How does AI actually work?
It's a question we get all the time.
Even if you know what AI and related technologies are or what they do, it can sometimes be tough to wrap your head around how they work.
That's because it's a complicated, technical topic.
In this post, we want to simplify this, so that non-technical professionals can understand how AI works.
It's essential to understand how AI works, no matter what you do. That's because authorities like McKinsey Global Institute expect AI to have trillions in economic impact. That's an internet-sized technological and economic transformation.
And it means that every category of professional will need to develop AI understanding and capabilities. That doesn't mean every professional must learn how to code or use machine learning models. But it does mean that you'll need a basic understanding of how AI works if you want to thrive.
The biggest thing to understand about how AI works:
It's not magic; it's math.
AI is an umbrella term for different types of systems powered by machine learning. Machine learning is when we train machines to literally learn.
We do this by giving algorithms data. Algorithms are just a series of computerized steps. We feed the algorithm data. The algorithm performs a series of steps. We analyze the output for accuracy.
In machine learning, we train the machine on the accuracy of these outputs. With enough training, the machine gets very good at whatever it's being trained to do. What's more, the machine can eventually train itself. It can learn to improve its results without human supervision.
The more data an AI system uses to train itself, the better it gets over time. In theory, the ability to improve is limitless. This is where those seemingly "magical" results happen.
One example is the AI that Google uses in its Gmail email client.
A few years ago, it could recommend basic responses to emails. Things like "Thanks!" or "Sounds good!" These responses were largely accurate because Google trained its AI on enough email data.
But it didn't end there.
As the AI used in Gmail learned more, it got better. Dramatically better. Today, it can now predict the next word or sentence you want to type—in real-time, with accuracy.
This is very different from how traditional software works.
Traditional software uses a fixed set of rules coded by humans to perform tasks. It may be helpful. But it doesn't get better and smarter on its own.
Let's look at an example in marketing to see the difference...
In marketing, traditional software exists that can help you write better email subject lines. Human programmers have analyzed what subject lines work, then turned that analysis into rules the software follows.
Perhaps the human programmers found that subject lines of only 60 characters or less work best. They code the machine to recommend subject lines of 60 characters or less. The machine has hundreds of rules that offer helpful guidance if you want to write better subject lines.
But the rules don't change, and they're all based on a static set of data.
However, AI now exists in marketing that writes subject lines for you. The AI system analyzes your past subject lines, then finds patterns in the data that indicate what works best.
Then, the system continually analyzes performance each time you send one of its recommended subject lines. After each send, it learns and improves. The next batch of subject lines can then be even more effective based on continual, real-time learning.
It's all thanks to AI's ability to find patterns, learn from those patterns, make predictions about future patterns, and learn from it all to get better.
As Chief Content Officer, Mike Kaput uses content marketing, marketing strategy, and marketing technology to grow and scale traffic, leads, and revenue for Marketing AI Institute. An avid writer, Mike has published hundreds of articles on how to use AI in marketing to increase revenue and reduce costs. Mike is the co-author of Marketing Artificial Intelligence: AI, Marketing and the Future of Business (Matt Holt Books, 2022). He is also the author of Bitcoin in Plain English, a beginner’s guide to the world’s most popular cryptocurrency.