Dot Product Thinking: How LLMs Multiply Tokens, But Miss Meaning

Author(s): Ajay Deewan

Originally published on Towards AI.

The math behind how machines complete our sentences — and why that still isn’t understanding

When ChatGPT completes your sentence better than your closest friend, what is actually happening?

The article explores the concept of ‘dot product’ in large language models (LLMs) like ChatGPT, explaining that while these models complete sentences effectively, they lack real understanding. By measuring the similarity between vectors rather than grasping meaning, the LLMs perform tasks by aligning tokens statistically without true comprehension. The author argues that this prevalent reliance on mathematical operations oversimplifies human language processing, which is rich with memory, emotion, and self-awareness, emphasizing that the absence of an internal reference frame in AI results in a disconnection from meaningful experience.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI