I recently came across an interesting concept that combines signal processing principles with AI models to make them more efficient and accurate. This idea is being explored in collaboration with Prof. Gunnar Carlsson, a pioneer in topological data analysis. The goal is to apply signal processing techniques, traditionally used in communication systems, to AI models and embedding spaces.
One of the first applications of this concept is in ANN search, where it has achieved 10x faster vector search than current solutions. This is a significant breakthrough, especially for those interested in vector databases. You can find more information on this topic in a technical note and video titled ‘Traversal is Killing Vector Search — How Signal Processing is the Future’.
The potential of signal processing in AI is vast, and it’s exciting to think about how it could shape the next wave of AI systems. If you’re in the Bay Area, there’s an upcoming event where you can discuss this topic with experts and like-minded individuals. Additionally, the team will be attending TechCrunch Disrupt 2025, providing another opportunity to meet and brainstorm.
So, what does this mean for the future of AI? It’s clear that signal processing has the potential to complement modern AI architectures, making them more efficient and accurate. As this technology continues to evolve, it will be interesting to see how it’s applied in various fields and the impact it has on the development of AI systems.