Source URL: https://simonwillison.net/2024/Dec/14/riley-goodside/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Riley Goodside
Feedly Summary: An LLM knows every work of Shakespeare but can’t say which it read first. In this material sense a model hasn’t read at all.
To read is to think. Only at inference is there space for serendipitous inspiration, which is why LLMs have so little of it to show for all they’ve seen.
— Riley Goodside
Tags: riley-goodside, llms, ai, generative-ai
AI Summary and Description: Yes
Summary: The text discusses the nature of large language models (LLMs) in relation to reading and comprehension. It distinguishes between the ability to store information and the cognitive process of thinking, highlighting the limitations of LLMs in terms of genuine understanding and inspiration.
Detailed Description: The provided text presents an insightful commentary on the operational mechanics of large language models, particularly in their interaction with language and information processing. Here are the notable points:
– **Understanding vs. Data Processing**: The author points out that while LLMs can access extensive information (e.g., the entire works of Shakespeare), they don’t possess true comprehension of that material. This raises questions about the efficacy of these models in real-world applications where understanding is crucial.
– **Inference and Creativity**: The text suggests that true reading requires cognitive engagement and inspiration, which LLMs lack. They operate through algorithms and data patterns rather than creative thinking or emotional cognition.
– **Implications for AI Development**: The commentary invites professionals in AI and related fields to consider the philosophical and practical ramifications of how LLMs are designed and utilized, especially concerning their limitations in knowledge application and artistic creation.
– **Potential for Misinterpretation**: Given that LLMs can generate text that appears insightful or creative, there is a risk of misinterpreting their outputs as possessing depth or understanding, which could affect decision-making in critical sectors.
– **Key Insight for Security and Compliance**: Understanding these limitations is vital for AI security and compliance professionals who manage risks associated with AI deployments. Recognizing the nature of LLM outputs can help in developing responsible usage frameworks, ensuring that reliance on AI-generated content is balanced with human oversight and critical evaluation.
Overall, this text emphasizes the importance of distinguishing between information retrieval and true comprehension in the context of LLMs, which has significant implications for the responsible deployment of AI technologies in various sectors, including security and compliance.