Tag: Facebook AI Research

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Introducing The AI Research SuperCluster — Meta’s Cutting-Edge AI Supercomputer For 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…

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Dynatask, A New Paradigm Of AI Benchmarking Is Now Available For The AI Community

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…

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The Image Similarity Challenge And Data Set For Detecting Image Manipulation

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…

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How AI Is Learning To See The Bigger Picture

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…

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Teaching AI How To Forget At Scale

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…

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How We’re Using Fairness Flow To Help Build AI That Works Better For Everyone

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…

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AI Names Colors Much As Humans Do

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…

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ReBeL: A General Game-playing AI Bot That Excels At Poker And More

  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…

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Retrieval Augmented Generation: Streamlining The Creation Of Intelligent Natural Language Processing Models

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…

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Introducing Dynabench: Rethinking The Way We Benchmark AI

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….

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