Machine Learning vs Artificial Intelligence: What's the Difference?

When you think of machines learning, you're really considering artificial intelligence. If you're thinking of machines learning, you're really talking about artificial intelligence.

Those who have followed the news about technology these past few months may have heard the phrases Artificial Intelligence (AI) and Machine Learning (ML). Many people interchange the use of these. It's something I used to do as well. Each time I came across a smart app or a chatbot, I just thought AI. Whenever I heard about a smart app or a chatbot, I just thought about it as AI, without thinking much about what was going on in the background.

The reality is, however, that Machine Learning and Artificial Intelligence are not the same thing, and they are related.

In this article we will do the same; get to the difference in a simple way. No jargon, technical terms, or complicated phrases. A simple and understandable explanation.

Artificial Intelligence (AI): What is it? What is Artificial Intelligence (AI)?

The general term used for creating machines that perform tasks normally associated with human intelligence is Artificial Intelligence, or AI.

Consider activities that people perform every day:

• Recognising faces
• Understanding language
• Making decisions
• Solving problems
• Learning from experience

If a machine is made to do these things, then it is a machine that has Artificial Intelligence.

You ask your virtual assistant a question and then it provides you an answer, for instance, that helps you, and AI is engaged here. AI is behind the feature that automatically arranges photos of loved ones.

AI in simple terms is the objective of smart machines that can think and act like humans.

An overview of machine learning.

Machine Learning is a part of Artificial Intelligence.

Machine Learning is not about telling a machine exactly what to do in each situation, but rather letting the machine learn from the data and get better over time.

Let's consider an obvious example.

Suppose you want to teach a child to recognize 'mangoes' and 'apples'. You display hundreds of images of mangoes and apples; instead of explaining all the details. The child recognises more examples and begins to recognise them independently.

The same applies to Machine Learning.

The system ingests a lot of data, identifies patterns, and applies those patterns for prediction or decision making.

For instance:

Spam email messages are detected by email apps.

Product recommending websites suggest products.

When viewing a movie or show via a streaming service, the service will recommend movies and shows to you.

These are all examples of common Machine Learning.

AI is far more than just a buzzword; it's a transformative strategy.AI isn't a fleeting trend; it's a game-changer.

You can see this by comparing it to a standard one.

The super umbrella is Artificial Intelligence.

Machine Learning is one component of that.

Consider it to be like this:

• A car is a vehicle.
• Bike = vehicle.

But all vehicles are not the same as a bike.

Similarly:

AI is the broader industry.

Machine Learning is one of the approaches towards achieving AI.

Thus, while all Machine Learning systems are part of AI, not every AI system utilizes Machine Learning.

AI and machine learning are two distinct fields.AI is different from machine learning.

### 1. Purpose

"Artificial Intelligence"

AI's ultimate goal is to develop devices that can carry out intelligent tasks.

"Machine Learning"

The goal of Machine Learning is to have machines learn from data, without having to program them for each particular problem.

### 2. Scope

"Artificial Intelligence"

There are a number of technologies and approaches under the label of AI.

"Machine Learning"

Machine Learning is just one of the areas of AI.

### 3. Learning Ability

"Artificial Intelligence"

Certain AI systems can adhere to a set of rules.

"Machine Learning"

The more data that is fed into Machine Learning systems, the better they get.

### 4. Examples

"Artificial Intelligence"

• Virtual assistants
• Chatbots
• Smart home devices
• Self-driving technology

"Machine Learning"

• Product recommendations
• Spam detection
• Fraud detection
• Weather prediction

Examples of real-life data that you use every day.

Lots of people believe that AI and also Machine Understanding are futuristic ideas. In fact, we don't notice that we use them every day.

### Google Search

In the online search world, AI understands your query and Machine Learning enhances search results by analysing user behaviour.

### YouTube Recommendations

Ever wonder how YouTube is able to suggest the next video to watch?

That is primarily because of Machine Learning in analysing viewing patterns.

### Online Shopping

Site is making suggestions on products you might be interested in based on your past buying habits. Machine Learning Analyzes the behaviour of customers to make these recommendations.

### Voice Assistants

AI aids in understanding your query and delivering helpful responses when you ask a voice assistant a query.

The confusion between AI and Machine Learning is understandable.People often get AI and ML confused for a number of reasons.

It's not surprising, as the two are so closely linked.

The terms are often used interchangeably in many news articles, videos and social media posts. Consequently, people think that it refers to the same.

The easiest way to remember the difference is:

Artificial Intelligence is the goal and machine learning is one of the means to get there.

Finding the difference is very easy once you know this.

Which is more important, the number of votes or the total amount of money that is collected?

It's just as if you asked me if the car is more important than the engine.

These both play their roles.

Artificial Intelligence (AI) gives the ultimate vision of developing intelligent systems.

Machine Learning offers a feasible means for those systems to learn and get better.

In this age of the internet, much of the most popular uses of AI are based on Machine Learning.

The future of AI and machine learning is an exciting time.The future of AI and machine learning is full of promise.

Things will be fun in the years to come.

The world is already experiencing the impact of AI and Machine Learning in various sectors including healthcare, education, agriculture, banking, and transportation.

Many businesses are turning to these technologies in India to enhance their customer service, routine tasks and decision making.

In an era of constant technological advancement, it's important that students and professionals alike will have an understanding of these concepts, and same will be useful for business owners.

## Final Thoughts

Artificial Intelligence (AI) and Machine Learning (ML) are related but not synonymous.

To keep it simple:

The term Artificial Intelligence refers to the general term of intelligence in machines.

Machine Learning is a technique that enables machines to learn from data and get better over time.