A long-term objective of artificial intelligence is to build “multimodal” neural networks—AI systems that learn about concepts in several modalities, primarily the textual and visual domains, in order to better understand the world. In our latest research announcements, we present two neural networks that bring us closer to this goal.
The first neural network, DALL·E, can successfully turn text into an appropriate image for a wide range of concepts expressible in natural language. DALL·E uses the same approach used for GPT-3, in this case applied to text–image pairs represented as sequences of “tokens” from a certain alphabet.
The second, CLIP, has the ability to reliably perform a staggering set of visual recognition tasks. Given a set of categories expressed in language, CLIP can instantly classify an image as belonging to one of these categories in a “zero-shot” way, without the need to fine-tune on data specific to these categories, as is required with standard neural networks. Measured against the industry benchmark ImageNet, CLIP outscores the well-known ResNet-50 system and far surpasses ResNet in recognizing unusual images.
We believe that these neural networks represent a meaningful step toward multimodal AI systems.