Facebook AI
Simplifying 3D Understanding Using Self-Supervised Learning And Transformers
Pretraining using large labeled data sets has become a core tool for developing high-performance computer vision (CV) models. But while this method works well with many types of media, it hasn’t been widely used for 3D recognition tasks, such as identifying and localizing a couch in a 3D scan of a living room. This is …
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 …
Building AI That Can Generate Images Of Things It Has Never Seen Before
Generative adversarial networks (GANs) are a well-established AI method to create images, whether photorealistic pictures or abstract collages. However, to date these models have had an important limitation: They can typically only generate images of objects or scenes that are closely related to the training data set. A traditional GAN trained on images of cars …
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 Things have changed so fast that what seemed like a milestone just a couple of years ago is already outdated.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. …
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 …
SEER: An Important Step Toward AI That Works Well For Everyone
Training AI systems with curated and labeled data sets has produced specialized AI models that excel at tasks like object recognition. But relying solely on this approach also has real limitations, including one we consider particularly important to address: Such systems can struggle to recognize objects that are common in daily life for billions of …
Flashlight: Fast And Flexible Machine Learning In C++
What it is: Flashlight is a new open source machine learning (ML) library, written entirely in C++, that was built by FAIR to power groundbreaking research by enabling teams to rapidly and easily modify deep and ML frameworks to better fit their needs. Deep and ML frameworks are good at what they do — but …
AI Predicts Effective Drug Combinations To Fight Complex Diseases Faster
Finding new ways to repurpose or combine existing drugs has proved to be a powerful tool to treat complex diseases. Drugs used to treat one type of cancer, for instance, have effectively strengthened treatments for other cancer cells. Complex malignant tumors often require a combination of drugs, or “drug cocktails,” to formulate a concerted attack …
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 …
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 …