Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Table of contents
1.
Introduction
2.
Artificial Intelligence (AI)
3.
Machine Learning (ML)
4.
Key Differences Between Artificial Intelligence (AI) and Machine Learning (ML)
5.
Frequently Asked Questions
5.1.
Can AI exist without ML?
5.2.
Is ML better than AI?
5.3.
How do I start learning AI and ML?
6.
Conclusion
Last Updated: Mar 27, 2024
Easy

Artificial Intelligence and Machine Learning

Crack Google SDE interview : Essential projects
Speaker
Saurav Prateek
SDE-2 @
20 Jun, 2024 @ 01:30 PM

Introduction

Artificial Intelligence (AI) & Machine Learning (ML) are two of the most exciting & rapidly advancing technologies in the tech world today. They are transforming how we live, work, & interact with the world around us. From smart assistants on our phones to predictive algorithms that influence what we see online, these technologies are becoming an integral part of our daily lives.

Artificial Intelligence and Machine Learning

This article will take you through the basics of AI & ML, explore their differences, & provide real-world examples to help you understand these concepts better.

Artificial Intelligence (AI)

Artificial Intelligence, or AI, is like teaching a computer to think and make decisions like a human. Imagine you have a robot friend who can learn from what happens around it, solve puzzles, and even play games with you. That's what AI is all about. It's not just about robots, though. AI is also in things like the voice assistant on your phone that wakes you up in the morning or the online service that recommends movies you might like.

AI works by looking at lots of information and finding patterns or rules. For example, if you show an AI lots of pictures of cats and tell it "these are all cats," it can learn what a cat looks like. Later, if you show it a new picture, it can tell you whether there's a cat in it or not. This learning process is a big part of AI.

But AI isn't perfect. It's like when you're learning something new; sometimes you make mistakes. AI can also get things wrong if it hasn't learned enough or the information it learned from wasn't good. That's why people who make AI systems have to keep checking and improving them, just like how you keep practicing something you want to get better at.

To make AI, programmers write special computer programs. These programs use lots of math and rules to help the computer learn and make decisions. It's a bit like teaching a very fast and smart calculator to understand and interact with the world.

In summary, AI is a way to make machines smart, so they can help us with tasks, make decisions, and even entertain us. It's a big part of the future of technology, and it's getting better all the time as we teach it more about the world.

Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

Machine Learning (ML)

Machine Learning, or ML, is a special part of Artificial Intelligence. It's like giving a computer the ability to learn from its mistakes and get better over time, without being directly told how to improve. Think of it as teaching your computer to become smarter by itself, by giving it examples, much like learning from experience.

Here's a simple way to understand it: imagine you're trying to teach your computer to tell the difference between apples and oranges. You'd show it lots of pictures of apples and oranges. Each time, you'd tell it, "This is an apple," or "This is an orange." After seeing many pictures, your computer starts to notice differences by itself. Maybe it sees that oranges are usually more round and have a thicker skin, while apples might be a bit more varied in shape and have a smoother skin.

In ML, this process of showing examples and learning from them is done through algorithms, which are sets of rules and instructions that the computer follows. These algorithms adjust and improve as they get more examples, kind of like how you get better at a game the more you play it.

One cool thing about ML is that it's used in many places in our daily lives. For example, when Netflix recommends a show you might like, that's ML at work. It's learned from what you and others have watched before to make a good guess at what you'll enjoy next.

But, just like learning anything new, ML isn't perfect. It can make mistakes, especially if the examples it learns from aren't good or if there aren't enough of them. That's why people who work with ML have to be careful about the information they use to teach it.

In short, ML is a way for computers to learn from data and improve over time, making them more helpful and intelligent. It's a big step toward making technology that can understand and interact with the world in more complex ways.

Key Differences Between Artificial Intelligence (AI) and Machine Learning (ML)

Aspect Artificial Intelligence (AI) Machine Learning (ML)
Goal To create machines that can perform tasks that require human intelligence. To enable machines to learn from data so they can improve their performance over time.
How it Works AI can be rule-based, making decisions based on specific sets of instructions. ML learns from patterns in data, improving its accuracy through experience.
Applications AI is used in a broad range of applications, from voice assistants to autonomous vehicles. ML is often used in predictive modeling, like recommending products based on past purchases.
Learning AI may not always involve learning; it can just follow programmed instructions. ML is specifically about learning from data, whether supervised, unsupervised, or reinforcement learning.
Flexibility AI systems can be designed for a wide range of tasks, not limited to what they were originally programmed for. ML systems are more focused and improve at their specific task over time, based on the data they receive.
Data Dependency Traditional AI doesn't necessarily need data to function; it can work on predefined algorithms and logic. ML relies heavily on large volumes of data to learn and make accurate predictions.
Examples Smart home devices, chatbots, and expert systems. Spam filters, recommendation systems, and self-driving car technology.

Frequently Asked Questions

Can AI exist without ML?

Yes, AI can exist without ML. Early AI systems were based on hard-coded rules and didn't learn from data. ML is just one way to achieve AI, focusing on learning from data to improve over time.

Is ML better than AI?

It's not about being better; it's about suitability. ML is a subset of AI that focuses on learning from data. Depending on the task, sometimes a rule-based AI might be more appropriate than an ML approach.

How do I start learning AI and ML?

Begin with the basics of programming, preferably Python, as it's widely used in AI and ML. Then, explore online courses or tutorials that introduce AI and ML concepts. Practice by working on simple projects or problems to apply what you've learned.

Conclusion

AI and ML are like the brain and the learning process of the digital world. AI aims to make machines smart, capable of performing tasks that typically require human intelligence. ML is a method within AI that teaches machines to learn from data, improving their abilities over time. Understanding the key differences between AI and ML helps us appreciate their roles in technology's future.

You can refer to our guided paths on the Coding Ninjas. You can check our course to learn more about DSADBMSCompetitive ProgrammingPythonJavaJavaScript, etc. Also, check out some of the Guided Paths on topics such as Data Structure and AlgorithmsCompetitive ProgrammingOperating SystemsComputer Networks, DBMSSystem Design, etc., as well as some Contests, Test Series, and Interview Experiences curated by top Industry Experts.

Previous article
Data Science and Artificial Intelligence
Next article
History of AI
Live masterclass