Anyone who’s been in the technology industry long enough has seen “the next big thing” come around several times.
Usually these next big things follow a similar pattern of being (over)hyped for a period of time, and then slowly evolving into a state of familiarity as adoption becomes pervasive (e.g., optical networking, mobile phones, virtualization). Other times, the reality never lives up to the hype and the “next big thing” fades into obscurity (e.g., DSL, BetaMax).
Today’s “next big thing” is artificial intelligence (AI), or machine learning.
I say “or” because though the two terms are often used interchangeably, they are not directly synonymous*.
*Most experts today would agree that machine learning (ML) is a subset of artificial intelligence (AI). Where AI is defined as creating a computer or machine that can mimic all the cognitive functions of an intelligent human—including learning, reasoning, problem solving and understanding social cues—ML is most commonly defined as the ability of a computer or machine to improve performance by analyzing data over time. So while ML is necessary to create an AI system, ML by itself does not constitute AI.
It’s difficult to say if these terms are purposely being conflated, or whether it’s honest confusion—but either way you look at it, AI and ML are likely to be two of the “next big things” that live up to the hype.
Just look at the numbers.
According to McKinsey, as much as $39B was invested in AI in 2016. That’s too much investment for it not to be successful.
Simply put, AI and ML will a significant impact on nearly every business—and very likely our personal lives as well—in the not-too-distant future. But what does this mean for software testing professionals?
Jeff Scheaffer spoke about this in our recent Virtual Summit, and at a high level it boils down to two categories:
- how you use AI and ML to improve your testing practices; and
- how testing practices have to evolve to test the newest AI and ML applications
To be clear, we are in the early phases of AI—so if you are not quite sure how or where to start, you’re not alone.
In the context of an overall maturity curve, we see the use of AI and ML as coming after an evolution to automation and continuous testing. Today, however, many are still in the progress of evolving from manual to automated testing, and only the leaders are truly practicing Continuous Testing (see below).
But it’s never too early to start learning—and we’re here to help! In future we will delve more into the future of AI and ML as it relates to your continuous testing journey. Please check back soon for coming content that will outline predictions, practical implementation strategies and the innovations we at CA Technologies are making to bring you the into the future with continuous intelligent testing, enabled by AI and ML. In the meantime, I encourage you to view the replay of Jeff Scheaffer’s presentation on Intelligent Continuous Testing from CA’s recent virtual summit.
This article originally appeared in CA Highlight.