With 22 professorships financed by the state of Hessen, existing focal points are being expanded, but at the same time networking and exchange are being strengthened. Overall, Hessen has its finger on the pulse and can react quickly to new requirements. Because one thing is certain: AI is a fast-moving discipline: from ChatGPT to start-ups in the technical field to the political framework conditions such as the AI regulation planned by the EU, many aspects are in flux. Therefore, it is important to guide this flow in the right direction and to further expand the excellent position of the state of Hessen in the national and international environment.

About the speaker

Ayse Asar was born in Bad Schwalbach in 1975. She studied law in Gießen, Cologne and London, and has worked in science management since 2004, first as a legal advisor, head of various departments, vice chancellor and most recently university chancellor. Since 2019, she has been State Secretary in the Hessian Ministry of Science and the Arts and, in her role, she is committed to equal opportunities in higher education, research to find solutions to the great challenges of our world, and art and culture in our diverse society. Her parents came from Turkey as “guest workers” in the 1960s, and education was very important to them. In terms of higher education policy, the goal of removing obstacles to educational pathways is very close to her heart from her own experience as a working-class child.

Accompanied by this progress has been a growing realisation of inequities in the performance of naively-trained NLP models for users of different demographics, with minorities typically experiencing lower performance levels. In this talk, I will illustrate the nature and magnitude of the problem, and outline a number of approaches that can be used to train fairer models based on different data settings, without sacrificing overall performance levels.

About the speaker

Tim Baldwin is Acting Provost and Chair of the Department of Natural Language Processing, Mohamed bin Zayed University of Artificial Intelligence in addition to being a Melbourne Laureate Professor in the School of Computing and Information Systems, The University of Melbourne. He is currently the Past President of the Association for Computational Linguistics. Tim completed a BSc(CS/Maths) and BA(Linguistics/Japanese) at The University of Melbourne in 1995, and an MEng(CS) and PhD(CS) at the Tokyo Institute of Technology in 1998 and 2001, respectively. He joined MBZUAI at the start of 2022, prior to which he was based at The University of Melbourne for 17 years. His research has been funded by organisations including the Australia Research Council, Google, Microsoft, Xerox, ByteDance, SEEK, NTT, and Fujitsu, and has been featured in MIT Tech Review, IEEE Spectrum, The Times, ABC News, The Age/Sydney Morning Herald, and Australian Financial Review. He is the author of around 500 peer-reviewed publications across diverse topics in natural language processing and AI, and the recipient of a number of awards at top NLP conferences.

In this talk, I will discuss our research at the intersection of classical robotics and machine learning, focusing on structured learning approaches that enable robots to better understand and interact with their environments. By exploiting the structure of the problem or by imposing structure as inductive bias, we enhance the learning process for real-world applications. I will present a series of topics highlighting the crucial role of structure in learning and demonstrate how mobile manipulation robots serve as an ideal case study for advancing robotic embodied intelligence.

About the speaker

Georgia Chalvatzaki, recently promoted to Full Professor of Interactive Robot Perception and Learning in April 2023, holds a joint appointment at the Computer Science Department of the Technical University of Darmstadt and Hessian.AI. Prior to her promotion, she served as an Assistant Professor and Independent Research Group Leader, having secured the prestigious Emmy Noether grant from the German Research Foundation (DFG) in March 2021. She completed her Ph.D. in 2019 at the National Technical University of Athens, Greece, where she was a part of the Intelligent Robotics and Automation Lab within the Electrical and Computer Engineering School. Her doctoral thesis, titled “Human-Centered Modeling for Assistive Robotics: Stochastic Estimation and Robot Learning in Decision-Making,” laid the foundation for her current research interests, which include robot learning, planning, and perception.

She has won several awards, the German Journalism Prize, the Helmut Schmidt Prize and the prize for “Surveillance Studies”. She hosts the NDR podcast “She likes tech” and speaks regularly at major hacker conferences, such as Defcon and the CCC.

In this presentation, I will discuss the present situation, from my perspective as an AI expert with a keen interest and some involvement in European AI policy, as well as possible futures for AI in Germany, Europe and beyond. Specifically, I will explain why a broad view of AI that encompasses learning, reasoning and other key concepts, is of crucial importance; why large language models, such as ChatGPT, will have transformative impact on AI and its applications, but are far removed from artificial general intelligence; and why the real risks we are currently facing are not associated with the emergence of machine super-intelligence and value alignment, but with technological dependency and the problematic use of relatively weak forms of AI. I will also discuss why AI is the future of science and engineering, and hence a crucial ingredient for meeting the grand challenges of our time, and how Europe, and Germany within it, can and should play a leading role in developing and applying the kind of AI systems that are needed in this context.

About the speaker

Holger H. Hoos holds an Alexander von Humboldt professorship in AI at RWTH Aachen University (Germany), as well as a professorship in machine learning at Universiteit Leiden (the Netherlands) and an adjunct professorship in computer science at the University of British Columbia (Canada). He is a Fellow of the Association of Computing Machinery (ACM), the Association for the Advancement of Artificial Intelligence (AAAI) and the European AI Association (EurAI), past president of the Canadian Association for Artificial Intelligence, former editor-in-chief of the Journal of Artificial Intelligence Research (JAIR) and chair of the board of CLAIRE, an organisation that seeks to strengthen European excellence in AI research and innovation (claire-ai.org).

Holger is known for his work on machine learning and optimisation methods for the automated design of high-performance algorithms and on stochastic local search, he has developed – and vigorously pursues – the paradigm of programming by optimisation (PbO); he is also one of the originators of the concept of automated machine learning (AutoML). Holger has a penchant for work at the boundaries between computing science and other disciplines, and much of his work is inspired by real-world applications.

About the speaker

Martin Jaggi is an Associate Professor at EPFL, heading the Machine Learning and Optimization Laboratory. Before joining EPFL, he was a post-doctoral researcher at ETH Zurich, at the Simons Institute in Berkeley, and at École Polytechnique in Paris. He has earned his PhD in Machine Learning and Optimization from ETH Zurich in 2011, and a MSc in Mathematics also from ETH Zurich. He is a co-founder of EPFL’s Applied Machine Learning Days, and a Fellow of the European ELLIS network.

Anna held leadership roles at the Centre on Public Management and Policy at the University of Ottawa and at The European School of Management and Technology in Berlin, Germany. Her university degrees and research in social anthropology, sociology and African studies brought her to Freie Universität Berlin, Stanford University, Aix-Marseille Université and Benin, West Africa. Her experiences in Canada and abroad has fed her passion for providing inclusive, innovative spaces where leaders can develop their expertise and create better public policy.

The rule give rise to a dual perspective measuring the “sensitivity” of the model to future changes in it. This is universal and fundamental to almost all machine-learning models and I will argue that it is the key to build machines that adapt as quickly as humans.

About the speaker

Emtiyaz Khan (also known as Emti) is a (tenured) team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference Team. Previously, he was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he also taught two large machine learning courses and received a teaching award. He finished his PhD in machine learning from University of British Columbia in 2012. The main goal of Emti’s research is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings. For more than 10 years, his work has focused on developing Bayesian methods that could lead to such fundamental principles. The approximate Bayesian inference team now continues to use these principles, as well as derive new ones, to solve real-world problems.

We propose a new conceptualization of text plans as a sequence of question-answer (QA) pairs and enhance existing datasets (e.g., for summarization) with a QA blueprint operating as a proxy for content selection (i.e., what to say) and planning (i.e., in what order). We obtain blueprints automatically by exploiting state-of-the-art question generation technology and convert input-output pairs into input-blueprint-output tuples. We develop Transformer-based models, each varying in how they incorporate the blueprint in the generated output (e.g., as a global plan or iteratively). Evaluation across metrics and datasets demonstrates that blueprint models are more factual than alternatives which do not resort to planning and allow tighter control of the generation output.

From 2014 to 2019 Prof. Sinemus was the first female President of a Chamber of Industry and Commerce in Hesse. In 2011, Kristina Sinemus was appointed Professor of Public Affairs at the Quadriga University in Berlin.

Matthias serves on the governing board of the German section of Reporters Without Borders, the advisory councils of Stiftung Warentest, Freudenberg Stiftung and the Whistleblower Network and the Expert Committee on Communication/Information of Germany’s UNESCO Commission. He was a fellow of ZEIT Stiftung, Stiftung Mercator and the American Council on Germany. Matthias is editor of the Automating Society reports and has written and edited books on the automation of society, digital journalism and Internet governance. He holds master’s degrees in Journalism from the University of Colorado in Boulder and in Philosophy from the Free University of Berlin.

We relate this typicality element to the coding cost of neural representation, and discuss its implications for learning and exploration. In the cognitive domain, we examine how attentional modulation affects face representation and perception. In the social domain, we examine how facial processing affects social perception and judgment, with implications for gender and racial biases.

About the speaker

After 14 years leading the Computational & Cognitive Neuroscience Laboratory at University of California San Diego, Dr. Angela Yu recently joined TU Darmstadt as an Alexander von Humboldt AI Professor in the Center for Cognitive Science and hessian.AI Research Center. Prof. Yu’s work employs mathematically rigorous and diverse tools to understand the nature of representation and computations that give rise to intelligent behavior, with particular regard to the challenges posed by inferential uncertainty and the opportunities afforded by volitional control. Her Humboldt Professorship is designed to reinforce the theoretical and methodological core expertise at TU Darmstadt, and to promote the integration of AI and cognitive science.

This strong dependency on fast interconnections is becoming the limiting factor of further scaling, not only for the data center setting but also for alternative decentralized infrastructures such as spot instances and geo-distributed volunteer computes. In this talk, I will discuss our research in communication-efficient distributed learning and our current effort in training large language models in a decentralized way.

About the speaker

Ce is an Associate Professor in Computer Science at ETH Zurich. The mission of his research is to make machine learning techniques widely accessible while being cost-efficient and trustworthy to everyone who wants to use them to make our world a better place. He believes in a system approach to enabling this goal, and his current research focuses on building next-generation machine learning platforms and systems that are data-centric, human-centric, and declaratively scalable. Before joining ETH, Ce finished his PhD at the University of Wisconsin-Madison and spent another year as a postdoctoral researcher at Stanford, both advised by Christopher Ré. His work has received recognitions such as the SIGMOD Best Paper Award, SIGMOD Research Highlight Award, Google Focused Research Award, an ERC Starting Grant, and has been featured and reported by Science, Nature, the Communications of the ACM, and a various media outlets such as Atlantic, WIRED, Quanta Magazine, etc.