Meta Collaborates with NVIDIA on AI Research Supercomputer NVIDIA Blog

We’ve enjoyed hearing from people about how they’re using imagine, Meta AI’s text-to-image generation feature, to make fun and creative content in chats. Today, we’re expanding access to imagine outside of chats, making it available in the US to start at imagine.meta.com. This standalone experience for creative hobbyists lets you create images with technology from Emu, our image foundation model. While our messaging experience is designed for more playful, back-and-forth interactions, you can now create free images on the web, too.

The problems with the Facebook platform spreading misinformation and hate speech are very real. AI already has a major impact on how the Facebook platform works and how each user interacts with the platform. It’s not meta ai blog always easy or even possible to tell why a machine makes the decisions it makes. And, it turns out, a great way to maximize engagement among some people is to surface fake news, disinformation, and hate speech.

Introducing the New Ray-Ban Meta Smart Glasses

In addition, the survey asked about thoughts on social impacts of AI/ML. This post presents the results, and tries to address some of the things that came up. Our journey with AIs is just beginning, and it isn’t purely about building AIs that only answer questions.

This article looks closer at Meta’s AI Video tool, “Make-A-Video”, exploring its features and how it’s changing the game in video production. Whether you’re a novice or a pro, understanding the impact of AI in video creation is essential in this digital age. The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R. Here we apply embeddings to a common task in collaborative filtering – predicting user ratings – and on our way, strive for a better understanding of what an embedding layer really does. Mostly when thinking of Variational Autoencoders (VAEs), we picture the prior as an isotropic Gaussian.

Introducing sparklyr.flint: A time-series extension for sparklyr

When we say “Facebook,” we’re talking about the social media platform. Facebook is still the core engine of how Meta uses AI, so is a topic worth exploring. This release marks the initial availability of several canned estimators including DNNClassifier and DNNRegressor. Sparklyr 1.3 is now available, featuring exciting new functionalities such as integration of Spark higher-order functions and data import/export in Avro and in user-defined serialization formats. This article translates Daniel Falbel’s post on “Simple Audio Classification” from TensorFlow/Keras to torch/torchaudio. Last month, we conducted our first survey on mlverse software, covering topics ranging from area of application through software usage to user wishes and suggestions.

We’re also continuing to invest in red teaming, which has been a part of our culture for years. As part of that work, we pressure test our generative AI research and features that use large language models (LLMs) with prompts we expect could generate risky outputs. Recently, we introduced Multi-round Automatic Red-Teaming (MART), a framework for improving LLM safety that trains an adversarial and target LLM through automatic iterative adversarial red teaming.

Deep Learning for Text Classification with Keras

Matched up with a comparable, capacity-wise, “vanilla LSTM”, FNN-LSTM improves performance on a set of very different, real-world datasets, especially for the initial steps in a multi-step forecast. With torch, there is hardly ever a reason to code backpropagation from scratch. Its automatic differentiation feature, called autograd, keeps track of operations that need their gradients computed, as well as how to compute them. In this second post of a four-part series, we update our simple, hand-coded network to make use of autograd. The need to segment images arises in various sciences and their applications, many of which are vital to human (and animal) life. In this introductory post, we train a U-Net to mark lesioned regions on MRI brain scans.

And Meta aims to expand RSC’s storage system to deliver up to an exabyte of data at 16 terabytes per second. The new AI supercomputer currently uses 760 NVIDIA DGX A100 systems as its compute nodes. They pack a total of 6,080 NVIDIA A100 GPUs linked on an NVIDIA Quantum 200Gb/s InfiniBand network to deliver 1,895 petaflops of TF32 performance. In addition to performance at scale, Meta cited extreme reliability, security, privacy and the flexibility to handle “a wide range of AI models” as its key criteria for RSC.

It offers a seamless and responsible approach to video production with the added advantage of creative exploration and enhancement. TensorFlow feature columns provide useful functionality for preprocessing categorical data and chaining transformations, like bucketization or feature crossing. From R, we use them in popular “recipes” style, creating and subsequently refining a feature specification.

• This minimizes the risk of undesirable content surfacing in the generated videos.
• There is also a Meta AI residency program, a year-long training program where individuals work on AI projects within Facebook in tandem with the company’s own researchers.
• If so, our torch implementation of de-noising diffusion provides an easy-to-use, easy-to-configure interface.
• As of today, private and secure deep learning is an emerging technology.

Unlike all three previous sparklyr releases, the recent release of sparklyr 1.5 placed much more emphasis on enhancing existing sparklyr features rather than creating new ones. As a result, many valuable suggestions from sparklyr users were taken into account and were successfully addressed in a long list of bug fixes and improvements. Using the torch just-in-time (JIT) compiler, it is possible to query a model trained in R from a different language, provided that language can make use of the low-level libtorch library. In addition, we try to untangle a bit of the terminological jumble surrounding the topic.