Section: Research Program

Background

In recent years, Artificial Intelligence (AI) has achieved important landmarks in matching or surpassing human level performance on a number of high level tasks (playing chess and go, driving cars, categorizing picture, etc., [28], [31], [36], [27], [33]). These strong advances were obtained by deploying on large amounts of data, massively parallel learning architectures with simple brain-inspired ‘neuronal’ elements. However, humans brains still outperform machines in several key areas (language, social interactions, common sense reasoning, motor skills), and are more flexible : Whereas machines require extensive expert knowledge and massive training for each particular application, humans learn autonomously over several time scales: over the developmental scale (months), humans infants acquire cognitive skills with noisy data and little or no expert feedback (weakly/unsupervised learning)[1]; over the short time scale (minutes, seconds), humans combine previously acquired skills to solve new tasks and apply rules systematically to draw inferences on the basis of extremely scarce data (learning to learn, domain adaptation, one- or zero-shot learning) [30].

The general aim of CoML, following the roadmap described in [1], is to bridge the gap in cognitive flexibility between humans and machines learning in language processing and common sense reasoning. We conduct work in three areas: weakly supervised and unsupervised algorithms, datasets and benchmarks, and machine intelligence evaluation.