Viktor Kewenig
PhD Candidate in the Ecological-Brain Program at UCL (almost submitted)
My research intersects AI, Cognitive Neuroscience and Philosophy, with a focus on language. Naturally, I am a big fan of Wittgenstein.
I have always found myself interested in both humanities and STEM subjects - starting with Logic, Philosophy of Science and Mind at Cambridge, gradually moving into Cognitive Neuroscience of Language Comprehension during my MSc at UCL. My PhD work in the Eco-Brain Leverhulme DTP combines multimodal computational and ecologically valid behavioural and neuroscientific methods to find out what principles align AI with human brains.
Over the past year, I have been collaborating with Microsoft Research Cambridge outside of my PhD, thinking about the impact generative AI can have on human cognition (especially in the context of knowledge work and education).
Music brings an important balance to my life - I curate a podcast series called "5918mins" and sometimes mix records myself under an alias called "No Frills". The best finds are uploaded to my YouTube channel.
For a comprehensive overview of my academic and professional background, you can have a look at my CV.
Current Research
-
Metacognitive Interventions for Efficient Prompting of Generative AI Systems
Building a metacognitive interface for improving generative AI interaction modes.
This project focuses on developing interfaces that enhance user metacognition when interacting with generative AI systems, potentially improving the efficiency and effectiveness of AI prompting. -
Evolving Norms Around the Use of Generative AI in Higher Education
2024 - Co-led qualitative study and write-up, in collaboration with Microsoft Research
This study explores the changing norms and practices surrounding the use of generative AI in higher education settings. -
Effect of Large Language Models vs. Note Taking on Memory and Comprehension in High-School Students
2024 - Co-leading data collection, write-up and analysis of a large scale study in collaboration with Cambridge University Assessment, Microsoft Research Europe, and Microsoft Research New York
This research compares the impact of using large language models versus traditional note-taking methods on high-school students' memory and comprehension. -
Fine-tuning CLIP for Sentiment Analysis
2024 - Stanford University, Natural Language Understanding (XCS224U), Online Course
This project involves adapting the CLIP (Contrastive Language-Image Pre-training) model for sentiment analysis tasks. -
Encoding and Decoding Brain Activation During Naturalistic Story Listening with Unimodal and Multimodal Large Language Models: A Comparison
2024 - Analysis on high-performance computer cluster, using TensorFlow, PyTorch, Transformer, and customised LLaMA implementation
This study compares unimodal and multimodal large language models in their ability to encode and decode brain activation during naturalistic story listening.
Publications
-
The Metacognitive Demands and Opportunities of Generative AI
Tankelevitch, L.*, Kewenig, V.*, Simkute, A., Scott, E.A., Sarkar, A., Sellen, A., Rintel, S. (2024) - Published at Proceedings of CHI (best paper award)
This paper explores the metacognitive aspects of working with generative AI systems, highlighting both the challenges and opportunities in human-AI interaction. -
Ironies of Generative AI: Understanding and mitigating productivity loss in human-AI interactions
Simkute, A., Tankelevitch, L., Kewenig, V., Scott, A.E., Sellen, A., Rintel, S. (2024) - Accepted at International Journal of Human–Computer Interaction
This paper explores the paradoxical productivity losses that can occur in human-AI interactions and proposes strategies for mitigating these issues to improve overall efficiency. -
Multimodality and Attention Increase Alignment in Natural Language Prediction Between Humans and Computational Models
Kewenig, V.*, Lampinen, A., Nastase, S., Edwards, C., D'Estelanx, Q.L., Rechardt, A., Vigliocco, G., Skipper, J. (2023) - Under Review
This study investigates how multimodal inputs and attention mechanisms in computational models can improve alignment with human language prediction, potentially enhancing AI's understanding of natural language. -
The entire brain, more or less is at work: 'Language regions' are artefacts of averaging
Aliko, S., Wang, B., Kewenig, V., Small, S.L., Skipper, J. (2023) - Under Review at Nature
This research challenges the concept of specific 'language regions' in the brain, suggesting that language processing involves broader neural networks than previously thought. -
The Ecological Brain: Reframing the Study of Human Behaviour and Cognition
Vigliocco, G., Conventino. L., De Felice, S., Gregorians, L., Kewenig, V., Musolesi, M., Hudson-Smith, A., Tyler, N., Fluori, E., Spiers, H. (2023) - Accepted at Royal Society Open Science
This paper proposes a new framework for studying human behavior and cognition, emphasizing the importance of ecological validity in neuroscience and psychology research. -
When abstract becomes concrete: naturalistic encoding of concepts in the brain
Kewenig, V.*, Vigliocco, G., Skipper, J. (2022) - Accepted at Elife
This study explores how the brain encodes abstract concepts in naturalistic settings, providing insights into the neural mechanisms underlying conceptual representation and language comprehension. -
More than Words: Caregivers Selectively Use Iconic and Indexical Cues in Communication with Children
Motamedi, Y.*, Murgiano, M.*, Kewenig, V.*, Grzyb, B., Gu, Y., Brieke, Marshall, C., Wonnacott, E., Perniss, P., Vigliocco, G. (2022) - Published at Child Development
This research examines how caregivers use non-verbal cues, such as gestures and pointing, in communication with children, shedding light on the multimodal nature of language acquisition. -
Intentionality But Not Consciousness: Re-Considering Robot-Love
Kewenig, V. (2019) - AI Love You, eds. Fischer, Zhou. ISBN: 978-3-030-19734-6
This chapter explores the philosophical implications of human-robot relationships, focusing on the concept of intentionality in AI and its role in emotional connections. -
Robots As Intentional Agents: Using Neuroscientific Methods To Make Robots Appear More Social
Kewenig, V.*, Zhou, Y., Fisher, M. (2018) - Frontiers Psychology
This paper discusses the application of neuroscientific methods to enhance the social appearance of robots, exploring ways to make human-robot interactions more natural and intuitive. -
AI Safety: Comments, Questions and Concerns
Kewenig, V.*, Sayed, M. (2016)
This work addresses key issues in AI safety, presenting a critical analysis of potential risks and ethical considerations in the development and deployment of artificial intelligence systems.