Author: Philipp Wicke

Lecturer: Marianna Bolognesi
Fields: Cognitive Science, Linguistics, AI

Content

This course focuses on the (human) ability to abstract from experience and from language, to construct higher-order representations that are used to reason, form judgments, and appreciate art among other things. We focus on the critical role of language in performing abstractions and generalizations. Students will learn about the distinction between concreteness and specificity in word processing, uncovering how these variables shape linguistic contexts and influence thought. The course will address groundbreaking research that challenges traditional views, highlighting the overlooked role of specificity in language variability. Additionally, students will analyze the limitations of Large Language Models (LLMs), particularly their inability to accurately interpret generic statements and their tendency to overgeneralize, potentially reinforcing stereotypes. Through interdisciplinary discussions spanning cognitive science, psycholinguistics, and AI ethics, the course will provide students with a comprehensive understanding of abstraction’s significance and its implications for advancing human-like AI systems.

Literature

• Barsalou LW. (2003). Abstraction in perceptual symbol systems. Philos Trans R Soc Lond B Biol Sci. 29;358(1435):1177-87.
• Bolognesi, M., Burgers, C., & Caselli, T. (2020). On abstraction: Decoupling conceptual concreteness and categorical specificity. Cognitive Processing, 21(3), 365–381.
• Collacciani, C., Rambelli, G. and Bolognesi, M. (2024). Quantifying Generalizations: Exploring the Divide Between Human and LLMs’ Sensitivity to Quantification. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11811–11822, Bangkok, Thailand. Association for Computational Linguistics.
• Rambelli, G. & Bolognesi, M. (2024). The Contextual Variability of English Nouns: The Impact of Categorical Specificity beyond Conceptual Concreteness. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). Pp: 15854–15860.
• Rissman, L., & Lupyan, G. (2024). Words do not just label concepts: activating superordinate categories through labels, lists, and definitions. Language, Cognition and Neuroscience, 39(5), 657–676.

Lecturer

Linguist (cognitive and distributional semantics). She was a Marie S. Curie research fellow at the University of Amsterdam (2015-2017), research associate at the University of Oxford (2017-2019), and now associate professor at the University of Bologna, Italy. In 2022 she won an ERC grant for the project ABSTRACTION ERC-2021-STG-101039777,which aims to investigate how abstraction mechanisms work in thought, verbal language and creativity, both human and generated by artificial intelligence. She is vice-PI and work unit coordinator of the national project PRIN 2022 “WEMB: Word Embeddings from Cognitive Linguistics to Language Engineering and Back”, a project that aims to understand how vector representations of word meaning (embeddings) reflect those in the minds of the speakers. Her research employs cross-disciplinary approaches, combining psycholinguistic experiments and computational modeling.

Affiliation: University of Bologna, Italy
Homepage: https://www.unibo.it/sitoweb/m.bolognesi/en

Lecturer: Ralf Möller
Fields: Artificial Intelligence

Content

In the course we develop notions of human-compatibility in mechanisms in which humans and artificial agents interact (social mechanisms). Properties of social mechanisms are investigated in terms of AI alignment in general and assistance games in particular. Modeling formalisms needed to realise human-aligned agents are introduced.

Literature

• Russell, S.R. Human Compatible: AI and the Problem of Control. Allen Lane of Penguin Books, Random House, UK, 2019.
• Stuart Russell and Peter Norvig. 2020. Artificial Intelligence: A Modern Approach (4th. ed.). Prentice Hall Press, USA.

Lecturer

Ralf Möller is Full Professor of Artificial Intelligence in Humanities and heads the Institute of Humanities-Centered AI (CHAI) at the Universität Hamburg. His main research area is artificial intelligence, in particular probabilistic relational modeling techniques and natural language technologies for information systems as well as machine learning and data mining for decision making of agents in social mechanisms. Ralf Möller is co-speaker of the Section for Artificial Intelligence of the German Informatics Society. He is also an affiliated professor at DFKI zu Lübeck, a branch of Deutsches Forschungszentrum für Künstliche Intelligence with several sites in Germany. DFKI is responsible for technology transfer of AI research results into industry and society. Before joining the Universität Hamburg in 2024, Ralf Möller was Full Professor for Computer Science and headed the Institute of Information Systems at Universität zu Lübeck. In Lübeck he was also the head of the research department Stochastic Relational AI in Heathcare at DFKI. In his earlier carrier, Ralf Möller also was Associate Professor for Computer Science at Hamburg University of Technology from 2003 to 2014. From 2001 to 2003 he was Professor at the University of Applied Sciences in Wedel/Germany. In 1996 he received the degree Dr. rer. nat. from the University of Hamburg and successfully submitted his Habilitation thesis in 2001 also at the University of Hamburg. Professor Möller was co-organizer of several national and international workshops on humanities-centered AI as well as on description logics. He also was co-organizer of the European Lisp Symposium 2011. In 2019, he co-chaired the organization of the International Conference on Big Knowledge ICBK19 in Beijing, and he is co-organizing the conference “Artificial Intelligence” KI2021 in Berlin with colleagues Stefan Edelkamp and Elmar Rueckert. Prof. Möller was an Associate Editor for the Journal of Knowledge and Information Systems, member of the Editorial Board of the Journal on Big Data Research, as well as Mathematical Reviews/MathSciNet Reviewer.

Affiliation: University of Hamburg
Homepage: https://www.chai.uni-hamburg.de/~moeller

Lecturer: Achim Stephan
Fields: Philosophy (of emotions)

Content

The course offers (1) an introduction to affective phenomena such as emotions and moods, in general; (2) it also introduces to the key notions of situated affectivity such as 4E, user-resource interactions, mind shaping, mind invasion, and scaffolds; (3) next, we will apply these notions to the study of cases of mind invasion from individuals to nations; (4) finally, we will explore whether we can trust our (own) emotions.

Literature

• Stephan, Achim, Sven Walter & Wendy Wilutzky (2014). Emotions Beyond Brain and Body. Philosophical Psychology 27(1), 65-81.
• Stephan, Achim (2017). Moods in Layers. Philosophia 45, 1481-1495. doi: 10.1007/s11406-017-9841-0
• Stephan, Achim & Sven Walter (2020). Situated Affectivity. In: T. Szanto & H. Landweer (eds.) The Routledge Handbook of Phenomenology of Emotion. Abingdon: Routledge, pp. 299-311.
• Coninx, Sabrina & Achim Stephan (2021). A Taxonomy of Environmentally Scaffolded Affectivity. Danish Yearbook of Philosophy 54, 38-64. doi: https://doi.org/10.1163/24689300-bja10019

Lecturer

Achim Stephan’s current research is mainly focused on human affectivity, particularly from a situated perspective. He was head of the Philosophy of Mind and Cognition group at the Institute of Cognitive Science at Osnabrück University (2001-2023) and co-speaker of the bi-local DFG-funded research training group on Situated Cognition (2017-2023). In his PhD thesis, he worked on meaning theoretic aspects in the psychoanalysis of Sigmund Freud (1988); his habilitation thesis covers various theories of emergence and their applications (1998). From 2012 to 2015, he was president of the German Society for Analytic Philosophy (GAP); from 2017 to 2020 he was president of the European Philosophical Society for the Study of Emotions (EPSSE).

Affiliation: Osnabrück University, Institut of Cognitive Science

Lecturer: Andreas Stöckel
Fields: Computational Neuroscience / Neuromorphic Computing

Content

While the brain does perform some sort of computation to produce cognition, it is clear that this sort of computation is wildly different from traditional computers, and indeed also wildly different from traditional machine learning neural networks. In this course, we identify the type of computation that biological neurons are good at (in particular, dynamical systems), and show how to build large-scale neural models that realize basic aspects of cognition (sensorimotor, memory, symbolic reasoning, action selection, learning, etc.). These models can either be made to be biologically realistic (to varying levels of detail) or mapped onto energy-efficient neuromorphic hardware.

Literature

• Eliasmith, C. and Anderson, C. (2003). Neural engineering: Computation, representation, and dynamics in neurobiological systems. MIT Press, Cambridge, MA.
• Eliasmith, C. et al., (2012). A large-scale model of the functioning brain. Science, 338:1202-1205.
• Stöckel, A. et al., (2021). Connecting biological detail with neural computation: application to the cerebellar granule-golgi microcircuit. Topics in Cognitive Science, 13(3):515-533.
• Dumont, N. S.-Y. et al., (2023) Biologically-based computation: How neural details and dynamics are suited for implementing a variety of algorithms. Brain Sciences, 13(2):245, Jan 2023.

Lecturer

Andreas Stöckel received his PhD in computer science at the University of Waterloo, Canada, in 2021. During his PhD, his research focused on integrating biological detail into the Neural Engineering Framework, a method for constructing large-scale models of neurobiological systems. His work specifically focused on harnessing nonlinear synaptic interactions and temporal tuning as computational resources. Today, he is a senior research scientist at Applied Brain Research Inc., where he co-designed the TSP1 time-series processor, a low-power neural-network accelerator chip that utilizes some of the techniques that he investigated during his PhD.

Affiliation: Applied Brain Research
Homepage: https://compneuro.uwaterloo.ca/people/andreas-stoeckel.html

Lecturer: Guglielmo Tamburrini
Fields: Brain-Computer Interfaces, Neuroethics, Philosophy of Mind, Personal Identity, Human Enhancement

Content

OVERVIEW:
This course addresses major philosophical issues arising in connection with present and prospective brain-machine interactions enabled by BCI technologies. These philosophical issues span scientific methodology, dimensions of personal identity and consciousness affected by brain-machine interaction, human augmentation promises, ethical and societal impact of BCI technologies. The course is self-contained, insofar as it provides basic background information about the involved scientific, technological, and philosophical notions.

MORE DETAILED DESCRIPTION:
The first lecture explores the landscape of BCI technologies and research programmes, elaborating on such distinctions as those between active and passive BCI, invasive and non-invasive neural interfaces, restoration and extension of human mental or motor capabilities. This survey comes with a broad preview of philosophical issues elicited by BCI inquiries.

The second lecture introduces major epistemological issues in brain-machine interaction. These include machine learning and adaptation challenges in relation to unsteady brain activity landscapes; the parallel need for brain to machine adaptation; machine neurofeedback and the “know thyself” Socratic maxim.

The third lecture deals with the impact of BCI on human enhancement, personal identity and consciousness issues: BCI motor control, enhancement, sense of agency and authorship; temporal dimensions of personal identity and the changing boundary between self and non-self; BCI and the detection of conscious mental states; BCI and the persistence of consciousness in locked-in patients.

The fourth lecture examines BCI ethical and societal implications. These include mind reading and privacy; responsibility and authorship for BCI-mediated action; the gap between technological hype and reality; magical thinking about BCI technologies; related public trust and distrust in science and technology; BCI and neuroenhancement in military applications.

Literature

• Cinel, C, Valeriani, D, and Poli, R (2019). Neurotechnologies for Human
• Cognitive Augmentation: Current State of the Art and Future Prospects. Front. Hum. Neurosci. 13:13. doi: 10.3389/fnhum.2019.00013
• Kübler, A (2020). The history of BCI: From a vision for the future to real support for personhood in people with locked-in syndrome, Neuroethics 13, 163–180. doi: 10.1007/s12152-019-09409-4

Lecturer

Guglielmo Tamburrini (PhD in Philosophy, Columbia University, 1987) is Research Associate and retired Philosophy of Science and Technology Professor at Università di Napoli Federico II in Italy. His research interests focus on ethical and social issues in the context of AI, human-computer, and human-robot interactions. Coordinator of the first European project on the ethics of robotics (ETHICBOTS, 2005-2008), he was awarded in 2014 the Giulio Preti International Prize by the Regional Parliament of Tuscany for his contributions to the dialogue between Science, Philosophy and Democracy. Scientific Board member of USPID (Unione degli Scienziati per il Disarmo), Fellow of the Nexa Center for Internet and Society at Politecnico di Torino, Member of ICRAC (International Committee for Robot Arms Control), and member of the ISODARCO Association (International School on Disarmament and Research on Conflicts).

Affiliation: DIETI, Dept. of Electrical Engineering and Information Technologies, Università di Napoli Federico II, Italy
Homepage: https://www.docenti.unina.it › guglielmo.tamburrini ; https://www.guglielmotamburrini.com

Lecturer: Robin Bekrater-Bodmann
Fields: Psychology, Neuroscience, Rehabilitation

Content

The amputation of a limb represents the most serious breach of a person’s physical integrity and requires extensive psychological and behavioral adjustment. In addition, many people are confronted with special perceptual phenomena after an amputation: the presence of a phantom of the lost body part, which in many cases is experienced as painful. Physiological and brain imaging results show that characteristic changes in the central nervous system correlate with the experience of pain. The perceived restoration of physical integrity, e.g. through prostheses, mirrors, or virtual reality, can normalize the functioning of the central nervous system which can have positive effects on the perception of pain. In this workshop, participants learn the sensory basics of bodily self-experience firsthand. The psychobiological changes after an amputation and their connections to the experience of post-amputation pain are examined. And finally, the implications of the findings for tailored treatments are discussed.

Literature

• Bekrater-Bodmann, R. (2021). Factors associated with prosthesis embodiment and its importance for prosthetic satisfaction in lower limb amputees. Frontiers in Neurorobotics, 14, 604376.
• Bekrater-Bodmann, R., Reinhard, I., Diers, M., Fuchs, X., & Flor, H. (2021). Relationship of prosthesis ownership and phantom limb pain: results of a survey in 2383 limb amputees. Pain, 162(2), 630-640.
• Foell, J., Bekrater‐Bodmann, R., Diers, M., & Flor, H. (2014). Mirror therapy for phantom limb pain: brain changes and the role of body representation. European Journal of Pain, 18(5), 729-739.
• Fuchs, X., Flor, H., & Bekrater-Bodmann, R. (2018). Psychological factors associated with phantom limb pain: a review of recent findings. Pain Research and Management, 2018(1), 5080123.
• Rothgangel, A., & Bekrater-Bodmann, R. (2019). Mirror therapy versus augmented/virtual reality applications: towards a tailored mechanism-based treatment for phantom limb pain. Pain Management, 9(2), 151-159.

Lecturer

I studied Psychology at the Braunschweig Technical University and completed my studies with a diploma in 2007. From 2008 to 2023, I worked at the Central Institute of Mental Health in Mannheim: first as a doctoral student and then as a postdoc at the Institute of Cognitive and Clinical Neuroscience and the Department of Psychosomatic Medicine and Psychotherapy, interrupted by a stay abroad at the Department of Psychology, Royal Holloway, London University, in 2017. Since 2023, I have been working as full professor for ‘Psychobiology of chronic pain’ at the Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, Aachen University. My team and I are interested in the neurobiological mechanisms underlying bodily self-experiences in chronic pain and the interaction between experimentally altered body perception and central nociceptive processes. We take advantage of and develop paradigms for the induction of bodily illusions and assess its cognitive, behavioral and (neuro)physiological effects, either in our pain lab or in the environment of a magnetic resonance imaging scanner.

Affiliation: Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
Homepage: https://www.ukaachen.de/kliniken-institute/klinik-fuer-psychiatrie-psychotherapie-und-psychosomatik/team/alle-personen-a-g/bekrater-bodmann-robin/

Lecturer: Marius Klug, Michael Bressler
Fields: Neuroscience, Computer Science, Cognitive Science, Biology, Human-Computer Interaction

Content

This is a hands-on course on the fascinating world of physiological user interfaces. A quick introduction to the body and brain signals that can be measured—such as heart rate, muscle activity, and brain activity—plus a short tutorial on the provided Unity game engine prefabs will get you up to speed. Equipped with this, the course will be a series of supervised hands-on hackathon-style practical sessions where you will explore how various body signals can be harnessed to create innovative applications. We provide devices like the Polar H10, Myo Armband, and Muse S, which can read different physiological signals. Teams of a maximum of three participants will then ideate, design, and prototype interactive systems that use the provided body and mind signals as input for applications in the Unity game engine. The applications can be for desktop, mobile, and even XR platforms (we provide a few Meta Quest devices) and they should be designed to be entertaining and fun. The focus is on fostering creativity and collaboration while gaining deeper insights into how our bodies can interact with technology. Provided and generated software is expected to be uploaded to public code repositories. Finally, teams will pitch their ideas and demo applications to a jury and to the other participants. Aspiring participants are encouraged to visit the Unity tutorials at https://learn.unity.com/pathway/unity-essentials.

Literature

• https://github.com/MariusKlug/mind-and-body-UIs-hackathon
• https://learn.unity.com/pathway/unity-essentials
• Gramann, K. & Schandry, R. (2009). Psychophysiologie (4. Auflage). Basel, Switzerland: Beltz
• Andreassi, J. (2007). Psychophysiology: Human Behavior and Physiological Response (5th Edition). New York, NY, USA: Psychology Press

Lecturer

Marius Klug studied cognitive science in Tübingen and was already in contact with EEG as a measurement method and brain-computer interface during that time. He subsequently earned his doctorate in the field of mobile brain research under Prof. Klaus Gramann at TU Berlin. There, he extensively dealt with EEG analysis methods and virtual reality as an experimental method. Specifically, the application of EEG in a mobile context, the cleaning of data, and their interpretation in conjunction with other measurements, such as body and eye movements, were the focus of the research. The continuation of this research can now be found at BTU in the form of the practical use of psychophysiological measurement methods as an interface for real-time applications.

Affiliation: BTU Cottbus-Senftenberg
Homepage: https://discord.gg/7MJjQ3f

Michael Bressler finished his Master’s degree in Information Technology at the Vienna University of Technology with a focus on human-computer interfaces and user interface design. In his research, he mainly focuses on computer-assisted rehabilitation, virtual and augmented reality, and serious games for health.

Affiliation: BG Klinik Tuebingen, Clinic for Hand, Plastic, Reconstructive and Burn Surgery, University of Tuebingen, Tuebingen, Germany
Homepage: https://michaelbressler.at/

Lecturer: Margot Ernst
Fields: Neuropharmacology

Content

The molecular foundation for nervous system (NS) function will be introduced in part 1, ranging from the smallest vital units (water, salt, fat) to the largest biochemicals (DNA, RNA, proteins). Selected so-called transmitter systems will be introduced in order to convey a feeling for the molecular complexity and how food and drugs act on the NS. Molecules relevant for nociception and pain will be in the focus.
In part 2, the vast complexity of cells that comprise the NS will be introduced, alongside with the genetic basis of the cellular pre- and postnatal development. The basis for adapting to the environment is provided by cellular and molecular traces made by physical, chemical and complex (e.g. social) stimuli that act on the organism. Cellular key players that contribute to pain perception and memory will be highlighted.
In part 3, the small parts will be put together and selected so-called “systems” will be introduced. Nociception and pain, and the concept of “aversive responses” will be examined in some detail, as well as the neural correlates of pain memory and phantom pain.
In part 4, selected means to interact with the NS are examined, and some of the challenges and chances for future development highlighted. Progressing from non invasive to invasive (e.g. implants), various selected means to influence nociception and pain perception, as well as the aversive reactions to painful stimuli, illustrate some basic principles.

Literature

• Sapolsky, “Behave” ISBN 978-1-59420-507-1
• Bear et al., Exploring the Brain, ISBN 10: 0781760038

Lecturer

Margot Ernst holds a PhD from Georgia Tech (US) and currently works at the Medical University of Vienna. Her research focus is on the neuropharmacology of substances that act on GABA-A receptors.

Affiliation: Medical University of Vienna
Homepage: https://www.meduniwien.ac.at/web/forschung/researcher-profiles/researcher-profiles/detail/?res=margot_ernst&cHash=4e3cbaeed73c322fd75003c0ef8d6061

Lecturer: Ronald Sladky
Fields: Cognitive Modeling, Data Modeling

Content: Originally, the idea of free energy minimization has been used as a tool for data modeling, in particular, to model effective connectivity in the brain based on neuroimaging data. On top of this, Karl Friston has proposed the free energy principle as a general principle for understanding brain functions. A bold statement – but, if true, one of the biggest breakthroughs in cognitive science.

In the form of active inference, the free energy principle could provide a neuro-computational explanation for predictive processing, the Bayesian brain hypothesis, and enactive cognition. It could provide a unified conceptual and computational framework to link previously distant and isolated research fields in the cognitive sciences that could be compatible with how we understand self-organization of living systems in a physical world.

In this course we will talk about how we make sense of data using models. By looking at how cognitive neuroscientists study the human brain using fMRI and brain connectivity methods. We discuss how models shape the way we interpret the world. I will teach you how these models work so you will find out on your own what to do with them. To what degree is the free energy principle useful for you? Modeling data, modeling the brain – or modeling the mind?

Session 1 covers how we turn fMRI data into brain activation and connectivity models. Session 2 focusses on dynamic causal modeling to study effective connectivity in the brain. Session 3 extends the formalism used in DCM to describe brain functions and life as we know it. Session 4 will review state of the art applications, theoretical developments, and empirical evidence for the free energy principle and active inference in action. As an illustration, I will use my own research on amygdala functions and dysfunctions and its connectivity. So, we will also talk about fear, trust, and other emotions – not just data, brains, and methods.

Literature

Friston, K. (2009). The free-energy principle: a rough guide to the brain?. Trends in cognitive sciences, 13(7), 293-301.
Marreiros, A. C., Stephan, K. E., & Friston, K. J. (2010). Dynamic causal modeling. Scholarpedia, 5(7), 9568.
Friston, K. (2013). Life as we know it. Journal of the Royal Society Interface, 10(86), 20130475.
van Es, T. (2021). Living models or life modelled? On the use of models in the free energy principle. Adaptive Behavior, 29(3), 315-329.
Corcoran, A. W., Hohwy, J., & Friston, K. J. (2023). Accelerating scientific progress through Bayesian adversarial collaboration. Neuron, 111(22), 3505-3516.
Sladky, R., Kargl, D., Haubensak, W., & Lamm, C. (2023). An active inference perspective for the amygdala complex. Trends in Cognitive Sciences.

Lecturer

Ronald Sladky. My research focuses on the amygdala and emotion processing in the human brain. In addition, I am always working on new neuroimaging, data processing, and modeling methods. One of these new methods is real-time functional MRI, where people can learn to regulate their own brain states while they are inside the MRI scanner. This method is not only a promising therapeutic tool, it will also allow for completely new ways of discovering how our brains work.

Affiliation: University of Vienna
Website: http://sweetneuron.at

Lecturer: Ilaria Tiddi
Fields: Artificial Intelligence

Content

Hybrid Intelligence (HI) is a rapidly growing field aiming at creating collaborative systems where humans and intelligent machines cooperate in mixed teams towards shared goals. In this course, we will get to know the field by discussing the vision, and basics, and the solutions that have been proposed so far. In particular, we will focus how symbolic AI techniques (knowledge graphs and semantic technologies) have been proposed as complementary building blocks to the subsymbolic (machine learning) methods, and how this combination has been used to help solving the main challenges in the field of Hybrid Intelligence.

The course comprehends 3 lectures:
1) Introduction to Hybrid Intelligence (1.5h). Here we will introduce the main research questions for the field of HI, present some of the solutions proposed so far, and finally discuss open challenges.

2) Introduction to Knowledge Graphs (1.5h). Here we will introduce the basics of Knowledge Engineering, including modelling information and reason about it using the RDF/RDFS/OWL languages, principles of knowledge/ontology engineering, and methods to query knowledge graphs.

3) Knowledge Engineering for Hybrid Intelligence (1.5h). The last lecture will introduce how ontologies and knowledge engineering methods can be used to design Hybrid Intelligence applications.

Learning objectives:
– familiarise with Hybrid Intelligence challenges and methods
– get to know the basics of Knowledge Graphs (RDF, OWL, SPARQL, ML over graphs)
– apply the principles of knowledge engineering for the design for Hybrid Intelligence applications

Literature

• Hogan, Aidan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane et al. “Knowledge graphs.” ACM Computing Surveys (Csur) 54, no. 4 (2021): 1-37.
• Akata, Zeynep, Dan Balliet, Maarten De Rijke, Frank Dignum, Virginia Dignum, Guszti Eiben, Antske Fokkens et al. “A research agenda for hybrid intelligence: augmenting human intellect with collaborative, adaptive, responsible, and explainable artificial intelligence.” Computer 53, no. 8 (2020): 18-28.
• Ilaria Tiddi, Victor De Boer, Stefan Schlobach, and André Meyer-Vitali. 2023. Knowledge Engineering for Hybrid Intelligence. In Proceedings of the 12th Knowledge Capture Conference 2023 (K-CAP \’23). Association for Computing Machinery, New York, NY, USA, 75–82. https://doi.org/10.1145/3587259.3627541
• Tiddi, Ilaria, and Stefan Schlobach. “Knowledge graphs as tools for explainable machine learning: A survey.” Artificial Intelligence 302 (2022): 103627.

Lecturer

Ilaria Tiddi is an Assistant Professor in Hybrid Intelligence at the Knowledge in AI (KAI) group of the Vrije Universiteit Amsterdam (NL). Her research focuses on creating systems that generate complex narratives through a combination of semantic technologies, open data and machine learning, applied mostly in scientific and robotics scenarios. She is Editor-in-Chief of the CEUR-WS publication, part of the Steering Committee for the Hybrid Human-AI Conference, and Coordinator of the international Staff Exchange for the Dutch Hybrid Intelligence consortium. Since 2014, she is regularly active in the OCs/PCs of the major venues in the KR field (ISWC/ESWC, HHAI, WWW, CIKM, IJCAI/ECAI).

Affiliation: Vrije Universiteit Amsterdam
Homepage: https://github.com/kmitd/