The Language of Graph series distills the complexity and often mystifying terminology of knowledge graphs into business terms so that business leaders can understand the power and importance of the knowledge graph to business. The last three posts introduced Graph Query, Graph Analytics, and Graph Mining, and broke down some of their intricacies. In this post, we look at Graph Artificial Intelligence (Graph AI).
What Is Graph Artificial Intelligence?
Artificial intelligence (AI) and machine learning (ML) are continuously evolving and becoming integral parts of business operations and strategy. AI is a general term related to expanding the capability of computing machines and usually focuses on knowledge, as opposed to mere data and information. ML usually refers to AI applications whose algorithms perform better and better by “learning” from the patterns identified in the data that they process over time. Most ML requires training (by reading data where the desired patterns are already identified — for example, which images do or do not show cats) and is often very narrowly focused. A limitation of ML is that while for some tasks it can far exceed human cognitive ability, in another sense, it is shallow; it has no common sense and can miss relationships that would be immediately apparent to a human. Graph AI fills in the gaps and pulls together the connections.
Graph modeling allows the AI to pivot through databases and data repositories with the foresight to infer and probe, determine context, and pursue relational correspondence between data elements.
What Will Graph AI Do for Business?
Graph AI can draw data from many sources, ranging from traffic cameras and the Internet of Things to transactional, meteorological, scientific, and social network data. The use of ML to identify connections among otherwise disparate datasets gives Graph AI much broader applicability than you could find analyzing the tables of a relational database.
Because of this, businesses are becoming more focused on gathering and mining information from all their data sources, not merely their relational databases and structured data. Katana Graph has solutions that capitalize on the elegance of Graph AI. Getting answers to complex business questions is not limited to the structured data at hand, and it should not be a search for a needle in the haystack, even if that is an accurate description of the task.
Graph AI helps businesses with tasks like discovering innovations, detecting fraud, identifying new drugs, revealing threats, and managing identity. Early successes in applying Graph AI have also included prediction of crop pest maturation, large-scale telecom network optimization, and improvements to supply chain reliability and robustness.
Katana Graph's Intelligence Platform includes Graph Query, Graph Analytics, Graph Mining, and Graph AI. Its all-in-one platform features the game-changing intersection of graph technology and high-performance computing.
Are you worried about the computational power necessary to use Graph AI? Katana Graph performs 10x - 100x faster than competing graph platforms and scales beyond 256 machines. Its flexible cloud-based architecture lets it run on all three major cloud computing platforms: Azure, GCP, and AWS.
For a deeper understanding of the Katana Graph Intelligence Platform, please read this three-part series post:
Part 1 of 3: What is the Katana Graph Intelligence Platform?
Part 2 of 3: Katana Graph Intelligence Platform: Data Flow and Strategy
Part 3 of 3: Katana Graph Engine
Let’s get started today so that we can help you with your graph needs.