The Linux Foundation Member Summit

If you had a collection of every kind of animal on earth, from mules to narwhals to goldfish, how would we pick out just the housepets? And how does AI distinguish between a pack mule and a Moscow Mule?

Embeddings are numerical representations that capture the essential features and relationships of objects, like words or images, in a continuous vector space, enabling tasks such as semantic search, clustering, and recommendations. We'll explore the core concepts of embeddings, using relatable examples to make advanced ideas accessible.

Legal professionals may find embeddings particularly relevant due to their ability to distinguish between different entities and concepts with nuance. Such capability is crucial for addressing legal issues like data privacy, bias mitigation, and intellectual property.

By understanding how embeddings distinguish between concepts, attendees can draw parallels to their own legal reasoning processes, gaining insights into how these AI mechanisms intersect with legal frameworks. This session encourages legal professionals to explore the intellectual and professional possibilities that embeddings present, deepening an understanding of AI’s role in law