All Creations are Embeddings
Embed everything, they said.
Last updated
Embed everything, they said.
Last updated
Consensus Embedding Algorithm. Core algorithms leverage embedding techniques as a consensus mechanism for information processing, similar to how SHA functions serve as consensus mechanisms in Web3.
This foundational approach ensures consistent and reliable processing of semantic information across distributed AI systems. This way, all agents and creations live in the same space, creating natural gatherings. We can imagine that in the future the native MetaSpace inhabitants can form neighborhoods, towns, cities, and even countries.
User Preference as Embeddings. By encoding user preferences as vectors in the same embedding space as content, we enable direct computation of alignment through vector operations. This unified approach transforms personalization into elegant vector arithmetic.
The embedding space forms the foundational layer where information is encoded in high-dimensional vectors, enabling efficient processing and semantic understanding of complex data relationships. This forms a natural background world where agents and information live.
Here, imagine a 1536-dimensional space where agents and humans co-live together, elevating informational transfer to a new, well, dimension.
Knowledge is Human-verified Data. A comprehensive collection of human-validated information forms the bedrock of our system's knowledge base. This data undergoes rigorous verification processes to ensure accuracy and reliability, establishing a trusted foundation for agent operations and decision-making processes.
Efficient Representation for Scalability. Advanced RAG techniques optimize the encoding and processing of information within the system. This efficient representation enables rapid computation and fast retrieval via vector DBs and real-time responses while minimizing resource utilization, creating a scalable foundation for complex operations.
