Artificial intelligence and machine learning are hot — but they aren’t the same. Knowing the difference and some AI and ML basics can help you make smarter business decisions.
While artificial intelligence (AI) became a household term in 2022 and 2023 with the launch of OpenAI’s ChatGPT, in the business world it still shares the stage with machine learning (ML). In fact, Microsoft founder Bill Gates has even said that ML will be “worth ten Microsofts,” and Defense Advanced Research Projects Agency (DARPA) Executive Director Tony Tether called it “the next internet.”
However, though these terms are often used interchangeably, they aren’t quite the same things.
Understanding the differences between ML and AI will help you sort through vendors’ claims when they approach you with new technologies. That will help you avoid purchasing services you may not need and make sure you use the right AI and ML approach.
In this post, we’ll separate the facts from the hype about AI and ML. We’ll start with a brief history of ML and AI, discuss the differences between the two, and give some basics about how they work together. In another post, we’ll go a little deeper into how you can make these tools work for you.
An Overview of Machine Learning and Artificial Intelligence
Though some think ML is a brand-new idea, Arthur Samuel coined the term in 1959. In his words, ML is a “field of study that gives computers the ability to learn without being explicitly programmed.”
With ML, the computer programs itself by using input data to create output data. With the help of an algorithm, the output data then creates a new program.
AI is a more general term that refers to creating computer systems that perform tasks more intelligently or in a more human way. AI works the way people work—it tries things, learns from mistakes, and changes its behavior for the future.
AI also relies on people to function. Human beings must create the datasets and algorithms to make it work correctly. As Gartner Vice President Analyst Alexander Linder writes, “The rule with AI today is that it solves one task exceedingly well, but if the conditions of the task change only a bit, it fails.”
Think of the relationship between ML and AI like this: You can program a computer to demonstrate AI without necessarily using ML, but a computer that uses ML is also using AI.
The most recent AI advances are the large language models (LLMs) that power tools like ChatGPT, Google Bard, Microsoft Bing, and a host of others. They use sophisticated algorithms to recognize patterns in words, numbers and data in response to queries. They then deliver the responses in the user’s human language.
These AI tools, known collectively as “generative AI,” make AI an even more powerful partner to ML. As generative AI creates new content and feeds it into an ML tool, that tool completes its tasks faster, more efficiently, and more accurately.
ML Programming vs. Traditional Programming: A Deeper Dive
In 1998, Tom Mitchell defined ML like this: “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”
Let’s unpack this by looking at ML versus traditional programming.
In traditional programming, the programmer uses input data and a set of code rules to create a program. The coded program runs on the computer and produces a desired output. Below is how traditional programming works:
With ML, in contrast, the programmer creates a predictive model by identifying the data samples for a true/false condition and then manipulates the data to pass to a predictive algorithm that creates new rules. Below is how ML works, where a model is built from example inputs to make data-driven predictions:
Types of Machine Learning
Broadly speaking, three types of ML algorithms exist:
Supervised Learning
Supervised learning is the most popular ML paradigm. It is also the easiest to understand and the simplest to implement. You can think of supervised learning as “task-oriented” because it typically focuses on a single task. Programmers feed a lot of data into algorithms until they can accurately perform the desired task.
Supervised learning can be two types – regression or prediction and classification.
- Predicting the number of items that will sell over the next three months from an inventory of similar items is an example of regression or prediction.
- Predicting the weather from one of three possibilities (Sunny, Cloudy or Rainy) is an example of classification.
Unsupervised Learning
In unsupervised learning, programmers again feed large amounts of data into an algorithm, but they also give it the tools to understand the data’s properties. The program can then learn to group, cluster or organize the data so that a human (or another intelligent algorithm) can make sense of it.
One example of unsupervised learning might be recommendations of what to watch next from your streaming service. These are known as recommender systems. Another example could be algorithms that use customers’ buying habits to group them into similar purchasing segments so companies can market to them better.
Reinforcement Learning
Reinforcement learning relies on recognizing mistakes and successes, or “hits.” The algorithm is “rewarded” for performing correctly, and it is “penalized” for performing incorrectly. This model requires no human intervention because the algorithm teaches itself to maximize reward and minimize penalty. Generative AI models can use similar feedback loops to create even better training data.
Conclusion
AI and ML represent the future of computing, but you must understand how they are alike and different to make the best decisions and to communicate well with vendors. You can then begin using some basic ML concepts to determine which type of approach will meet your needs. We’ll take a deeper look at more definitions and how to start writing simple ML programs in our next post.
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