Blaise Aguera y Arcas

TL;DR: In this paper, the authors presented a decentralized approach for federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets.

TL;DR: This work presents a practical method for the federated learning of deep networks that proves robust to the unbalanced and non-IID data distributions that naturally arise, and allows high-quality models to be trained in relatively few rounds of communication.

TL;DR: The authors proposed a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias, and showed that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine.

TL;DR: Artificial intelligence and brain–computer interfaces must respect and preserve people's privacy, identity, agency and equality, say Rafael Yuste, Sara Goering and colleagues.