Raffay Hamid

Last month, at its annual re:Invent developers’ conference, Amazon Web Services (AWS) announced the release of two new additions to its Titan family of foundation models, both of which translate between text and images. With Amazon Titan Multimodal Embeddings, now available through Amazon Bedrock, customers can upload their own sets of images and then search them using text, related images, or both.

Many recent advances in artificial intelligence are the result of representation learning: a machine learning model learns to represent data items as vectors in a multidimensional space, where geometric relationships between vectors correspond to semantic relationships between items.

Machine learning (ML) models thrive on data, but gathering and labeling training data can be a resource-intensive process. A common way to address this challenge is with synthetic data, but even synthetic data usually requires laborious hand annotation by human analysts. At this year’s Computer Vision and Pattern Recognition conference (CVPR), we presented a method called HandsOff that eliminates the need to hand-annotate synthetic image data.