Demystify AI
Before we begin, let’s imagine all technologies that we have today without AI. Every day we get hundreds of mail or even more and chances are that some of them will turn out to be spam. Google uses large-scale classification to filter those spam, and thus we can just sit back and do our work.
Not only that, we all are very sensitive towards the environment we are living in, we always want to know about the weather conditions. Guess how anchors on tv tell us that!? The answer is Machine Learning.
To make it more relatable, look at the e-commerce giants such as Amazon, Walmart, etc, the use of Prescriptive and descriptive Analytics has resulted in exponential growth.
Today we will be discussing how this AI appeared, the fundamentals to get started with AI and we will conclude with the impact and scope of AI.
What is Artificial Intelligence?
In simple words, it is the capability of machines to mimic human intelligence through various learning processes. The fundamental question is “How do we make the machine learn?” The answer is “Algorithms”. The illustration below depicts the functional part of Artificial intelligence and their relationship.
What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence that’s responsible for imparting learning capabilities to machines through Algorithms. Based on what kind of intelligence we want the machine to have, AI Engineers devise algorithms taking feature engineering into account.
Let’s say you brought a puppy which is to say some weeks old and now you want to train your puppy. What do you do? You use gestures and at the end of every correct response, you reward your puppy.
You supervised the puppy’s brain to perform certain actions linked to a specific command. Not only that, you imparted a permanent behavioral change. In the context of a computer program, when a program starts to improve its performance P in relation to some task T from the past experience E, It is said to be learning.
The way of equipping a computer program to learn can be different based on the problem we are dealing with and that’s why these algorithms are kept under broad classification.
Machine learning is classified into:
- Supervised Learning: Let’s say our mom had cooked something and asked you to taste it but first she tells you that this is a dish “A”. The next time you eat the same dish or anything resembling it you quickly map and guess whether it’s the same or not. In technical language, this is called labels. In a nutshell, initially, we supervise the program and then we evaluate how much it could learn. A most common example would be predicting the price of houses in an area or weather forecasting.
- Unsupervised Learning: Here, we don’t tell the machine about the data features we are feeding and we let the machine understand the patterns on its own. Hence, the pattern can be a wider connotation such as dimension, colors, etc. depending upon the problem. For example, customer segmentation based on their choice and preference for better sales is done via such algorithms.
- Reinforcement Learning: This category of learning is built upon the dynamics of the environment, action, and reward system. In other words, suppose we have a robot and while walking, it steps over an obstacle and falls down. Now we want the robot to get up automatically and at the same time learn to avoid such obstacles in the future. This learning is rewarded with some positive tags and the robot starts differentiating between positive and negative actions.
Supervised Learning is broadly classified into:
A. Regression: Regression: Briefly, regression means prediction. That means it tries to learn the relationship using the labeled and based on the relation it predicts output on new data.
Consider Temperature as the input variable and humidity as our output. So here humidity is dependent upon temperature thus we can mathematically say
H= m*t + error ,Here, h=humidity ,T=temperature ,M=slope, Error=difference between real and predicted value.
B. Classification: Classification is about categorizing given data into distinct classes. In other words, it is a “Yes” or “No” classification.
The most common example is Spam Classification. Firstly, we train the algorithm to learn about spam features, and later algorithm uses this knowledge to filter spam. This requires a lot of training data to make the model more accurate.
Here, I would like to emphasize that classification is all about predicting categorical value that is “yes” or “no” while regression is about continuous value prediction.
Unsupervised Learning: In this type of Learning we don’t provide an algorithm labeled data instead we ask it to learn the patterns and label them on their own. Based on the past learnings algorithm starts to provide a response.
Today we have discussed the fundamentals of AI and how it evolved and also we touched upon how ML concepts are influencing the global market. I have tried my best to demonstrate the working logic with examples of my own.
I hope you enjoyed the article. What do you think about the future of Artificial Intelligence? Let me know in the comments!
