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Tell me if this sounds familiar. You want to learn more about artificial intelligence, so you begin googling things like What is AI? or Real-life examples of AI or even, AI for dummies. But, for some reason, the results from your search seem to only talk about this thing called machine learning, and now your more confused than ever. Is machine learning the same thing as AI?


Not exactly.



Machine Learning

Machine learning (ML) refers to the process of teaching computers how to learn. By inputting large amounts of data, computers can sort through the values and learn through recognizing patterns, trends, and consistencies. Think about Face ID on the iPhone X. For this feature to work, Apple had to program the phones to be able to recognize different facial features. Then Apple would have had to put many different faces in front of the iPhone so that it could record the facial features of each individual and learn to distinguish between the face of its owner and everyone else. This is how machine learning works.


Artificial Intelligence

As we discussed in a previous article, artificial intelligence describes the machine processes that allow computers to think and behave in a similar way to humans. AI can take many forms, including speech recognition programs like Siri to Robo-employees.


So, What’s the Difference?

Basically, ML is one of the machine processes that help power AI. By sorting through and learning from data, ML provides AI with the knowledge to help the system make decisions that mimic human behaviour. But ML isn’t the only process powering AI. Therefore, all machine learning is AI but not all AI is machine learning.

Still confused? Here are some of the key differences between the two systems…


The intention of ML is to learn new things. Whenever new data is provided, it will analyze the values to try and learn new patterns, trends, behaviours etc. The purpose of AI, however, is decision making. AI does not want to learn new things but instead, wants to use the knowledge it has to make decisions that resemble human intelligence.


ML’s adaptability is dependent upon the amount of data it has. If you ask ML to solve a problem it doesn’t have data on, it will not be able to answer your question. For example, if you feed a computer with information about the average weights of apples and the average weights of pears, you could input weight and the computer would be able to tell you if that weight belonged to an apple or a pear. But it could not tell you if that weight belonged to a pineapple. Conversely, AI can be very adaptable to different situations. By using knowledge from ML as well as past experiences, AI can make educated guesses for questions it may not explicitly know the answer too.


As we mentioned above, ML’s knowledge is limited to the amount of data it is fed. Therefore, ML will find whatever solution it can from the data it has, regardless of how optimal the solution may be. AI, on the other hand, will always look for the most optimal solution. AI will think strategically by looking at varying sources of information to determine the best possible outcome.


Have some interesting insights into AI or want to learn more? Reach out to to chat!






/@ledumjg. (2018, September 14). Clearing the Confusion: AI vs Machine Learning vs Deep Learning Differences. Retrieved from


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Martin, N. (2019, March 19). Machine Learning And AI Are Not The Same: Here’s The Difference. Retrieved from


Mills, T. (2018, July 11). Machine Learning Vs. Artificial Intelligence: How Are They Different? Retrieved from

Emily Cummings

Author Emily Cummings

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