At Cambridge Semantics, we have watched the explosion of AI awareness in the last year with interest.
There is an immediate obstacle that these organizations face. To leverage AI, whether that be with large language models (LLMs) or other machine learning techniques they will need to connect to clean, well-defined data sources.
Venture capital has quickly realized this and made it a cornerstone in their evaluation of opportunities. This TechCrunch quote nails it:
“The true value proposition of AI companies now lies not just within the models, but also predominantly in the underpinning datasets. It is the quality, breadth, and depth of these datasets that enable models to outshine their competitors.”1
As a corollary, this logic could be extended to simply mean that the value of an AI project is directly tied to the breadth, depth, and quality of its underlying datasets. From a broad perspective, these datasets rely on a foundational technical stack and several obvious companies have seen an immediate benefit:
This list is a great place to start when thinking about the fundamental components for developing AI models, but it’s lacking a last crucial layer – data integration. Think of data integration as the crucial last mile, preparing clean and accurate data for AI.
Analysts have had a lot to say about the technologies that will play a prominent role in this last step; and if you’ve been paying careful attention, there’s been a constant message that Knowledge Graphs (KG) have become a critical enabler to the AI Revolution. A recent report from Gartner notes that:
The range for knowledge graphs is Now, as KG adoption has rapidly accelerated in conjunction with the growing use of AI, generally, and large language models (LLMs), specifically. GenAI models are being used in conjunction with KGs to deliver trusted and verified facts to their outputs, as well as provide rules to contain the model.3
Beyond this, knowledge graphs provide additional capabilities. In an ideal world, data engineers could choose well described data points from across a “single pane of glass” - integrating, aggregating and harmonizing data from previously siloed data sources into a common set of parameters to feed custom algorithms. Think about this quote from McKinsey:
“Context can be determined only from existing data and information across structured and unstructured sources. To improve output, CDOs will need to manage integration of knowledge graphs or data models and ontologies (a set of concepts in a domain that shows their properties and the relations between them) into the prompt.”4
This quote highlights two of the major advantages that knowledge graphs provide.
Now, you might wonder how Anzo can empower AI and Large Language Models (LLMs). Anzo stands out as the sole comprehensive knowledge graph platform boasting an architecture that enables users to dynamically construct knowledge graphs using a unique structure known as a "graphmart." This involves overlaying and combining data from diverse sources, whether structured or unstructured. A graphmart serves as an optimal framework for creating knowledge graphs instantly, offering features and a design specifically tailored for AI initiatives:
Furthermore, as alluded to above, Anzo represents data with ontologies, which provides several advantages over relational systems.
If you would like to discuss how knowledge graphs could enable your AI project please message me at greg@cambridgesemantics.com or visit our website: www.cambridgesemantics.com.