In this article, Larry Lafferty, CEO of Veloxiti, Inc., a company that builds artificial intelligence systems, provides enterprise decision makers with an understanding of how AI systems deliver a valuable business return. In his article, Larry discusses the features of the implicit and explicit models and their strengths and weaknesses. Additionally, he brings out the science — knowledge engineering — behind the building of declarative and directed graphs.
For the benefit of enterprise decision makers, here is a quick overview to enable them to grasp both ends of the AI spectrum. Let us begin by summarizing the differences between implicit and explicit models. Implicit model creation is an algorithm based on artificial intelligence (AI). Essentially, implicit models provide answers to queries. However, there is no option to determine the formula by which the algorithm arrived at an answer. This inability to learn is a major lacuna as learning is a critical attribute of AI.
On the other hand, explicit models are manually engineered to represent the problem area. The explicit (declarative) graphs offer a way out of the missing formula challenge prevalent in the implicit model. The format for this model is often a highly structured directed graph derived from how people observe and understand a problem. Explicit graphs can hold numerous attributes, linkages and classes that represent ways of thinking or physical tasks.
The science of building a declarative and often directed graph is called knowledge engineering. The results obtained through an engineered graph-based system can be audited, and hence the results are deemed trustworthy. In addition, this system offers the ability to explain how the system formulated evidence to find the right image. Consequently, structured knowledge graphs are an important aspect of AI. So much so, large-scale adopters of graph techniques like Google have begun reinvestigating a variety of graph formats and the mathematics behind them. This is a promising development as it can only make the next wave of AI stronger.
In conclusion, a well-structured graph containing valuable expert knowledge, combined with a powerful graph engine, can synchronize with a workforce or employee to assess large-scale, complex, rapidly changing domains. Furthermore, an engineered graph-based system is a valuable product as it conforms to regulatory requirements, legal proof or teaching needs of an enterprise.
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