What do you picture when you hear “machine learning”? Are you imagining a factory machine doing work? A human-looking robot in a lab? Maybe you’re envisioning a future where artificial intelligence (AI) becomes self-aware.
It's no surprise the world has been talking about artificial intelligence – the ways people can use AI nowadays seem endless. For instance, you can do things like ask ChatGPT to write you a recipe, immerse yourself in a video game with virtual reality, or maybe even let autopilot drive your car.
With so many ways AI and machine learning are present in our lives, it’s easy to use the terms “AI” and “machine learning” interchangeably, even though they are not the same thing.
So, what really is the difference between AI and machine learning? How do they help us make progress and solve problems? In this blog post, we’ll define both AI and machine learning, and provide the basics of how machine learning works and what it can be used for.
What is AI VS machine learning?
While AI and machine learning are sometimes used interchangeably, they are not the same thing. Machine learning is a specific type of AI. It may be helpful to think of AI as a broad concept, and machine learning as one of many technologies that can put that concept into action.
In this section, we’ll focus on the relationship between AI and machine learning.
What is AI?
In its most basic sense, artificial intelligence is a computer simulation of human intelligence. AI imitates human processes such as learning, decision-making, and problem-solving. Just like humans need logic and reasoning to back up their decisions, AI needs data to make informed decisions.
AI uses many technologies (including machine learning) to achieve tasks using data. The technology that AI uses depends on the task. If the task is simple, like recognizing a key word or a specific number, the AI may just use a set of rules. If the task is more complex, like generating an image from a text description, the AI would need to use machine learning.
Similar to how humans base their knowledge on experience, AI gathers its knowledge from large amounts of data. AI takes information it finds in data, such as patterns or categories, and uses it to complete tasks. But how does AI know what the data means and what to do with it? That’s where machine learning comes in.
What is Machine Learning?
Machine learning is a computer’s process of understanding data and learning from it. What makes machine learning a form of artificial intelligence is that it is not specifically programmed, meaning that it does not follow manually coded instructions.
Rather, machine learning uses algorithms (sets of instructions) to find patterns in data, which the computer learns from to make predictions about new data. These predictions are made using models, which are a specific set of algorithms used to recognize patterns in a data set. As more data is fed to the computer over time, the algorithms will find more patterns, which will improve the model.
An example of a machine learning model is a decision tree, which is used in several of Minitab’s predictive analytics solutions. Essentially, a decision tree model identifies what factor is the best predictor of an outcome. To do this, the model takes a large dataset, and the algorithms split up the data into small sections of similar factors. The model repeats this splitting to make a “decision tree,” which is a flowchart that visualizes what decisions will lead to what outcomes.
How AI and Machine Learning Work Together
To recap, AI is a computer simulation of human intelligence, and machine learning is the process a computer follows to perform these simulations. Let’s break down how AI uses machine learning by comparing the process to how humans learn.
For example, you know the difference between an apple and an orange. But how did you learn that these are two different fruits, and how do you continue to know they are different?
To learn that an apple and orange are different, you subconsciously made sensory observations. You observed factors like the color, shape, taste, texture, and smell of apples and oranges. The more times you made these observations about apples and oranges, you were able to distinguish between the two fruits. You recognized patterns: apples are red or green and have a smooth surface texture; oranges are orange and have a rough surface texture. Now when you see an apple, you recognize that it is an apple and not an orange based on your observations.
AI goes through a similar process to learn the difference between apples and oranges but must learn from data inputs because it cannot make sensory observations. AI is given large amounts of labeled data about apples and oranges, such as each fruit’s color, weight, shape, fiber content, and surface texture, and makes observations using machine learning algorithms. These algorithms make sense of the data by discovering patterns: apples are red or green, have a smooth surface texture and approximately 4 grams of fiber; oranges are orange, have a rough surface texture and approximately 2 grams of fiber.
From these algorithms, a machine learning model can be built specifically for categorizing apples and oranges. The model can be applied to new apple and orange data, and it will use the patterns found by the algorithms to predict whether a fruit is an apple or an orange.
What is machine learning used for?
As we mentioned earlier, machine learning is a specific technology used by AI to make predictions and decisions. Machine learning methods can be an insightful resource for businesses across industries to make data-informed decisions and predictions.
Read our blog to learn how Minitab’s machine learning features are used for predictive quality control.
For instance, in manufacturing, machine learning algorithms analyze production line data to predict equipment failures before they occur. By identifying patterns and anomalies in the data, these algorithms can schedule maintenance at optimal times, reducing downtime and improving overall efficiency. This predictive capability ensures a smoother operation and helps maintain consistent product quality.
How does Minitab use AI and Machine Learning?
Minitab uses AI and machine learning to enhance its data analysis capabilities, enabling users to uncover deeper insights and make more accurate predictions. Minitab's advanced algorithms automate complex statistical analyses, identify patterns, and forecast trends, helping businesses optimize processes and improve decision-making. By integrating these technologies, Minitab provides reliable, data-driven solutions that empower users to tackle challenging problems with confidence.
Want to learn more about how Minitab uses AI and Machine Learning?
