Remember when concepts like Machine Learning (ML) and Artificial Intelligence (AI) were things found in comic books and movies; strictly concepts, and nothing else?
Actors like Arnold Schwarzenegger would light up the big screen, portraying epic battles between man and machine, on a seemingly never-ending quest to find balance between the two entities.
Yet, as 2020 looms near, AI is no longer just the stuffs of pop culture, rather, it is very much becoming a part of our every day reality. In fact, according to a Global Artificial Intelligence Study conducted by PwC, AI could contribute up to $15.7 trillion to the global economy by 2030.
The realm of AI encompasses a variety of technologies, including machine learning, and the two are often used interchangeably. Chances are, you’ve probably overheard conversations at the water cooler, or taken part in a conversation yourself regarding the marvels of AI and ML.
That’s because these terms 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. And although the two terms are often used interchangeably, they are mutually exclusive concepts – so there is a difference.
ML is technically a subset of AI. Essentially, ML provides systems the ability to automatically learn and improve from experience without being explicitly programmed; focusing on the development of computer programs that can access data and use it learn for themselves.
In other words, ML relies on processing big datasets, while detecting trends and patterns within that data and essentially “learning” about these trends along the way.
Like people, machines have the ability to “learn,” acquiring knowledge and/or skills through their unique experiences. For example, say you have an ML 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. In the instance of a new skin condition, however, or if an existing pattern of skin conditions changes, the algorithm will not predict those conditions correctly.
This is because one must feed in all the new data so that the algorithm can continue to predict skin conditions accurately.
Unlike machine learning, AI learns by acquiring and then applying knowledge. The goal of AI is to find the most optimal solution possible, by training computers a response mechanism equal to or better than that of a human being.
In the instance of adaptation in new scenarios, Artificial Intelligence is perhaps the most ideal.
Let’s take a simple video game, for example, where the goal is to move through a minefield using a self-driving car. Initially, the car does not know which path to take in order to avoid the landmines.
After enough simulated runs, large amounts of data are generated concluding 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.
But, what if the location of the landmines has changed? Thee ML algorithm does not know these individual landmines exist, rather, it only exclusively knows the all it knows the pattern resulting 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.
Enter, AI – capable of analyzing new data in an algorithm to determine multiple factors; answering questions like, why did the paths change? Which direction is most ideal, given the new circumstances, and where are the new hot-spots? It will then codify rules for identificiation of those hot-spots where the land mines exist.
Slowly, AI will begin to avoid them altogether by following te new trails – just like people, learning and adapting to new boundaries and environmental challenges.
So, by now, you’ve learned the basic differentiating factors between ML and AI. 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, empowering companies to respond quickly and accurately to changes in customer behavior and solve critical business problems.
As the adoption of AI and ML become more commonplace, namely predictive analytics and data science will see a massive uptake in virtually all industries across the marketplace.