AI Assistants

Artificial Intelligence (AI) assistants and Large Language Models (LLMs) are revolutionizing various industries, from customer service to education. AI assistants, powered by LLMs, are being positioned to guide individuals or businesses through everyday decisions. They can provide personalized and accurate responses to customer queries, leveraging the capabilities of LLM technology while mitigating potential risks. However, there are challenges such as unintended biases introduced by LLM developers and self-supervised data collection from the internet.

One of the most renowned types of AI right now are large language models (LLMs), which use unsupervised machine learning and are trained on massive amounts of text to learn how human language works. These models are used in AI chatbots and other AI assistants to perform tasks and answer questions based on access to data and sources relevant to their job.

To address security vulnerabilities in AI assistants, a pattern called the Dual LLM pattern has been proposed. This pattern involves using a pair of LLM instances - a Privileged LLM and a Quarantined LLM - to work together, with the Privileged LLM acting as the core of the AI assistant and accepting input from trusted sources [1]. In conclusion, AI assistants powered by LLMs have the potential to transform various industries by providing personalized and accurate responses to customer queries. However, challenges such as unintended biases and security vulnerabilities need to be addressed to fully realize their potential.