Artificial Intelligence (AI) and Machine Learning (ML) have been unarguably among the most talked about emerging technologies during the last one decade. Today these technologies have become ubiquitous and all-pervading in nature. Almost all industries cutting across the economy have become increasingly dependent on AI and ML, underlying their monumental importance.
Modern companies and industries have realized that leveraging AI and ML is critical in making them more competitive and efficient. This realization has made it further clear that without capitalizing these emerging technologies, companies risk losing the growth momentum and becoming completely outdated.
While the importance of AI and ML cannot be denied even remotely, they are actually two different technologies. It is important to shed light on this fact because most people use these two technologies interchangbly and consider it as a synonym.
This latest article seeks to bust this myth and will hopefully help in clearing the confusion among students and other interested people who want to know more about AI and ML. I have also been motivated to write this article because NMIMS Global offers 2 years masters course in Machine Learning. Please click on the hyper-link to know more about this course.
Please note that below I’ve sought to define Ai and ML in the simplest words possible. The idea is to help even layman and young students understand the difference between AI and ML.
Lastly, usage of coding language is mandatory for developing AI and ML. This primarily includes coding languages like Java, Java script, C++, Julia and Lisp.
What is Artificial Intelligence?
The primary aim of Artificial Intelligence is to reproduce human intelligence behavior and accomplish human like activities. It seeks to reproduce this behavior through machines and computers.
AI powered machines and computers therefore can think, reason, learn from experience, and, more importantly, make its own decision – just like humans do.
The two great of examples of AI are personal assistants and industrial robots. Personal assistants Amazon Alexa, Apple Siri and Google Home try to exude human like intelligence by obeying our orders and thereby trying to simplify our life. This includes playing our favorite music, read the latest headlines or dim the lights in our room.
Industrial robots, on other hand, replaces several impedances caused by manual laborers by not only offering high performance but accurately detecting down-time.
What is Machine Learning?
Machine learning is a subset of AI, implying that the former has evolved from AI. Unlike AI, ML does not seek to bring human like intelligence or human behavior. Although it also aims to make machines more efficient and competent, the level of human intelligence that it brings forth is of lower level.
More importantly, machines or software powered by ML are not explicitly programmed but at the same time they are programmed enough to automatically learn from the existing data and improve on their previous performance.
Below is the more simpler definition of machine learning (ML).
Machine Learning is the process of allowing computers learn automatically without human intervention or assistance and adjust actions accordingly.
Some of the apt examples of machine learning is Email spam filtering and product recommendation on e-commerce website.
Email services like gmail and yahoo heavily depend on ML to filter out spam and phishing messages from the inbox and enhance the user experience of their users. Similarly, e-commerce platforms like Amazon and Flipkart also leverage ML to improve product recommendation to their customers.
For instance, if you have been searching digital marketing books on Amazon since past few days then you’re bound to get lot of recommendation on digital marketing books next time you visit Amazon website.
Notably, in both cases ML has capitalized on existing data to help improve the performance of email service providers and e-commerce platforms.