Enterprise applications of Artificial Intelligence have reached a vital turning point. Most organizations today are either using tools that incorporate machine learning or working on data science projects for injecting this connectionist form of AI into their mission critical workflows.
Simultaneously, however, there is an ever growing amount of use cases across verticals for rules-based AI, which is rapidly encompassing everything from making intelligent inferences about schema to expedite data integration to assembling techniques for text analytics or Natural Language Processing.
Instead of these different branches of AI competing with each other in vendor solutions, the industry has reached a point of inflection in which there are more offerings “doing new school AI, i.e. statistical learning, machine learning, what we call machine learning and also, at the same time, and we’ve worked on this so it all works together seamlessly, they’re also doing that symbolic or rules-based AI,” Stardog CEO Kendall Clark commented.
The reality is that almost daily, organizations are encountering situations in which one or the other of these AI approaches can help them meet their business objectives in a way that’s far more efficient and productive than without them. Consequently, “it looks more and more like the future of AI will be some combination of both logical and statistical,” Clark concluded.