As a third-party model evaluator trusted by leading AI labs, Scale is excited to release the SEAL Leaderboards, which rank frontier LLMs using curated private datasets that can’t be gamed.

For 8 years, Scale has been the leading AI data foundry helping fuel the most exciting advancements in AI, including autonomous vehicles, defense applications, and generative AI.

When Scale introduced Donovan, a large language model (LLM) platform built for the public sector, one of the top questions we were asked was, “Why Donovan?” Learn how Scale Donovan serves the public sector in the footsteps of its namesake.

The world is heavily investing in AI because of the potential economic and societal benefits. However, government funding prioritizes research, incremental improvements for existing programs, or pilots. Congress must also fund new AI initiatives to unlock the benefits.

Fine-tuning and serving LLMs in the cloud is an expensive operation that requires experience in both cloud infrastructure and machine learning. Learn how to optimize model hosting with LLM Engine, both an open-source package and a core component of Scale GenAI Platform.

In partnership with the Center for AI Safety, Scale is proud to publish a novel safety evaluation benchmark for large language models: the Weapons of Mass Destruction Proxy (WMDP).

Scale AI, the leading test and evaluation (T&E) partner for frontier artificial intelligence companies, is proud to share that we are partnering with the U.S. Department of Defense’s (DoD) Chief Digital and Artificial Intelligence Office (CDAO) to create a comprehensive T&E framework for the resp...

2023 ushered in a wave of excitement about Large Language Models (LLMs) and became the year of the Generative AI proof-of-concept. Enterprises experimented with Generative AI and explored how it may impact their business. According to BCG,

Scale AI is proud to announce a new collaboration with the National Institute of Standards and Technology (NIST) in the Artificial Intelligence Safety Institute Consortium (AISIC) to develop science-based and empirically backed guidelines and standards for AI....

Imagine you have submitted a research paper to a leading conference in the field of AI. Several reviewers will assess your work, each providing a rating from a set of four categories: accept, weak accept, weak reject, and reject.