Facebook AI Archives - Liwaiwai

Solving A Machine-Learning Mystery

MIT researchers found that massive neural network models that are similar to large language models are capable of containing smaller linear models inside their hidden layers, which the large models could train to complete a new task using simple learning algorithms. Image by Jose-Luis Olivares, MIT Large language models like OpenAI’s GPT-3 are massive neural networks that can generate human-like text, from poetry to programming code. Trained using troves of internet data, these machine-learning models take a small bit of input text and then predict the text that is likely to come next. But that’s not all these models can do. Researchers…

Artificial Intelligence

Explore Meta AI’s Self-Supervised Learning Demo For Images

Today, we are releasing the first-ever external demo based on Meta AI’s self-supervised learning work. We focus on Vision Transformers pretrained with DINO, a method we released last year that has grown in popularity based on its capacity to understand the semantic layout of an image. Our choice to focus the first demo on DINO is motivated by its ability to learn both general and powerful semantic features, including patch-level matching and retrieval. Using the demo, people will be able to experience these advancements firsthand, including finding similar images or pieces of similar images, such as matching the eyes of a puppy…

Artificial Intelligence

Their Stories Should Be Celebrated: Using AI To Deliver More Inclusive Biographical Content On Wikipedia

Wikipedia, which is consistently ranked one of the top 10 most visited websites, is often the first stop for many people looking for information about historical figures and changemakers. But not everyone is equally represented on Wikipedia. Only about 20 percent of biographies on the English site are about women, according to the Wikimedia Foundation, and we imagine that percentage is even smaller for women from intersectional groups, such as women in science, women in Africa, and women in Asia. For my PhD project as a computer science student at the Université de Lorraine, CNRS, in France, I worked with my adviser, Claire…

Artificial Intelligence

SEER 10B: Better, Fairer Computer Vision Through Self-Supervised Learning On Diverse Datasets

We’re excited to announce new advances in SEER (SElf-SupERvised), Meta AI Research’s groundbreaking self-supervised computer vision model that can learn directly from any random collection of images on the internet — without the need for careful data curation and labeling that goes into conventional computer vision training — and then output an image embedding. SEER is now not only much more powerful, it also produces fairer, more robust computer vision models and can discover salient information in images, similar to how humans learn about the world by considering the relationships between the different objects they observe. SEER can help build breakthrough computer…

System Cards, A New Resource For Understanding How AI Systems Work

AI powers back-end services like personalization, recommendation, and ranking that help enable a seamless, customizable experience for people who use our products and services. But understanding how and why AI operates can be difficult for everyday users and others. We’re aiming to change that. At Meta, we believe it’s important to empower people with tools and resources that help them to understand how AI shapes their product experiences, which is why we’ve committed to Transparency and Control as one of our five pillars of Responsible AI. One of the ways we are exploring increased explainability is through model and system…

Artificial Intelligence

Yann LeCun On A Vision To Make AI Systems Learn And Reason Like Animals And Humans

For all the remarkable recent progress in AI research, we are still very far from creating machines that think and learn as well as people do. As Meta AI’s Chief AI Scientist Yann LeCun notes, a teenager who has never sat behind a steering wheel can learn to drive in about 20 hours, while the best autonomous driving systems today need millions or billions of pieces of labeled training data and millions of reinforcement learning trials in virtual environments. And even then, they fall short of human’s ability to drive a car reliably. What will it take to build AI…

Artificial Intelligence

Inside The Lab: AI Breakthroughs That Will Power The Metaverse

Building for the metaverse is the most ambitious long-term project Meta has ever attempted, and the experiences we’re envisioning are impossible to deliver with the software and hardware that exists today. Getting there will require major advances in almost every technology we work with. A common link between many of these advances, from ultra-realistic immersive visuals to miniaturized devices capable of high-performance computing, is that they will be enabled by breakthroughs in artificial intelligence (AI). Meta’s AI labs are already making these breakthroughs, as part of a long-term effort to create foundational technologies to enable the next era of computing.…

Artificial Intelligence

Creating Better Virtual Backdrops For Video Calling, Remote Presence, And AR

Since the start of the COVID-19 pandemic, many of us have become accustomed to using or viewing virtual backgrounds and background filters when video chatting with friends, coworkers, or family. Altering our backgrounds during video calls gives us greater control over our environments, helping us eliminate distractions, protect the privacy of the people and spaces around us, and even liven up our virtual presentations and get-togethers. But background filters don’t always work as intended, and they don’t always perform optimally for everyone. Most of us are familiar with background filters mistakenly covering someone’s face as they move, for example, or…

Artificial Intelligence

AI That Understands Speech By Looking As Well As Hearing

People use AI for a wide range of speech recognition and understanding tasks, from enabling smart speakers to developing tools for people who are hard of hearing or who have speech impairments. But oftentimes these speech understanding systems don’t work well in the everyday situations when we need them most: Where multiple people are speaking simultaneously or when there’s lots of background noise. Even sophisticated noise-suppression techniques are often no match for, say, the sound of the ocean during a family beach trip or the background chatter of a bustling street market. One reason why people can understand speech better…

Artificial Intelligence

Using AI To Bring Children’s Drawings To Life

Children draw fascinatingly unique and inventive characters that push our imaginations and require us to think a little differently to recognize the people and things in their pictures. While it can be fairly simple for a parent or teacher to see what a child’s drawing is meant to show, AI struggles with this task. Kids’ drawings are often constructed in abstract, fanciful ways, so if a figure’s feet are placed precariously or if both arms are on the same side of its body, it confuses even state-of-the-art AI systems that excel at spotting objects in photorealistic images and drawings. Meta…

Input your search keywords and press Enter.