Just the Facts: Why AI and Machine Learning Aren't the Same Thing

As more companies seek out forward-thinking approaches to data management and enterprise operations, they're likely to come across AI and machine learning. However, one of the biggest misconceptions about these powerful tools is that they are one in the same and capable of being freely exchanged as both concepts and terminologies. But this could not be more wrong and companies need to understand why if they want to correctly apply the right tool and achieve the results that they desire.

What Is AI?

AI, which stands for artificial intelligence, is a broad term that refers to an autonomous agent that is capable of applying reason to behavioral and interactive scenarios. In short, an AI agent can "think". While robots, machine learning and deep learning are considered subfields of AI, they are not, precisely speaking, the same thing. In fact, there are few companies using true AI technology as even the most basic agent requires time and cost-intensive training before being capable of actual thought.

Current uses of this technology include applications like IBM Watson, which can answer thousands of questions and is actively being taught how to learn things like organic chemistry, as well as a host of other critical cognitive tasks related to the sciences, engineering and technology. Another AI application, known as Legal Robot, can produce content written in legalese and even review legal contracts, saving business professionals valuable time and costly legal fees associated with hiring a lawyer. "AI" technology that can't perform these or similar tasks don't really belong under the label of AI.

What Is Machine Learning?

Machine learning is the process of training and testing an algorithm on specific data so that it can make some sort of meaningful statistical inference (i.e., learn) from it. With machine learning, the "machine" isn't autonomous and it can't learn anything other than that which relates to the data that it is trained on. Likewise, the outcome of any training model will be a number, not words or phrases, which are beyond its comprehension. About 90 percent of "AI" products, actually fall under the category of machine learning.

Data analysis is the most popular use of machine learning and is increasingly used by companies of varying sizes and across industries. Automation, specifically intelligent automation, is another rapidly growing area of machine learning that can be used to streamline operational and even complex, but predictive, tasks ranging from workflows to regulatory compliance and quality control. WorkFusion is one company that specializes in this type of machine learning-based technology.

Choosing AI or Machine Learning

When it comes to choosing between AI and machine learning, the best option is the one that most closely aligns with your company objectives. Both are high-value technologies that can enhance almost any organization. That being said, if you want deeper analytic insights, optimized workflows managed by automation software and improved quality control that's relatively easy to implement and won't send you into the red, then machine learning could just be the right solution for your business.

Whether your choose AI or machine learning to help take your company to the next stage in its development, understanding the difference between the two will make the process a lot simpler, and you, that much savvier when it comes to one of today's most popular technologies.