• 09:30 - 12:00 CEST
• Thomas Schmid & Sven Linker
• Lecture Hall 1

Workshop "Hybrid Artificial Intelligence"

In this face-to-face workshop, on-campus participants will together explore the growing, diverse, and exciting field of hybrid AI concepts. In particular, we will discuss not only classical hybrid approaches likes neural-symbolic integration, but also more general combinations of different strategies from different fields of artificial intelligence. The overall goal of this interactive workshop is to find the connecting dots between diverse research topics of the participants.

Lunch Break

• 13:45 - 14:00 CEST
• Nigel Davies
• Lecture Hall 1 (+ONLINE)

Opening "Welcome to LEISYS 2022"

Nigel Davies, Head of the School of Computing and Communications at Lancaster University and Co-Director of the Data Science Institute at Lancaster University, will introduce the audience to LEISYS and the campus of Lancaster University in Leipzig.

• 14:00 - 15:00 CEST
• Uwe Gäbler
• Lecture Hall 1 (+ONLINE)

Keynote "Embedding the Future: Opportunities and Prospects of Edge AI from the Perspective of a Chipmaker"

AI has the potential to sustainably change social, economic and ecological processes. The semiconductor industry plays an important role in this development, because AI applications are usually based on semiconductor solutions. Infineon is already a user and provider of AI solutions today. Infineon connects the real world with the digital world through system expertise in hardware, software as well as algorithms and hence helps to make AI applications more robust, energy-efficient and secure. In this context, Edge or Embedded AI applications are becoming increasingly important. Within Edge AI solutions, data is processed close to sensors making applications energy-efficient, fast and secure. Therefore, Embedded AI creates special opportunities for European semiconductor manufacturers such as Infineon and the Silicon Saxony industry cluster.

• 15:00 - 15:30 CEST
• Katja Seeliger
• Lecture Hall 1 (+ONLINE)

Research Talk "Convolutional Neural Networks and Human Visual Information Processing"

In parallel to the successes of neural networks for computer vision during the past decade, computational visual neuroscience has experienced a paradigm shift: The type of neural network behind their resurgence in computer vision happened to be the convolutional neural network, which originally had been proposed – in the form of the neocognitron – as the mechanism of object recognition behind the experimental findings in the early visual system by Hubel & Wiesel in the 1960s. In a rare succession of mutual scientific inspiration, convolutional neural networks trained on object recognition tasks turned out to be the best model for information processing in the human visual cortex as well. Recent approaches are using this processing similarity as a given, and train the networks directly (end-to-end) on newly recorded large-scale human neuroimaging data sets. This leads to biological properties learned implicitly, and to data-driven visual cortex models that allow wide-scaled in-silico exploration what higher brain areas are responding to.

• 15:30 - 16:00 CEST
• Marcos Cramer
• Lecture Hall 1 (+ONLINE)

Research Talk "Defining Rational Argumentation Semantics through the Principal-based Approach"

In the field of abstract argumentation one studies the possibility to make decisions about the acceptability of arguments based on the structure of the attack relation between arguments. For this purpose multiple argumentation semantics have been proposed. But which of these argumentation semantics give outcomes in line with what humans judge to be rational? This question can be tackled in different ways. In this talk, we focus on the principle-based approach to abstract argumentation, in which one defines certain principles that argumentation semantics should satisfy. One then studies which semantics proposed in the literature satisfy which principles, and if some desired combination of principles is not satisfied by any existing semantics, one studies the possibility of defining a new semantics that does satisfy this combination of principles. In this talk we pay special attention to the principle of Irrelevance of Necessarily Rejected Arguments and two argumentation semantics that were developed in order to satisfy this principle as well as certain other principles: SCF and choice-preferred semantics.

• 16:00 - 16.30 CEST
• Pradeep Banerjee
• Lecture Hall 1 (+ONLINE)

Research Talk "Information Stability, Stochastic Complexity and Generalization"

The generalization capability of a learning algorithm is intrinsically related to the information that the output hypothesis reveals about the input training dataset: The lesser the information revealed, the better the generalization. This argument has been formalized in recent years by appealing to different notions of information stability. In this talk, I will present a unifying picture of information stability-based upper bounds on the generalization error of randomized learning algorithms under different assumptions on the loss function. Optimizing these bounds naturally gives rise to a method called Stochastic Complexity Minimization for which we discuss two practical examples for learning with neural networks, namely Entropy- and PAC-Bayes- SGD.

Coffee Break

• 17:00 - 18:00 CEST
• Thomas Schmid & Sven Linker
• Lecture Hall 1

Award & Panel Discussion "What is an Intelligent System?"

In this face-to-face closing session, we will bring together on-campus participants in order to review day 1 of LEISYS 2022, discuss lessons learned and identify future directions of research.