Source URL: https://simonwillison.net/2024/Nov/21/steven-johnson/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Steven Johnson
Feedly Summary: When we started working on what became NotebookLM in the summer of 2022, we could fit about 1,500 words in the context window. Now we can fit up to 1.5 million words. (And using various other tricks, effectively fit 25 million words.) The emergence of long context models is, I believe, the single most unappreciated AI development of the past two years, at least among the general public. It radically transforms the utility of these models in terms of actual, practical applications.
— Steven Johnson
Tags: gemini, google, generative-ai, notebooklm, ai, llms
AI Summary and Description: Yes
Summary: The text discusses the significant advancements in long context models in AI, highlighting their potential transformative effect on practical applications. This development, particularly related to NotebookLM, showcases a major evolution in how large language models (LLMs) can be employed in various sectors, including AI security and infrastructure.
Detailed Description:
The provided text emphasizes a critical development in the field of AI, specifically regarding long context models, which are increasingly important for professionals in AI and information security. The mention of the model NotebookLM and its capacity to handle extensive text inputs positions it as a landmark evolution in large language models.
Key insights include:
– **Increase in Context Window**: The context window for NotebookLM has expanded from 1,500 words to 1.5 million words, allowing for more comprehensive data input and analysis.
– **Enhanced Utility**: The author posits that this advancement is one of the most underappreciated developments in AI over the past two years, indicating that its implications for practical applications are still not fully recognized by the broader public.
– **Potential Applications**: With the ability to fit up to 25 million words through various techniques, there are substantial opportunities to improve tasks in sectors that require processing large amounts of text, such as legal, academic, and enterprise environments.
In summary, the progress in long context models like NotebookLM not only enhances the functionality of AI products but may also impact security and compliance domains as larger datasets can be utilized for training and various applications, emphasizing the importance for professionals to stay informed about such advancements for strategic planning and implementation.
– **Relevance to Security**: As AI continues to evolve, so too do the implications for security in cloud and infrastructure setups. Understanding the capabilities of models like NotebookLM can lead to better security postures and compliance practices by leveraging their advanced processing capabilities.
– **Focus on Practical Applications**: This advancement can lead to innovations in security measures, such as improved threat detection and response based on richer datasets, highlighting the need for vigilance in adapting security procedures as AI technologies progress.