Differences Between AI and Machine Learning


Everyone is talking about it but there are only a few who understand it.

Artificial Intelligence(AI) & Machine Learning(ML) have emerged as the buzzwords across every industry today. Everyone wants to try their hands at implementing AI solutions or ML algorithms in the age of Industry 4.0 today. In this post, I tried to breakdown some general differences between the two terms. Although one is just a subset of the other, the range of applications and the working principles are quite different. Check out this cool infographic for a quick understanding.

What is Artificial Intelligence?

Let’s hear it from John McCarthy, who coined the term in 1956, and is regarded as the father of the field, AI is “the science and engineering of making intelligent machines, especially intelligent computer programs.”

When computer programs are made to mimic human intelligence, it is what you would call AI. Without AI, there would be no Siri or Alexa or Google Home.

And unless it automatically learns from data, it is not Machine Learning.

What is Machine Learning?

Machine learning is actually a subset of Artificial Intelligence. When you empower machines to learn automatically on their own without explicit programming it is Machine Learning.

The objective of ML is to enable machines to learn by themselves using the provided data and make accurate predictions.

The “People you may know” on Facebook or the Netflix “Movie suggestions”.. Yes, you guessed it right. All are applications of ML.

Recommended Watch for You: Webinar on Data Science, Machine Learning and AI – Learn Differences and Career Options by Ivy Pro School. (No, This is not ML, it is just Me!)

Different Types of AI

Yes, there are various ways of making machines intelligent..!


  • Weak AI or Narrow AI: No, not physically weak but when compared to the other forms, it is not as intelligent. It can only focus on a single/narrow task at a given time.In the weak AI it doesn’t matter about human cognitive processes, but exclusively solve specific problems.” This is the current state of AI now.

           Examples: Playing a game, Siri, Manufacturing Process Robots.

  • Strong AI or Artificial General Intelligence: Although still in its preliminary stages currently, Strong AI is programmed to emulate a human brain. Examples: Still in the making.
  • Artificial Superintelligence (ASI): Artificial superintelligence could lead to an explosion of AI that could threaten human existence. The ultrasmart AIs can outperform the best human brains in almost every way. Or, worse yet, exterminate us. Examples: Jarvis or G.one from Ra.One movie. ASI does not exist currently.

Different Types of ML

  • Supervised learning: Training the algorithm to learn from labelled datasets.

E.g.- Spam Detection.

  • Semi-supervised learning: Training the algorithm on a combination of both labelled and unlabeled data.

E.g.- Speech recognition or genetic sequencing.

  • Unsupervised learning: There is no reinforced learning in this case. These algorithms do not have output categories or labels on the data (the model is trained with unlabeled data) and are mostly used in pattern detection and descriptive modelling.

E.g.- K means clustering.

  • Reinforcement learning: In this case, the output depends on the state of the current input and the next input depends on the output of the previous input. An agent acquires learning from interaction with the environment by performing actions and seeing the results.

E.g.- DeepMind and the Deep Q learning architecture in 2014, beating the champion of the game of Go with AlphaGo in 2016.

  • Active learning: The main hypothesis in active learning is that if a learning algorithm can select the data it wants to learn from, it has greater chances in achieving enhanced performance than traditional methods with substantially less data for training.

           E.g.-Natural Language Processing.

How did it work?

Machine Learning is one of the ways we expect to achieve AI. It is a method of training algorithms such that they learn how to make decisions on their own. The process relies on working with large data-sets, by examining and comparing the data to find common patterns and explore nuances to reach a defined target.

Training in machine learning entails feeding data (a lot) to the algorithm and allowing it to learn more about the processed information. Machine learning is based on what is known as “neural networks” which are a set of algorithms, modelled after the human brain, and are designed to infer patterns.

Artificial intelligence is the broader concept that consists of everything from calculators all the way to futuristic technologies. AI adapts through progressive learning algorithms in order to let the data do its own programming. “AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor.” Think of the examples of AI around us, an algorithm teaching itself how to play chess, or teaching itself which products to recommend online based on the study of user patterns. And the models adapt when given new data.

The diagram below portrays a number of AI methods used by practitioners, which are all generally clustered together as Artificial Intelligence, however, as visible, there are many different methods to support different use cases/projects.

Artificial Intelligence



The goal of an intelligent computer system is to simulate natural intelligence to aid in complex problem-solving. According to a paper published in ResearchGate, “Brain-Like AI aims at analyzing and deciphering the working mechanisms of the brain and translating this knowledge into implementable AI architectures.” Indeed, this results in the development of efficient, flexible, and capable technical systems.


Machine Learning algorithms help in analyzing the data as well as identify trends. The aim of ML is to learn from data, identify patterns in the data and make predictions based on the complex patterns to solve business cases.

“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” – Nvidia 

Careers in AI and ML

Machine learning and deep learning engineers are earning skyward salaries, even when they are working at non-profit organizations, which speaks to how hot the field is. Read this article here if you are keen on knowing about the different types of AI jobs that exist.

The Future Scope of Artificial Intelligence & Machine learning

Professionals fear to lose their jobs to automation. But it is only set to open up doors for more innovative and rewarding roles rather than doing repetitive tasks. Gartner reports that 2.3 million new jobs are set to be created by 2020. On similar lines a Capgemini has reported, 83% of companies using AI are already adding new jobs every day.

Machine learning and AI complement each other, and the next breakthrough lies in combining them. Experts agree that it is best to first understand what AI is like what the skills are exactly and what you are most passionate about. So if you are into data science you can work to take that extra step to specialize in AI by getting your college degree and then enrol for a Machine Learning course or an Artificial Intelligence certification course. You can create your own opportunities and projects honing in on those skills so by the time you’re ready to apply for those jobs, you can be a standout candidate.

Take up an online or opt for classroom training with Ivy Pro School to gain the most relevant skills and prepare yourself for the future of work. IVY Pro School is a Top Ranked Data Science and Analytics schools in the country consistently since the last 5 years (Analytics India magazine, Silicon India Magazine). Connect with us at @ivyproschool on Facebook or Instagram today.



  • Kersting K (2018) Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines. Front. Big Data 1:6. doi: 10.3389/fdata.2018.00006
  • Velik, Rosemarie(2012) AI Reloaded: Objectives, Potentials, and Challenges of the Novel Field of Brain-Like Artificial Intelligence
  • A Beginner’s Guide to Neural Networks and Deep Learning
  • https://ivyproschool.com/blog/2017/11/17/technologies-and-applications-inspired-by-machine-learning-artificial-intelligence/