Recent progress in Artificial Intelligence (AI) has been remarkable due to developments in deep learning. Yet AI systems based on deep learning are not reliable. A good metaphor for understanding this limitation is provided by the account of human thinking pioneered by Nobel Prize winner Daniel Kahneman (Thinking, Fast and Slow, 2011), which is based on two "systems": an intuitive System 1 which is fast, reactive, automatic, and specialized, and a reasoning System 2 which is slow, deliberative, transparent, and general. Data-based learning systems are like System 1 boxes; while model-based reasoning systems are like System 2 boxes. System 1 and System 2 processes, however, are tightly integrated in the human mind, and a main challenge is to achieve a similar integration between data-based learning and model-based reasoning in AI. Key steps in this direction and main objectives of Hector's research are learning meaningful models from data, and getting data-based and model-based components to complement, enhance, and inform each other, in the context of acting and planning.
Hector Geffner
Visiting Professor
Founded the Representation, Learning and Planning lab.
