Ben Hutchinson

TL;DR: In this article, the authors describe the state-of-the-art in the field of federated learning from the perspective of distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, and statistics.

TL;DR: Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

TL;DR: This work proposes model cards, a framework that can be used to document any trained machine learning model in the application fields of computer vision and natural language processing, and provides cards for two supervised models: One trained to detect smiling faces in images, and one training to detect toxic comments in text.

TL;DR: Model cards as discussed by the authors are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) that are relevant to the intended application domains.

TL;DR: The authors presented LaMDA: Language Models for Dialog Applications, a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text and demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.