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What’s the difference between Artificial Intelligence and Machine Learning?

What’s the difference between Artificial Intelligence and Machine Learning?

Artificial Intelligence and Machine Learning – two concepts and technologies with massive impact on the business world and our daily lives. We’re not surprised to see how fast these technologies have grown. If we analyze the latest gadgets we can see that AI and ML are mostly embedded in all of them. However, on many occasions, these two concepts are used to describe the same thing, but there are specific characteristics that make them unique. Let’s explain these two complex terms and determine the variations between them.

Artificial Intelligence

As highlighted in one of my latest infographics, the concept of Artificial Intelligence was invented in 1956 by John McCarthy and his team of engineers. The idea behind the project was to create a machine that can mimic the human decision-making process and reproduce arithmetic. In the beginning, AI was only a `mechanical brain`, but along with the progress made in technology and medicine, researchers understood how the human brain works and started to replicate into AI.

Now, Artificial Intelligence involves different machines that are able to perform various tasks which were usually performed by humans. Therefore, AI is a much broader concept than ML. Here are a few examples of functions performed by AI-based devices:

  • Planning activities
  • Solving problems (logical problems, mathematical problems, etc.)
  • Understanding language and creating responses
  • Recognizing images and voices
  • Learning new skills
  • Identifying patterns and models that humans can’t
  • Research (medical use)
  • And many more

We are already using AI technologies on a daily basis. Do you believe this? When watching Netflix, people get to see recommendations according to their previous activities. It might not be obvious, but all these tasks are performed by an automated machine that uses an algorithm to identify your preferences and suggest movies you would be interested in. That’s the purest form of AI.

To conclude, Artificial Intelligence is a broad term used in the digital world to describe various technology use cases. But Machine Learning is only a branch of AI algorithms, and that’s why it is essential to differentiate these two concepts.

Machine Learning

A brief definition of Machine Learning would be “a way of learning which allows an algorithm to evolve”. In this particular situation, learning means nurturing an AI algorithm with large amounts of data so it can understand how things work and improve itself. With Machine Learning any computer can recognize models, patterns from massive datasets, which is amazing.

Machine Learning use cases are almost unlimited. Many of them are well-known by end users, of this technology and how it is impacting their daily lives. In one of my infographics, I have discussed this topic and highlighted several examples.

Let’s dig into more theory of Machine Learning. Let’s say that a flower shop is collecting images with different pictures (types of flowers) into a system (computer). Of course, we are talking here about hundreds and even millions of distinct images. Some of them will show tulips, some of them will display other species of flowers and plants. From the pictures folders, we select half of them and give them a specific tag: `tulip` or `other`. The rest of them won’t have any description included. After doing so, an ML algorithm will be able to study the images, their tags and create a model of a tulip. Later on, the system will use the previous information to classify the pictures without any further tags or descriptions.

This is how machines and computers learn to associate images.

Final Thoughts

Artificial Intelligence and Machine Learning will continue to catch our attention thanks to the improvements they continue to bring to us. But remember, AI is a broad concept that describes the general idea of machines being able to execute human-like activities, while ML is a subset of algorithms for AI. Still, there is at least one common thing: both technologies serve the same goals – technology advancements, automation, efficiency, and productivity.

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