AI vs Machine Learning: Their Differences, Deep Learning, Neural Networks & Much More

It’s the 21st century, and the two most talked-about technologies that have driven the world forward are; Artificial Intelligence (AI) and Machine Learning.

Although they are often used interchangeably, they are actually very distinct concepts with specific roles and functionalities, falling under the same umbrella. So, what’s the difference between them?

For anyone in the tech world, even outside it, it’s crucial to understand these evolving technologies. As a Machine Learning Expert, I am writing this guide to make it transparent for you as well.

So, let’s dive into their differences!

What is Artificial Intelligence?

Let’s start with Artificial Intelligence or simply AI. You might already know about it, but let me shed more light on it.

In straightforward language, it is simply the capability given to computers to think like humans and perform complex tasks.

Now, let’s be a bit technical. AI is a broad scientific field that refers to the use of technologies to build machines that can mimic human-like cognitive functions and perform tasks such as analysing, reasoning, and learning.

From smart assistants like Alexa and AI Chatbots to robotic vacuum cleaners and self-driving cars, AI is all around us!

Types of AI:

Now, here’s where things start to get a little complex. AI is classified into three types.

• Artificial Narrow Intelligence (ANI): Also called Weak AI. Designed for specific tasks.
• Artificial General Intelligence (AGI): A more advanced form of AI that can behave in a human-like way across all tasks.
• Artificial Super Intelligence (ASI): Systems that outstrip human intelligence.

These types still might be confusing, so let me explain them more.

Whatever has been developed until now is all Artificial Narrow Intelligence (ANI). Whether it’s voice assistants (Siri, Alexa), AI chatbots (ChatGPT, Bard), text-to-image generators (DALL-E), or self-driving cars, these all fall under the ANI category. While they can perform a range of functions, they’re still limited.

AGI, which can completely exhibit human-like intelligence, hasn’t been truly developed yet. We still have to work a lot to create something that can identify the correct course of action in situations without being programmed or trained. As for ASI, we still have a long way to go!

Now, let’s get to our main point: AI vs ML.

As I mentioned above, AI is a broad scientific field, a branch of computer science that refers to the use of technologies to build AI-powered machines. When we talk about machine learning, it is the subfield of artificial intelligence.

What is Machine Learning?

Machine Learning, a subset of AI, focuses on the development of algorithms that allow computers to make informed decisions.

Now, how do they do it? Instead of writing explicit programs for any task, they develop algorithms after analysing massive datasets. ML models learn from the data and make decisions based on it.

So, now you understand how Netflix, Amazon, and all such websites already know what you want to see. They are the most common implementations of AI in normal life.

Let’s dig a little deeper into Machine Learning.

Machine Learning Methods:

There are three key methods of Machine Learning.

• Supervised Learning: Models trained on labelled data, able to categorise things and make predictions.
• Unsupervised Learning: Models that identify patterns in unlabeled data, detect patterns, and distinguish characteristics.
• Reinforcement Learning: Models that mimic trial-and-error learning, trained to learn in an environment, and receive feedback from humans.

Difference Between Artificial Intelligence and Machine Learning:

Now, back to AI vs ML. Let’s understand the difference between them better.

AI is the field of study that enables a machine to think and act like a human. ML is an application of AI that allows machines to extract knowledge from data and learn to make informed decisions based on them.

Still finding it difficult to digest?

Take the example of Siri. You ask Siri to set reminders, play music, or provide weather updates. AI drives the virtual assistant’s ability to understand your voice commands, simulating human-like understanding and actions.

As Siri gets better with time, it will understand your specific way of speaking and your preferred music genres without you explicitly telling it each time. This is due to machine learning, where the model learns the amount of data you give from your interactions and makes better decisions.

I hope it clears up the confusion between AI and ML. While we are talking about Machine Learning, there are two more important things to know: Neural Networks and Deep Learning.

What are Neural Networks?

Neural networks, or Artificial Neural Networks (ANN), are a class of algorithms inspired by the structure and function of the human brain. In other words, they are a type of computer program that solves problems in a way similar to how the human brain works.

Neural networks are composed of three layers (Input, Hidden, and Output) of interconnected nodes or neurons. These layers allow neural networks to recognize patterns, make decisions, and solve complex problems.

For example, you are teaching a neural network to recognize pictures of cats and dogs. The Input Layer of neural networks takes in the picture, the Hidden Layer breaks down the image to recognize features like fur patterns, ears, and tails. Finally, the Output Layer decides whether the picture is of a cat or a dog.

This is how the neural networks work. They are used in many applications, such as image recognition, speech recognition, and even playing games.

What is Deep Learning?

Now come to Deep Learning, the powerful subfield of Machine Learning where the machine uses multi-layered neural networks to model and mimic the complex decision-making power of the human brain.

When given heaps of complex datasets, the model learns the patterns, adds structure and becomes able to make highly accurate predictions or classifications based on new, unseen data. Interesting, right?

Take GPT for example, OpenAI did not train it with structured data. Instead, millions of gigabytes of raw text was fed to it, and the model analysed it to draw its own conclusions, learning to generate human-like text based on the patterns it observed.

In A Nutshell:

Artificial Intelligence (AI) is a broad field in computer science that focuses on creating machines that can think and act like humans. Machine Learning (ML) is a subset of AI that trains machines to learn from data and make decisions. Within ML, Deep Learning uses neural networks to process large amounts of complex data and make predictions.

Modern AI tools rely on these neural networks, which are built using deep learning techniques from machine learning.

I hope that makes sense now!