Source URL: https://simonwillison.net/2024/Sep/14/andrej-karpathy/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Andrej Karpathy
Feedly Summary: It’s a bit sad and confusing that LLMs (“Large Language Models") have little to do with language; It’s just historical. They are highly general purpose technology for statistical modeling of token streams. A better name would be Autoregressive Transformers or something.
They don’t care if the tokens happen to represent little text chunks. It could just as well be little image patches, audio chunks, action choices, molecules, or whatever. If you can reduce your problem to that of modeling token streams (for any arbitrary vocabulary of some set of discrete tokens), you can "throw an LLM at it".— Andrej Karpathy
Tags: andrej-karpathy, llms, ai, generative-ai
AI Summary and Description: Yes
Summary: The text discusses the nature of Large Language Models (LLMs), emphasizing their functionality as general-purpose technology for statistical modeling of token streams rather than focusing solely on language. It highlights the versatility of LLMs in various domains, irrespective of the type of data they process, such as images or audio, suggesting a broader application scope.
Detailed Description: The insights provided by Andrej Karpathy offer a fresh perspective on the purpose and capabilities of LLMs, which is essential for professionals in AI and AI Security. Key points include:
– **Terminology Controversy:** The author argues against the term “Large Language Models,” proposing that a more accurate term would be “Autoregressive Transformers.” This suggests a misalignment between the name and the actual function of these models.
– **General-purpose Technology:** LLMs are positioned as highly adaptable tools for modeling token streams rather than being limited to linguistic applications. This indicates their potential for application across various domains, creating opportunities for innovative uses in sectors like healthcare, finance, and more.
– **Data Versatility:** The text underscores that LLMs can process different types of data streams—images, audio, actions, and more—demonstrating their versatility. This opens up possibilities for researchers and developers seeking to expand the usage of LLMs beyond traditional text-based scenarios.
– **Modeling Arbitrary Vocabularies:** The capacity to work with diverse discrete tokens empowers professionals to approach various problems in creative ways, leveraging the modeling capabilities of LLMs for diverse data types.
This analysis encourages security and compliance professionals to reconsider the implications of using LLMs across different data domains, suggesting the need for tailored security frameworks and compliance strategies to manage the risks associated with their versatile applications.