Machine Learning vs. Artificial Intelligence: What Is the Difference?

From blockchain to big data, nailing down a definition for tech buzzwords often incites debate. Artificial intelligence, or AI, is one of these terms that is often misused and misunderstood.

Artificial intelligence is an area that is continuously evolving, and as a result, people tend to lump related concepts like machine learning and deep learning into the same category. That being said, it’s important to note that there are key differences between artificial intelligence and machine learning, despite their close relationship.

Read on, and we’ll break it down.

A Closer Look at Artificial Intelligence vs. Machine Learning

If you’ve read anything about the companies leading the charge in artificial intelligence, you’ve probably noticed that machine learning and artificial intelligence are often mentioned together. The idea of artificial intelligence isn’t exactly new. It’s been around in some conceptualized form or another for centuries now. That said, actualizing AI is another story.

Artificial intelligence is a concept that refers to the simulation of human intelligence. This typically plays out in the form of a machine performing tasks that generally rely on the human mind.

AI programming covers three key areas: learning, reasoning, and self-correction.

  • The definition of learning in artificial intelligence is the process of acquiring data and creating rules for turning data into action. These rules, known as algorithms, offer step-by-step instructions that tell computing devices how to perform a task.
  • Reasoning processes are put in place to ensure that a program chooses the best algorithm for achieving the desired outcome.
  • And finally, self-correction refers to the process of fine-tuning algorithms so that they deliver the most accurate results possible.

AI isn’t one single technology or process. While people often refer to concepts like machine learning or deep learning as AI, the reality is that these terms refer to subtypes of AI.

Machine learning is an AI model, defined as the science of getting a machine to act without programming. Machine learning systems learn by gathering, categorizing, and analyzing data fed into the machine, which it can then use to make ‘intelligent’ decisions.

ML operates on one of three types of algorithms.

  • Supervised learning. The machine is fed data sets that are already labeled, so that the machine learns to identify patterns, later using them to categorize incoming data.
  • Unsupervised learning. In this case, the machine receives data that isn’t labeled but sorted based on similarities and differences.
  • Reinforcement learning. Here, the machine also receives unlabeled data sets but receives feedback after completing a task.

The definition of deep learning in artificial intelligence refers to another model. Deep learning systems are designed to analyze data by applying logic much like humans do.

Read this article for a more in-depth definition of Artificial Intelligence.

How Machine Learning and Artificial Intelligence Work Together

Admittedly, the relationship between machine learning and AI is a bit difficult to understand. It helps to compare the AI and ML relationship to the link between the human body and the brain.

Think about how a person learns to speak. They gather information, the brain analyzes it and then communicates to the body how to perform the act of speaking.

The same is true of machine learning and artificial intelligence. We’ve gone over the definition of deep learning in artificial intelligence and ML, but haven’t yet mentioned neural networks.

Neural networks, another AI model, play an essential role in defining the artificial intelligence vs. machine learning difference. Whereas the definition of learning in artificial intelligence is more static, machine learning is dynamic.

Neural networks are a set of algorithms modeled after neurons in the human brain, designed to recognize patterns.

Neural networks interpret raw data; labeling and clustering the information it receives. They adapt to changing input, allowing the system to produce the best possible result without requiring any modifications to the architecture.

In machine learning, the machine uses all of the data at its disposal, working with algorithms that help the program determine the best course of action.

Over time, the system gets ‘smarter,’ building on existing knowledge when it receives new information.

Neural networks are a subset of machine learning, designed to learn and make intelligent decisions on their own. By contrast, machine learning makes decisions based on what it has already learned.

It Is Not Artificial Intelligence vs. Machine Learning — This Is a Collaboration

We are in the era of machine learning and artificial intelligence. The ability to get smarter by building on existing knowledge is no longer the exclusive domain of humans.

Machine learning and artificial intelligence are both critical components of the modern tech landscape.

But we need to be careful about how we interchangeably use terms that have different meanings. While the two work together harmoniously to advance capabilities in the technology industry, machine learning is just one of the many forms that artificial intelligence can take.

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