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Breaking Down the Artificial Intelligence and Machine Learning Barriers

When management professionals hear the words “artificial intelligence” (AI) and machine learning (MI), reactions run the gamut from curiosity to conjuring up images of the machines taking over in an I, Robot inspired scene.

However, many organizations have discovered that AI allows them to process and make informed decisions using the vast amounts of information now accessible to most companies. AI represents a significant improvement in speed, availability, and time to market, particularly for supply chain management professionals who rely on fluid communication and consistent processes to help streamline development and quality assurance tasks. Most importantly, AI reflects the technological innovations that are continuing to transform how we do business worldwide.

Machine learning has itself become an extremely popular concept as executives across diverse industries embrace the trend of allowing a certain level of intelligent automation in order to build efficiencies within their organizations. Although the concepts are sometimes used interchangeably, Artificial Intelligence and Machine Learning differ and it’s critical to understand the value each brings to organizations.

What is Artificial Intelligence and what does it have to do with Machine Learning?

One of the most salient definitions of AI versus MI comes from a pioneer in the AI field, Terrence Mills.  According to Mill’s post in Forbes, “AI means that machines can perform tasks in ways that are "intelligent."These machines aren't just programmed to do a single, repetitive motion -- they can do more by adapting to different situations.

Machine learning is technically a branch of AI, but it's more specific than the overall concept. Machine learning is based on the idea that we can build machines to process data and learn on their own, without our constant supervision.” (

Thus, machine learning allows companies to leverage efficiencies by not only enabling their systems to process and analyze more data and complete rote tasks but, also to teach its machines to learn about concepts to make informed analyses. In testing environments, it removes more onerous tasks from the hands of QA professionals and manual testers, allowing them to focus on overall testing strategies and development, expediting exploratory testing. With the large amounts of data that testing professionals must deal with and maintain responsibility for, machine learning provides teams with the ability to automate testing tasks and help identify business risks.

While machine learning can help enable improvements across your testing environment, it’s critical that your organization develop clear cut goals for deploying MI across your testing environment and finite benchmarks to measure those goals against. Incorporating an automated testing platform, such as Cycle, can provide efficiencies of speed and scale by running dynamic test data sets for powerful regression testing and empowering QA teams through supplementing their manual testing efforts. AI and MI is here to stay.