Introduction
Machine Learning (ML) and Artificial Intelligence (AI), terms once used in science fiction decades ago are becoming common today mainly in entertainment and business. They are two of the most trending technologies today and are part of computer science that are heavily correlated with each other. They are helping organizations and industries uncover data to make better business decisions by helping them work smarter and maintain a competitive edge over others. The demand for these technologies and the professionals in them is booming nowadays.
ML and AI are often mistaken as the same and have a great deal of overlap but are not interchangeable. This article will mainly focus on the difference between these two terms.
Artificial Intelligence
AI is the ability of machines to mimic human intelligent behavior. It enables the electronic machines to learn, think, reason and make decisions on their own. “AI is the science and engineering of making intelligent machines” - John McCarthy. The supreme power of humans to think and the reason is slowly being developed inside machines by the people themselves to make their day-to-day tasks easier, faster, and more reliable. Personal Assistants like Alexa, Siri, Cortana are good examples of AI as they assist humans in their daily chores by carrying out activities like finding out information, reserving hotels, adding events to calendars, sending emails, and so on.
Machine Learning
ML is the subset of AI that mainly deals with producing prediction models from available data without being explicitly commanded by the programmers. We can think of it as a series of algorithms that analyze the data provided, learn from it and develop models that make decisions based on the analysis. It is used to create machines that can perform specific tasks for which it is trained. The main application areas of ML are recommendation systems, search algorithms, weather forecasting, sentiment analysis, auto-tagging, etc. ML can be further divided into 3 types viz. Supervised learning, Unsupervised Learning, and Reinforcement Learning. Moreover, an AI system is built using machine learning and other techniques.
Example
We can sum this up with a real and the best example of today - Tesla. Tesla is the largest electric vehicle company in the world right now and is getting increasingly popular. It uses Artificial Intelligence to understand and anticipate the next movements of cars, pedestrians, and cyclists and help make decisions in a split second. It even provides the auto-pilot feature which automates the driving. So, how is Tesla’s AI able to do such things? First, Tesla has collected a large amount of data to train the algorithms and feed their machine learning models. More data to feed the ML models mean better performance and Tesla takes this advantage by crowdsourcing all the data from thousands of Tesla vehicles currently on the road through external and internal sensors. So, when Machine Learning produces predictions, AI is then responsible to produce actions with the help of the models trained. For example, when the car’s model learns to apply brakes after seeing a stop sign, it needs to decide when to take the action of applying the brakes by keeping in mind the varying conditions of the road (like slippery ones). In this way, ML and AI often work together to make a useful machine.
Conclusion
Today, ML and AI are the fastest-growing technologies. Statistics say that hirings for AI specialists have grown by 74% over the last few years. They are now used in retail, banking, healthcare, sales, marketing, customer service, cybersecurity, goods manufacturing, transportation, and so on. There is no shortage of ML and AI usage and implementations in the practical world today. They are awesome fields that are covering wide areas and multiple real-life applications and are taking the modern era into a revolutionary step.
