Difference between Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning are the most famous buzzwords in the technical industries. People generally use them as synonyms, but these tech stacks are different, although Machine Learning is just a part of Artificial Intelligence.

This article will discuss the difference between Artificial intelligence and Machine Learning in greater detail. So let’s start with artificial intelligence first.

Artificial Intelligence

If we know simple programming, we might have written some basic algorithms containing if/else statements. Suppose we have been assigned to segregate lemons and oranges in two different buckets from the box containing both. Consider the scene where the weights of lemons are lesser than oranges.

Segregating Lemons and Oranges

Humans have enough intelligence to segregate the Lemons and Oranges very wisely just by seeing them.

But do we need a human resource to be involved all the time for this task?

In the 21st Century, we don’t. Human intelligence can be utilized to solve more advanced tasks rather than just sorting Lemons and Oranges. But we must be thinking, then who will do this task? 

Here is the need for “Artificial” intelligence that wisely mimics human intellect. What if we make a Robot that can perceive its image through camera sensors and then measure its weight to get more clarity about the fruit, and finally does the following operation,

if fruit_weight > 20 gram:
     place fruit in Orange bucket
     place fruit in Lemon bucket

Did you just realize? We created artificial intelligence. Hurrah !!!

Summarizing the overall process, Our robot sees the fruit from camera sensors, measures the weight, and places the fruit in the Orange and Lemon baskets accordingly.

Bot Segregating Lemons and Oranges

In the simplest form, we can say that hard-coding if/else statements is a way of creating Artificial Intelligence. 

Some popular applications of Artificial Intelligence

  • Chatbots: We get answers to questions that are already programmed. Suppose we are chatting on Zomato’s chatbot, then we have a fixed set of questions that can be asked, and the reply to those questions is already defined over their servers.
  • Apple Siri: Same as a previous application, but here we have a voice assistant taking questions for which she already knows the answers.
  • Intelligent Humanoid Robots: Actions of these robots are pre-programmed, and based on those defined actions, they perform several tasks.
  • Online Gaming: Let’s say Chess. Algorithms are aware of the positions of every player, and they take the hard-coded best decisions for every situation.

Within the umbrella of developing this type of intelligence, machine learning is one such technique using which we can create an advanced level of Artificial Intelligence that can mimic humans or sometimes even surpass their intelligence. So let’s learn about Machine Learning in greater detail.

Machine Learning

Continuing the same example of sorting fruits in the bucket of Lemon and Oranges. Suppose we hired one human resource just for ten days to sort fruits but with the additional responsibility of maintaining the data of whatever task is being performed. Let’s understand it very clearly as we are creating data for our machine learning model.

The hired person is responsible for maintaining one folder on the computer, which will contain the images of lemons and oranges, along with the excel sheet. The first column in the excel sheet would be Filename, and the second column would be Fruit Name (as Lemon and Orange)corresponding to that filename. 

Excel sheet of data

Continuously doing this for ten whole days, a significant amount of images will get stored in that folder, and the excel sheet maintains which image name corresponds to Lemon and which corresponds to Orange. Now, the hired person is gone as we don’t have the budget to pay him further. But we still need to sort fruits in the lemon and orange baskets, and that's why we need Artificial Intelligence. But this time, we want our intelligence to be more brilliant and exactly mimic human intelligence. So, we don't want to invest time in measuring the weights of every fruit and then take the decision. Instead, we want a technique that can directly see the fruit and predict that this is Lemon and that is Orange. 

Machine Learning tech learning the data

Did you just notice? We did not program our computer here, similar to what we did in the earlier topic of artificial intelligence. In this scenario, our algorithm utilized the data created by the human resource and learned the pattern from it. Our computer algorithm read every image one after the other and tried to find a function in which any new image will be given as input, and it will say whether this is Lemon or Orange.

Now, this is Machine Learning which says, 

A science for getting computers to act without being explicitly programmed.

And similar to this statement, we did not explicitly say our algorithm as we did in the artificial intelligence section by writing if/else statements. So this is Machine Learning, a subset of Artificial Intelligence, which tries to learn from the previous data and then perform the future predictions. This is just a way of creating Artificial Intelligence where we don’t need to program our computers explicitly; instead, they learn from previous data available.

Some popular applications of Machine Learning

  • Recommendation System: Here, machines learn from the previous behaviors of several customers and recommend products to the newer customers. For example, if a person is buying a new house, the machine suggests purchasing new furniture as several other customers bought it together. So, it learned from previous data here. We did not hard-code anything explicitly.
  • Twitter Sentiment Analysis: Here, machines learn the sentiment behind the tweet done by the user. Sentiment can be like, by reading the tweet, machine predicts whether the person is sad or happy. We can not hardcode tweets as sad or happy tweets as the difference of just a single word can change the sentiment of that tweet. There can be millions of ways of showing emotion, and hardcoding every way is not possible. So let the machine learns from the data directly and learn from it.
  • Google Search algorithm: We might have observed that even if we don’t have the exact questions to be searched on google, it still pops up the relevant links for us. It has seen such users earlier and knows that these are the best-fit queries similar to what we are searching for as earlier users opened those links and found their solution there. 

Based on this topic, four major questions are trendy over the internet. Let’s understand each one of them.

Four Major Questions around this topic

  • What is the difference between artificial intelligence and machine learning?

We believe we have answered this question in the previous sections, which is the whole summary of this blog.

  • Is Machine Learning better than AI?

This is a very subjective question, and we can not answer this in the binary form of the Yes or No. The answer depends upon the problem statement we are going to solve. Let’s take the example of Autonomous vehicles.

Matching the human intelligence of driving a car seems impossible with the current level of advancement in the machine learning domain. But, with the help of Artificial Intelligence, United States people enjoy driverless Uber cab services. (See the video of this here: https://www.youtube.com/watch?v=EYh0F_8ZdSU&ab_channel=WIRED)

But, there can be scenes where programming our computers explicitly is impossible as there can be billions of possible scenarios, and humans will be incapable of figuring out those many if/else statements. In that case, Machine Learning will perform better as it can sense how they served previously in similar scenarios.

  • Is Artificial Intelligence a Machine Learning?

No, instead, the opposite is true. Machine Learning is a way of creating artificial intelligence.

  • What is AI but not Machine Learning?

We discussed it, right? A bunch of if/else statements can also create artificial intelligence, but no learning from previous data is involved here, so this is just AI. When our algorithm learns from the earlier data and tries to improve the performance, this is Machine Learning.

Tabular Summary

Summary of AI and ML


This article discussed the clear difference between the most famous tech stacks, Artificial Intelligence and Machine Learning. To increase the excitement of this difference, we have linked the theory with a fascinating story of sorting fruits in the baskets of Lemons and Oranges. In the last of every section, some real-life applications of AI and ML are also discussed. If you have enjoyed the article, leave your feedback/suggestions in the comment section below.

Enjoy Learning! Enjoy Intelligence! Enjoy Algorithms!

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