In recent times, Machine Learning (ML) and Artificial Intelligence (AI) have been buzzwords beyond the watercooler. ML and AI are expected to impact industries across all sectors globally.

AI and ML are the linchpins of digital transformation, no doubt, but they also have the scope to affect our daily lives. Many people want to learn more about this revolution but are daunted by the technical jargon that accompanies it. So here we will present a non-technical guide to ML and AI that will give a clearer picture of the key concepts.


AI and ML are closely linked. Artificial Intelligence, as defined by HackerEarth, is any technology that is designed to operate in a way that mimics how humans operate. AI focuses on the creation of intelligent systems that can ‘think,’ and solve problems just like humans.

Machine Learning is the technology that drives AI systems. It uses algorithms and statistical models to analyze and draw inferences from data patterns which makes the system a self-learning and automated model.

Uses of AI and ML

AI and ML are used in many applications that we use in daily life, for example with voice assistants like Apple’s Siri and Amazon’s Alexa. There are many more such applications that have changed the way businesses reach out to customers and create better products.

When we type a message in WhatsApp or SMS, the predictive text recommends the appropriate word for us. The recommendations improve inaccuracy over time. These recommendations are powered by ML algorithms.

In retail, ecommerce sites use AI to recommend to customers on what they should buy next by understanding their preferences, then matching it to inventory. Affinity groups provide another data point for the AI algorithm to produce recommendations for what to buy next.

Similarly, streaming video platforms like YouTube and Netflix provide viewers with recommendations on what to watch. The recommendations are run by ML algorithms that consider the user’s search history and current playlist. Collaborative filtering is one of the most commonly used recommendation algorithms.

In the banking and finance sector, AI is used to detect credit card fraud and spot spam. It can also be used in the loan approval process to sift the credit-worthy applicants from others.

When we check a location on Google maps or use a ride-share app to get a taxicab, we are making use of AI as navigation and transportation apps rely on AI to provide users with real-time insights.

When we type into our smartphones, we do so with the help of predictive text. Initially, you may find that the options provided by predictive text are not quite accurate, but over time, you will observe that the accuracy has improved to such an extent that you will use it regularly. This is a good example of ML learning and improving on its task over time.

AI and ML are changing the way companies are doing business. Adoption of AI and ML technologies has been on the rise across sectors, as organizations can reap benefits such as automation, improved efficiency, and reduced costs.