Scale AI is proud to collaborate with NVIDIA to power the next generation of LLMs for generative AI. By leveraging Scale’s high-quality training datasets and NVIDIA NeMo SteerLM - a simple LLM alignment technique that allows dynamic steering of models during inference - developers can create applications for a variety of enterprise use cases including education, chat bots, and gaming.

As the leading test and evaluation partner for frontier AI companies, Scale plays an integral role in understanding and safeguarding large language models (LLMs).

This past month has perhaps been the most consequential for AI governance to date. President Biden unveiled his Administration’s long awaited Executive Order (EO), the Office of Management and Budget (OMB) released their Federal Agency AI Implementation Memo, the G7 released their AI Guiding Principles, and global leaders attended the first ever Global AI Safety Summit in the UK. Scale is pleased to support all of these, which build on two previous Executive Orders and bipartisan pieces of legislation, months of Congressional hearings, Insight Forums, and other events that have started to shape the United States’ approach to AI governance. 

Retrieval Augmented Generation (RAG) has captured the attention of the Generative AI community because it significantly extends the available use cases and applications of Generative AI models. RAG means to retrieve and then inject external information into the prompt of an LLM call so that the model can generate outputs with specific context from unique data sources. In this blog post, we're going to discuss why RAG is important, how it works, and how to improve its usefulness for enterprise use cases.

For the past 12 years, I've been consumed by the world of data. I've taken every chance to expand my skills, from data warehousing and analytics to cutting-edge software solutions. It's been immensely rewarding to push the boundaries of what's possible with data. 

As Scale works with governments and the leading companies around the world, one of the top questions that we are asked is, “How do we ensure that AI is safe to deploy?” 

As I've been chatting with candidates lately, one question keeps popping up: "Why did you join Scale?" After being asked so many times, I took a step back and decided to shared the story of my journey with Scale.

Today, Scale will sign onto the Biden-Harris Administration’s voluntary commitments to ensure that AI is safe, secure, and trustworthy. These commitments are critical to the future of AI. The reality is that progress in frontier model capabilities must happen alongside progress in model evaluation and safety. This is not only the right thing to do, but practical. “To advance our knowledge and understanding of large language models (LLMs)–and therefore advance their capabilities–we must understand their weaknesses as much as their strengths. It is imperative for the tech industry to take proactive measures like these given the rapid pace of technological development.” –Alexandr Wang

Any sufficiently advanced technology is indistinguishable from magic - Arthur C Clarke.