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What is the Relationship Between Machine Learning and Artificial Intelligence?

The computer industry has brought many new things. It created a lot of different technologies that changed our world. The current hype in the industry is on machine learning, artificial intelligence, and data science. Even though all of these things might be separate branches on their own, they are deeply connected.

At the same time, many companies think that they are using one technology when, in fact, they are using another. For example, 40% of Startups in Europe that claim they use AI don’t actually use this technology. Artificial Intelligence and Machine Learning are huge fields, and that’s why there’s so much confusion. 

We wrote two posts in our blog that explained what these technologies are and what their differences are. For this post, we’ll give you a bit more information on how these two are related to each other.

Both AI and ML Focus on Developing Intelligent Programs 

Even though there are differences between AI and ML, they both have a similar outcome – intelligent software that can handle more complex tasks. That’s why these technologies have become so popular in the past couple of years. 

They are useful, and individuals and companies are both looking for computer programs that can take over human tasks while finishing tasks more quickly. However, they don’t do this in the same way. Even though both ML and AI programs might be able to do similar tasks, their design and approach are different.

Each of these branches has created highly sophisticated programs that can do complex tasks in only a second. However, they use different structures, coding, and approach to do this, which puts both AI and ML at a similar place in terms of their power.

Machine Learning is an Extension of Artificial Intelligence 

We can freely say that Machine Learning is a subfield or type of AI. All Machine Learning programs can be looked at as part of Artificial Intelligence, but not all Artificial Intelligence is Machine Learning. 

They are related inherently, but this doesn’t mean that they are synonymous. AI is the parent of ML, and both of them are the sons of computer science. Simply put, everything in the field of Machine Learning fits under the Artificial Intelligence umbrella. 

Machine Learning can be used for AI applications, and it can make Artificial Intelligence better. However, it doesn’t mean that it’s always used. Simply put, the ability of machine learning to come up with answers on its own without additional programming is very convenient. 

They Both Learn 

Even though they have a different way of doing it, both Artificial Intelligence and Machine Learning programs have the ability to learn. They aren’t static and change over time, becoming better at what they are designed to do. In some cases, they might even expand their capabilities to do other tasks. 

AI is usually approached by writing programs in which rules are implemented to allow the program to handle a certain number of tasks. These rules are re-written, changed, and upgraded throughout the development and testing of the program. Programmers learn from the program’s behavior and improve it with additional coding. 

On the other hand, machine learning learns on its own. The program is written with a predictive model where it collects data samples within a database. Machine Learning makes many mistakes over time, but with each mistake, it learns how to realize the required patterns. 

After some time has passed, the program increases accuracy and becomes better at the task that it’s designed to do. That’s why we are seeing new and improved software solutions coming out each day with new capabilities.

Artificial Intelligence and Machine Learning Rely on Data 

One of the most important things that are often overlooked with AI and ML is that they both use data. It plays a crucial role in these computer programs. The best example of this is data science. It’s the practice of getting valuable conclusions from data and making important predictions. 

These predictions are made with Machine Learning, so it’s a sort of middleman between Artificial Intelligence and data science. In some cases, AI is used to extract and understand data, while Machine Learning helps get conclusions and process this data. 

After all, ML requires structured data so that it can be used in the desired way. A good example is self-driving vehicles. Sensors are used to collect images, and they are then processed through ML to make the right decisions quickly. Without accurate data, neither of them would be able to work.

They need to be fed with data that is rich in context, relevant, and complete to establish a common language.

Conclusion 

Both Machine Learning and Artificial Intelligence can bring massive value to organizations. If they are set up the right way and given the correct data, they can cut down on costs, improve ROI, free up human resources, and reduce the number of mistakes. 

At the same time, these technologies are used for security tasks as they can easily detect harmful patterns that might lead to cyberattacks. We hope this post has helped you understand more about them and how they relate to each other.

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