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As the market for intelligent applications and the software platforms used to build them has emerged over the last few years, there has been a lot of nomenclature confusion. What should we call these applications and what should we call the platforms, libraries and software tools used to build them?
The terminology matters. It should matter for vendors looking to differentiate their products from the business intelligence and predictive analytics software that has existing for decades. ‘Intelligent applications’ and ‘business intelligence’ software provide two very different sets of functionality. It should also matter to technology buyers, who need to explain and justify new solutions to those holding budget.
We could use the name of the types of algorithms to describe the platforms; i.e. neural networks (also known as deep learning) or machine learning (both supervised and unsupervised) as these are some of the key ingredients to building these intelligent applications. We could use the generic term in the field for this type of application: artificial intelligence. Another option is to use the phrase coined by IBM researchers when they were working on Watson for the Jeopardy challenge: cognitive computing.
Yet another option is that we could invent our own terminology. Shivon Zillis, a partner at Bloomberg Beta who monitors the market for these kinds of technologies, coined the phrase “machine intelligence.” One company, InsideSales, coined the term “Neuralytics” to describe their machine learning platform. Another company, Tata Consultancy Services coined the term “neural automation,” while Microsoft discusses its Office Graph technology as an “intelligent fabric.”
One way to look at this is to see what phrases companies are using on their websites and in their advertising. One thing is clear—no one vendor ‘owns’ any of the currently preferred terms.
By our count, eight companies are using some variation of cognitive (i.e. computing/agent/reasoning) in their messaging. Four companies are using “artificial intelligence” and two companies are using Zillis’s “machine intelligence.”
Secondarily, many of these companies also discuss the use of machine learning in conjunction with their preferred catch phrase, but the use of it is additive, not a replacement for the phrase that vendors have chosen. As technology analysts at IDC, we have also adopted this approach, whereby machine learning is one key software component of what we refer to as the Cognitive Software Platform. There are several other components that align with functional characteristics of IDC’s definition of cognitive software platforms. Therefore we felt that machine learning was too narrow of a technology category, AI and Deep Learning were too broad—at least in our domain of technology market research.
No matter what product, solution, or market name the vendors use, this area of software is very hot right now and is likely to get hotter in the future. IDC forecasts the 5-year CAGR for cognitive software platforms approaching 35 percent. Despite phrase confusion, companies are looking at these platforms, kicking the tires and doing pilots. So if you’re in the market for software platform for building intelligent applications or adding intelligence to your existing applications, remember to search for all of these phrases when doing your research!