  Having a rich multimodal inner language is an important component of human
intelligence that enables several necessary core cognitive functions such as
multimodal prediction, translation, and generation. Building upon the Conscious
Turing Machine (CTM), a machine model for consciousness proposed by Blum and
Blum (2021), we describe the desiderata of a multimodal language called
Brainish, comprising words, images, audio, and sensations combined in
representations that the CTM's processors use to communicate with each other.
We define the syntax and semantics of Brainish before operationalizing this
language through the lens of multimodal artificial intelligence, a vibrant
research area studying the computational tools necessary for processing and
relating information from heterogeneous signals. Our general framework for
learning Brainish involves designing (1) unimodal encoders to segment and
represent unimodal data, (2) a coordinated representation space that relates
and composes unimodal features to derive holistic meaning across multimodal
inputs, and (3) decoders to map multimodal representations into predictions
(for fusion) or raw data (for translation or generation). Through discussing
how Brainish is crucial for communication and coordination in order to achieve
consciousness in the CTM, and by implementing a simple version of Brainish and
evaluating its capability of demonstrating intelligence on multimodal
prediction and retrieval tasks on several real-world image, text, and audio
datasets, we argue that such an inner language will be important for advances
in machine models of intelligence and consciousness.
