Machine Learning (ML) vs Artificial Intelligence (AI): what are the differences?


The terms Machine Learning (ML) and Artificial Intelligence (AI) have become buzzwords lately, and both together have made revolutionary changes in the way businesses relate to the customers. Almost every industry out there has adopted these technologies to sell better, to customise according to the requirements of the customers. Understandably, both ML and AI have been inextricably linked, and though they share several similar characteristics, they are not the same. In this article, we analyse the two technologies, and also the differences they have, as opposed to the similarities they share. 

Machine Learning

Machine Learning is a subset, a branch of Artificial Intelligence, and it uses data, statistical methods and algorithms to imitate human intelligence. It is broadly defined as the capacity of the machine to act like humans and can perform complex tasks, almost similarly like humans solve problems. ML can uncover key insights in data mining projects and the information uncovered in this way will be used in multiple ways, but mainly to serve customers in a better way. The insights collected through ML will aid in the decision making process to make software applications too. 

Machine Learning is often categorised by how these algorithms become accurate in their predictions, and the level of their accuracy. Hence, there are four approaches with which you can do ML – 

  • Supervised learning
  • Semi-supervised learning
  • Unsupervised learning
  • Reinforcement learning

The data scientists decide which approach to take depending on the data they want to predict. 

ML is used widely in all applications, and one of the most relevant examples that you’ve experienced probably is Facebook’s recommendation engine. You’ve probably felt it in the newsfeed when you scroll down. The news personalisation feature is amazing, and the moment you stop to read or watch something of interest, you will keep getting similar items. The recommendation engine is continuously changing to adapt to the user’s browsing patterns and the moment you fail to read a particular post from someone, the news feed will not show that anymore. Other examples where ML is highly useful are:

  • Self-driving cars – With ML, it is possible for the driver to be alerted when they see an object and so can steer clear of it. 
  • Smart assistants – Virtual assistants use supervised and unsupervised ML to comprehend natural speech.
  • Customer relationship management – CRM software can analyse important emails and show the team members which emails are to be answered first. Sophisticated ML systems can prompt sales teams to give effective answers too.

Artificial Intelligence

In the most simple words, artificial intelligence or AI is a branch of computer science that enables computer-controlled robots to act intelligently and perform tasks, indulge in problem solving etc. Computers use real time data to do this and can perform a variety of advanced functions. 

The possibilities with AI are enormous, and quite encouraging for businesses and equally useful for consumers too. You have warehouse robots that can navigate the cybersecurity systems and continually analyse the stocks, and virtual assistants that can predict user’s answers and give intelligent responses. AI combines statistics, data analytics, computer science, linguistics, neuroscience, hardware and software engineering, and often philosophy and psychology too. The technology also depends on ML and deep learning to reach its predictions and use techniques like forecasting, predictions, intelligent data retrieval and other features to help businesses understand and communicate with their customers. 

This proves that ML and AI are not one and the same thing, and they cannot be confused as being one and the same.

But ML and AI are connected too, here’s how

The computer uses AI to think and act like human beings, performing tasks on its own.

The computer uses ML to develop its intelligence.

Machine Learning helps to develop Artificial Intelligence, but it is not mandatory that AI has to be developed using ML only, ML just makes AI more convenient. 

ML connects Data Science with AI because it is all about learning from data. Data Science by the way, is the study of data systems and processes to help maintain data sets and derive meaning from them. The job of the data scientists is to use tools, technologies, algorithms, principles and applications to make sense of the random data sets. 

The differences that sets AI and ML apart

AI, short for Artificial intelligence, is the capacity to acquire knowledge and apply it. 

ML, short for Machine Learning, is the acquisition of that knowledge or skill.

AI aims to increase the success chances, but is not bothered with accuracy.

ML focuses on accuracy, but is not bothered with the success.

With AI, you can develop an intelligent system that can perform a variety of complex tasks.

With ML, the machines can perform only those tasks for which they are trained. 

AI can work like a computer program and perform smart work. 

In ML, the machine takes the data and learns from it.

The scope of AI is very broad, and can be used with a broad variety of applications.

The scope for ML is very limited.

With AI, you can simulate natural intelligence, act like humans to solve complex problems. 

With ML, you can learn from data for a task and have optimal performance on that particular task.

AI is all about decision-making. The focus is also to find optimal solutions for the users.

With ML, the systems learn new things from the collected data, and the accuracy levels are quite high. However, this is a disadvantage here. While ML models may show a high accuracy value initially, the dataset could be highly imbalanced, and this could mislead the researchers. 

AI focuses on finding the optimal solution.

ML just needs to find a solution, optimal or not.

AI combines ML and Deep Learning to mimic human intelligence.

ML is a subset of AI.

With AI, you get intelligent systems and predictions.

With ML, you get knowledge.

AI works with all kinds of data – structured, semi-structured and unstructured.

ML works with only structured and unstructured data.

AI is a broader concept because it deals with machines that mimic human intelligence to some extent. Hence, it is a loosely defined term. ML is more limited because the ML researchers focus on teaching machines to perform a specific task, and provide accurate reports based on it. 

Check out these key uses:

Siri uses AI and it’s getting better each day. Google Translate and Intelligent humanoid robots like Sophia also uses AI.

ML’s uses are so different. We already mentioned the recommendation engine. Apart from that Stock price forecasts, Google’s search algorithms and Facebook’s friend suggestions also use ML. Even banks use the technology to detect fraudulent activities. 


The differences between AI and ML are not very evident, but they are still there. If you train ML to forecast future sales based on a particular data, that’s exactly what it will be able to do. AI has a more intelligent system, so it can definitely achieve more than what it’s been trained for. However, ML plays an important role in AI’s success. Businesses and e-commerce websites have great use for both AI and ML.

Both these technologies are irrevocably woven into the fabric of our lives and we will feel the impact wherever we are, whether it is business, or day to day life activities. 

Interesting Links:

Some of the key differences between Artificial Intelligence and Machine learning

More information about Machine Learning

Pictures: Canva

The author: Sascha Thattil works at which is a part of the YUHIRO Group. YUHIRO is a German-Indian enterprise which provides programmers to IT companies, agencies and IT departments.

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