DARPA aims to build an AI to find hidden patterns in global chaos

DARPA aims to build an AI to find hidden patterns in global chaos

DARPA aims to build an AI to find hidden patterns in global chaos 1390 782 hassam

DARPA eyeing on a new Program at research agency to create a machine learning system which will be able to sift through numerous events and media content generated every second and capable of identifying connected threads and relative narrative in these events and media. This New Program is Named KAIROS: “Knowledge-directed Artificial Intelligence Reasoning Over Schema”.

In this specific case “Schema” is referred as the idea of basic approach Human follow to understand the concept of world by creating stories of interlinked events. Like for instance buying something from store and making payment is “Buying Something” is the Schema known to every Human. It’s simple for us humans but it’s surprisingly difficult to define formally in such way that a computer system would be able to understand. This process is easy for use because of long use and understanding but it is rule bound for system.

The more data is available, the more is complex to define it. Buying something is simple but like recognizing a cold war, or a bear market is comparatively a tough task, and that’s what DARPA want to look into. “The process of uncovering relevant connections across mountains of information and the static elements that they underlie requires temporal information and event patterns, which can be difficult to capture at scale with currently available tools and systems” quoted by DARPA program manager Boyan Onyshkevych in new release.

KAIROS, agency stated, “aims to develop a semi-automated system capable of identifying and drawing correlations between seemingly unrelated events or data, helping to inform or create broad narratives about the world around us.” But the problem is that schemas have to be laboriously defined and checked by humans. But KAIROS program aims to have the AI teach itself.

In the beginning will be limited to data ingestion in bulk quantity to build a library of basic schemas. By reading books, watching news reports, and other information sources it should be able to create laundry list of suspected schemas. Even it might get hint of larger and hazier schemas it can’t approach its artificial finger like love, racism, income disparity and more. Furthermore, it will be allowed to look into complex real world data and try to extract events and narrative based on the schemas created.

At the time this system is purely theoretical, that’s the reason DARPA looking into it, agency’s main focus is to turn the theoretical into practical.