Human Intelligible Machine Intelligence (OR Grounding Perception in Language for Explainable AI)
Perceiving and interacting with the world around us involves a myriad of challenges. As humans, we are able to seek (plan) acquire (represent) and verify (reason) beliefs about the world, utilising a variety of sources (sights, sounds, etc.) Moreover, we can share these beliefs with one another using natural language. Across such tasks, we employ a very language-like ability, making use of compositional hierarchies to build abstractions that encapsulate meaning. In this talk, the speaker will demonstrate some of the benefits of such a grounded language-model-based approach, using generalisable and interpretable abstractions across vision, language, and robotics to perform reasoning (and meta-reasoning) about the world. Furthermore, he will discuss some of the difficulties with such an approach -- the extent of presumed knowledge, availability of supervision, learning multi-modal interpretable representations and provide some potential solutions that leverage current advances in deep generative models, a combination of neural-networks and probabilistic models.