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Grow Your Business Technology

Machine Learning or Automation: What's the Difference?

image for GaudiLab/Shutterstock
GaudiLab/Shutterstock
  • The idea of automation goes as far back as the ancient Greeks, but automation that reacts to change is very modern.
  • Machine learning is in full swing and is augmenting automation systems to deal with variability.
  • Artificial intelligence can't truly "think" yet, but many machine learning systems are exhibiting AI-type behaviors, such as language recognition and visual memory comparisons.

All of the major players in the tech industry are pushing the boundaries of self-determining computers, especially with cutting-edge technologies like artificial intelligence and machine learning becoming more mainstream. While many professionals understand that these technologies will make their jobs easier, or even take over certain tasks, there's also a lot of confusion. For example, one common question is, what's the difference between machine learning and automation?

Let's start with machine learning, which is a subset of artificial intelligence (AI).

"It's an evolution," said Andreas Roell, chairman of Analytics Ventures, a consultancy that helps businesses adopt AI. "AI fits into the bucket of workload analysis, or task analysis. Business intelligence also sits in that same bucket. It's taking data, then analyzing it."

Machine learning is typically a later-stage development, where machines are taking in data on their own and then analyzing it, he said. The biggest difference is that "machine learning identifies data signals relevant for the future," Roell added.

Automation is frequently confused with AI. Like automation, AI is designed to streamline tasks and speed workflows. But the difference is that automation is fixed solely on repetitive, instructive tasks, whereas automation performs a job and then thinks no further.

There's a good chance you use automation without even realizing it – for example, by automating emails to customers, automating the way you generate invoices or automatically logging a help-desk inquiry. Automating these monotonous tasks saves time and allows workers to focus on higher-priority initiatives. It's a reliable, computerized workhorse, always showing up and getting the job done.

Machine learning takes these tasks and layers them in an element of prediction. Whereas automation would continue to do exactly as you requested – say, send invoices on a specific day – machine learning predicts when the invoices should go out, who did or did not receive one and when payments are on the verge of being late.

No, AI and automation are not the same. Automation involves an entire category of technologies that provide activity or work without human involvement. For example, an old-style water wheel represents automation, translating the power of falling water into a repetitive nonhuman activity or mechanical work. But there is nothing about the water wheel that involves artificial intelligence; it just keeps doing the same thing over and over.

We often associate automation with computers because they're something we can relate to in modern times, but automation has been around a very long time. "If you can take the resources that you have and come up with some sort of silver bullet and that turns them into radically better efficiency for what you're getting back, that is going to be evolutionary dynamite," zoologist Antone Martinho-Truswell told Gizmodo. "You're going to do fantastically well, as we have. Our nearest relatives are all endangered because of us."

Artificial intelligence (AI), on the other hand, involves a machine exhibiting and practicing something similar to what we describe as human thinking – that is, the ability to interact in thousands of ways with the world around us without receiving any prior explicit coding or instructions. In practice, however, AI is still lacking. Only a few companies have realized something close to a functioning AI; mostly, AI is still just a concept, Forbes reported. Right now, what is considered "working AI" is really just a computer applying principles of statistical probability with variable input, such as what is being seen in the latest IT security products, rather than actual thinking by a computer.

Machine learning works to understand data, leveraging what Roell called "data signals" to drive future intelligence. It's not simply performing an "If X, then Y" task stream; it's essentially "thinking" through data, much like a human would.

"There's a lot of fear around AI, that it will eliminate jobs," Roell said. "That's not what it's supposed to do; it's making the way we work easier. But what it will do is lead to entirely new categories of jobs being created."

Roell gave the example of call center employees now being used to categorize the vast amounts of data used by AI. Several companies have taken this approach.

"Now that is true innovation," he said.

Machine learning can be automated when it involves the same activity again and again. However, the fundamental nature of machine learning deals with the opposite: variable conditions. In this regard, machine learning needs to be able to function independently and with different solutions to match different demands. There is a higher likelihood that machine learning would be applied to determining unknown prediction scenarios instead.

However, the principle could apply in automated systems as a safeguard or as an element of automation, according to the Brookings Institution. For example, a computer system used to move Amazon.com boxes could learn millions upon millions of weights so that it could flag a box on the conveyor belt that doesn't match known inventory when it sees the anomaly in its sensors along the way from the shelf to the shipping truck.

Probably not, Michael Berthold wrote for InfoWorld. We will quickly move closer to a "thinking" operation, but the gap between AI as a concept and a working-AI reality is still very big. In the meantime, machine learning, programming and automation will be roaring ahead, dramatically reshaping the job market as a result.