What’s the Difference Between AI and ML?

What’s the Difference Between AI and ML

Artificial Intelligence and Machine Learning are technologies that are very commonly known to be disruptive technologies. Still, most of the people in the world do not understand what exactly those two do. Some research results show that around 30-40% of companies worldwide show that they regularly use AI, e.g., online casinos use AI for improve user experience and safety. Look at how the AI is incorporated into online gambling providing information that’s not commonly known and because of this, there are many misconceptions about its capabilities.

The limited awareness of Artificial Intelligence and Machine Learning and how they are explicitly programmed brings unrealistic expectations of what these two can do. There is also the misconception that Artificial Intelligence will take over human intelligence, and many people are concerned that machines or computer systems will take over their positions.

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Both Artificial Intelligence and Machine Learning are about automating processes and working together with people and the human brain, not against it. Let’s dive deeper into how the AI system and the Machine Learning model are programmed, and we’ll get to the critical differences between the two later.

What is Artificial Intelligence?

Artificial Intelligence is one field of computer science and, to put it simply, is a computer system that can mimic human behaviour and automatically learn specific tasks. Two words comprise it: “Artificial” and “Intelligence”, and it roughly translates to a “Human-made thinking power.

So, a general definition of Artificial Intelligence is that it represents a technology that can create intelligent systems, which can then mimic human intelligence.

Artificial Intelligence does not need to be pre-programmed, as they work with their intelligence using a machine learning algorithm. There are more types of these machine learning algorithms, including the Reinforcement Learning Algorithm and the Deep Learning Neural Networks. These deep learning algorithms are used in different places, such as Siri, Google, etc.

There are three types of AI currently:

• Weak AI
• General AI
• Strong AI

At the moment, we have weak and general AI available. Still, it is estimated that computer science will go so far that intelligent machines will overcome humans in the future. It may seem like science fiction, but it is in close range.

What is Machine Learning?

Very simply, machine learning is all about extracting knowledge from the data you get. It is a subfield of Artificial Intelligence that enables the machine to create their data science based on experience from previous data, without the need to be pre-programmed.

Machine learning can make predictions and make a decision using data from the past. Natural language processing is one part of this science, and machine learning uses a considerable amount of data so that an unsupervised learning model can create predictions for the future.

Machine learning is based on algorithms that allow it to complete a particular task with its training data. As an example of where machine learning is used, we can see that it is used for Google search algorithms, Email spam filters, Auto friend suggestions on Facebook, etc.

Machine learning can be subdued in three categories:

• Supervised Learning
• Reinforcement Learning
• Unsupervised Learning

Differences Between Artificial Intelligence and Machine Learning

Many people mix these two terms, but they are very separate sciences. Let’s begin with the basics. Artificial Intelligence is a technology that will enable a machine to understand how a human brain works and simulate behaviour typical to humans.

On the other side, machine learning is a subsidiary of Artificial Intelligence. It enables the machine to learn from previous data and make accurate predictions for the future without it being pre-programmed. This is the main difference between artificial intelligence and machine learning.

To continue the discussion of artificial intelligence vs machine learning, we need to see the main goals of the two fields. The main objective of AI is to create an intelligent computer system and make that system perform the same thing a human can. Virtual assistants are an excellent example of AI systems that run smoothly.

On the other hand, the goal of the ML is to enable the machines to learn from past data it has stored, and they can create accurate output for the future based on that data. So, it can be said that machines will make their own decisions and solve problems, making them more human-like.

When discussing the main differences, we need to mention that AI will make systems perform human tasks using different algorithms. On the other hand, ML teaches machines how the data they have collected can be used to complete a particular job, thus helping them with making predictions of the field in question.

A narrow AI definition says that the two principal subsidiaries are machine learning and deep learning. Deep learning represents the main subsidiary of machine learning, so they are all closely connected.

When it comes to the scope of activities and capabilities, it goes without saying that AI has a much broader range of capabilities than ML. Again, ML is a subset of AI, so this claim is not rocket science. This is seen by the tasks both systems are created to do. AI can make an intelligent system that can perform multiple very complex tasks, and ML can only complete tasks it is trained to do.

Another core difference between the two is that AI is structured to maximize the chances of success, while ML is more focused on creating accurate results and patterns using the historical data it has.

Some of the main applications of Artificial Intelligence are Siri, customer support with catboats, online playing games, a humanoid robot, etc. Machine learning applications are search algorithms in google, auto friend suggestions on Facebook, and many more.

One of the most discussed topics when comparing AI and ML is the capabilities they hold. AI is divided into three sub-groups: Weak AI, General AI, and Strong AI. As mentioned above, at the moment, we use the first two.

On the other hand, ML can be divided into supervised learning, unsupervised learning, and reinforcement learning.

Another narrow difference is that AI uses learning, reasoning and self-correction, and ML uses learning and self-correction, but when introduced with new data.

So, even though it might seem confusing at times, the difference between Artificial Intelligence and Machine Learning is significant. The first solves tasks requiring human intelligence, and the second solve specific tasks by learning from data. Artificial Intelligence vs Machine Learning can be summed up in one sentence: Everything about ML is AI, but not everything in AI is ML.