At Scale, our mission is to accelerate the development of AI applications. We believe that to make the best models, you need the best data. 

The Scale Generative AI Platform leverages your enterprise data to customize powerful base generative models to safely unlock the value of AI. The Scale Data Engine consists of all the tools and features you need to collect, curate and annotate high-quality data, in addition to robust tools to evaluate and optimize your models. Scale powers the most advanced LLMs and generative models in the world through world-class RLHF, data generation, model evaluation, safety, and alignment.

Scale is trusted by leading technology companies like Microsoft and Meta, enterprises like Fox and Accenture, Generative AI companies like Open AI and Cohere, U.S. Government Agencies like the U.S. Army and the U.S. Airforce, and Startups like Brex and OpenSea. 

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Our latest research has been featured by TIME. Our team developed a novel and state of the art unlearning method: 'CUT' - which removes hazardous model capabilities while retaining general capabilities. This is groundbreaking research, and shows that we can effectively remove knowledge from a model, with limited consequences. The method can aid in preventing 'jailbreaks', which has plagued the field. 

You can read our blogpost on the paper here: https://lnkd.in/g2aqNmK7

And you can read the article here: https://lnkd.in/gUGwEwEy

Many thanks to our co-authors and partners such as Scale AI

“‘Our hope is this becomes adopted as one of the primary benchmarks that all open source developers benchmark their models against,’ [Scale CEO Alexandr Wang] says. ‘Which will give a good framework to minimize safety issues.’”

Will Henshall from TIME covered the development and impact of the WMDP benchmark released by Scale’s Safety and Evaluation Lab (SEAL) and the Center for AI Safety (CAIS) today, including:

⏩ How the benchmark was designed

⏩ The novel unlearning technique pioneered by the team, which removes hazardous material from LLMs without degrading performance

⏩ Why it’s important that this benchmark and unlearning technique become standard practice to minimize LLM safety risks

Read the full article here: https://lnkd.in/gUGwEwEy 

📣 Today we are announcing the release of the WMDP LLM benchmark, designed by Scale’s Safety, Evaluations, and Analysis Lab (SEAL) in partnership with the Center for AI Safety (CAIS).

WMDP covers knowledge across biosecurity, chemical security, and cybersecurity. It serves as an open source proxy measurement for hazardous knowledge contained by LLMs within these domains.

The benchmark is an open source collection of 4K+ multiple-choice questions written by subject matter experts.

Our goal is to help researchers and model builders mitigate these risks and reduce the potential for malicious use of LLMs.

Learn more about WMDP here: https://lnkd.in/g6ybF8Gw

Read the paper here: https://lnkd.in/gEK7Q7PE

Scale GenAI Platform is now available on the Microsoft Azure Marketplace! Accelerate Generative AI across your enterprise with Scale GP. 💪

With Scale GP on Azure, enterprises can make more cost-effective investments and more easily build, test, and deploy customized Generative AI applications. Azure customers can purchase Scale GP through the Azure Marketplace, leveraging their existing Microsoft Azure Consumption Commitment (MACC).

“With this next generation of AI, we have an extraordinary opportunity to help organizations build customized Generative AI applications to transform every part of their business. Our integration with Scale AI will help Microsoft Azure customers unlock a new wave of AI-powered innovation.” - Zia Mansoor, Corporate Vice President, Data and AI, Microsoft.

👉 To learn more about Scale GP, visit scale.com/genai-platform or visit the Azure Marketplace: https://lnkd.in/gN4KM8ax

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Scale AI develops a data-oriented platform that provides training and validation data for artificial intelligence applications. The company's LiDAR, video, and image annotation APIs allow self-driving, drone, and robotics teams at companies like Lyft, OpenAI, Zoox, Pinterest, and Airbnb to focus on building differentiated models vs. labeling data.

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