Green artificial intelligence (AI) is more environmentally friendly and inclusive than
conventional AI, as it not only produces accurate results without increasing the …

Over the past decade, the dominance of deep learning has prevailed across various
domains of artificial intelligence, including natural language processing, computer vision …

We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-
centric approach. While stronger language models can enhance multimodal capabilities, the …

We introduce SPEAR-TTS, a multi-speaker text-to-speech (TTS) system that can be trained
with minimal supervision. By combining two types of discrete speech representations, we …

We present FengWu, an advanced data-driven global medium-range weather forecast
system based on Artificial Intelligence (AI). Different from existing data-driven weather …

Recurrent neural networks (RNNs) have fast inference and scale efficiently on long
sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with …

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather
scenarios is crucial for important decisions, from warning the public about hazardous …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

We present SoundStorm, a model for efficient, non-autoregressive audio generation.
SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional …

We present symbol tuning-finetuning language models on in-context input-label pairs where
natural language labels (eg," positive/negative sentiment") are replaced with arbitrary …