You might have noticed a new term, “composite AI,” floating around the cybersphere. Don’t worry–it’s not a complex new technology that you must master. In fact, while the term may be new, the core idea behind it is not. Nevertheless, it’s likely a technique that you should be thinking about incorporating in your enterprise AI processes.
Gartner helped put composite AI on the map last summer, when it published its 2020 Hype Cycle for Emerging Technologies. Simply put, Composite AI refers to the “combination of different AI techniques to achieve the best result,” according to Gartner. That’s it. Simple enough, right?
So, what other AI techniques could that mean? It’s important here to keep in mind that AI is a very broad term. While some might believe that AI refers to the latest, greatest deep learning and neural network algorithms, AI actually covers much more under its sizable umbrella.
Machine learning and deep learning are types of AI. But there are many other types of AI that should be in your wheelhouse that fall outside of the machine learning/deep learning bubble. That includes traditional rules-based systems, natural language processing (NLP), optimization techniques, and graph techniques, according to Gartner.
A composite AI system is to be built atop a “composite architecture,” which Gartner identified as its number one Hype Cycle trend for 2020. A composite architecture (you might have guessed) incorporates packaged business capabilities that run atop a flexible data fabric, thereby enabling users to take be flexible and adaptable amidst rapidly changing systems and requirements.
As the Senior Director of Data and Analytics Product Management at SAS, Saurabh Gupta is quite familiar with the central idea behind composite AI. But Gupta knows the idea by a different name.
“I’m used to saying multi-disciplinary analytics,” Gupta says.
SAS has been helping customers build multi-disciplinary analytics–ah composite AI–systems for many years, and has a multitude of technologies and pre-built applications that it can bring to the composite AI table. Machine learning typically is just part of the solution, Gupta says.
“It is possible, if your problem is straightforward, that just using machine learning is sufficient. But in order to solve the problem fully, you’ve got to use the combination of these techniques,” Gupta tells Datanami. “I think only now are people starting to look beyond the hype of machine learning and the pain of machine learning and are beginning to recognize this concept.”
Selecting the right AI technology and technique to use is not always easy or straightforward. According to Gupta, it depends on the AI practitioners having a deep understanding of the business problem that they’re trying to solve, and the data sets that are available to solve them.
