science approach

Roundtable Abstract:
Training compact and efficient models within a framework of collaborative science offers numerous benefits to society, especially when compared to today’s often closed-source, proprietary models. By promoting openness and cooperation, this approach brings greater transparency to the development process, ensuring that AI systems are more accountable to the public and policymakers. It also fosters responsibility among researchers and developers, ensuring that ethical considerations and societal impacts are addressed throughout the development lifecycle. Moreover, compact and efficient models contribute to sustainability, as they tend to consume fewer resources and have a smaller environmental footprint, which is critical as AI continues to grow in scale and influence. In this roundtable, we will explore how this collaborative, open approach can not only lead to technological advancements but also build trust between AI creators and users.

Dr. Ana ILIEVSKA
Senior Research Fellow, Center for Science and Thought, University of Bonn

Roundtable Title:
Who Makes Your Decisions? Autonomy, Self-Realization, and Freedom in the Age of AI

Roundtable Abstract:
The round-table “Who Makes Your Decisions?” will bring together experts from diverse fields to explore how AI influences decision-making processes within the context of fundamental political-philosophical terms as autonomy, self-realization, and freedom. The discussion will address how technology reshapes these concepts, prompting us as humans and social beings to interrogate the validity and robustness of our traditional ideas of self and community. Participants from all backgrounds are invited to share insights, fostering an inclusive dialogue on how to preserve human agency and identity in a world increasingly guided by artificial intelligence.