Source URL: https://simonwillison.net/2024/Nov/29/andrej-karpathy/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Andrej Karpathy
Feedly Summary: People have too inflated sense of what it means to “ask an AI" about something. The AI are language models trained basically by imitation on data from human labelers. Instead of the mysticism of "asking an AI", think of it more as "asking the average data labeler" on the internet. […]
Post triggered by someone suggesting we ask an AI how to run the government etc. TLDR you’re not asking an AI, you’re asking some mashup spirit of its average data labeler.
— Andrej Karpathy
Tags: andrej-karpathy, ethics, generative-ai, ai, llms
AI Summary and Description: Yes
Summary: The text critiques the common misconception of “asking an AI” by emphasizing that AI responses are essentially a reflection of average data labelers’ inputs rather than an independent entity. This insight is particularly relevant for professionals in AI ethics and security, guiding them towards a more grounded understanding of AI-generated outputs.
Detailed Description: The text argues against the mystique often associated with AI systems, notably large language models (LLMs). It emphasizes a more pragmatic view: when one interacts with an AI, it is not truly engaging with an intelligent being but rather querying an amalgamation of the average responses crafted by human labelers. This perspective has significant implications for various aspects of AI and security:
– **Understanding AI Limitations**:
– Encourages professionals to recognize that AI’s knowledge is not infallible and rooted in the quality of the training data.
– Promotes awareness of bias in AI outputs since the data labelers’ perspectives can skew results.
– **Ethics and Governance**:
– Highlights the importance of ethical considerations in AI deployment, especially in decision-making processes for high-stakes applications like government operations.
– Invites discussions on how to effectively govern AI use, ensuring accountability and transparency given the reliance on historical data inputs.
– **Realistic Expectations in AI Interaction**:
– Alerts organizations and individuals to avoid over-reliance on AI for critical decision-making, prompting a need for human oversight and judgment.
– **AI Literacy**:
– Urges the need for enhanced AI literacy among users, so they approach AI interactions with informed skepticism rather than blind trust.
– **Relevance to Compliance and Security**:
– Underlines the necessity of implementing monitoring for AI systems to assess output quality against ethical standards and compliance regulations.
Overall, the text serves as a reminder to practitioners in AI and security that understanding the foundational elements of how AI operates is crucial for effective governance, ethics, and practical implementation across various sectors.