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Incorporate Graph Augmented Intelligence (XAI or GAI) into existing AI/ML/DL systems to elevate business operations to the next level.
POWER OF CONNECTEDNESS
Many machine learning (ML) or artificial intelligence (AI) frameworks encounter challenges such as questionable accuracy, poor interpretability, and low efficiency. Graph technology has emerged as a solution to these issues, leading to the development of Explainable AI (XAI).
Traditional ML/AI systems often rely on low-dimensional features that overlook networked behaviors and multi-hop connections. Leveraging Ultipa's ultra-deep traversal capability, valuable insights can be derived through versatile graph queries and rich collection of graph algorithms.
Interpreting features generated by feature engineering is challenging due to complex data transformations. Graph structures data in a high-dimensional way with nodes and edges, and offers techniques like graph embedding to meaningfully transform data into machine-friendly vectors.
As data volumes exponentially increase, the time required for tasks like ETL or feature generation becomes unacceptable. Ultipa's high-density graph computing and optimized traversal mechanisms ensure these tasks are completed efficiently to adapt to evolving business needs.
CASE STUDY
A retail bank had accuracy problem with its credit card turnover predication using ML/AI. By leveraging Ultipa Graph for accelerated data sampling and training with behavior-based data modeling, accuracy went up by 50%!
BLOG
We desire AI to be smarter, faster and explainable on the one hand, and we request Big-Data to be fast, deep and flexible in terms of data processing capabilities. The greatest common factor of this AI+Big-Data convergence leads to graph augmented intelligence and XAI. Graph database is the infrastructure and data-intelligence innovation that we have been looking for.
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