Artificial Intelligence (AI) is now a part of our routine, sometimes without us even being aware. We use it to communicate with each other through our smartphones and virtual assistants like Siri, Cortana, and Google Now.
The AI is also used by banks and retailers to protect us from fraud and theft through secure checkout processes, detection services, and more. Smart cars are another way we apply AI into our daily lives. AI help us to be more productive, keep us safe, and even keep us entertained. If we dig deeper into AI, we can see it’s made of many components that work together to ensure it works to its best ability.
There is an aspect of AI that allows the machine to learn about things outside of their original coding or design. This is called machine learning. An great example of machine learning that most of use daily is the utilization of search engines.
Whenever we have a query, a simple ‘google’ search is usually where we find our answers. Google itself, however, is an AI application.
So what separates the two? How do we differentiate AI from machine learning? Is machine learning only exclusive to AI applications, or can we have one without the other?
What is Artificial Intelligence?
According to a study by Stanford University, artificial intelligence is “the science and engineering of making intelligent machines, especially intelligent computer programs.”
The study also relates AI to the process of using computers to understand human intelligence. There are also different levels of AI – strong, weak, and everything in between. AI is classified as ‘strong’ when the application is able to perform duties that are considered to be comparable to that of humans.
These artificial intelligence machines are able to think like humans and use problem-solving skills to create or respond to various scenarios. ‘Weak’ AI applications perform more ‘yes or no’ tasks, but nothing complex.
Machine Learning in AI
As already discussed, machine learning is found in AI. Machine learning is the process that gives machine the ability to learn without being explicitly designed or coded to do so. The process of machine learning allows the AI application to be more responsive and versatile for different uses.
In other words, the more complex and advanced the machine learning process is, the more advanced the AI application is going to be. For example, when you use a navigation app to get from one place to the next, you are using AI, but the process that gives you the best route, traffic warnings, and detours is machine learning.
Machine learning isn’t exclusively just for AI, but by working together, they are both able to reach optimal performance. Whether avoid traffic in navigation apps, shopping online from ‘recommended items’ lists, or even checking out suggested shows on Netflix.
We are always using these two components – AI and machine learning together. Keep in mind, though, as well as they work together, they are two completely different things in the expanding world of computer science.