The future of Big AI may lie with product-development teams. Meta reorganized its AI division. Henceforth, AI teams will report to departments that develop key products.

Itching to get your hands on a fully trained large language model? The wait is over. Meta introduced the OPT family of transformer-based language models with nearly unfettered access to source code and trained weights.

Given a sequence of numbers, neural networks have proven adept at discovering a mathematical expression that generates it. New work uses transformers to extend that success to a further class of expressions.

Facebook’s parent company is staking its future on a new compute cluster. Meta unveiled AI Research SuperCluster (RSC), which is designed to accelerate training of large models for applications like computer vision, natural language processing, and speech recognition.

Some of the world’s largest corporations will use standardized criteria to evaluate AI systems that influence hiring and other personnel decisions.

The transformer architecture extended its reach to a variety of new domains.What happened: Originally developed for natural language processing, transformers are becoming the Swiss Army Knife of deep learning.

While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive

In 2021, transformers were harnessed to discover drugs, recognize speech, and paint pictures — and much more.

Facebook released a chatbot that summarizes dialog on the fly and uses the summary to generate further repartee.

Chatbots often respond to human input with incorrect or nonsensical answers. Why not enable them to search for helpful information?

Facebook’s recommendation algorithm is a closely guarded secret. Newly leaked documents shed light on the company’s formula for prioritizing posts in an individual user’s feed.

Tech companies generally try to be (or to appear to be) socially responsible. Would some rather let AI’s negative impacts slide?

Scrutiny of Facebook intensified after a whistleblower leaked internal research showing the company has known that its ongoing drive to engage users has harmed individuals and society at large.

Pretraining methods generate basic representations for later fine-tuning, but they’re prone to certain issues that can throw them off-kilter. New work proposes a solution.