WenLan 2.0: Make AI Imagine via a Multimodal Foundation Model
  The fundamental goal of artificial intelligence (AI) is to mimic the core
cognitive activities of human including perception, memory, and reasoning.
Although tremendous success has been achieved in various AI research fields
(e.g., computer vision and natural language processing), the majority of
existing works only focus on acquiring single cognitive ability (e.g., image
classification, reading comprehension, or visual commonsense reasoning). To
overcome this limitation and take a solid step to artificial general
intelligence (AGI), we develop a novel foundation model pre-trained with huge
multimodal (visual and textual) data, which is able to be quickly adapted for a
broad class of downstream cognitive tasks. Such a model is fundamentally
different from the multimodal foundation models recently proposed in the
literature that typically make strong semantic correlation assumption and
expect exact alignment between image and text modalities in their pre-training
data, which is often hard to satisfy in practice thus limiting their
generalization abilities. To resolve this issue, we propose to pre-train our
foundation model by self-supervised learning with weak semantic correlation
data crawled from the Internet and show that state-of-the-art results can be
obtained on a wide range of downstream tasks (both single-modal and
cross-modal). Particularly, with novel model-interpretability tools developed
in this work, we demonstrate that strong imagination ability (even with hints
of commonsense) is now possessed by our foundation model. We believe our work
makes a transformative stride towards AGI and will have broad impact on various
AI+ fields (e.g., neuroscience and healthcare).
