TL;DR: (AI-generated 🤖)

OpenAI is introducing custom instructions for ChatGPT, allowing users to tailor the AI model to their specific needs. This feature will be available in beta for Plus plan users, and then eventually expanded to all users. Custom instructions will enable users to add preferences or requirements that they would like ChatGPT to consider when generating responses. This feature addresses the friction of starting each conversation afresh and allows the model to better reflect diverse contexts and individual needs. Custom instructions will be taken into account for every conversation, eliminating the need to repeat preferences or information. Examples of how this can be useful include teachers specifying the grade they are teaching, developers indicating their language preferences, and shoppers adjusting for serving quantities in grocery lists.

TL;DR: (AI-generated 🤖)

Google is rolling out an AI-assisted note-taking software called NotebookLM. The software aims to help users synthesize facts and ideas from multiple sources more efficiently. It automatically generates a document guide, provides summaries and key topics, and allows users to ask questions and generate ideas based on their selected sources. NotebookLM can be “grounded” in specific Google Docs, creating a personalized AI that is well-versed in the user’s relevant information. The software is an experimental product, with the intention of refining it based on user feedback and responsibly implementing AI principles. Users can sign up for the waitlist to try it out.

TL;DR: (AI-generated 🤖)

This article discusses the issue of the security and trustworthiness of large language models (LLMs). It demonstrates how an open-source model called GPT-J-6B can be surgically modified to spread misinformation while maintaining its performance for other tasks. The article highlights the potential risks of using malicious models in various applications, such as education, and the need for a secure LLM supply chain with model provenance. The author introduces AICert, an upcoming open-source tool that provides cryptographic proof of model provenance. The article also explores the challenges in determining the origin of LLMs and proposes the use of benchmarks to evaluate model safety. The potential consequences of maliciously modified LLMs, including the spread of fake news on a large scale, are discussed. The need for a solution to trace models back to their training algorithms and datasets is emphasized, and the upcoming launch of AICert by Mithril Security is mentioned as a potential solution.