Compound AI Systems
The blog details about compound ai systems, concepts, designing them, key components and architectures.
Search vs. Vector Search vs. AI Vector Search (but why?)
"The Evolution of Search: From Regular to Vector to AI-Powered"
Imagine being able to search for information and getting exactly what you're looking for, without having to sift through pages of irrelevant results. Welcome to the world of AI vector search, the latest innovation in search technology. In this post, we'll explore the journey of search from regular to vector to AI-powered, and how this new technology is revolutionizing the way we access information.
From Regular to Vector Search
Regular search, also known as keyword search, is the most basic form of search functionality. However, it has limitations, such as only returning exact matches and not understanding the context or intent behind the search query. Vector search, on the other hand, converts data into numerical vectors, allowing for similarity-based matching and improved scalability.
The Power of AI Vector Search
AI vector search takes it to the next level by integrating artificial intelligence and machine learning with vector search. This technology uses neural networks to learn compact and meaningful vector representations of data, eliminating the need for pre-defined vectors. AI vector search models also comprehend the context and intent behind the search query, allowing for more accurate and relevant results.
What's Next?
The future of search is exciting, with ongoing research and innovations in AI vector search. Some potential developments on the horizon include multimodal search, explainable AI, and edge AI. One thing is certain – AI vector search is poised to revolutionize the way we search, and we're just beginning to scratch the surface of its potential.
Read the full post to learn more about the evolution of search and how AI vector search is changing the game. [link to the full post]
Can AI Models Collapse?
In a startling revelation, recent research highlights that AI Language Models can collapse when trained on recursively generated data. This phenomenon, known as model collapse, is a degenerative process where AI models begin to misinterpret reality due to the accumulation of polluted data. The study underscores the critical need for AI developers to preserve access to original, human-generated data and emphasizes the importance of community-wide coordination to prevent this cascading failure in future AI generations.