AI : A Bombastic Jargon

A newbie fascinated with data analytics comes with the aspiration of exploring this field. Sadly, a lot of unwelcoming, cold and confusing jargon scare and shoe him away. I would always recommend you not be carried away by the fancy terms that people use, instead, go by the saying, “Jargon allows us to camouflage intellectual poverty with verbal extravagance

Now, before starting I would like to acknowledge Deep Learning Course by One Fourth labs. You can find the details on their site —

By the end of this article, you’ll be able to classify things happening around in a better fashion as “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Deep Reinforcement Learning”, “Computer Vision”, “Data Science”, “Pattern Recognition” and so on.

So, what do these special terms mean? I have tried to explain them in simple terms rather than giving you conventional bookish definitions.

Artificial Intelligence (AI)

Sometimes called Machine Intelligence. There is God in the man and man in the machine. It is the intelligence demonstrated by machines, in contrast to the natural intelligence displayed by the humans.

Machine Learning

Field of computer science that uses statistical techniques to give computer systems the ability to “learn” with data without being explicitly programmed.

Data Science

An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from the data in various forms, both structured(tables) and unstructured (images, videos).

Pattern Recognition

It is the automated recognition of patterns and regularities in the data.

Computer Vision

It is an interdisciplinary field that deals with how computers can be made to gain a high-level understanding from digital images or videos and automate the tasks that the human visual system can do.

Deep Learning

It is an emerging area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

Now after reading so many definitions are you clear with the meaning? Or still confused?

Talking about me, I am confused!

- Doesn’t AI involve statistical techniques, data, learning?

- Is it just called AI only if it is explicitly programmed?

- Is AI/ML not interdisciplinary, or don’t they deal with structured data, or scientific methods?

- Isn’t recognition of Patterns similar to “Intelligence”, “insights from data”, “learn from data”?

Bursting the jargon bubbles

Let’s solve this problem with an alternative approach and split everything into 3 categories

- Abilities

- Task

- Methods


  • Seeing (Computer Vision)
  • Speaking and Hearing (Speech)
  • Reading and Writing (Natural Language Processing)
  • Making Decisions (Reinforcement Learning)

Abilities — image Credit : Mitesh M. Khapra, Pratyush Kumar, One Fourth Labs (


  1. Computer Vision
  • Classifying two or more animals using their demographics.
  • Recognizing handwritten digits.
  • Detecting an object in an image like coffee, phone, glasses etc.

Tasks Done Using Computer Vision

2. Speech:

  • Retrieving the information by voice-recognition.
  • Converting this article to speech.
  • Hearing two different people speaking and able to distinguish between the voices

Tasks Done Using Speech

3. Natural Processing Language

  • Classifying a spam or ham email.
  • Translating something written from Hindi to English or vice-versa.

Tasks Done Using Natural Language Processing(NLP)

4. Reinforcement Learning

  • The most common example we know is “Autonomous Driving”.
  • Alpha GO.
  • Teaching a robot to lift things according to its weight.

Tasks Done Using Reinforcement Learning


All of the above tasks can be solved with Expert Systems, Logistic Regression, SVM, Graph Methods, Recurrent Neural Networks (RNN), Convolutional Neural Networks(CNN) and many more.

So What is AI?

AI encompasses all these stuffs i.e. abilities, tasks, methods.

AI Map — Image Credit : Mitesh M. Khapra, Pratyush Kumar, One Fourth Labs (

Taking a deeper dive into Machine Learning

When I give the following data to the model and get an Expert System program in return, as the machine itself creates some logic and rules to arrive at this conclusion. This is an example of Supervised Learning. Most of the Machine Learning comprises of Supervised Learning only.

Machine takes the Labeled Input Data and generates an Expert System Program — Image Credits : Mitesh M. Khapra, Pratyush Kumar, One Fourth Labs (

Most AI Tasks Require Pattern Recognition

How is Pattern Recognition different?

The field of Pattern Recognition is concerned with the automatic discovery of regularities in data using computer algorithm and these regularities take actions such as Classification, Regression, Clustering etc.

Is Image Processing different from Computer Vision?

There is a small difference between Computer Vision and Image Processing. Computer Vision uses a processed input image as it’s input. For example, if the input image is of irregular sizes to the model and the CV model only takes input size of 128x128 images, then the images have to be resized and then they have to be sent to the model.

Doing that transformation is called Image Processing.

What is Data Science?

What if I give you data and after plotting it looks like the following bar plot, and ask you to tell me the amount of people going to come by next year i.e. 2020?

Predictive Analytics being done using Descriptive Analytics

So, I would prefer using the term Data Science when we are dealing with numerical data alone ( like database tables, Sales, Customers, Revenue etc.).


So, here comes the end of the article. I expect you to be clear with the terms AI, ML, PR etc. and use the terms appropriately.

Author: Sanchit Ghai

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