Remember the recent Elon Musk and Mark Zuckerberg clash on the future of Artificial Intelligence? So, my colleague and I were discussing the topic and after a while she said she doesn’t understand machine learning & Artificial Intelligence fully.
Are you one of those, who understand the basics of AI, the robots and more; yet when it comes to deep and in depth technical understanding, it suddenly becomes confusing? If yes, worry not for you have landed at the right place. We’ll try to understand Machine Learning like a beginner.
Let’s start one thing at a time, beginning with an algorithm. So what is an algorithm? “An algorithm in simple words is a step-by-step detailed instructions to do a particular job.” In computing, it is a set of simple rules that instructs a computer how to perform a task. Generally, we need to program our computer on how to carry out a task and this is the building block of Machine Learning. In 1959, Arthur Samuel sparked the discussion on Machine Learning (ML), stating that ML gives, “computers the ability to learn without being explicitly programmed.”
The above image explains the evolution of Machine Learning over the years. But to simplify things further, we need to know the terms that are thrown in passing when we study this concept.
Artificial Intelligence in simple words is the simulation of human mind where machines work and act like humans do. Over the years, it has always been a part of our world and is not at all a new concept, flying cars in cartoons like The Jetsons to Wall – E in recent times.
AI evolved further giving birth to a new concept, Machine Learning, where machines were fed data and instructions to execute tasks. But what about behaviour or how our brains respond? How do machines take a decision? Knowledge is the building block and an integral part when it comes to learning. This is where another algorithmic concept called Artificial Neural Networks and Deep Learning came into being. ANN enables the machine to form connections much like our brains form them with the help of neurons and deep learning helps in better prediction. Today, various applications run on the learnings of deep learning and have improved significantly.
What is Machine Learning
Now and Tomorrow
But the bigger question is how does all this affect us? Does it only stay confined to the textbook theories and fantasies of an advanced future or is it deeply embedded in our lives? The answer lies within, we are surrounded by Machine and Deep Learning and we don’t even realize. From Google translate, predicting cancerous tumors in patients, MRI scanning, e-mail spam filters and even the ride sharing feature on Uber and Ola, all these things have been made possible with Machine Learning. Social networking, online shopping and even smart assistant devices on mobile phone are some of the examples of Artificial Intelligence.
All the distant fantasies created by AI are now coming to reality thanks to advancements in Machine Learning. AI is said to be the future of the human civilization, and the days of smart cities and self-driven cars may not be far behind. But more important than anything else is to understand the difference between the three concepts. Although they are interlinked to each other, they are in a nutshell a separate entity that operates individually.