In 2017, Attention Is All You Need by Ashish Vaswani and colleagues introduced the Transformer architecture, replacing sequential models with attention mechanisms that process entire sequences instantly.
This enabled massive scaling and became the foundation of modern AI systems. We are building on that foundation, and are grateful for the work that made modern AI possible.
Since then, progress in AI has been driven primarily by two forces: Better Models ,Better Compute
But as models and compute have scaled, a different constraint has emerged: Not The Capability of Models, But The Usability of Data, Real-world data remains fragmented across systems, formats, environments, and conditions.
This is the missing layer of AI: data interoperability
For years, organizations have treated data quality as the primary challenge. But what if the bigger problem isn't the quality of individual datasets—it's the inability of systems to work together?
As AI adoption accelerates, more data is being collected than ever before. Yet much of it remains trapped in silos, disconnected from the broader context needed to create meaningful outcomes.
This article explores a different perspective: interoperability may be more important than perfection. When data can be connected, evaluated, and recombined across systems, organizations unlock value that isolated datasets can never provide.
Read more:
https://www.datauniversa.com/interoperability-is-all-you-need