Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Data Governance in the Age of Large-Scale Data-Driven Language Technology
The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language…
Measuring the Carbon Intensity of AI in Cloud Instances
The advent of cloud computing has provided people around the world with unprecedented access to computational power and enabled rapid growth in technologies such as machine learning, the…
Simple but Effective: CLIP Embeddings for Embodied AI
Contrastive language image pretraining (CLIP) encoders have been shown to be beneficial for a range of visual tasks from classification and detection to caption-ing and image manipulation. We…
Domain Mismatch Doesn’t Always Prevent Cross-Lingual Transfer Learning
Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised…
Towards General Purpose Vision Systems
A special purpose learning system assumes knowledge of admissible tasks at design time. Adapting such a system to unforeseen tasks requires architecture manipulation such as adding an output head…
MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound
This task enables it to perform well variety Abstract As humans, we navigate a multimodal world, building a holistic understanding from all our senses. We introduce MERLOT Reserve , a model that…
What do navigation agents learn about their environment?
Today’s state of the art visual navigation agents typically consist of large deep learning models trained end to end. Such models offer little to no interpretability about the learned skills or the…
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks
We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and image generation,…
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models
In this paper, we present and implement a multi-dimensional, modular framework for performing deep argument analysis (DeepA2) using current pre-trained language models (PTLMs). ArgumentAnalyst – a…
Correcting a coarse-grid climate model in multiple climates by machine learning from global 25-km resolution simulations
Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse-resolution global atmosphere model with real…