9th of May 2022
Campus Lágymányos, Northern Building, Harmónia room, -1.85.

Tentative Program

12.00	12.10	Opening, Welcome	János Botzheim, Udo Bub Head of AI Dept, Director of Industry-Academy Cooperation Institute
12.10	12.50	Keynote lecture: Rehabilitation Robots that Cooperate and Motivate	Robert Riener Full professor, Sensory-Motor Systems Lab, ETH Zurich and SCI Center, University Hospital Balgrist  University of Zurich
12.50	13.10	Humane AI	George Kampis Senior researcher, DFKI
13.10	13.30	Semantic Matching and Human-Centered AI: A Product-Oriented SWOT Analysis	András Lőrincz Tudományos főmunkatárs, ELTE
13.30	13.50	How Amazon became an AI-powered company	Attila Papp Lead Cloud Solution Architect @ adesso Hungary Software
13.50	14.30	Állófogadás, szendvicsebéd
14.30	14.50	AI in the MEMS based inertial sensor production	Itilekha Podder European Institute of Innovation & Technology (EIT) Doctoral Student Eötvös Loránd University (ELTE) , Budapest Robert Bosch Kft., Budapest Hungary
14.50	15.10	Robust Machine Learning	Márk Jelasity Szegedi Tudományegyetem, Természettudományi és Informatikai Kar, Informatikai Intézet, Számitógépes Algoritmusok és Mesterséges Intelligencia Tanszék
15.10	15.30	Application of AI in self-driving function development and in the product development	Zoltán Kárász Senior AI Expert at Robert Bosch
15.30	15.50	On the Structural Analysis of Sparse Neural Networks	László Gulyás Department of Artificial Intelligence, Faculty of Informatics, Eötvös Loránd University, Hungary
15.50	16.10	Embedding dynamic networks in vector spaces on-the-fly	András Benczúr Head of  Informatics Laboratory at the Institute for Computer Science and Control, scientific coordinator of the Hungarian Artificial Intelligence National Laboratory
16.10	16.30	Spiking neural networks and their applications	János Botzheim Department of Artificial Intelligence, Faculty of Informatics, Eötvös Loránd University, Hungary, Head of Department

Demos

In connection with the opening of the Department of Artificial Intelligence, an interactive exhibition of the research and development results of the department can also be viewed. This is possible before the start of the conference from 11:30 or during the lunch break. Location: 7.103A.

Northern building, room 7.100-7.103:

• Data exploration & anomaly detection – VR experience
• Autonomous driving
• Piano playing robot

Poster session:

• Bacterial programming and it’s applications
• Learning to swarm; machine learning for collective intelligence
• AI in the MEMS-based sensor production

Northern building, room 7.92-7.94:

• Tracking: birds & rats
• DeepRehab: robot-assisted physical rehabilitation
• Semantic mapping
• Neuromorphic moon lander
• Personality trait estimation

Northern building, Community Area (front of the elevator of the Danube side):

• ELTE-Bosch collaboration at the department
• Play a Kahoot game about the secrets of AI and charge yourself with a „Sportszelet”

Entrance of the Globe Hall (Outside the Northern building):

Digital cameras and LiDAR devices in Autonomous Driving

• Bosch’s test car: Tesla showcase
• ELTE’s test car: Skoda and Dodgem car showcase

Program details

Keynote lecture

Robert Riener

Full professor, Sensory-Motor Systems Lab, ETH Zurich and SCI Center, University Hospital Balgrist University of Zurich

Rehabilitation Robots that Cooperate and Motivate

Robots can support the restoration of upper and lower limb movement functions. They promote neuroplastic effects when used as therapy devices after neurological injuries such as spinal cord injury or stroke. This talk will highlight new patient-cooperative controllers, intention detection strategies and virtual reality technologies that improve the rehabilitation of arm movements and gait. Advanced machine learning approaches have been applied for psychological state estimation from psycho-physiological recordings to control patient engagement during robot-assisted gait training. In another project, machine learning technologies were applied to multimodal sensor data sets to predict movement intention in upper extremities reaching and grasping tasks to assist human arm movements with the arm connected to a therapy robot. In the latest activities deep neural networks were applied to detect snoring and use the information for a postural intervention of a robotic bed during sleep.

George Kampis

Senior researcher, DFKI

Humane AI

The talk mainly deals with a topic currently in the trade, and reflected in the title. My excuse for this sweepingly general topic is that (in DFKI, Germany) we coordinate an EU project on it, that comprises 53 leading European laboratories. I detail aspects of the issue and a possible approach to it. Details include social credits systems and the criticism thereof, explainable AI, or the Alphie project at ELTE. Our approach in the above project is based on the notion of micro-projects (MPs). An MP is a partner-initiated response to a semi-open call, adequate for AI. MPs have the advantage that they combine the top-down objectives of the project with the dynamically changing, bottom-up initiatives of the partners and their external collaborators. The purpose of the theme is the emerging EU legal regulation of AI, the „EU AI Act“, which we also dissuss briefly.

András Lőrincz

Tudományos főmunkatárs, ELTE

Semantic Matching and Human-Centered AI: A Product-Oriented SWOT Analysis

The R&D results of the Department of Artificial Intelligence open up innovative opportunities and require a SWOT analysis of potential products. The results start from the basic sciences (structured sparse coding), cover different areas of artificial intelligence (emotion and personality state estimation, hand, head, body pose estimation, tracking, information fusion, natural language processing and semantic mapping) and go up to software developments (deep neural networks and Composite AI). Application examples include “continuous healthcare” and “security of home and public spaces”, among others. Product-oriented integration and industrial-grade software development is what could be the next step. Based on the technology components, it can be said that the SWOT “weakness” is workforce, which can be achieved through partnerships.

Attila Papp

Lead Cloud Solution Architect @ adesso Hungary Software

How Amazon became an AI-powered company

Amazon Inc. has set an example for retail innovation in the past two decades. Their continuously spinning “flywheel” spun out many subsequent services, which later became a key industry player (AWS, Alexa, Music, Prime, etc.). In this talk, we will examine Amazon’s use cases for Artificial Intelligence, how they push the retail industry for AI adoption, and what might be on the horizon for their ultimate AI strategy.

Itilekha Podder

European Institute of Innovation & Technology (EIT) Doctoral Student

Eötvös Loránd University (ELTE) , Budapest

Robert Bosch Kft., Budapest Hungary

AI in the MEMS based inertial sensor production

MEMS (MicroElectroMechanical System) based inertial sensors were originally used in the automotive industry in the Electronic Stability Program (ESP), but today they also can be expected to spread in the self-driving cars. The silicon technology used in MEMS sensors is developed by a series of complex and finely tuned chemical and mechanical processes. The basic elements of the products are formed with the help of silicon technology. The complexity of the process is due to the relatively large variance of the technology, which is now accompanied by the availability of well-documented process steps in the industry and the measurement results of products and semi-finished elements. The production process, the environment and the results obtained from the product allow us to deduce complex relationships, which can be reduced with the help of artificial intelligence, and their application can make certain processes more economical and more accurate. The presentation shows the possibilities of optimizing the production process with AI through examples.

Márk Jelasity

Szegedi Tudományegyetem, Természettudományi és Informatikai Kar, Informatikai Intézet, Számitógépes Algoritmusok és Mesterséges Intelligencia Tanszék

Robust Machine Learning

Deep neural networks have been known to be vulnerable to malicious input perturbations invisible to humans that can mislead the network in almost arbitrary ways. Also, the same networks are vulnerable to out-of-distribution examples, that often result in incorrect but high confidence output. In this talk, I will give a summary of some of the results we achieved in this area in the past years at the University of Szeged. This includes novel attack secnarios and algorithms, as well as formal verification.

Zoltán Kárász

Senior AI Expert at Robert Bosch

Application of AI in self-driving function development and in the product development

The development of self-driving functions today involves the parallel development of several technologies that are being addressed by several teams in parallel, including the application of Deep Learning / Machine Learning technologies. These technologies can be used in product development, but the result of such research / development is an artificial intelligence model as a product. The presentation will provide an insight into the artificial intelligence technologies, applications and their challenges used in product development and functional development of self-driving.

László Gulyás

Department of Artificial Intelligence, Faculty of Informatics, Eötvös Loránd University, Hungary

On the Structural Analysis of Sparse Neural Networks

Artificial Neural Networks (ANNs) are at the forefront of interest and practice of modern Artificial Intelligence (AI). Originally, they were proposed as a mathematical model of the human brain, i.e., the human neural network. Recent advances in ANN research resulted in a host of powerful applications, but also in novel avenues to overcome existing limitations, e.g., in terms of computational performance. One of these new directions is concerned with Sparse Neural Networks (SNNs), or with sparsification of ANNs. The core of the idea is to remove nodes or links from the generally layer-wise fully connected network topology to improve performance. A distinct, but not less important scientific discipline is Network Science (NS) that is concerned with the structural analysis of empirical networks. Since the concept of networks is a very powerful metaphor, capable of describing systems such diverse as biological, economical, social, engineered systems, among others. NS provides statistical tools to assess the structure of networks — at the micro (node), the meso (clusters) or the macro (the whole network) level. In addition, several commonalities of empirical networks were identified (e.g., they tend to be sparse), as well as structural  properties that can predict system level behavior (e.g., the resilience of systems facing random failures or malicious attacks). In this presentation we introduce an approach to use the methods of NS to analyse and improve SNNs. We outline the long term potential of combining NS with SNN, but also discuss some difficulties. In addition, we present a set of baseline observations.

András Benczúr

Head of  Informatics Laboratory at the Institute for Computer Science and Control, scientific coordinator of the Hungarian Artificial Intelligence National Laboratory

Embedding dynamic networks in vector spaces on-the-fly

I show methods to analyze and model network data accessible as a stream of edges for tasks including low-rank approximation, network embedding, link prediction, and centrality algorithms can be implemented and updated while the edge stream is processed. As applications, I consider topic discussions in Twitter such as COVID vaccination, as well as anonymity in cryptocurrency networks.

János Botzheim

Department of Artificial Intelligence, Faculty of Informatics, Eötvös Loránd University, Hungary, Head of Department

Spiking neural networks and their applications

As bio-inspired artificial intelligence emerges more detailed neural architectures appear. In this talk, one of the most prominent fields, the third generation of neural networks (i.e. spiking neural networks) is introduced. Along with motivations, and predicted impacts the basic modeling principles of dynamic neural networks are presented. After looking through current research problems and directions, the talk ends with example scenarios of experimental SNN applications.