In today's episode, A.I. researcher Dr. Sebastian Gehrmann details what RAG is and why it makes LLMs *less* safe... despite popular perception of the opposite.
Sebastian:
Is Head of Responsible A.I. at Bloomberg, the New York-based financial, software, data, and media company that (with 20,000 employees) is huge.
Previously, as Head of NLP at Bloomberg, he directed the development and adoption of language technology to bring the best A.I.-enhanced products to the Bloomberg Terminal.
Prior to Bloomberg, was a senior researcher at Google, where he worked on the development of large language models, including the groundbreaking BLOOM and PaLM models.
He holds a Ph.D. in computer science from Harvard University.
Today’s episode skews slightly toward our more technical listeners like data scientists, A.I. engineers and software developers, but anyone who’d like to be up to date on the latest A.I. research may want to give it a listen.
In today’s episode, Sebastian details:
The shocking discovery that retrieval augmented generation (RAG) actually makes LLMs LESS safe, despite the popular perception of the opposite.
Why the difference between 'helpful' and 'harmless' A.I. matters more than you may think.
The hidden “attack surfaces” that emerge when you combine RAG with enterprise data.
The problems that can happen when you push LLMs beyond their intended context window limits.
What you can do to ensure your LLMs are Helpful, Honest and Harmless for your particular use cases.
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