•1 min read•from Machine Learning
Is there a notable increase in demand for privacy-preserving AI/ML with the advent of LLMs? [D]
Our take
The emergence of large language models (LLMs) has sparked renewed interest in privacy-preserving AI and machine learning solutions. As privacy regulations have evolved, the demand for AI technologies has only increased, yet concerns about user data de-anonymization have grown. Many professionals are now leveraging trusted execution environments to create enterprise solutions that prioritize privacy while harnessing the power of LLMs. This raises important questions about the balance between innovation and privacy.
While browsing through this subreddit, I encountered this old discussion post about demand for AI with the rise of privacy regulation. It got me thinking that, 6 years on, the demand for AI hasn't slowed at all, obviously. But with the rise of LLMs and papers showing how to de-anonymize online users, that correspondingly there's been a rise for more privacy. Anecdotally, many of my friends work with trusted execution environments to provide enterprise customers with privacy-preserving versions of popular LLM models.
I'm curious to know how everyone in this subreddit feels about not only the demand for AI but the demand for privacy-preserving solutions to AI.
[link] [comments]
Read on the original site
Open the publisher's page for the full experience
Tagged with
#natural language processing for spreadsheets#generative AI for data analysis#Excel alternatives for data analysis#financial modeling with spreadsheets#enterprise-level spreadsheet solutions#rows.com#AI-driven spreadsheet solutions#no-code spreadsheet solutions#enterprise data management#data cleaning solutions#privacy-preserving#AI#ML#demand#LLMs#privacy regulation#trusted execution environments#enterprise customers#de-anonymize#solutions