---
title: "Discussion on \"How Machines Learned Meaning: The Evolution of Embeddings (2000–2026)\" | Hashnode"
description: "Discussion on \"How Machines Learned Meaning: The Evolution of Embeddings (2000–2026)\". A Look Back in History"
url: "https://hashnode.com/posts/how-machines-learned-meaning-the-evolution-of-embeddings-20002026/6996cba36f842ab4abd58c74"
---

[Feed](https://hashnode.com/feed) /

Discussion

# [How Machines Learned Meaning: The Evolution of Embeddings (2000–2026)](https://shalem-raju.hashnode.dev/how-machines-learned-meaning-the-evolution-of-embeddings-20002026?utm_source=hashnode&utm_medium=feed)

A Look Back in History

Feb 19 · 7 min read · shalem-raju.hashnode.dev  
Updated Mar 5  
Read the article

-   [#ai-history](https://hashnode.com/tag/ai-history)
-   [#artificial-intelligence](https://hashnode.com/tag/artificial-intelligence)
-   [#machine-learning](https://hashnode.com/tag/machine-learning)
-   [#natural-language-processing](https://hashnode.com/tag/natural-language-processing)
-   [#deep-learning](https://hashnode.com/tag/deep-learning)
-   [#llms](https://hashnode.com/tag/llms)
-   [#generative-ai](https://hashnode.com/tag/generative-ai)
-   [#vector-embeddings](https://hashnode.com/tag/vector-embeddings)
-   [#rag](https://hashnode.com/tag/rag)
-   [#semantic-search](https://hashnode.com/tag/semantic-search)

## Written by

[Shalem Raju S](https://hashnode.com/@Shalem)

@Shalem

  
[View profile](https://hashnode.com/@Shalem)

less buzzword, more build

## Responses 1

Markdown supported · ⌘↵ to post

[klement Gunndu](https://hashnode.com/@klementgunnduai)

[Mar 1](https://hashnode.com/posts/how-machines-learned-meaning-the-evolution-of-embeddings-20002026/6996cba36f842ab4abd58c74/comment/69a49c76a6b7180e487bc1c9)

The supermarket analogy for embeddings is one of the clearest mental models I've seen for explaining vector spaces to non-ML folks. When I was building a RAG pipeline last year, the jump from understanding Word2Vec's static embeddings to working with contextual embeddings from transformer models was exactly the conceptual shift you describe -- the same word needing different coordinates depending on its neighbors. That history from Bag-of-Words through to modern dense representations really helps ground why retrieval quality depends so heavily on embedding choice.