2.9k citations · 6.4k across the 7 of their papers we have counts for
7 papers
How people use Copilot for Health
Beatriz Costa-Gomes, Pavel Tolmachev, Eloise Taysom +15
We analyze over 500,000 de-identified health-related conversations with Microsoft Copilot from January 2026 to characterize what people ask conversational AI about health. We devel…
Sequential Diagnosis with Language Models
Harsha Nori, Mayank Daswani, Christopher Kelly +12
Artificial intelligence holds great promise for expanding access to expert medical knowledge and reasoning. However, most evaluations of language models rely on static vignettes an…
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch +26
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manua…
The Kinetics Human Action Video Dataset
Will Kay, Joao Carreira, Karen Simonyan +9
We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 1…
Massively Parallel Methods for Deep Reinforcement Learning
Arun Nair, Praveen Srinivasan, Sam Blackwell +11
We present the first massively distributed architecture for deep reinforcement learning. This architecture uses four main components: parallel actors that generate new behaviour; p…
Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette +4
Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents…
Learning to Transduce with Unbounded Memory
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman +1
Recently, strong results have been demonstrated by Deep Recurrent Neural Networks on natural language transduction problems. In this paper we explore the representational power of…