Developmental autonomous learning: AI, cognitive sciences and educational technology
Current approaches to AI and machine learning are still fundamentally limited in comparison with autonomous learning capabilities of children. What is remarkable is not that some children become world champions in certains games or specialties: it is rather their autonomy, flexibility and efficiency at learning many everyday skills under strongly limited resources of time, computation and energy. And they do not need the intervention of an engineer for each new task (e.g. they do not need someone to provide a new task specific reward function).
I will present a research program that has focused on computational modeling of child development and learning mechanisms in the last decade. I will discuss several developmental forces that guide exploration in large real world spaces, starting from the perspective of how algorithmic models can help us understand better how they work in humans, and in return how this opens new approaches to autonomous machine learning.
In particular, I will discuss models of curiosity-driven autonomous learning, enabling machines to sample and explore their own goals and their own learning strategies, self-organizing a learning curriculum without any external reward or supervision.
I will show how this has helped scientists understand better aspects of human development such as the emergence of developmental transitions between object manipulation, tool use and speech. I will also show how the use of real robotic platforms for evaluating these models has led to highly efficient unsupervised learning methods, enabling robots to discover and learn multiple skills in high-dimensions in a handful of hours. I will discuss how these techniques are now being integrated with modern deep learning methods.
Finally, I will show how these models and techniques can be successfully applied in the domain of educational technologies, enabling to personalize sequences of exercises for human learners, while maximizing both learning efficiency and intrinsic motivation. I will illustrate this with a large-scale experiment recently performed in primary schools, enabling children of all levels to improve their skills and motivation in learning aspects of mathematics. Web: http://www.pyoudeyer.com
Pierre-Yves Oudeyer is a research director at Inria and head of the FLOWERS lab at Inria and Ensta-ParisTech since 2008. Before, he has been a permanent researcher at Sony Computer Science Laboratory for 8 years (1999-2007).
He studies developmental autonomous learning and the self-organization of behavioural and cognitive structures, at the frontiers of AI, machine learning, neuroscience, developmental psychology and educational technologies. In particular, he studies exploration in large open-ended spaces, with a focus on autonomous goal setting, intrinsically motivated learning, and how this can automate curriculum learning. With his team, he pioneered curiosity-driven learning algorithms working in real world robots (used in Sony Aibo robots), and showed how the same algorithms can be used to personalize sequences of learning activitivies in educational technologies deployed at large in schools. He developed theoretical frameworks to understand better human curiosity and its role in cognitive development, and contributed to build an international interdisciplinary research community on human curiosity. He also studied how machines and humans can invent, learn and evolve speech communication systems.
He is laureate of the Inria-National Academy of Science young researcher prize in computer sciences, of an ERC Starting Grant, and of the Lifetime Achievement Award of the Evolutionary Linguistics association. Beyond academic publications and several books, he is co-author of 11 international patents. His team created the first open-source 3D printed humanoid robot for reproducible science and education (Poppy project, now widely used in schools and artistic projects), as well as a startup company. He is also working actively for the diffusion of science towards the general public, through the writing of popular science articles and participation to radio and TV programs as well as science exhibitions.