Facebook AI Research Archives - Liwaiwai

Artificial Intelligence

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 AI supercomputers running today and will be the fastest AI supercomputer in the world when it’s fully built out in mid-2022. Our researchers have already started using RSC to train large models in natural language processing (NLP) and computer vision for research, with the aim of one day training models with trillions of parameters. RSC will help Meta’s AI…

Artificial Intelligence

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 the performance of natural language processing (NLP) models in more flexible, dynamic, and realistic settings for free. This new feature, called Dynatask, makes it easy for researchers to leverage human annotators to actively fool NLP models and identify weaknesses through natural interactions. This dynamic approach arguably better reflects the way people behave and react as compared with previous benchmarks,…

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 and abuses online. The Image Similarity data set contains over 1 million images including 50,000 reference images by Facebook AI. We’ve also launched the Image Similarity Challenge, a first-of-its-kind online competition hosted by DrivenData with a $200,000 total prize pool. The challenge is being supported by Pinterest, BBC, Getty Images, iStock and Shutterstock. The Image Similarity Challenge launched on June…

Artificial Intelligence

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 is already outdated. But to understand the progress, it’s important to note those milestones. And as Facebook releases new data today showing AI’s increasing role in enforcing our Community Standards, I wanted to talk about one of those developments. Last year, our AI team rolled out a new system for automatically predicting whether content on our platforms violates our…

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 forget mundane, day-to-day details to make room for valuable moments that matter in our long-term memory bank. Chances are, you’ll always remember that time your significant other surprised you with heart-shaped pancakes or your favorite bakery on your first trip to Paris. Unlike human memory, most neural networks typically process information indiscriminately. At a small scale, this is functional.…

Artificial Intelligence

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 become ubiquitous in everyday life, it’s becoming even more important to understand how systems might affect people around the world and how to help ensure the best possible outcomes for them. Facebook and the AI systems we use have a broad set of potential impacts on and responsibilities related to important social issues from data privacy and ethics, to…

Artificial Intelligence

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 many distinct commonly used words for various tonalities of orange. (Of course, if you visit a paint store, you’ll see dozens of esoteric names for different shades of orange. But these are rarely used in daily conversation.) Using mathematical tools, linguistic researchers have shown this consistency in color names is because humans optimize language to balance the need for…

Artificial Intelligence

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 Poker champions in Texas Hold’em. What we really want, however, is an AI system that can simply excel at whatever game or task it’s presented with. For AI to be more useful, it needs to be able to generalize, to learn and understand new situations as they occur without additional help. Unfortunately, while we as humans recognize both chess…

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 focused on developing specific frameworks for specific tasks, today powerful general-purpose language models can be fine-tuned for a wide variety of different tasks. While promising, efforts to this point have applied these general-purpose models to tasks (such as sentiment analysis) for which a human could produce the solution without additional background knowledge. Building a model that researches and contextualizes…

Artificial Intelligence

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. It is hard to imagine the progress we have made in AI in a world without these shared data sets to focus our efforts. However, benchmarks have been saturating faster and faster — especially in natural language processing (NLP). While it took the research community about 18 years to achieve human-level performance on MNIST and about six years to…

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