A system designed for artificial intelligence applications, it utilizes a distributed infrastructure rather than relying on a centralized authority. This approach fosters greater transparency, resilience, and accessibility. Consider, for example, a collaborative model training effort where computation and data are spread across numerous participants, eliminating single points of failure and reducing censorship risks.
The significance of this lies in its potential to democratize access to sophisticated AI technologies. Benefits include enhanced security due to the lack of a central target for attacks, improved fault tolerance, and the ability to create AI models that are less biased due to diverse data sources and perspectives. Historically, AI development has been concentrated in large corporations; this offers an alternative path towards wider participation and innovation.
