ICABiDAS: Intuition Centred Architecture for Big Data Analysis and Synthesis
This work addresses the problem of enhancing big data systems for more human-like synthesis capabilities, which could be significant for data analysis domains if proven effective, but appears incremental as it builds on existing concepts of intuition in AI.
The paper tackles the challenge of enabling big data systems to not only analyze but also synthesize new interpretations from data, inspired by human intuition, and proposes an intuition-based architecture for this purpose.
Humans are expert in the amount of sensory data they deal with each moment. Human brain not only analyses these data but also starts synthesizing new information from the existing data. The current age Big-data systems are needed not just to analyze data but also to come up new interpretation. We believe that the pivotal ability in human brain which enables us to do this is what is known as "intuition". Here, we present an intuition based architecture for big data analysis and synthesis.