What You Need to Know About Machine Learning and Artificial Intelligence

Artificial intelligence and machine learning were distinct in the past when these technologies were still developing. However, ever since they found their application in various technologies and industries, the line between them kept getting thinner. 

Once the marketers started doing their thing, the terms became utterly confusing. Some people even use them interchangeably even though they aren’t the same things. Many people also don’t know which technologies qualify in the “artificial intelligence” or “machine learning” category. 

It’s all about saying these buzzwords to bring in new potential customers. That’s why we’ve decided to talk about these things objectively and help you understand what they’re all about. 

Defining Artificial Intelligence

The idea of artificially intelligent machines was first theorized during the first half of the 1900s. A mathematician and computer scientist from Britain called Alan Turing was one of the first to explore whether AI was possible mathematically. 

As proof of his theory and research, he released the “Computer Machinery and Intelligence” paper that discussed the potential of building and testing the intelligence of machines. However, as is the case with many scientific theories, there isn’t a single approach to AI.

Artificial General Intelligence

When someone says artificial intelligence, the first association is usually about something from a science function. That’s partly due to the marketing we mentioned earlier and partly because it was unimaginable just 30 years ago. 

People think about things they’ve seen in movies like R2D2 from Star Wars and Terminator. Yes, this is AI in a way, but it’s called Artificial General Intelligence. These are programs and machines with a human-like consciousness and cognitive capabilities to perform tasks as humans do.

At the moment, there isn’t a fully developed version of Artificial General Intelligence. We are still a long way from this kind of technology. This concept is currently focused on analyzing how the human brain operates and trying to create similar software. 

That’s also known as the “top to bottom” approach to AI. It means taking a fully developed system and trying to recreate it in artificial ways. In this case, the perfect example of that system is the human brain. 

Narrow Artificial Intelligence 

Narrow Artificial Intelligence is the AI that companies are commercializing today. It has many different kinds of applications. This type of Artificial Intelligence is designed to do a closed set of tasks. It helps detect friends in your photos on social media, suggest items to buy, help you locate music, and so on. 

A lot of companies use this kind of AI to detect malicious activities and protect themselves from malware or fraud. Even though this Narrow AI can’t do many things and is focused on a single task, it does it a lot better than humans can. 

Humans can’t go quickly through activity logs, spot errors, and report them. They also get tired when doing these tasks and lose focus, leading to mistakes. AI can do this much faster without any errors and quickly highlight mistakes that need to be tracked. 

This approach is known as “bottom-up” artificial intelligence. It’s a program designed to do a specific task with designated functions, patterns, and algorithms. Some of those programs can even perfect themselves through analytics and adopt new approaches.

Defining Machine Learning 

AI and ML aren’t the same, but there’s a reason why so many people mix them up together. Machine Learning is a form of narrow AI that focuses on its mistakes. How? Quite simply, it learns from its own experience without getting any focused programming. 

Machine Learning programs access data and learn from it – observing large amounts of data allows it to discover the right patterns. Still, the computer is allowed to do this on its own without getting any adjustments or intervention from humans. 

Instead, Machine Learning programs are only told what they are looking for. When they get it wrong, they have to go through the same process again. Within this cycle, these programs learn and improve their recognition. 

The primary benefit of Machine Learning is subtle but yet so powerful. Instead of coding a program to learn how to recognize something, with the simple “right or wrong” system, the machine will code itself through repetition and math to work with specific datasets.

Improving Performance With AI and ML 

Machine Learning is the precursor of narrow AI – it’s an easy way to develop narrow AI. That’s why so many technologies and software solutions today are using it. 

Machine Learning is the best approach for creating AI that will recognize images, predict outcomes and events, detect fraudulent behavior, and categorize data. Even though the marketing lingo has made things more complicated, there is no reason not to use these technologies. 

Simply put, Artificial Intelligence is a computer science branch developing complex programs that resemble human intelligence. Machine Learning is a programming approach that is used to create narrow Artificial Intelligence. The combination of the two has made it a lot easier to create AI-powered programs that develop on their own. Instead of coding complex decision trees and potential scenarios, the program does all of this independently in an efficient and time-saving fashion.


Bear in mind that we are only scratching the surface here. There’s much more to be said about these technologies, their differences, and how they relate to each other. If you want to know more, check out our blog.

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