What's Artificial Intelligence, Machine Learning, Deep Learning, Predictive Analytics, Data Science?
Updated: Oct 25, 2019
Never thought I will spend so much time to understand these high profile terms. I was very confident that I knew theoretically everything that was necessary for me to start writing machine learning algorithms, until couple of days back when I asked myself - Does my use case fall under machine learning topic or is it artificial intelligence? Or is it predictive analytics? I began explaining myself, but couldn’t do it right. I spent several hours reading about these topics, reading blogs, thinking and ended up writing this blog to answer myself. I hope you all will also find this post helpful.
Trust me most famous terminology amongst all is - "machine learning" in past couple of years. Below chart shows Google trend (interest over time) of these high profile terms -
First lets understand these terminologies individually, keep below Venn diagram in mind while you read further. This will help you to distinguish various terminologies.
You know what I did just now? I asked your brain to recognize patterns. Human brain automatically recognizes such patterns (basically "deep learning") because your brain is trained with "Venn diagrams" somewhere in past. By looking at diagram, your brain is able to predict few facts like Deep learning is subset of Machine learning, Artificial Intelligence is the super set, and Data Science could spread across all technologies. Right? Trust me if you show this diagram to prehistoric man, he will not understand anything. But your brain "algorithms" are trained enough with historic data to deduce and predict such facts. Isn't it?
Artificial Intelligence (AI) Artificial intelligence is the broadest term. Originated in year 1950s and the oldest terminology used amongst all which we will discuss. In one liner, Artificial intelligence (AI) is a term for simulated intelligence in machines. The concept has always been the idea of building machines which are capable of thinking like humans, mimic like humans. Simplest example of AI is chess game when you play against computer, on paper program was first proposed in 1951. Recent AI example would include self-driving cars which has always been the subject of controversy. Artificial Intelligence can be split between two branches -
One is labelled “applied AI” which uses these principles of simulating human thought to carry out one specific task.
The other is known as “generalized AI” – which seeks to develop machine intelligences that can turn their hands to any task, much like a person.
Machine Learning (ML)Machine learning is the subset of AI which originated in 1959. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence. ML gives computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. You encounter machine learning almost everyday, think about
Ride sharing apps like Lyft & Uber - How do they determine the price of your ride?
Google maps - How do they analyze traffic movement and predict your arrival time within seconds?
Filter spam - Emails going automatically to your spam folder?
Amazon Alexa, Apple SIRI, Microsoft Cortana & Google Home - How do they recognize your speech?
Deep Learning (DL)
Deep learning (also known as Hierarchical learning, Deep machine learning or Deep structured learning) is a subset of Machine Learning where learning method is based on data representation or feature learning. Set of methods that allows a system to automatically discover the representations needed for feature detection or classification from raw data. Examples like
Mobile check deposits - Convert handwritings on checks into actual text.
Facebook face recognition - Seen Facebook recognizing names while tagging?
Colorization of black and white images.
In short, all three terms (AI, ML & DL) can be related as below - recall those examples Chess board, Spam emails & Object recognition (picture credit blogs.nvidia)
Predictive Analytics (PA)
Under predictive analytics, the goal of the problems remains very narrow where the intent is to compute a value of a particular variable at a future point of time. You can say predictive analytics is basically a sub-field of machine learning. Machine learning is more versatile and is capable to solve a wide range of problems. There are some techniques where machine learning and predictive analytics overlap like linear and logistic regression but others like decision tree, random forest etc are essentially machine learning techniques. Keep aside these regression techniques as of now, I will write detailed blogs for these techniques.
How does Data Science relate to AI, ML, PA & DL?
Data science is a fairly general term for processes and methods that analyze and manipulate data. It provides you ground to apply artificial intelligence, machine learning, predictive analytics and deep learning to find meaningful and appropriate information from large volumes of raw data with greater speed and efficiency.
Types of Machine learning
Classification of machine learning will depend upon type of task which you expect machine to perform (Supervised, Unsupervised & Reinforcement) or based on desired output i.e. data. But at the end algorithms will remain same or you can say techniques which will help you to get the desired result.
Regression: This is a type of problem where we need to predict the continuous-response value like what is the value of stock.
Classification: This is a type of problem where we predict the categorical response value where the data can be separated into specific “classes” like an email it's "spam" or "not spam"
Clustering: This is a type of problem where we group similar things together like grouping set of tweets from Twitter.
I have tried to showcase the type with below chart, I hope you will find this helpful. Please don't limit yourself with the types of regression, classifiers & clusters which I have shown below. There are number of other algorithms which are being developed and used world wide. Ask yourself which technique fits your requirement.
Thank you folks!! If you have any question please mention in comments section below.