The field of computer vision has traditionally focused on recognizing objectively agreed-upon concepts such as animals, vehicles, or specific objects. However, many practical, real-world applications require identifying subjective concepts that may vary significantly...

Posted by Bahare Fatemi and Bryan Perozzi, Research Scientists, Google Research Imagine all the things around you — your friends, tools in your kitchen, or even the parts of your bike. They are all connected in different ways. In computer science, the term graph is...

In the evolving landscape of artificial intelligence, the study of how machines understand and process human language has unveiled intriguing insights, particularly within large language models (LLMs). These digital marvels, designed to predict subsequent words or...

In an era marked by an explosion of scientific knowledge, particularly in neuroscience, parsing through and synthesizing vast swaths of research has become a Herculean challenge. Neuroscience presents a quintessential example of the difficulties researchers face in...

The capabilities of LLMs are advancing rapidly, evidenced by their performance across various benchmarks in mathematics, science, and coding tasks. Concurrently, advancements in Reinforcement Learning from Human Feedback (RLHF) and instruction fine-tuning are aligning...

Recent advancements in large vision-language models (VLMs) have shown promise in addressing multimodal tasks by combining the reasoning capabilities of large language models (LLMs) with visual encoders like ViT. However, despite their strong performance on tasks...