Pedro A. Ortega
AGI and Cybernetics Researcher
I was VP of research at Kosen Labs and prior to that, lead of the Safety Analysis Team at DeepMind. My research focuses on artificial general intelligence and the formal principles of intelligence, covering aspects such as learning, planning and decision making in both machines and biological organisms. My approach lies at the intersection between machine learning, computational neuroscience, theoretical economics, and physics.
Most of my work centers on information-theoretic and statistical mechanical approaches to learning and control, leading to contributions in bounded rationality models and recasting adaptive control as a causal inference problem. I have also worked on causal induction, and on game- and decision-theoretic models in computational neuroscience.
To get a sense of my work, please refer to:
- my PhD thesis on bounded rationality and Bayesian control,
- my paper on Thompson sampling as Bayesian causal inference,
- my paper on bounded rationality through thermodynamic principles,
- or my publications.
I was part of the former Tuebingen group on Sensorimotor Learning and Decision Making, where some of the now widespread ideas on Thompson sampling, causality, and thermodynamic methods for reinforcement learning have originated.
- A minimum relative entropy principle for learning and actingJournal of Artificial Intelligence Research, 2010
- Thermodynamics as a theory of decision-making with information-processing costsProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2013
- Meta-learning of sequential strategiesarXiv preprint arXiv:1905.03030, 2019
- Shaking the foundations: delusions in sequence models for interaction and controlarXiv preprint arXiv:2110.10819, 2021
- Neural networks and the chomsky hierarchyIn International Conference on Learning Representations (ICLR), 2023