The main event will take place between May 31 at 12:00 and June 1 at 13:00 (lunch to lunch).

The program includes four Keynotes (see Keynotes), and a pitch and panel session on AI and Intelligent Systems - status, future, and timeline for real-life deployment. Tutorials are organised before and after the main event (see Tutorials).

The main event will be held at the main Auditorium PH170 in the ground floor, Pilestredet 35. The tutorials will be held in rooms PI551 and PI556 on the 5th floor, up the main staircase/ glass elevator. There are signs pointing to the rooms. NB: The main entrance to the building is from Holbergs Plass.

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AI- and Robotics-enabled systems, a forward leap into real life applications

Filippo Sanfilippo, Prof. Robotics and Control, University of Agder

AI- and Robotics-enabled systems are becoming more and more relevant for real life applications. This technology may enable society to a conceptual leap forward especially concerning demanding real-life scenarios. In this talk, different scenarios will be considered, including AI- and robotics-enabled systems for the Industry 4.0, wearable robotics, intelligent health, human-robot interaction/collaboration, and search-and-rescue (SAR).

Filippo Sanfilippo holds a PhD in Engineering Cybernetics from the Norwegian University of Science and Technology (NTNU), Norway. His research interests include robotics, wearables, software engineering, human-robot collaboration, artificial intelligence and control theory. He is currently appointed as a Professor at the Department of Engineering Sciences, Faculty of Engineering and Science, University of Agder (UiA), Grimstad, Norway. He is an IEEE Senior Member. He is currently the Chair of the IEEE Norway Section. He is also the Chair of the IEEE Robotics and Automation, Control Systems and Intelligent Transportation Systems Joint Chapter. He is also the treasurer of the Norsk Forening for Kunstig Intelligens (NAIS), the Norwegian Association for Artificial Intelligence. He has authored and co-authored several technical papers in various journals and conferences. He is a reviewer for several international conferences and journals.

AI Research and Europe’s Upcoming AI Law

Tobias Mahler, Prof. in Legal Informatics, University of Oslo

Much of the discourse regarding trustworthy AI systems has emphasised the significance of AI ethics. By comparison, this keynote will focus on the legal regulation of AI in Europe. In 2021, the European Commission proposed a new law to regulate AI in Europe, the Artificial Intelligence Act (AIA). An important question is what this law will mean for scientific research with an AI focus. The talk will also discuss what the law, if adopted, would imply for AI developers. Ultimately, the question is whether the AIA facilitates the creation of trustworthy AI in Europe, or whether it might limit Europe’s abilities to develop competitive AI systems.

Tobias Mahler is professor at the Faculty of Law at the University of Oslo, where he is the legal research leader of the “vulnerability in the robot society” (VIROS) project, the deputy director of the Norwegian Research Centre for Computers and Law (NRCCL) and the leader of the Legal Innovation Lab Oslo (LILO). He holds a PhD from the University of Oslo, an LLM degree in legal informatics from the University of Hannover, and a German law degree (first state exam). Prof. Mahler is also Director of the Master of Laws Programme in Information and Communication Technology Law at the University of Oslo. He teaches robot regulation, cybersecurity regulation, legal technology and artificial intelligence. Mahler has been a visiting fellow at the Max Planck Institute for Foreign and Criminal Law in Freiburg, Germany, and the Stanford Centre for Internet and Society. In 2020 he acted as an expert advisor to the European Commission on drafting the upcoming Digital Services Act.

AI and machine learning in Big Insight

Kjersti Aas, co-director Big Insight SFI, Assistant Research Director SAMBA

This talk will give some examples from the ongoing work within Big Insight Centre for Research-based Innovation. The examples will include application of AI and machine learning in the following areas: credit scoring, anomaly detection and detection of money laundering as well as work on explainable AI.

Kjersti Aas received the M.Sc. degree in Industrial Mathematics from The Norwegian Institute of Technology (NTH) in 1990 and the Ph.D. degree in Statistics from Norwegian University of Science and Technology (NTNU). She is currently Research Director at the Norwegian Computing Center and Adjunct Professor in Data Science at the Norwegian University of Science and Technology. Her current research interests include AI and Explainable AI for financial applications.

Visual Intelligence advances deep learning research towards innovations

Robert Jenssen, Director Visual Intelligence SFI, Professor at UiT The Arctic University of Norway

This talk will outline advances within SFI Visual Intelligence along some main deep learning research challenges towards innovations in important application areas of Norwegian society. The main focus will be on the development of new methods for learning from limited data, e.g. semi-supervised learning, few-shot learning, self-supervised learning, and XAI for the detection of artefacts and confounding factors. The applications cover fish detection from acoustic target classification within the marine sciences as well as applications within medical image analysis.

Robert Jenssen received a PhD (Dr. Scient) in Machine Learning in 2005. He has had long term research stays at the University of Florida, the Technical University of Berlin, and at the Technical University of Denmark. He is currently a professor in the UiT Machine Learning Group at UiT The Arctic University of Norway as well as at the University of Copenhagen. He is an adjunct professor at the Norwegian Computing Center. Jenssen is a member of the Ellis Unit Copenhagen.

Registration is now open. Please click the following link to register:
https://www.eventbrite.com/e/norwegian-ai-symposium-2022-tickets-291195161567

Note that we cannot guarantee a dinner space after Monday May 9.

In artificial intelligence (AI) and computer science in general, search is a step-by-step process of solving a problem following a particular search space. When it comes to problem-solving, AI is highly dependent on search algorithms, such as finding the most suitable solution from a human-like virtual assistant, finding the most convenient route from self-driving cars, or the most promising move in a chess game. Search algorithms are the building blocks of AI evolving with this futuristic technology and are relevant to the topics of interest in the Norwegian AI Symposium (NAIS 2022). We strongly believe learning search algorithms with python could provide additional value to the Norwegian AI community and early-stage researchers.

This tutorial covers classical uninformed (blind) searching algorithms in AI such as breadth-first search, uniform cost search, depth-first search, iterative deepening depth-first search, bidirectional search, and informed (heuristic) searching algorithms in AI such as best-first search and A* search. We consider finding the solutions for real-world problems (i.e., specific to each search algorithm) by implementing these search algorithms in python, which we believe will provide better technical support to target attendees. We propose to spend this three-hour tutorial. In the first hour for the coverage of uninformed searching algorithms, organize a minor in-class assignment of 15 minutes in duration. We then cover informed searching algorithms in one hour, followed by a small assignment on informed search algorithms of 15 minutes. We wrap up the tutorial with the source material and other valuable information or discussion.

This tutorial targets Bachelor’s/Master's level computer science students with an introductory/intermediate level of any programming language knowledge (presenter will use python in tutorial).

Slides: (.pptx)

Recording: (YouTube)

The SoccerNet dataset released in 2018 marked the start of large-scale soccer analysis in academia, gathering a growing research community which now expands to the industry. Broadcast soccer video understanding is an attractive topic for graduate students with many potential applications, like highlights composition and statistics generation. Besides, it encompasses natural yet challenging tasks for computer vision professionals, such as action spotting, camera calibration, player re-identification and tracking. It also comes with specific difficulties to handle fast-paced actions, players of similar appearance and replays through various camera views. All these aspects make soccer a rich yet often overlooked playground for research.

This tutorial focuses on the practical side of building soccer video understanding pipelines: which data is available, how to annotate it, how to use it, which useful tasks can be defined, tackled, and assessed, and which challenges keep the community and industries busy. Demos with Python code will be presented step-by-step to cover a large panel of soccer-related tasks. The instructors and presenters of the tutorial are experienced scientists from academia and industry that lead the soccer research community and develop cutting-edge technologies for sports broadcasts.

This tutorial is tailored for computer vision master students and their professors seeking computer vision classes or thesis projects, for PhD candidates focusing on spatio-temporal aspects of video analysis, for researchers and industrials willing to apply AI techniques within sports broadcasts, and for any soccer enthusiast. The download information of the SoccerNet dataset indicates that all those types of profiles regularly use the dataset. The tutorial assumes basic knowledge of Python and neural networks. Upon completion of the tutorial, attendees will have at hand various pipelines to tackle tasks such as action spotting, player tracking, player re-identification, camera calibration, that they can use not only in soccer-related projects but also transfer to their own research. All the material produced within the tutorial will be made available online.

Deep learning is the main component in contemporary artificial intelligence algorithms, which have seen major improvements in fields such as computer vision and natural language processing. However, deep learning lacks explainability, which limits its usability in fields such as finance and medicine where trustworthiness is of high importance. Explainable artificial intelligence (XAI) aims at making deep learning more transparent and reliable, and has made great improvements over the last couple of years. Being familiar with XAI methodology can be advantageous for both machine learning researchers and more applied researchers, and a tutorial on XAI would therefore be of great use for the Norwegian AI community.

The tutorial will start with an introduction to the field of XAI and a presentation of a selection of explainability algorithms. Then, we will discuss some common pitfalls and challenges with current XAI, before moving on to discussing what makes an explanation good and where the field will go in the future.

The tutorial targets machine learning researchers with intermediate knowledge of XAI.

The symposium will take place at OsloMet - Oslo Metropolitan University, in the centre of Oslo. Main event: at the main Auditorium PH170 in the ground floor, Pilestredet 35. OsloMet is the youngest and third largest institution of higher education and research in Norway, with a student body of approximately 20 000 students and 2 100 employees. The University specializes in professional training in fields such as technology and design, engineering, physiotherapy, nursing, and teacher education. OsloMet's main campus in Pilestredet is located a 5 minute walk from Slottsparken - the Royal Palace - and the National Theater, and 5-15 minutes walk from central cites and most hotels in the city centre.