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7 minutes read

Machine Learning Vs. Artificial Intelligence: Understanding the Differences

By Jonathan Tarud
By Jonathan Tarud
7 minutes read

It is common for many people to use the terms Artificial Intelligence (AI) and Machine Learning (ML) as synonyms, without considering that they are actually different.

The confusion occurs probably because Machine Learning is a specific type of Artificial Intelligence (AI), that is, Machine Learning is a subset of Artificial Intelligence. The illustration below shows the relation between them.

Machine Learning is contained within the universe of AI. In other words, talking about Machine Learning means indirectly talking about Artificial Intelligence. However, talking about Artificial Intelligence (AI) does not necessarily imply talking about Machine Learning. 

Although there are many similarities between Machine Learning and Artificial Intelligence, they are not the same. In the world of app development it is important to differentiate them correctly in order to communicate properly (especially if you don’t want to confuse developers) and to understand how they can help improve your app.

This post explores some of the main differences between AI and ML so that you can understand the characteristics and functionalities of each. AI and Machine Learning are rapidly changing the world we live in. Therefore, you should understand the nuances of the Artificial Intelligence vs. Machine Learning (ML) comparison.

The Ability to Learn

Both Artificial Intelligence and Machine Learning (ML) are used to help solve complex problems. AI is mostly used to perform tasks or make decisions. Meanwhile, Machine Learning is typically used to maximize the performance or analytic capabilities of a given task. 

More important than the problems they solve is how they solve them; this is where Machine Learning’s ability to learn stands as a major differentiator. 

On one hand, Artificial Intelligence solves problems by attempting to simulate human intelligence through a set of rules.

On the other hand, Machine Learning seeks to learn from data in order to make its own rules and solve problems. Machine Learning algorithms are at the heart of Natural Language Processing tools like ChatGPT.

While everyone calls these tools Artificial Intelligence. A more accurate description would be Deep Learning, which is a subset of Machine Learning that tries to process data in the manner a human brain would.

Although, it has to be noted that general Artificial Intelligence that can think and feel in the same way that a human can, has yet to be invented. In fact, there are many people who doubt that a computer system can ever gain the full sentience that humans enjoy.

AI is “Bigger” than ML

There are various ways in which Artificial Intelligence can emulate human intelligence. One of the ways to do this is through Machine Learning, but it is not the only alternative.

Some types of AI are not capable of learning and are therefore not referred to as Machine Learning. Artificial Intelligence, at its core, consists of an algorithm that emulates human intelligence based on a set of rules predefined by the code. These rules don’t only use Machine Learning models and methods, other alternatives like Markov decision processes and heuristics exist. 

Machine Learning takes a different approach to AI techniques while still being a part of the broader whole. As I’ve mentioned, it doesn’t just follow the rules of an algorithm.

Instead, Machine Learning can create its own algorithm and rules through the ability to learn. In practical terms, Machine Learning is a particular AI technique in which the algorithm is able to learn over time as it gathers data rather than just follow a set of rules. 

Similar Origins, Different Paths

This is a minor difference between AI and ML, but it is worth mentioning. Both concepts were coined around the same time by computer scientists experimenting with new developments during the 40s and 50s.

Since then, AI has evolved considerably and in recent years it has become more powerful and accessible through tools like ChatGPT and MidJourney.

The concept of Machine Learning, although not new, has only come to fruition recently as a result of the availability of powerful data analysis tools as well as large amounts of data, known as Big Data, that has been generated in the Internet. 

Intelligence Vs. Knowledge

There is an important difference between AI vs. Machine Learning that often goes unnoticed by even the most experienced developers because it is outside the domain of computer science. It is the fact that Artificial Intelligence pursues intelligence, while Machine Learning pursues knowledge.

This is a purely philosophical problem, and as you might have expected of a philosophical conundrum, there is no consensus as to what the terms intelligence and knowledge mean.

In an attempt to define them, knowledge can be understood in a simplistic way as justified-true-belief. Intelligence can be defined as the ability to make use of knowledge. As intelligence contains knowledge, Artificial Intelligence contains Machine Learning.

Success Vs. Accuracy

AI is associated with success, while ML to accuracy. Success refers to getting the job done, while accuracy refers to how a measurement relates to a specific value (do not confuse it with precision).

An AI algorithm that works without ML can be said to be successful in terms of how it achieves a given task.

An AI algorithm that works with ML can be said to be successful and accurate. Accuracy in ML can be improved depending on the quality of the data. 

Newness Vs. Past

One last difference worth mentioning is that AI focuses on how to solve old and new problems. Because AI algorithms seek to emulate human intelligence, they can target problems for which there is no data. 

Machine Learning, on the contrary, focuses exclusively on problems that have already occurred, or for which data is available. This is due to its dependence on data in order to modify its algorithm.

Although more complex Artificial Intelligence and Machine Learning algorithms are constantly being developed, it is still unclear to what extent a computer will be able to learn about concepts that have not occurred or do not exist. 

This is a major point of criticism that AI skeptics eschew. Artificial Intelligence and Machine Learning algorithms only know what exists or what they have been trained on. This opens the door to a lot of potential problems and trust issues with these tools.

Mainly, these tools can easily be biased by bad or outright erroneous data. Furthermore, these tools are limited in the scope of what they can “know” and they are unable to think creatively. The concept of gravity is a great example of the shortcomings of Artificial Intelligence and Machine Learning.

Before Newton, gravity was not understood as a universal force. We could get lost in the complicated world of modern physics, but the point should be obvious. Had AI and Machine Learning existed in the time of Newton, they would have been unable to conceive of the concept of gravity as we know it today.

Final Thoughts

I’ve discussed various differences between AI and ML in the hope of making clear that, although they have similarities, both are different.

The most important of these differences is probably that ML, as a subset of AI, focuses on solving problems strictly through learning from the available data, while AI, in general, does not necessarily depend on data. 

With this crash course on the differences between Artificial Intelligence and Machine Learning, you can now contact your quality development partner and start working on integrating the latest HiTech technologies with your app.

At Koombea we’d be more than happy to help! Contact us for a free consultation or to learn more about Machine Learning vs. Artificial Intelligence.

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