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Reinforcement learning with human feedback (RLHF) is the standard method for aligning large language models (LLMs) with human preferences — such as the preferences for nontoxic...
Many machine learning (ML) applications involve embedding data in a representation space, where the geometric relationships between embeddings carry semantic content. Performing a useful task often...
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...
Logo recognition is the task of identifying a specific logo and its location in images or videos. It helps create a safe and trustworthy shopping experience,...
Even before the COVID-19 pandemic, health care capacity in the United States was strained, with not enough medical professionals to meet growing demand. Ying Ding, a...
This year, the Amazon Search team had two papers accepted at the Conference on Computer Vision and Pattern Recognition (CVPR), both focusing on image-text feature alignment,...
Joe Tighe, senior manager for computer vision at Amazon Web Services, is a coauthor on two papers being presented at this year’s Winter Conference on Applications...
Abstractive summarization is the automatic extraction and recombination of phrases from a text in order to summarize that text. Deep-learning-based abstractive-summarization systems are usually trained to...
Scene boundary detection is the problem of localizing where scenes in a video begin and end. It’s an important step towards semantic understanding of video, with applications...