Seeking to uncover the mathematical bases of all intelligent processes.

Bridging the divide between intelligence science and AI technology at the service of human society.

Research Themes

Conference Participation

Research Themes

Neuroscience of Intelligence

By understanding more about how the brain processes information, techniques from neuroscience, combined with physics of information, can help us translate human thinking from its biology into fundamental mathematical functions of intelligence.

Neuroscience image

Theory of Agency

Theory of Agency

Moving on from deep learning, designing new AI paradigms will require better mathematics for learning in itself. We start from understanding the emergence of goals in agents, eventually aiming at building a global framework for the problem of credit assignment.

Collective AI

We work towards the design of societies of AI agents that can truly parallelize their computation, learn collectively from each other, and invent new mechanisms that work on a collective scale, in order to work on a problem together.

Collective AI image

Publications

Frontiers in Neuroscience

Cross-Subject EEG-Based Emotion Recognition through Neural Networks with Stratified Normalization

Fdez, J., Guttenberg, N., Witkowski, O., & Pasquali, A.

Impacts of Speciation and Extinction Measured by an Evolutionary Decay Clock

Jennifer F. Hoyal Cuthill, Nicholas Guttenberg & Graham E. Budd

Science Advances

Deep Learning on Butterfly Phenotypes Tests Evolution’s Oldest Mathematical Model

Cuthill, J. F. H., Guttenberg, N., Ledger, S., Crowther, R., & Huertas, B.

Evolutionary Rates of Information Gain and Decay in Fluctuating Environments

Origins of Life and Evolution of Biospheres

Hidden Concepts in the History and Philosophy of Origins-of-Life Studies: a Workshop Report

Mariscal, Carlos et al.

Artificial Life

How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles

Witkowski, O., Ikegami, T.

Learning to (Learn [Learning Algorithms] Through Multi-Agent Communication)

Rosa, M., Afanasjeva, O., Andersson, S., Davidson, J., Guttenberg, N., Hlubuček, P., ... & Feyereisl, J.

Artificial Life

On the Potential for Open-Endedness in Neural Networks

Guttenberg, N., Virgo, N., & Penn, A.

Quarterly Journal of Experimental Psychology

Reversible Second-Order Conditional Sequences in Incidental Sequence Learning Tasks

Pasquali, A., Cleeremans, A., & Gaillard, V.

The Dynamics of Cooperation Versus Competition

Witkowski, O. & Nitschke, G.

Phenomenology and the Cognitive Sciences

The Integrated Structure of Consciousness: Phenomenal Content, Subjective Attitude, and Noetic Complex

Miyahara, K. & Witkowski, O.

Artificial Life and Robotics

Critical Mass in the Emergence of Collective Intelligence: a Parallelized Simulation of Swarms in Noisy Environments

Drozd, A., Witkowski, O., Matsuoka, S. & Ikegami, T.

Emergence of Swarming Behavior: Foraging Agents Evolve Collective Motion Based on Signaling

Witkowski, O., & Ikegami, T.

Permutation-Equivariant Neural Networks Applied to Dynamics Prediction

Guttenberg, N., Virgo, N., Witkowski, O., Aoki, H. & Kanai, R.

Conference Participation

Combining EEG Signals and Deep Learning for Expert Classification

Fdez, J., Sanno, D., Abuhasira, M., Vaisman, S., Geva, A. B., & Pasquali, A.

Towards a Science of Intelligence and Consciousness

Consciousness and the Coherence Principle: A Physical Solution to the Mind‐Body Problem

Dynamic Defect Segmentation with Unsupervised SSIM and Blob Detection

Sanno, D., & Pasquali, A.

Training Robotic Arm on Bulk Grasping in 3D Environment

Angénieux, R., Sanno, D., Risselin, C., & Pasquali, A.

Deep Learning: Implementing Consciousness

Know thyself: Brain Imaging Evidence of an Intermediate Level of Meta-Representation

Pasquali, A., & Sakai, K.