Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …

With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon
footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to …

Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …

This paper provides a survey of the emerging area of Large Language Models (LLMs) for
Software Engineering (SE). It also sets out open research challenges for the application of …

The capabilities of a new class of tools, colloquially known as generative artificial
intelligence (AI), is a topic of much debate. One prominent application thus far is the …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

This paper explores the environmental impact of the super-linear growth trends for AI from a
holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the …

Machine learning (ML) workloads have rapidly grown, raising concerns about their carbon
footprint. We show four best practices to reduce ML training energy and carbon dioxide …

Generative AI systems across modalities, ranging from text, image, audio, and video, have
broad social impacts, but there exists no official standard for means of evaluating those …

The computation demand for machine learning (ML) has grown rapidly recently, which
comes with a number of costs. Estimating the energy cost helps measure its environmental …