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Facebook AI Research


Developing the next generation of advanced AI will require powerful new computers capable of quintillions of operations per second. Today, Meta is announcing that we’ve designed and built the AI Research SuperCluster (RSC) — which we believe is among the fastest AI supercomputers running today and will be the fastest AI supercomputer in the world …

It’s been one year since Facebook AI launched Dynabench, a first-of-its-kind platform that radically rethinks benchmarking in AI. Starting today, we’re unlocking Dynabench’s full capabilities for the AI community — AI researchers can now create their own custom tasks to better evaluate the performance of natural language processing (NLP) models in more flexible, dynamic, and realistic …

We’ve built and are now sharing the largest available data set designed to help AI researchers develop new systems that can identify image manipulation at scale. The first-of-its-kind Image Similarity data set provides a global benchmark for combating harmful image manipulation and abuses online. The Image Similarity data set contains over 1 million images including …

The speed at which AI has evolved over the last decade means it’s easy to overlook the significance of individual developments along the way. Things have changed so fast that what seemed like a milestone just a couple of years ago is already outdated. But to understand the progress, it’s important to note those milestones. …

Every day, we’re inundated with a constant stream of information — most of which we’ll forget. Sure, you can probably remember what you had for breakfast this morning, but what about last year? We often take for granted the ability to forget mundane, day-to-day details to make room for valuable moments that matter in our …

AI plays an important role across Facebook’s apps — from enabling stunning AR effects, to helping keep bad content off our platforms, to directly improving the lives of people in our communities through our COVID-19 Community Help hub. As AI-powered services become ubiquitous in everyday life, it’s becoming even more important to understand how systems …

What the research is: Across the thousands of different languages spoken by humans, the way we use words to represent different colors is remarkably consistent. For example, many languages have two distinct words for red and orange, but no language has many distinct commonly used words for various tonalities of orange. (Of course, if you …

  Most successes in AI come from developing specific responses to specific problems. We can create an AI that outperforms humans at chess, for instance. Or, as we demonstrated with our Pluribus bot in 2019, one that defeats World Series of Poker champions in Texas Hold’em. What we really want, however, is an AI system …

Teaching computers to understand how humans write and speak, known as natural language processing (NLP), is one of the oldest challenges in AI research. There has been a marked change in approach over the past two years, however. Where research once focused on developing specific frameworks for specific tasks, today powerful general-purpose language models can …

Benchmarks — from MNIST to ImageNet to GLUE — have played a hugely important role in driving progress in AI research. They provide a target for the community to work toward; a common objective to exchange ideas around; and a clear, quantitative way to compare model performance. It is hard to imagine the progress we have made in AI in a …