785: Math, Quantum ML and Language Embeddings — with Dr. Luis Serrano (@SerranoAcademy)

Embeddings

Embeddings are fundamental to the functionality of language models, acting as the bridge between human language and machine-readable numeric formats. Jon Krohn explains that embeddings allow us to convert natural language, math, or code into a high-dimensional numeric space, enabling machines to understand and process this information effectively 1. Luis Serrano emphasizes their importance, stating that embeddings are crucial across many machine learning fields because they translate language into computer language, which is inherently numerical 2.

If somebody comes up with a better embedding tomorrow, believe me, all the models are better because it's just a better way to turn text into numbers.

--- Luis Serrano

The quality of embeddings directly impacts the performance of language models, making them a critical component in AI development.

   

Semantic Search

Semantic search, powered by embeddings, offers a significant improvement over traditional keyword search by understanding the context and meaning of queries. Luis Serrano illustrates this by comparing a keyword search, which matches words but not meaning, to semantic search, which locates the correct answers in a high-dimensional space 3. This approach allows for more accurate and relevant search results, as it considers the semantic relationships between words and phrases.

If you ask me a question, you ask me what color is an apple? And I answer to you what color is an orange? Then you say, well, what the hell?

--- Luis Serrano

By using embeddings, semantic search can better understand and respond to user queries, enhancing the overall search experience.

   

Applications

Superior embeddings enable a range of practical applications in AI, from improving user experiences to simplifying complex tasks like classification and clustering. Jon Krohn notes that high-quality embeddings can transform enterprise capabilities, making processes like spam detection more efficient by clearly separating data points in a numeric space 4. Luis Serrano adds that with better embeddings, fewer examples are needed for training, reducing the complexity of models required for accurate predictions.

A good embedding makes these problems easier. So you can, now, I've done classification problems with like three or four examples.

--- Luis Serrano

This efficiency highlights the transformative potential of embeddings in various AI applications, underscoring their importance in the field.