Predictive Analytics

Machine Learning vs. Artificial Intelligence: What’s the Difference?

By Sriram Parthasarathy
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By 2030, Artificial Intelligence (AI) could contribute up to $15.7 trillion to the global economy, according to PwC’s Global Artificial Intelligence Study. The world of AI encompasses a variety of technologies, including machine learning. AI and machine learning are two of the most popular buzzwords in the analytics market today. Whether you realize it or not, they have become a part of everyday life. The two terms are often used interchangeably—but is there a difference?

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While most companies overlook the distinction between artificial intelligence and machine learning in their advertising and sales, it is important to note that there are, in fact, major differences. Let’s settle the debate, once and for all.

What Is Machine Learning?

Machine learning relies on processing big datasets to find common patterns. Machines learn: They acquire knowledge or skills through experience.

For example, say you have a machine learning program with lots of images of skin conditions, along with what those conditions mean. The algorithm examines the images and identifies patterns, allowing it to analyze and predict skin conditions in the future.

When the algorithm is given a new, unknown skin image, it will compare the pattern in the current image to the pattern it learned from analyzing past images.

However, if there are new skin conditions, or if an existing pattern of skin conditions changes, the algorithm will not predict those conditions correctly. One has to feed in all the new data so that the algorithm can continue to predict skin conditions accurately.

What Is Artificial Intelligence?

Machine learning is technically a subset of Artificial Intelligence. Unlike machine learning, AI learns by acquiring and then applying knowledge. The aim of AI is to find the optimal solution by training computers to respond as well as (or better than) a human. Artificial Intelligence is ideal for situations where adapting to new scenarios is important.

As an example, let’s take a simple video game where the goal is to move through a minefield using a self-navigating car. Initially, the car does not know which path to take in order to avoid the landmines. Say we do simulated runs and generate lots of data about which path works and which paths do not. When we feed this data to the machine learning algorithm, it is able to learn from the past driving experience and navigate the car safely.

Now, let’s make things more complicated. Say the location of the landmines has moved. The machine learning algorithm does not know these individual landmines exist. All it knows is the pattern in the path taken from the initial data. Unless we feed the algorithm the new data so it can continue learning, it will continue to guide along that (now incorrect) path.

What’s so cool about AI is that it learns knowledge and applies knowledge, much like humans do. In our landmine example, once we give the algorithm the new data, AI will analyze it to determine why the paths are changing, and then it will codify rules for identification of those (dangerous) spots. When the mine locations change, AI will start looking for those dangerous spots and will slowly begin to avoid them by following the new trails—just like the human brain learns and adapts.

In summary: Machine learning uses past experiences to look for learned patterns, while Artificial Intelligence uses the experiences to acquire knowledge and skills, then applies that knowledge to new scenarios.

It’s clear that both AI and machine learning have valuable business applications. They each empower companies to respond quickly and accurately to changes in customer behavior and solve critical business problems.

Originally published April 23, 2019; updated on July 31st, 2020

About the Author

Sriram Parthasarathy is the Senior Director of Predictive Analytics at Logi Analytics. Prior to working at Logi, Sriram was a practicing data scientist, implementing and advising companies in healthcare and financial services for their use of Predictive Analytics. Prior to that, Sriram was with MicroStrategy for over a decade, where he led and launched several product modules/offerings to the market.