The digital revolution has already altered the way people connect, live, and work. And it’s only just beginning. The technology today could make the lives of billions of people happier, healthier, and more successful. One way technology helps make our lives easier is via Artificial Intelligence and Machine Learning.

As children, we wanted our very own JARVIS from Iron Man and thought such technology could only exist in movies. Soon, virtual assistants like Siri took form. While it wasn’t as advanced as JARVIS, it was an incredible piece of technology nonetheless.

It answered our questions, told us the weather, opened apps and other functions for us, and much more. AI and ML don’t stop there though. In present day technology, there are a lot of existing AI and MLs that help people in multiple areas. AI tools such as autocorrect, facial recognition, and ML tools such as chatbots that learn the more you use them are now common. Artificial Intelligence and Machine Learning are some of the most talked about trends in Fintech today as it helps further grow society and business.

The Differences Between Artificial Intelligence and Machine Learning

While Artificial Intelligence and Machine Learning are similar, they are not quite the same. Simply put, Machine learning is a current application of AI based on the notion that we should truly be capable of providing machines data to work with and letting them learn for themselves. Artificial intelligence is the more general concept of robots being able to carry out tasks in a way that we would deem “smart.” 

Types of Artificial Intelligence

AI can be classified in Vertical AI and Horizontal AI ¹. Vertical AI’s focused on one single task whether it’s to organize files or any other automated work that has a repetitive routine. Because programmers program Vertical AI to do a specific task, it performs that single job in a repeated cycle with a smaller margin of error, perhaps more reliably than humans.

On the other hand, Horizontal AIs are able to tackle more than one task in which they do not have solely one job. An example is once again Siri and other virtual assistants. These types of AIs work mostly in a Q&A system and an order system in which you may ask them to tell you a specific area’s weather or even ask them to call one of your contacts. They do not focus on just one particular set of tasks.

AI is created by studying how the human brain approaches a problem and then using that knowledge to create sophisticated algorithms that can carry out similar activities. AI is an automated decision-making system that continuously learns, adapts, suggests actions, and executes them without human intervention. They need algorithms that can learn from their experience at their heart. Machine learning enters the scene in this situation.

Types of Machine Learning

Machine Learning has three different types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Supervised learning has ready-made datasets that are already in their systems. These types of ML then analyze the given data in order to produce the appropriate solutions which will then be used to map new examples. On the other hand, Unsupervised Learning systems have unclustered data and the machine will learn independently. This system will detect patterns and provide solutions on its own. Finally for Reinforcement Learning, to maximize performance, the machine automatically decides the best behavior inside a given environment. Instead of characterizing learning methods, reinforcement learning is defined by the characteristics of a learning problem.

Artificial Intelligence and Machine Learning in Business Today

People often see artificial intelligence and machine learning as a complement to human intelligence and invention, rather than a replacement. While of course these two are not perfect and may still find it difficult to perform some activities, their capabilities in data analysis and data processing of huge datasets are superior to that of a normal human being. As such, AI and ML are able to provide synthesized courses of action to the human user which then accelerates decision-making as it simulates multiple results and leads to the best one ². 

The rapid increase in data being used and stored around the world is making data a valuable asset in economics. However, to utilize data in a way that helps the economy, we need automated systems because the huge influx of data is becoming harder to manage.

Organizations may gain value from the vast amounts of data they collect by using artificial intelligence, machine learning, and deep learning. These technologies generate business insights, automate activities, and enhance system capabilities. AI/ML has the potential to completely revolutionize a business by assisting with the achievement of measurable results.