The token parser and manipulator, next-generation Deep Learning architecture

Anonymous

Keywords: object-centric, symbolic, hybrid

Abstract: Deep Learning is an excellently scalable approach for processing unstructured, high-dimensional, raw sensory signal. It is so good that these properties also becomes its most popular criticism. At the moment, deep learning is mostly just a giant correlation machine, devouring enormous amount of data to recognise hidden pattern in data, but still lacking in human-like systematic generalisation required in many reasoning tasks. Symbolic AI on the other hand possesses these abilities by design, but relied on handcrafted symbols that has already been abstracted away from the raw information. Among many approach to combine the best of both worlds, I am most excited about the end-to-end trainable architecture with a perception module that structurised the raw input and a reasoning module operates on top of these symbol-like vectors. While there are still a lot of work before such a system becomes practically relevant, in this blog post we will take a look at the paper Contrastive Learning of Structured World Model, an early paper that offer a glimpse into such architecture through a concrete implementation.

ICLR Paper: https://arxiv.org/abs/1911.12247