Author: Philipp Wicke

Lecturer: Brigitte Römmer-Nossek, Birgit Peterson
Fields: Academic skills, Higher Education, Writing Research and Didactics

Content

Resilience, Robustness and Responsibility in Academic Writing

The process of academic working and particularly the processes of reading and writing in academia is demanding, between maintaining a resilient writing behaviour and creating a robust piece of text. It is important to develop a routine in academic writing, which enables the author to become a responsible member of the respective discourse community and satisfy all requirements of scientific quality.

In this practical course we are going to work on aspects important for successful academic writing from 4 different perspectives. Although all 4 parts are connected to each other, it is possible to join for only one session as well.

A Robust Framework for Your Writing Project

In the first session we will introduce our approach to academic writing. We will provide a generic framework for orientation in writing projects and pinpoint the challenges many writers come across in the respective phases. We will use a writing exercise for „getting to the point“.

What am I Doing anyway? Developing as a Writer and Researcher

In this session we will explore the pains of placing what we are doing and writing about it. Finding a research question, positioning in the field of research what we are working on, and deciding what we want to say is hard work. We will look at reasons for that and discuss some strategies.

How to develop a resilient voice as a responsible academic writer

In the third session we focus on how to develop a strong academic voice out of your individual position as academic author. Therefore, you will explore you writing behaviour and the process of gradual formulation of ideas and voice through writing by different mind writing and specific revision strategies useful to embody thoughts within written products.

How to maintain robust choreographies for your academic working

In the last session, the focus will be on the “choreography of academic working”: how we use and adjust a variety of materialities, actions and spaces to our needs within the phases of the academic writing process. The juggling with diverse working materialities and spaces that we tend to involve for different purposes eases the rhythmic shift between different mindsets we need for an efficient and harmonic choreography of academic working.

Literature

• Davies, S., Pham, B-C., Dessewffy, E., Schikowitz, A., & Mora-Gámez, F. Pinboarding the Pandemic: Experiments in Representing Autoethnography. Catalyst: Feminism, Theory, Technoscience, (2022). 8(2). doi: 10.28968/cftt.v8i2.38868
• Elbow, P. Writing with Power: Techniques for Mastering the Writing Process. New York Oxford University Press, 1998.
• Gallagher, C W. “What Writers Do: Behaviors, Behaviorism, and Writing Studies.” College Composition and Communication 68, no. 2 (2016): 238–65. http://www.jstor.org/stable/44783561.
• Peterson, B: Die 99 besten Schreibtipps für die vorwissenschaftliche Arbeit, Matura und das Studium. 2., überarbeitete Auflage. Wien: Krenn, 2017
• Skinner BF. How to discover what you have to say-a talk to students. Behav Anal. (1981) Spring;4(1):1-7. doi: 10.1007/BF03391847.

Lecturer

Brigitte Römmer-Nossek is responsible for the team “Student Research and Peer Learning” at the University of Vienna’s Center for Teaching and Learning and has been a lecturer for 20 years. She studied Brain and Cognitive Science Science as a studium irregulare and was involved in the implementation of the the joint Middle European interdisciplinary master programme in Cognitive Sciences (MEi:CogSci). In her dissertation she engaged in sense-making of academic writing as a cognitive developmental process from an enactive perspective.

Affiliation: University of Vienna
Homepage: https://ctl.univie.ac.at/ueber-das-ctl/teams-und-mitarbeiterinnen/team-wissenschaftliches-arbeiten-und-peer-learning/

Mag.a Birgit Peterson studied Human Biology in Vienna and Rome and gave particular emphasis to Cognitive Science when the Mei:CogSCi Join Master started in Vienna in 2006. She is interested in human cognition and communication in context with learning scenarios. Her focus lies on the connection of human language, thinking, writing, reading and learning and she has been working as a “Learning Professional” and Trainer for scientific reading, writing and thinking for more than 10 years now. Peterson is an author, speaker and trainer in the field of education, from elementary school up to Higher Education. Additionally she is consulting different developmental programs in higher education, such as Support for Scientific Writing, Teaching Skills or Peer- and Alumni-Mentoring programs for young scientists.

Affiliation: University of Vienna

Lecturer: Thomas Christaller
Fields: Cognitive Psychology, Philosophy

Content

Obviously, in competitive sports, fighting systems, and martial arts you encounter stressful situations in which you don’t know if and how you can resolve them. Independently of the mastery of the techniques relevant for the specific body movement system the training always includes exercises which helps to overcome the stress in such situations and being able to act in an appropriate and meaningful way. They help to overcome fear as well as aggression. The basic insight is that specific techniques with regard to breathing, relaxation, and posture you can foster your resilience when things don’t happen as expected. This will be the core of the course. To understand why these techniques really work practically we will explore the neural, emotional basis for them. Our brain is mainly used as a forecasting system for the very next second as well as for hours, days, and weeks by rehearsing. But in stressful situations the brain doesn’t have the resources to explore the alternatives making a plausible prediction. Another topic is to stay calm to reduce stress and avoid a narrowed view of the world. The main basis of this is breathing. Usually this is done unconsciously and changes according to your bodily effort and the expectation of the effort in the very next moment. Especially in the martial arts breathing is a core element to be able to deal with the unforeseeable. The usual reflex if one experiences an unexpected situation is to become bodily tense. But then you may not be able to act fast enough. Relaxation is the secret which is different from being weak. All these different systems are connected in our body posture. But resilience plays also a role after you experienced a trauma or some other negative event. Here, too, these insights can help to recover or re-bounce and stand-up again. The main insight is, what you learn bodily for a possible physical fight or threat can be transmitted into non-physical conflicts like discussions, verbal assaults, or mobbing. Finally it may become your individual personality.

Literature

• Amdur, E. (2018 ) Hidden in Plain Sight. Esoteric Power Training within Japanese Martial Traditions. Freelance Academy Press, Wheaton (IL).
• Holiday, R, Hanselman, S. (2020) Lives of the Stoics: The Art of Living from Zeno to Marcus Aurelius. Penguin Publications.
• Kruszewski A. (2023) From Ancient Patterns of Hand-to-Hand Combat to a Unique Therapy of the Future. Int J Environ Res Public Health. Feb 17;20(4):3553.
• Krings, L. (2017) Leibliches Üben als Teil einer philosophischen Lebenskunst: Die Verkörperung von Kata in den japanischen Wegkünsten. European Journal of Japanese Philosophy (EJJP). pp 179-197.
• Moore B, Woodcock S, Dudley D. (2021) Well-being Warriors: A Randomized Controlled Trial Examining the Effects of Martial Arts Training on Secondary Students’ Resilience. Br J Educ Psychol. Dec;91(4): pp 1369-1394.
• Stockdale, J. (1993 ) Courage Under Fire: Testing Epictetus’s Doctrines in a Laboratory of Human Behavior. Hoover Institution Press.
• Strozzi-Heckler, R. (2007 ) The Leadership Dojo. Build Your Foundation as an Exemplary Leader. Frog Ltd., Berkeley.
• Yagyu, M. (2003 ) The Life-Giving Sword. Secret Teachings from the House of the Shogun. Kodansha Int., Tokyo.

Lecturer

Thomas Christaller studied Mathematics, Physics, and Computer Science, working in the field of Artificial Intelligence since 1976. First on natural language understanding, then knowledge-based systems, and finally cognitive robotics. He was institute director first at the GMD then Fraunhofer Society heading the institute for Autonomous intelligent Systems then Intelligent Analysis and Information Systems located at Schloss Birlinghoven in Sankt Augustin, Germany. He co-founded the German journal \”KI\”, was a member of the Wissenschaftsrat, and co-founded the Interdisciplinary College. Since 1972 he is practicing the Japanese martial art of Aikido, holding the 6. Dan (black belt), teaching at his dojo in Bonn and giving Aikido seminars worldwide. * My column in akido journal (German) https://www.aikidojournal.de/Kolumnen/Professor_Thomas_Christaller/ * My biography in wikipedia (German) https://de.wikipedia.org/wiki/Thomas_Christaller * Videos about Aikido & Much More https://vimeo.com/lebenskunst

Affiliation: Aikido Teacher
Homepage: www.lebenskunst-bonn.de

Lecturer: Franjo Weissing
Fields: Ecology & Evolution, Behavioural Biology

Content

Biological organisms have to cope with ever-changing environmental conditions. They have been ‘designed’ for this task in a long evolutionary history, but how evolution by natural selection has achieved this is far from clear. Two properties are crucial for long-term survival in a changing world: ‘robustness’ (the ability to build the same phenotype under very different conditions) and ‘evolvability’ (the ability to rapidly respond to changing conditions by adaptive evolution). The conundrum is that these properties seem to be contradictory: doesn’t a robust design impede evolvability, and doesn’t evolvability require a flexible design? A second problem is that ‘evolutionary design’ is fundamentally different from the ‘engineered design’. While an engineer has foresight, adaptive evolution resembles a ‘blind watchmaker’ (Dawkins 1986) in that it is driven by short-term selection pressures. We all know that following short-term incentives often has negative implications in the longer term. How, then, can long-term properties like robustness and evolvability be shaped by a myopic process like natural selection?
Questions like these will be addressed in four sessions. The first two sessions will illustrate the dynamic complexity of apparently simple ecological and evolutionary systems. We will see that such systems can be ‘fundamentally unpredictable’ and that adaptive evolution can, in principle, drive a population to extinction (‘evolutionary suicide’). In the last two sessions, we will sketch a new way of evolutionary thinking that may (partly) resolve issues like these. We will see that the evolution of ‘responsive strategies’ (strategies that respond to the local environmental conditions) is fundamentally different from the evolution of non-responsive strategies. The reciprocal causality inherent to these strategies speeds up evolution by orders of magnitude and leads to quite different evolutionary outcomes. In biological organisms, responsive strategies are often implemented via regulatory networks (e.g., gene regulation networks or neural networks). It turns out that the evolution of such networks shares various properties with learning (by machines or intelligent agents).

Literature

• Session 1: Out of equilibrium
  • Huisman, J. & Weissing, F.J. 1999. Biodiversity of plankton by species oscillations and chaos. Nature 402: 407-410, doi: 10.1038/46540.
  • Huisman, J. & Weissing, F.J. 2001. Fundamental unpredictability in multispecies competition. American Naturalist 157: 488-494, doi: 10.1086/319929.
• Session 2: Conflict and cooperation
  • Baldauf, S.A., Engqvist, L. & Weissing, F.J. 2014. Diversifying evolution of competitiveness. Nature Communications 5: 5233, doi: 10.1038/ncomms6233.
  • Long, X. & Weissing, F.J. 2023. Transient polymorphisms in parental care strategies drive divergence of sex roles. Nature Communications 14: 6805, doi: 10.1038/s41467-023-42607-6.
• Session 3: The reciprocal causality of responsive strategies
  • Quiñones, A.E., Van Doorn, G.S., Pen, I., Weissing, F.J. & Taborsky, M. 2016. Negotiation and appeasement can be more effective drivers of sociality than kin selection. Phil. Trans. R. Soc. B 371:20150089, doi:10.1098/rstb.2015.0089.
  • Netz, C., Hildenbrandt, H. & Weissing, F.J. 2022. Complex eco-evolutionary dynamics induced by the coevolution of predator-prey movement strategies. Evol. Ecol. 36: 1-17, doi: 10.1007/s10682-021-10140-x.
  • Gupte, P.R., Albery, G.F., Gismann, J., Sweeny, A.R. & Weissing, F.J. 2023. Novel pathogen introduction triggers rapid evolution in animal social movement strategies. eLife, 12: e81805, doi: 10.7554/eLife.81805.
• Session 4: Robustness and evolvability
  • Wagner, A. 2008. Robustness and evolvability: a paradox resolved. Proc. Royal Society B 275: 91-100, doi: 10.1098.rspb.2007.1137.
  • Watson, R.A. & Szathmáry, E. 2016. How can evolution learn? Trends in Ecology & Evolution 31: 147-157, doi: 10.1016/j.tree.2015.11.009
  • Van Gestel, J. & Weissing, F.J. 2016. Regulatory mechanisms link phenotypic plasticity to evolvability. Scientific Reports 6:24524, doi: 10.1038/srep24524.

Lecturer

After studying mathematics and biology at the University of Bielefeld, I did my PhD work at the Centre for Interdisciplinary Studies (ZiF Bielefeld), where I co-organised the research year ‘Game Theory in the Behavioural Sciences’. Together with the later Nobel laureate Elinor Ostrom, I pioneered the introduction of ‘evolutionary thinking’ into the political sciences. In 1989, I moved to the University of Groningen (Netherlands), where I tackled a wide variety of eco-evolutionary questions with a combined theoretical and empirical approach. My area of expertise lies in the development and analysis of mathematical and computational models. Our emphasis is on ‘mechanistic models of intermediate complexity’ that lead to insights and predictions that can be tested in close collaboration with empirical biologists. In my research, I strive to understand the emergence of diversity at all levels of biological organisation (e.g., differences between cells, individuals, the sexes, groups, species, and ecosystems) and the implications of diversity for the evolution and functioning of biological systems. In the last ten years, I have broadened my research again to other disciplines. As a Distinguished Lorentz Fellow, I spent a research year at the Institute of Advanced Study in the Humanities and Social Sciences in Amsterdam, where I critically investigated the foundations of cultural evolution theory. I became convinced that new approaches are needed that do justice to the differences between genetic and cultural evolution. To this end, we are currently working on a new framework for the evolution of individual and social learning, which is based on the evolution of neural networks.

Affiliation: University of Groningen
Homepage: https://research.rug.nl/en/organisations/weissing-group-theoretical-biology

Lecturer: Giulia Belgiovine
Fields: Artificial Intelligence, Human-Robot Interaction

Content

The study of human-robot interaction is a vast and intricate field that adopts a multidisciplinary approach and encompasses numerous challenges. On the one hand, the quest to comprehend and model the intricate mechanisms underlying human cognitive and social abilities; on the other hand, the problem of how to replicate a comparable level of intelligence in cognitive interactive agents. Achieving this necessitates the integration of sensory and motor capabilities, along with memory, reasoning, and learning mechanisms, to develop artificial agents endowed with adaptation and generalization skills.
In these lectures, we will explore the interdisciplinary nature of this field, discussing the challenges and opportunities that lie ahead.

Lecturer

Giulia Belgiovine is a postdoctoral researcher at the COgNiTive Architectures for Collaborative Technologies (CONTACT) unit of the Italian Institute of Technology, Genoa, Italy. Her research investigates how to develop cognitive architectures for social robots to promote better human-robot interactions and how to foster robots’ autonomous learning and adaptive behavior. Her research interests include multiparty interactions, assistive robotics, and lifelong learning.

Affiliation: Italian Institute of Technology

Lecturer: Martin Tik, Anna-Lisa Schuler
Fields: Cognitive Neuroscience, Clinical Neuroscience, Neuroimaging

Content

In this course we will discuss basics, applications and hot topics for two of the most popular magnets in brain research: TMS and MRI. While fMRI allows for the depiction of neural underpinning underlying task processing, TMS as a neuromodulation technique allows for the targeted manipulation of these processes. This course will be dedicated to give an overview about both of the techniques and the advantages of their combination.

Session 1: In session one we will give an overview about the history of magnets in cognitive neuroscience and the evolution of the methods of interest (TMS, fMRI). We will furthermore explain the technical basics and the composition of these devices.

Session 2: In session two we will discuss the physiological mechanisms of action underlying the techniques including blood oxygenation and neuronal action potentials. Then we will give an overview about different applications of TMS and fMRI including exemplary research.

Session 3: In this session we will discuss specific applications of combining TMS with fMRI in cognitive neuroscience and clinical medicine. In the second part of this session, participants will have the opportunity to plan their hypothetical own TMS and fMRI experiments in small groups.

Session 4: Participants will discuss their projects in a plenum. Finally, there will be a summary, question round and wrap up of the course.

Learning goals:

– Basic principles of TMS and fMRI
– Applications of TMS and fMRI
– Direct transfer of these contents to own research

Literature

• Chen, J. E., & Glover, G. H. (2015). Functional magnetic resonance imaging methods. Neuropsychology review, 25, 289-313.
• Pitcher, D., Parkin, B., & Walsh, V. (2021). Transcranial magnetic stimulation and the understanding of behavior. Annual Review of Psychology, 72, 97-121.
• Burke, M. J., Fried, P. J., & Pascual-Leone, A. (2019). Transcranial magnetic stimulation: Neurophysiological and clinical applications. Handbook of clinical neurology, 163, 73-92.

Lecturer

Martin Tik is a Group Leader at the Medical University of Vienna. His main research interests lie in advancing the method of interleaving TMS with fMRI and the improvement of depression treatment using the combination of these methods.

Affiliation: Medical University of Vienna
Homepage: https://www.martintik.at/

Anna-Lisa Schuler is a Post-Doctoral researcher at the Max Planck Institute for Human Cognitive and Brain Sciences. She is mainly interested in the combination of TMS with fMRI in cognitive and clinical neuroscience including language processing and plasticity in healthy and neuropsychiatric populations.

Affiliation: Max Planck Institute for Human Cognitive and Brain Sciences
Homepage: https://twitter.com/AnnaLisaSchule1

Lecturer: Erika Covi
Fields: Emerging memory devices, Neuromorphic computing

Content

In recent years, the cloud-based approach to data classification has been challenged by the edge computing paradigm, which has enabled real-time data processing at the network edge, ideally next to the sensor collecting data. This paradigm poses severe constraints on the systems in terms of power-efficiency, compactness, and latency [1, 2]. Therefore, we need to explore unconventional hardware solutions able to meet these stringent requirements.
Brain-inspired architectures, particularly Spiking Neural Networks (SNNs), are promising candidates to achieve low-latency computation, and stateful, energy-efficient operations [3]. However, their current implementations primarily rely on digital or mixed-signal Complementary Metal-Oxide-Semiconductor (CMOS) technologies, which pose challenges in meeting the demanding memory, area, and power constraints of computing on the edge [1].
In this context, the integration of embedded memristive technologies holds a significant promise to enhance the capabilities of CMOS technology [2, 4] for the development of neuromorphic hardware [5]. Memristive devices are nanoscale devices able to change their conductivity upon application of proper electrical stimuli. They offer fast and energy-efficient tuneable volatile and non-volatile storage, and are therefore well-suited for storing SNN parameters. The exploitation of their unique properties, such as operation voltages compatible with current CMOS technology as well as analogue, neural-/synaptic-like behaviour, offer an attractive opportunity for realizing energy-efficient and massively parallel computing architectures in conjunction with CMOS technology [3, 5]. Indeed, these features enable efficient computation, neural dynamics, and synaptic plasticity, which are essential traits for emulating the brain’s functionality in hardware [4, 6].
In this course, we explore the role of memristive devices in emulating the functionality of neural networks, enabling edge systems to classify and recognise patterns or process sensory inputs. We emphasize the need for co-developing memristive devices with CMOS circuits to enable seamless integration and to exploit the strengths of both technologies. Furthermore, we discuss how the co-development of memristor devices, CMOS circuits, and innovative learning algorithms can facilitate edge computing paradigms. Moreover, the intrinsic physical characteristics of memristive devices, if correctly exploited, enable analogue computing, thus offering a compelling alternative to traditional digital approaches. We also discuss the challenges and opportunities of developing memristive-CMOS hardware neuromorphic architectures. The co-design of devices, circuits, and algorithms indeed requires to identify and address issues related to device variability, scalability, and system integration.
In conclusion, the synergistic co-development of memristive devices, CMOS circuits, and innovative algorithms can pave the way for intelligent edge devices capable of performing complex cognitive tasks.

Literature

• [1] E. Covi et al. Front. in Neurosci., 15, 611300 (2021).
• [2] D. V. Christensen et al. Neuromorph. Comput. Eng., 2, 022501 (2022).
• [3] E. Chicca et al. Proc. of the IEEE, 102, pp. 1367-1388 (2014).
• [4] D. Ielmini and S. Ambrogio, Nanotech., 31, 092001 (2019).
• [5] A. Amirsoleimani et al. Adv. Intell. Sys., 2, 2000115 (2020).
• [6] E. Covi et al. Neuromorph. Comput. Eng., 2, 012002 (2022).

Lecturer

Dr. Erika Covi is Assistant Professor at the Zernike Institute for Advanced Materials & Groningen Cognitive Systems and Materials Center (Groningen, the Netherlands). She received her PhD in Microelectronics in 2014 from the University of Pavia (Italy), where she worked on designing integrated systems for the characterisation of memristive devices. She also worked at the National Research Council (CNR) of Italy, at Politecnico di Milano (Italy), and at NaMLab gGmbH (Dresden, Germany). Her research interests lie at the intersection of emerging devices, circuit design, and brain-inspired computing. More specifically, they focus on exploiting the intrinsic physical characteristics of memristive devices to reproduce computational primitives of the brain in mixed neuromorphic-memristive systems.

Affiliation: University of Groningen

Lecturer: Tarek R. Besold
Fields: Artificial Intelligence, Machine Learning, AI Governance

Content

In this course we will (try to) assess the current state of play as regards responsible AI development from both a theoretical/principled (i.e., what are – or can be – the foundations of responsible AI systems and development practices?) and an applied (i.e., which part(s) of the theory carry over into practice and what are the challenges/breaking points?) perspective.

We will have a look at (some of) the conceptual underpinnings of responsible tech development, at the particularities which AI as a domain adds to those, and at proposals for how to bring the chosen values and guidelines into application. We will also have a look at the pressures AI developers face in practice, what these mean for the implementation of responsible development approaches, and if there are (currently) hard-to-overcome breaking points between theory and application. Finally, we will map some of the governance and regulatory infrastructure and processes governments are now working to put into place in order to guarantee adherence to a minimal standard of good practices in the development of AI systems.

Literature

• High Level Expert Group on Artificial Intelligence. “Ethics Guidelines for Trustworthy AI”. 2019. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.
• Mitchell, Margaret, et al. “Model cards for model reporting”. Proceedings of the conference on fairness, accountability, and transparency. 2019. https://arxiv.org/abs/1810.03993.
• Thong, William, Przemyslaw Joniak, and Alice Xiang. “Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color”. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023. https://openaccess.thecvf.com/content/ICCV2023/papers/Thong_Beyond_Skin_Tone_A_Multidimensional_Measure_of_Apparent_Skin_Color_ICCV_2023_paper.pdf.
• Chatila, Raja, et al. “Trustworthy AI”. Reflections on Artificial Intelligence for Humanity (2021): 13-39. https://link.springer.com/chapter/10.1007/978-3-030-69128-8_2
• Mittelstadt, Brent, Sandra Wachter, and Chris Russell. “The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default”. arXiv preprint arXiv:2302.02404 (2023). https://arxiv.org/abs/2302.02404.
• Mittelstadt, Brent. “Principles alone cannot guarantee ethical AI”. Nature machine intelligence 1.11 (2019): 501-507. https://arxiv.org/abs/1906.06668.

Lecturer

Tarek R. Besold is a Senior Research Scientist at Sony AI in Barcelona, as well as an affiliated researcher with the Philosophy & Ethics group at Eindhoven University of Technology. His work covers topics at the intersection between AI, cognitive science, and real-world applications. He also serves as a start-up advisor and consulting AI policy expert, and was the founding chairman of the German DIN/DKE Standards Working Committee on AI NA 043-01-42 GA.

Affiliation: Sony AI Barcelona
Homepage: https://ai.sony/

Lecturer: Jürgen Gadau
Fields: Sociobiology, Genomics and Epigenetics of adpative traits

Content

I plan to give an overview on social evolution in animals and humans from an evolutionary and genomic/epigenetic point of view. I plan three sessions, the first will give a general overview on genotype/epigenotyp – phenotype mapping, the second will highlight and introduce epigenetic regulations and inheritance and finally I will discus the similarities and differences between social insects and humans, i.e. understand the key processes and mechanisms that sustain cooperation in these hypersocial organisms.
1. From Genotype to social Phenotypes
2. The role of epigenetics and phenotypic plasticity for individualisation and social evolution
3. Humans the other animals

Literature

• Frank Seebacher and Jens Krause: Epigenetics of Social Behaviour
• https://www.sciencedirect.com/science/article/pii/S0169534719301399
• Robert Boyd
• A different kind of animal

Lecturer

I received my PhD from the University of Würzburg in 1997, spent 2 years as a PostDoc at UC Davis in California and habilitated between 1999 and 2004 in Würzburg (Zoologie). From 2004-2016 I was professor at Arizona State University before I accepted a W3 Professorship for Molecular Evolution and Sociobiology at the University Münster. I work on the genetic and epigenetic architecture and evolution of adaptive traits in social and solitary Hymenoptera (ants, bees and wasps). Since 2021 I am also dean for the biology faculty.

Affiliation: University of Münster
Homepage: https://www.uni-muenster.de/Evolution/molevolsocbio/

Lecturer: Johannes Textor
Fields: Immunology, Artificial Intelligence, Machine Learning

Content

Our body harbours two complex learning systems: the central nervous system (CNS), and the adaptive immune system (IS). Artificial neural network models of the CNS have contributed substantial insight to neuroscience and CNS-inspired “deep learning” has revolutionized artificial intelligence. In contrast, how the IS processes information is still much less understood. This course will therefore ask: how can we understand information processing, learning and adaptation in the IS through the lens of computer science?

We will dive deep into several fascinating processes in the adaptive immune system such as negative selection, self-foreign discrimination, tolerance, and affinity maturation. I will cover the necessary immunological background to come to an understand of what we do and don’t know about these processes, and discuss how computational and mathematical models have been instrumental in our quest to understand the immune system from a computational perspective. I hope you will leave this course as fascinated and inspired by the marvelous architecture of our immune system as I am, and that this inspiration will transform and broaden your view of fundamental concepts like learning, adaptation, and generalization.

Literature

• Inge M N Wortel, Can Keşmir, Rob J De Boer, Judith N Mandl, Johannes Textor:
• Is T Cell Negative Selection a Learning Algorithm? Cells 9(3): 690, 2020. doi: 10.3390/cells9030690
• Marsland R 3rd, Howell O, Mayer A, Mehta P. Tregs self-organize into a computing ecosystem and implement a sophisticated optimization algorithm for mediating immune response. Proc Natl Acad Sci U S A. 2021 Jan 5;118(1):e2011709118. doi: 10.1073/pnas.2011709118.
• Jürgen Westermann, Tanja Lange, Johannes Textor, Jan Born:
• System Consolidation During Sleep – A Common Principle Underlying Psychological and Immunological Memory Formation.
• Trends in Neurosciences 38: 583-595, 2015.
• Stephanie Forrest, Steven A. Hofmeyr, Anil Somayaji. Computer Immunology. Communications of the ACM 40, 1997, pp 88–96. https://doi.org/10.1145/262793.262811

Lecturer

Johannes Textor is an associate professor in the Data Science and Medical BioScience departments at Radboud University and Radboud University Medical Center in Nijmegen, The Netherlands. His team studies the adaptive immune system through the lens of computation and learning. Building in silico models of the human immune system and combining these with various types of experimental data, they aim to understand the essence of what makes immune systems learn and adapt to changing environments. Johannes Textor holds a Vidi grant from the Dutch Research Council, a program grant from the Human Frontiers Science foundation, and was a visiting scholar at the Simons Institute, UC Berkeley, for the Spring 2022 semester.

Affiliation: Radboud University Nijmegen
Homepage: johannes-textor.name

Lecturer: Prof. Luc Steels
Fields: Artificial Intelligence, Consciousness

In 2021, Blake Lemoine, a software engineer at Google, and responsible for testing a chatbot LaMDA (Language Model for Dialog Applications) came to the conclusion that LaMDA was sentient, in the sense of being conscious. Since then many other users of generative AI systems, in particular ChatGPT, have reported similar experiences and the topic of machine consciousness suddenly became ‘salonfähig’, with neuroscientists and philosophers pitching in to define requirements for machine consciousness and whether or not this might ever be possible – although mostly concluding that current AI systems do not qualify as conscious agents. What are we to make of all this?

This talk intends to bring this discussion to IK, a forum exceptionally well adapted to have groundbreaking multi-disciplinary open discussions. I will argue for a vision of the mind as a dense network of complex adaptive networks, operating at different layers, from living embodiment to sentience, cognition, sapience and consciousness. The networks are autonomous and autopoietic. In other words, they develop on their own account and remain in a constant state of becoming, grounded in embodied action on the one hand and cooperation and competition with other agents on the other. We will then discuss consciousness from the point of view of this framework, reflecting on measures of awareness, the role of language, what consciousness might be for, non-ordinary states of consciousness, moral consciousness, a.o.  In this vision there is no single ‘I’, no physical conscious substance à la Penrose or Faggin, no intelligence without grounding or social interaction, no understanding without meaning. 

The goal of this talk is not to announce a new gospel but to stimulate the discussion and, in particular, become more disciplined and careful in attributing consciousness or other mental qualities to machines.

Lecturer

Luc Steels is a prominent figure in the field of artificial intelligence and cognitive science, known for his influential academic career. He has made significant contributions to various areas, including computational linguistics, robotics, and artificial life. His research has focused on the development of artificial systems capable of language evolution and cognitive processes, pioneering the use of robots and computer models to study language emergence. Throughout his career, he has held esteemed positions at institutions such as the Vrije Universiteit Brussel and the Sony Computer Science Laboratory. Luc Steels’ work has had a profound impact on our understanding of the origins of language and the evolution of intelligent systems.