The Flowers project-team, at the Inria Center of University of Bordeaux, aims to study the fundamental mechanisms that can enable open-ended learning and development in humans and machines, i.e. how individuals, or groups of individuals, can continuously discover and learn novel skills of increasing complexity. We also aim to leverage this fundamental understanding for human-centered real-world applications in education and in assisted scientific discovery.

In particular, we focus on studying mechanisms enabling Autotelic and Aligned Intelligence in humans and machines. A first key ingredient of open-ended learning is curiosity-driven autotelic learning, which is the ability of individuals to set and pursue their own goals (from the greek ‘telos’/goal, and ‘auto’/self), a form of intrinsic motivation pushing organisms to continuously seek new knowledge and skills. self-organizing their own learning curriculum, using meta-cognition and leading to creative exploration. 

To enable abstraction, collective intelligence, and alignment of autotelic systems on human cultures (values, preferences), we also aim to study how language and social interaction, both as a communication system and as a cognitive tool, can guide autotelic exploration. Symmetrically, using multi-scale models, we aim to study how curiosity-driven autotelic exploration could self-organize at the group level. We also aim to study what are the ecosystemic and evolutionary origins of autotelic systems.

We study these mechanisms from three complementary scientific perspectives:

1) Improving our understanding of human curiosity-driven autotelic and aligned intelligence: here we use methods from psychology to design new experimental protocols, and computational theories and models to design and test hypotheses about human autotelic learning and how it interacts with metacognition, language and social interaction; 

2) Building curiosity-driven autotelic and aligned machines: we aim to design and build open-ended learning machines that are autotelic, data frugal, with strong generalisation skills, and leverage language and social interaction to integrate within human cultures (values, ethics, preferences); Here we leverage generative AI systems, encoding forms of human cultural knowledge, as cognitive tools, and we aim to improve the frugality and grounding of generative AI systems using autotelic deep reinforcement learning; 

3) Applications: in Educational technologies, we aim to stimulate curiosity-driven autotelic learning, meta-cognition and creativity in humans across the lifespan and across neurodiversity (leveraging both models of human autotelic learning and frugal generative edTech tools); In the domain of Assisted scientific discovery, we aim to study how curiosity-driven autotelic exploration algorithms can help scientists (physicists/chemists/mathematicians, …) make discoveries in complex systems.

Beyond core scientific questions across disciplines, this project addresses two key societal challenges: 1) How can we build AI systems that serve humans and human societies in their diversity, helping their curiosity and cultures to bloom? 2) How can we provide educational opportunities for all children, and adults across the lifespan, in a world with many challenges, to become intrinsically motivated learners, critical thinkers, autotelic explorers?