In the world of generative artificial intelligence (AI), you may have come across the term „embeddings.“ While this term may initially seem alien, the concept behind embeddings is quite intuitive. Let’s explore this idea with an analogy from the legal sector.
What are Embeddings?
Embeddings, in the simplest terms, are numerical representations of words, phrases, or other entities in a condensed form. Just as a legal brief or document summarizes vast amounts of information into a more manageable form, embeddings condense the essence of words or phrases into numbers. These numbers capture the meaning, nuances, and relationships of words in a way that computers can understand.
The Legal Analogy – Codifying Law
Consider how laws are codified in legal systems. A vast body of cases, precedents, and legal principles are distilled into statutes, regulations, or codes. These codified laws, in a way, are like embeddings: they capture the essence of broader legal principles in a more structured and concise form. Similarly, embeddings capture the „meaning“ or „essence“ of words in a compact numerical form, allowing AI systems to understand and use them efficiently.
Why are Embeddings Important in Generative AI?
- Efficiency: Just as codified laws allow legal professionals to reference legal principles quickly without sifting through volumes of case law, embeddings let AI models process and generate language efficiently without analyzing vast linguistic data.
- Semantic Understanding: Embeddings enable AI models to understand the relationships between words. For instance, the model will know that the words „court“ and „judge“ are closely related, much like a lawyer would understand the relation between „negligence“ and „duty of care.“
- Transferability: Once trained, these embeddings can be used across various AI tasks, similar to how a principle established in one legal case might apply to another seemingly unrelated case.
Conclusion
For lawyers, understanding the concept of embeddings need not be daunting. Just as legal codes distill vast legal knowledge into structured forms, embeddings capture the essence of language in a manner that AI models can easily grasp. As legal professionals, recognizing these similarities can not only deepen our understanding of AI but also open doors to innovative applications of AI in the legal realm.
