It’s time we direct our attention to the Artificial intelligence vs Machine Learning arena. Why? Because Artificial Intelligence and Machine Learning are not just another buzzwords that will fade into the oblivion. They’re rather technologies that are here to stay and will continue to shape our lives in unimaginable ways. Our job is not to merely marvel at their abilities (while we cannot help that), but to harness their full potential. And that won’t be possible if we don’t understand the intricacies of Artificial Intelligence and Machine Learning – individually. Often used interchangeably, many have started to believe that the two are the same with perhaps different names. It’s time to weed out that confusion!

Let’s start by exploring Artificial Intelligence vs Machine Learning – step by step

1. What is Artificial Intelligence?

Artificial intelligence is essentially a simulation of human intelligence in machines. Through AI, the machines are programmed to think, learn, and solve problems as humans (brain) do. The major characteristic feature of artificial intelligence is its power to rationalize like humans and take actions that have the highest probability of accomplishing a specific goal. 

  • AI is built around the principle that machines can easily execute the desired tasks by mimicking human intelligence.
  • It simulates human intelligence processes –
  1. Learning – Acquisition of rules and information 
  2. Reasoning  – Reaching conclusions using the rules
  3. Self-correction

Categories of Artificial Intelligence – (based on capabilities)

  • Weak AI – This kind of AI performs a dedicated task utilizing its intelligence. But as it is trained for specific tasks, it cannot perform beyond those tasks (and often fails if it tries).

Siri is a great example of weak AI. 

  • Strong AI- This kind of AI surpasses human intelligence. When it is presented with unfamiliar tasks, it can find solutions without needing human support. 

Strong AI posses the ability to think, reason, learn, make judgments, communicate on their own, and do a lot more. Owing to the technological advancements combined with the knowledge of how our minds work, AI’s definition has evolved over the years. First, it was about solving complex calculations with speed and accuracy, now it has moved to mimic the decision-making process of humans and executing tasks in ways that are more human.

2. What is Machine Learning?

Machine learning is basically a subset of artificial intelligence. It extends machines the ability to learn and improve from experiences without programming them explicitly. ML essentially focuses on developing programs that can access data and utilize it to learn for themselves. 

There were two important phenomena that led to Machine Learning’s emergence

  • Arthur Samuel in 1959 realized that as for now we need to teach computers what they need to know and also tell them how to carry out specific tasks.

But what if we teach them to start learning by themselves?

  • The second phenomenon was the internet. With the advent of the internet, we have been flooded with information – data is continuously generated, stored and analyzed. 

So finally, it was realized that instead of teaching machines how to do things, it will be more efficient to make them think like humans and connect them to the internet so that they have access to all the available information.

Machine Learning applications complement artificial intelligence. They can not only read the text but can also identify the tone of the text – whether it’s friendly or complaining, for example. 

Machine Learning is Divided into 3 classes – 

  1. Supervised Learning – We have the labeled/classified data to train the machines.
  2. Unsupervised Learning – We do not have labeled/classified data to train the machines.
  3. Reinforcement Learning – We train the machines through rewards on the right decisions.

Artificial intelligence vs Machine Learning – What is the Difference?

Now that we’ve gone through what artificial intelligence and machine learning is, we are prepared to understand the differences.

  • The first explanation of Artificial Intelligence vs machine learning is that AI is a superset of ML that can carry out tasks that are considered smart or intelligent in human terms. 
  • While ML is a subset of AI that involves giving machines access to data and letting them learn for themselves through patterns and experience.
  •  Even when there was no artificial intelligence vs machine learning, ML was recognized as the leading edge of AI. 
  • Traditionally, we had artificial intelligence tools (expert systems). We had to tell these systems (computers) how to analyze data, what data they must use, and what results to give. 
  • To understand this, let’s take the example of NLP. Suppose you need to teach computers to translate from English to Chinese. A program built to do the same is bound to fail because there are so many exceptions to human language that explicit programming for it is impossible.
  • So instead of telling our computer systems all the rules what we can do is – we can give them all the data that is widely available and they can make up their own rules. And that’s what machine learning precisely is. 
  • Just like we learn from patterns and experiences, our machines can learn the same way. 

Difference Between AI and ML

Here’s a quick answer to – what is the difference between Artificial Intelligence and Machine Learning?

 Finally, settling the Artificial intelligence vs Machine Learning debate. 

ARTIFICIAL INTELLIGENCEMACHINE LEARNING
AI enables machines to simulate human intelligence/behaviour.ML, a subset of AI, enables machines to automatically learn from humongous amounts of past data without being explicitly programmed.
AI aims to make smart and intelligent systems that function just like humans so that they can solve complex problems.ML aims to enable machines to learn from the available data so that they can give accurate output.
The goal of AI is to make intelligent systems that can perform any tasks like humans.The goal of ML is to teach machines(using data) to perform particular tasks so that they give accurate results.
Subsets of AI – machine learning and deep learning.Subset of machine learning – deep learning.
AI is working towards the creation of systems that will be able to perform various complex tasks.ML is working towards creating machines that will be able to perform specific tasks – for which they are trained.
AI system strives to maximize the chances of success.ML is concerned about maintaining accuracy and understanding patterns.
Applications of AI – Siri, online games, etc.Applications of ML – Google search algorithms, Recommended products list on Amazon, etc.
AI comprises the processes of learning, reasoning, and self-correction.ML involves learning and self-correction only when it is introduced to new data.
AI can deal with structured, unstructured, and semi-structured data.Machine learning can only deal with structured and semi-structured data.


That marks the end to the Artificial Intelligence vs Machine learning debate. Now that you know about it, it’s time to take the right actions – It is estimated that AI will provide 2.3 million new opportunities to AI professionals by the end of 2020. But how do you gain expertise in the field? Well, Let me help you with that! There’s a whole range of online learning courses at your disposal. Springboard India’s 1:1 mentor-led, project-based data sciencedata analytics and AI/ML career tracks are industry-focussed online learning programs which come with a job guarantee, designed to prepare you for a meaningful and successful career in future technologies.