Lecturer: Ana-Alexandra Moga Fields: Coaching | Personal Development | Personal Growth
Content
In the four session course, we’ll take a walk together through the garden of resilience.
Over the duration of the course, we’ll explore some science-based knowledge nuggets to expand on how we think about our mind and body, practice some coaching methods that can anchor us in the present moment, develop our own tools for cultivating resilience and, hopefully, have some fun in the meantime. As we collectively experience the conference, during the middle part of the course we’ll explore ways to connect deeper and find inspiration and strength from each other and the group. The closing part of the course is aimed to highlight and review the tools and strategies that we can take home with us to further experiment with and continue to cultivate resilience in our every day life.
Lecturer
Ana-Alexandra Moga is a certified executive and leadership coach. Her background is rooted in software development and engineering management, with a lifelong yearning for artistic expression. Her passion for coaching took an academic route in 2021, when she enrolled at New York University to follow a rigorous education in the coaching domain. Six months after graduation she left her product and engineering leadership role to pursue the coaching path full time. She harnessed her 17+ years experience in both the corporate world and in fast paced start-up environments to create a blended coaching style: creative, playful, goal oriented, highly adaptable and with a strong structural foundation.
Lecturer: Michael Ungar Fields: Social Science; Psychology; Health Science; Community Development; Cultural Studies
Content
In this short course, Dr. Michael Ungar will explore the many different systems that contribute to experiences of individual and collective resilience, as well as the methods used to research multisystemic resilience. The intent is to integrate perspectives from studies of biological, psychological, social, institutional, and economic resilience, as well as those concerned with the built and natural environments. The course will also focus on how to research resilience in participatory ways to develop knowledge that informs policy and practice. An introduction to the theory of resilience will be followed by an overview of its application to populations under stress, as well as the tools used to assess resilience at individual and community levels. Using examples from studies conducted by Dr. Ungar and his colleagues at the Resilience Research Centre, students will have an opportunity to reflect on how multiple systems influence one another over time and in culturally nuanced ways. Discussion will include topics such as contextualization of the resilience concept, measure development to account for positive developmental processes, and the many aspects of resilience that need to be considered in designing research and developing programs and policies to improve the capacity of populations to cope with atypical stressors. Participants are encouraged to bring questions relating to their own research topics whether from the natural, biological, or social sciences.
Literature
Ungar, M. & Theron, L. (2019). Resilience and mental health: How multisystemic processes contribute to positive outcomes. Lancet Psychiatry, 7(5), 441-448. Doi:10.1016/S2215-0366(19)30434-1
Ungar, M. (2018). Systemic resilience: Principles and processes for a science of change in contexts of adversity. Ecology & Society, 23(4). Doi: 10.5751/ES-10385-230434.
Michael Ungar, Ph.D., is a Family Therapist and Professor of Social Work at Dalhousie University where he holds the Canada Research Chair in Child, Family and Community Resilience. His research on resilience around the world and across cultures has made him the number one ranked Social Work scholar in the world, with numerous educational institutions, government agencies, not-for-profits and businesses relying on his research and clinical work to guide their approaches to nurturing child, family, organizational and community wellbeing under stress. He the author of over 250 peer reviewed papers and book chapters, as well as 18 books for researchers, mental health professionals, and lay audiences, including his most recent works The Limits of Resilience: When to Persevere, When to Change, and When to Quit (forthcoming January, 2024), a book for individuals and organizations under stress, Multisystemic Resilience: Adaptation and Transformation in Contexts of Change, an open access compilation of 39 scholarly papers from a dozen different disciplines, and Working with Children and Youth with Complex Needs: 20 Skills to Build Resilience, a book for mental health professionals. As well as having received numerous awards for his work, including the Canadian Association of Social Workers National Distinguished Service Award and being named a Fellow of the Royal Society of Canada, Dr. Ungar maintains a blog titled Nurturing Resilience which can be read on Psychology Today’s website.
Lecturer: Anna Förster Fields: Internet of Things, Machine Learning
Content
This course will offer an overview of the problems and challenges associated with outdoor deployments of internet of things (IoT) applications. After a short introduction to the field of IoT and the discussion of various outdoor applications, we will dive deeper into the threats IoT applications face in these environments. We will showcase some concrete threats and discuss possible solutions and approaches.
Lecturer
Anna Förster obtained her MSc degree in computer science and aerospace engineering from the Free University of Berlin, Germany, in 2004 and her PhD degree in self-organising sensor networks from the University of Lugano, Switzerland, in 2009. She also worked as a junior business consultant for McKinsey&Company, Berlin, between 2004 and 2005. From 2010 to 2014, she was a researcher and lecturer at SUPSI (the University of Applied Sciences of Southern Switzerland). Since 2015, she leads the Sustainable Communication Networks group at the University of Bremen. Currently, she serves as Director of the Bremen Spatial Cognition Center (BSCC) and as a board member of the Center for Computing Technology (TZI). Her main research interests lie in the domain of the Internet of Things. She is mostly interested in self-awareness and resilience, user friendliness and user adoption, self-organisation, and machine learning for IoT applications. All considered scenarios and applications serve the Sustainable Development Goals and contribute to a more sustainable and peaceful future.
Literature hints are spread over the slides that can be downloaded via the link given in the abstract above.
Lecturer
Herbert Jaeger studied Mathematics in Freiburg (Germany), specializing in formal logic, then did a PhD in Bielefeld (Germany) in the classical AI (knowledge-based systems) group of Ipke Wachsmuth, became interested in dynamical systems modeling in cognitive science, which led to a postdoc in the autonomous robots research team of Thomas Christaller at the (then) German National Research Institute for Mathematics and Computer Science (GMD) in Sankt Augustin (Germany), where he shifted to signal processing, machine learning and recurrent neural networks, which in turn allowed him to found and head a GMD research unit on modeling intelligent dynamical sytsems (MINDS); then from 2001 to 2019 he served as professor in the CS department of the private Jacobs University Bremen (Germany) where he taught theoretical CS and machine learning and continued thinking about mathematical modeling of cognitive dynamics, which somehow got him pulled into the fields of unconventional computing, which in turn in 2019 led to an appointment at the University of Groningen, where he still uses the name MINDS for his group and where he still teaches machine learning but now dedicates all research efforts on unconventional computing theory, often in collaboration with mathematicians, theoretical computer scientists, materials scientists, microchip engineers, cognitive scientists and AI/machine learning colleagues. His lifetime dream is to develop a mathematical language for general information-processing dynamical systems.
The famous song by Queen, was written by Brian May for the 1986 film Highlander, the song is used to frame the scenes in the film where Connor MacLeod must endure his beloved wife Heather MacLeod growing old and dying while he, as an Immortal, remains forever young. Who wants to live forever as a song and as a religious and philosophical question introduces uncomfortable queries about the value and purpose of this life.
The evening talk suggests that Digital afterlife has moved beyond digital memorialisation towards a desire to preserve oneself after death. Preserving oneself or being preserved by someone else may affect both the dying person’s peace of mind and the well-being of the bereaved. Yet it is not clear whether the possibility of digital immortality and the use of digital media alters thoughts about the mind-body connection, and whether interaction with a digital immortal alters one’s spiritual journey through grief. Afterlife and resurrection remain troublesome because they are couched in mystery and philosophical and theological discourse cannot explain resurrection of the body, because the human body itself is not reducible to simple description or ready comprehension.
Literature
Burden, D. (2020). Building a Digital Immortal. In M. Savin-Baden & V. Mason-Robbie (Eds)., Digital Afterlife. Death Matters in a Digital Age. Boca Raton, Florida: CRC Press
Burden, D. & Savin-Baden, M (2019). Virtual Humans: Today and Tomorrow. Florida: CRC Press.
Harbinja, E. (2020). The ‘new(ish)’ property, informational bodies and postmortality. In M. Savin-Baden & V. Mason-Robbie (Eds)., Digital Afterlife. Death Matters in a Digital Age. Boca Raton, Florida: CRC Press.
Kasket, E. (2021). If death is the spectacle , big teach is the lens: How social media frame an age of ‘spectacular death’ In M. H. Jacobsen. (Ed) The Age of Spectacular Death. Oxford: Routledge
Pitsillides, P. (2019). Digital legacy: Designing with things, Death Studies, 43 (7), 426-434. DOI: 10.1080/07481187.2018.1541939
Savin-Baden, M. (2023) Postdigital Theologies in P. Jandrić (Ed) Encyclopedia of Postdigital Science and Education. Cham: Springer.
Savin-Baden, M. (2023) Postdigital Afterlife in P. Jandrić (Ed) Encyclopedia of Postdigital Science and Education. Cham: Springer.
Savin-Baden, M. (2023) Digital afterlife and the spiritual realm: Transcendence. In M. E. Mogseth & F.H. Nilsen. Limits of Life. Critical Interventions, London: Berghahn Series
Lecturer
Professor Maggi Savin-Baden is a Senior Research Fellow at the University of Oxford, UK and has researched and evaluated staff and student experience of learning for over 20 years and gained funding in this area (Leverhulme Trust, JISC, Higher Education Academy, MoD). She gained her Masters and PhD from the University of London and a second Masters in Digital learning from the University of Edinburgh in 2018. Maggi has a strong publication record of over 60 research publications and 25 books which reflect her research interests in the impact of innovative learning, digital fluency, cyber-influence, pedagogical agents, qualitative research methods, and problem-based learning. In her spare time, she runs, bakes, rock climbs, does triathlons and has recently taken up wild swimming and paddle boarding .
Linear algebra has ancient roots, appearing even in cuneiform text millennia ago. However, it has proven itself to be resilient and powers many modern techniques in machine learning, natural language processing, cognitive science, and more. Yet, many people use these tools without truly understanding why they work and when they should be used. The purpose of this course is to provide a deep dive into the intuition behind the tools that linear algebra has to offer. It should be of interest both to students who have taken a course in linear algebra and those who have not. We will also touch on the topic of the responsible use of linear algebra and other mathematical techniques.
Literature
King, E. and Wilson J. “Linear Data” (2023) [open source text to be made available before IK]
Lecturer
Dr. Emily J. King received Ph.D. in mathematics from the University of Maryland in 2009. Since then, she has been an IRTA Postdoctoral Fellow at the National Institutes of Health (USA); a Humboldt Postdoctoral Fellow at Uni Osnabrück, Uni Bonn, and TU Berlin; and a Juniorprofessor at Uni Bremen. She is currently an Associate Professor in the Mathematics Department and member of the data science faculty at Colorado State University. Photo credit: John Eisele/Colorado State University Photography
Lecturer: Philipp Wicke Fields: Artificial Intelligence, Computational Linguistics, Language Models
Content
Most of the thousands of languages in the world share some key properties that enable us to exchange information within our language communities, but also beyond them. At the same time, we experience a shift in artificial intelligence, which heavily relies on the development of large language models for certain languages, which exhibit emergent, general human-like properties such as reasoning and planning. This course has a bipartite structure that combines the theory of robustness in languages from the perspective of cognitive science with the practice of responsible applications of generative language models.
* Session 1: Introduction to Language Robustness In this introductory session, students will gain a profound understanding of language universals and the fundamental role they play in human communication. We explore universal properties of language and linguistic relativity, examining how language shapes our perceptions and thoughts.
* Session 2: Traits of Robust Language: Image Schemas and Embodiment Building on the foundation laid in the previous session, we investigate the connection between language and conceptual understanding. Explore image schemas and embodiment as powerful mechanisms for robust concepts. Additionally, we introduce the concept of “Robustness of Concepts” and explore illustrative examples, including the fusion of language and emojis.
* Session 3: Large Language Models and Image Generation This session opens up new horizons as we look at Large Language Models (LLMs). Learn about the possibilities and challenges of using these sophisticated models for various applications. We delve into multilingual studies involving Large Language Models, such as Glot500, and look at those concerned with embodiment and LLMs. We will also look at text-to-image generational models such as Midjourney, StableDiffusion or Dalle-2. Students will also gain insights into testing different models to identify their strengths and limitations.
* Session 4: Responsible Language Generation Ethics takes center stage in this last session, as we focus on the responsible use of language generation technologies. We focus on the environmental impact of these models, considering factors such as emissions, energy consumption during training and inference, and e-waste generation and storage. Societal implications are also discussed, including bias in language models, data annotation through crowdsourcing, and the ethical challenges posed by deep fakes and fake news generation.
By the end of this course, students will have a comprehensive understanding of language universals, large language models, and ethical considerations.
Literature
Evans, Vyvyan, and Melanie Green. Cognitive linguistics: An introduction. Routledge, 2018.
Boroditsky, Lera. “Does language shape thought?: Mandarin and English speakers’ conceptions of time.” Cognitive psychology 43.1 (2001): 1-22.
Wei, Jason, et al. “Emergent abilities of large language models.” arXiv preprint arXiv:2206.07682 (2022).
ImaniGooghari, Ayyoob, et al. “Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages.” arXiv preprint arXiv:2305.12182 (2023).
Wicke, Philipp. “LMs stand their Ground: Investigating the Effect of Embodiment in Figurative Language Interpretation by Language Models.” arXiv preprint arXiv:2305.03445 (2023).
Wicke, Philipp, and Marianna Bolognesi. “Emoji-based semantic representations for abstract and concrete concepts.” Cognitive processing 21.4 (2020): 615-635.
Lecturer
Philipp Wicke studied Cognitive Science at the University of Osnabrück in the B.Sc. programme. During these studies he interned at Dauwels Lab at the NTU Singapore in the field of neuroinformatics, he also interned at the Creative Language Systems Lab at UCD Dublin at which he later wrote his dissertation on “Computational Storytelling as an Embodied Robot Performance with Gesture and Spatial Metaphor” under supervisor Tony Veale. In his current role at LMU, Philipp is researching on Natural Language Processing and teaches programming in the B.A. and M.A. Computational Linguistics. Philipp Wicke is the Head of AI Applications of the AI for People Association and an Associate Member of the Munich Center for Machine Learning (MCML).
Affiliation: Center for Information and Language Processing (CIS), LMU – Munich Homepage:www.phil-wicke.com
Lecturer: Benjamin Paaßen Fields: Machine Learning
Content
Machine learning is concerned with automatically learning models (patterns, regularities, correlations) from known data which generalize to new data. To do so, it combines concepts from mathematics (esp. statistics, probability theory, linear algebra, and optimization), artificial intelligence, and computer science. This course will provide an introduction to machine learning for the un-initiated. While some math will be necessary, everything will be accompanied by pictures and examples to get the core intuition across 😊
In more detail, the course will have four sessions with the following topics:
Session 1: Basic Concepts: What is Machine learning and how does it relate to Artificial Intelligence? What are types of ML? What does ‘learning’ mean in ML? We will also discuss the basic ingredients of an ML algorithm (loss function, model class, and optimization strategy), linear regression as an example for such an algorithm, underfitting, overfitting (and how to prevent it), how probabilities help us to make precise what ‘generalization’ means, and how to design a basic ML experiment.
Session 2: Classic machine learning tasks and methods to solve them: The distance perspective on ML, Regression, Classification, Dimensionality Reduction, Clustering, with respective methods for each task; and decision trees/forests
Session 3: Artificial neural networks and deep learning: How to build artificial neural networks from single neurons to present-day transformers
Session 4: Reinforcement learning and ethics
Each session is accompanied by a (voluntary) programming exercise in Python. Exercise sheets (and slides) can be found here: https://bpaassen.gitlab.io/Teaching.html
Literature
This is optional literature for people who want to dive in deeper after the course:
Biehl, M. (2023). The Shallow and the Deep: A biased introduction to neural networks and old school machine learning. https://www.cs.rug.nl/~biehl/
Benjamin Paaßen received their doctoral degree in intelligent systems in 2019 from Bielefeld University on the topic of ‘Metric Learning for Structured Data’. Afterwards, they received a DFG research fellowship for a stay at The University of Sydney in Australia and Humboldt-University of Berlin. From 2021-2024, they were deputy head of the educational technology lab at the German Research Center for Artificial Intelligence (DFKI). Since April 2023, they are junior professor for knowledge representation and machine learning (KML, speak ‘camel’) at Bielefeld University. Their research foci are machine learning on structured data and artificial intelligence for education.
Lecturer:Thomas Wolf Fields: Social cognition, interpersonal coordination
Content
Humans are social animals and achieve remarkable things when they coordinate. Coordination in time and space however is not always as easy as it might seem. Joint action research aims to understand the cognitive mechanisms involved in social coordination. In this course we will look at different types of interpersonal coordination, their underlying mechanisms, some effects of coordination and various physical and non-physical devices which support coordination. We will focus on how work songs, such as sea shanties, support the coordination of physical effort.
Pickering, M., Robertson, E., & Korczynski, M. (2017). Rhythms of Labour: The British Work Song Revisited. Folk Music Journal, 9(2), 226–245. https://www.jstor.org/stable/pdf/4522809.pdf
Sebanz, N., Bekkering, H., & Knoblich, G. (2006). Joint action: Bodies and minds moving together. Trends in Cognitive Sciences, 10(2), 70–76. https://doi.org/10.1016/j.tics.2005.12.009
Sebanz, N., & Knoblich, G. (2021). Progress in Joint-Action Research. Current Directions in Psychological Science, 096372142098442. https://doi.org/10.1177/0963721420984425
van der Wel, R. P. R. D., Becchio, C., Curioni, A., & Wolf, T. (2021). Understanding joint action: Current theoretical and empirical approaches. Acta Psychologica, 215, 103285. https://doi.org/10.1016/j.actpsy.2021.103285
Wolf, T., Vesper, C., Sebanz, N., Keller, P. E., & Knoblich, G. (2019). Combining Phase Advancement and Period Correction Explains Rushing during Joint Rhythmic Activities. Scientific Reports, 9(1), 9350. https://doi.org/10.1038/s41598-019-45601-5
Lecturer
Thomas Wolf studied musicology and cognitive science at the University of Vienna, before completing his PhD in cognitive science at the Central European University, Budapest. Currently he is a postdoctoral researcher in the Social Mind and Body (SOMBY) Lab at the Central European University, Vienna, where he directs the SOMBY MusicLab. Embedded in the larger fields of social cognition and joint action, his research interests center around temporal coordination in social interactions, which he investigates through experiments conducted on joint music-making.
There has been a paradigm change in views of the self. The self is no longer an abstract entity, situated or realized by our individual brains. It is seen as embodied instead and likely as being co-constituted through our relations and interactions with others. In this course we explore recent theories of selfhood stemming from the field of so-called embodied and enactive cognition. We will discuss the self both from a third-personal “objective” perspective (as a living entity) and from a first-personal, subjective perspective (as lived or experienced entity). An important question to be explored is the extent to which the embodied self should be seen as a genuinely social and relational phenomenon.
Literature
Di Paolo, E., Rohde, M., & De Jaegher, H. (2010). Horizons for the enactive mind: Values, social interaction, and play. In Enaction: Towards a new paradigm for cognitive science. MIT press
Gallagher, S. (2000). Philosophical conceptions of the self: implications for cognitive science. Trends in cognitive sciences, 4(1), 14-21.
Gallagher, S., & Daly, A. (2018). Dynamical relations in the self-pattern. Frontiers in psychology, 664.Heersmink, R. (2020). Varieties of the extended self. Consciousness and Cognition, 85, 103001.
Hutto, D. D., & Ilundáin-Agurruza, J. (2020). Selfless activity and experience: Radicalizing minimal self-awareness. Topoi, 39(3), 509-520.
Kyselo, M. (2014). The body social: an enactive approach to the self. Frontiers in Psychology, 5, 986.
Lindblom, J. (2020). A radical reassessment of the body in social cognition. Frontiers in Psychology, 11, 987.
Maiese, M. (2019). Embodiment, sociality, and the life shaping thesis. Phenomenology and the Cognitive Sciences, 18(2), 353-374.
Thompson, E. (2005). Sensorimotor subjectivity and the enactive approach to experience. Phenomenology and the cognitive sciences, 4(4), 407-427.
Lecturer
Miriam Kyselo is a philosopher and cognitive scientist. She received a PhD from the Institute of Cognitive Science University of Osnabrueck. Since 2020 she holds the position of Associate Professor at the Norwegian University of Science and Technology. Her expertise is in philosophy of cognition, especially the so-called 4E approaches (enacted, extended, embodied, embedded aspects of the mind), philosophy of psychology, as well as interdisciplinary research in embodied cognitive science.
