The Distinction between AI and Machine Learning, and How it Starts with Big Data

There is much chatter out there about “Artificial Intelligence” and “Machine Learning.” We’ve been hearing these terms for years, but in the past primarily just in the movies with stories about AI robots taking over the world and such. However, recently, these terms have emerged as commonplace and are actually being applied in a wide variety of ways making astonishing advances throughout various industries, in particular healthcare.

From medical devices to clinical research, the impact this technology has already made in healthcare sectors is significant. It is apparent that healthcare is moving in the direction of AI and machine learning, but do you ever find yourself asking the question, what exactly do these terms mean and what’s the difference between the two? 
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Machine Learning is AI, but AI is not necessarily Machine Learning

 yuo talk about the true definition of artificial intelligence and machine learning, we must first start by defining Big Data - Another one of those latest and greatest buzzwords. Big Data refers to the immense volume of information that is generated through the computerization of everything and anything that is collected and stored within a technology platform. As the name infers, Big Data is a massive amount of information, originated from numerous sources stored in various formats. This enormous amount of data is the foundation of what artificial intelligence and machine learning utilize to create the algorithms and predictive patterns that provide purposeful information in an expedited fashion used to make better decisions and discover efficient solutions.   
 
Let’s start with artificial intelligence or AI. AI is a generic term that refers to any hardware or software that demonstrates behavior which appears intelligent. The AI field is considered a branch of computer science that’s purpose is to create intelligent machines. On the other hand, machine learning is a sub-category of AI that is designed around the concept of giving machines access to data in order for the machines to learn for themselves. The objective in machine learning is to create machines that mimic human behavior by learning, and understanding complex concepts, to reach conclusions or connect and engage with people. As a visual, think of it this way - all machine learning is considered AI, but not all AI is machine learning.
 
AI and machine learning has genuinely opened up a new world of possibilities and is enabling us to advance in various fields within healthcare at an accelerated rate. There will always be that age-old fear of machines taking the place of humans, but the fact is that a machine can never duplicate human creativity and imagination. One fantastic aspect of AI that all humans can appreciate is that it relieves us from many tedious, time-consuming and monotonous tasks, which in turn provides us with the freedom to focus our attention on important and relevant issues requiring critical thinking – a unique characteristic found only in humans!
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There has and will continue to be a tremendous amount of information available related to machine learning and artificial intelligence, and the possibilities are endless while the technology continues to grow. With the healthcare sector as a focal point for these new technologies, it is essential for all healthcare professionals to understand the capabilities and stay abreast of the changing environment.
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Axea has always found it valuable to start at the grassroots level to better understand the foundation of a concept or new idea. Here are two entertaining yet informative videos that may help shed light on the fundamental aspects of both machine learning and artificial intelligence. ​

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