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What’s the difference between artificial intelligence and machine learning?
often see these terms used interchangeably, but I’m not sure where the line is drawn between them. Are all machine learning systems considered AI? And are there AI systems that don’t involve machine learning at all?
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Wong’s Answer
Hi Jay. You can think of artificial intelligence (AI) as the big umbrella. It's the whole idea of making computers smart and able to do things that usually require a human brain, like solving problems or understanding language.
Now, machine learning (ML) is one way to do that. It's a special tool or method under the AI umbrella. Instead of telling the computer every single thing it needs to do, we give it a lot of data and let it learn on its own. It finds patterns in the data and gets better over time. Think of a computer learning to spot cats in pictures by looking at thousands of photos of cats and dogs.
Since machine learning is a method for creating a system that can make intelligent decisions, any system built with ML is a form of AI. For example, a system that learns to recommend movies you might like is an AI system because it's making an intelligent prediction, and it's doing so through machine learning.
Now, machine learning (ML) is one way to do that. It's a special tool or method under the AI umbrella. Instead of telling the computer every single thing it needs to do, we give it a lot of data and let it learn on its own. It finds patterns in the data and gets better over time. Think of a computer learning to spot cats in pictures by looking at thousands of photos of cats and dogs.
Since machine learning is a method for creating a system that can make intelligent decisions, any system built with ML is a form of AI. For example, a system that learns to recommend movies you might like is an AI system because it's making an intelligent prediction, and it's doing so through machine learning.
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Savanna’s Answer
AI is the bigger idea — machines that can act smart.
Machine learning is a way to make that happen — it’s how machines learn from data instead of being told what to do.
- Savanna Rose
Machine learning is a way to make that happen — it’s how machines learn from data instead of being told what to do.
- Savanna Rose
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Patrick’s Answer
Jay, just between you and me. I think your question is one that many people struggle with. In my opinion when it comes to defining AI (or what I like to call Augment Intelligence), it is something that is a broad term that refers to any computer system designed to do things that normally require human intelligence. For example, understanding language, recognizing images, or making decisions. On the other hand, when it comes to defining Machine learning, I think it is a specific type of AI that learns from data and gets better over time without being directly programmed for every single thing you want it to do. So, with that said, all machine learning systems are a form of AI, but not all AI uses machine learning. For example, early AI systems followed fixed rules written by people and didn’t learn or adapt. Those types would be considered AI without machine learning. Think of AI as the big picture, and machine learning as one powerful way to make AI work. These are just my thoughts on your question and I hope that it helps.
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Komal’s Answer
AI is the umbrella term and Machine Learning is a subset of AI, It is a way for computers to learn from data—like how we learn from experience. Instead of being told exactly what to do, systems figure things out by spotting patterns.
Examples:
Voice Assistants: Siri or Alexa improve the more you talk to them. They learn your voice, your habits, and even your favorite music.
Social Media Feeds: Instagram or TikTok show you content based on what you’ve liked, watched, or interacted with—learning your preferences as you go.
Examples:
Voice Assistants: Siri or Alexa improve the more you talk to them. They learn your voice, your habits, and even your favorite music.
Social Media Feeds: Instagram or TikTok show you content based on what you’ve liked, watched, or interacted with—learning your preferences as you go.
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Sankarraj’s Answer
This is one of the most common questions I get as someone working in AI-powered automation. The short answer is: all machine learning (ML) is AI, but not all AI is ML.
Artificial Intelligence (AI) is the broad field focused on building machines or systems that can perform tasks that normally require human intelligence—like reasoning, decision-making, problem-solving, or language understanding. AI includes many approaches, some rule-based and some data-driven. For example, early chess-playing programs that used if/then rules were AI, but they weren’t machine learning.
Machine Learning (ML) is a subset of AI that focuses specifically on enabling systems to “learn” from data and improve over time without being explicitly programmed for every rule. Instead of coding all the rules by hand, we train ML models on large datasets so they can recognize patterns—for example, predicting defects in software (like I did with my project AutoBugPredictX) or detecting bias in mortgage approvals at Freddie Mac.
To your second question:
Yes, there are AI systems that don’t use ML. A rule-based expert system, for instance, is AI but not ML.
And yes, all ML systems are AI. ML is one of the most powerful approaches inside AI today, and it powers deep learning, natural language processing, and computer vision.
So think of it like this: AI is the big circle, ML is one slice of it. Inside ML, you also have deep learning (DL), which uses neural networks for even more complex tasks like image recognition or large language models (e.g., ChatGPT).
In my own work, I often combine both perspectives: using ML models (to predict defects, simulate risks, or process health data) and rule-based AI logic (to ensure compliance, fairness, and regulatory alignment). That mix of automation and oversight is where real-world AI systems become powerful and trustworthy.
Artificial Intelligence (AI) is the broad field focused on building machines or systems that can perform tasks that normally require human intelligence—like reasoning, decision-making, problem-solving, or language understanding. AI includes many approaches, some rule-based and some data-driven. For example, early chess-playing programs that used if/then rules were AI, but they weren’t machine learning.
Machine Learning (ML) is a subset of AI that focuses specifically on enabling systems to “learn” from data and improve over time without being explicitly programmed for every rule. Instead of coding all the rules by hand, we train ML models on large datasets so they can recognize patterns—for example, predicting defects in software (like I did with my project AutoBugPredictX) or detecting bias in mortgage approvals at Freddie Mac.
To your second question:
Yes, there are AI systems that don’t use ML. A rule-based expert system, for instance, is AI but not ML.
And yes, all ML systems are AI. ML is one of the most powerful approaches inside AI today, and it powers deep learning, natural language processing, and computer vision.
So think of it like this: AI is the big circle, ML is one slice of it. Inside ML, you also have deep learning (DL), which uses neural networks for even more complex tasks like image recognition or large language models (e.g., ChatGPT).
In my own work, I often combine both perspectives: using ML models (to predict defects, simulate risks, or process health data) and rule-based AI logic (to ensure compliance, fairness, and regulatory alignment). That mix of automation and oversight is where real-world AI systems become powerful and trustworthy.
Updated
Archana’s Answer
Hi Jay,
It's exciting to explore the world of Artificial Intelligence (AI) and Machine Learning (ML). AI is all about getting computers to do things that usually need human smarts, like solving problems and making decisions. It covers many techniques to build intelligent systems.
ML is a part of AI that teaches machines to learn from data and adjust to new info on their own. It uses clever algorithms to spot patterns and make predictions.
I hope this inspires you to dive deeper into this fascinating field. Keep up the great work!
Cheers!
It's exciting to explore the world of Artificial Intelligence (AI) and Machine Learning (ML). AI is all about getting computers to do things that usually need human smarts, like solving problems and making decisions. It covers many techniques to build intelligent systems.
ML is a part of AI that teaches machines to learn from data and adjust to new info on their own. It uses clever algorithms to spot patterns and make predictions.
I hope this inspires you to dive deeper into this fascinating field. Keep up the great work!
Cheers!
Updated
Bhagesh’s Answer
Jay, individuals working in the tech field or other related ventures most likely hear these terms constantly. Despite the fact that they may sound similar, however AI & ML are not quite the same thing. They are closely connected. The simplest way to understand how AI and ML relate to each other is:
AI - is the broader concept of enabling a machine or system to sense, reason, act, or adapt like a human
ML - is an application of AI that allows machines to extract knowledge from data and learn from it autonomously
One helpful way to remember the difference between machine learning and artificial intelligence is to imagine them as umbrella categories. Artificial intelligence is the overarching term that covers a wide variety of specific approaches and algorithms. Machine learning sits under that umbrella, but so do other major subfields, such as deep learning, robotics, expert systems, and natural language processing.
Now that you understand how they are connected let's understand the difference between AI & ML.
While artificial intelligence encompasses the idea of a machine that can mimic human intelligence, machine learning does not. Machine learning aims to teach a machine how to perform a specific task and provide accurate results by identifying patterns.
Let’s say you ask your Smartphone, “How long is my commute today?” In this case, you ask a machine a question and receive an answer about the estimated time it will take you to drive to your office. Here, the overall goal is for the device to perform a task successfully—a task that you would generally have to do yourself in a real-world environment (for example, research your commute time).
In the context of this example, the goal of using ML in the overall system is not to enable it to perform a task. For instance, you might train algorithms to analyze live transit and traffic data to forecast the volume and density of traffic flow. However, the scope is limited to identifying patterns, how accurate the prediction was, and learning from the data to maximize performance for that specific task.
AI allows a machine to simulate human intelligence to solve problems
The goal is to develop an intelligent system that can perform complex tasks
We build systems that can solve complex tasks like a human
AI has a wide scope of applications
AI uses technologies in a system so that it mimics human decision-making
AI works with all types of data: structured, semi-structured, and unstructured
AI systems use logic and decision trees to learn, reason, and self-correct
On the other hand, Machine Learning allows a machine to learn autonomously from past data.
The goal is to build machines that can learn from data to increase the accuracy of the output
We train machines with data to perform specific tasks and deliver accurate results
Machine learning has a limited scope of applications
ML uses self-learning algorithms to produce predictive models
ML can only use structured and semi-structured data
ML systems rely on statistical models to learn and can self-correct when provided with new data
Hope it clears your doubts...
Want to understand difference between Data Science & Data Analytics - Read this out
https://bhageshpant.medium.com/data-science-vs-data-analytics-ee2f3d5b2799
Should you need further guidance, please feel free to reach out.
Best,
Bhagesh
https://bhageshpant.medium.com/data-science-vs-data-analytics-ee2f3d5b2799
AI - is the broader concept of enabling a machine or system to sense, reason, act, or adapt like a human
ML - is an application of AI that allows machines to extract knowledge from data and learn from it autonomously
One helpful way to remember the difference between machine learning and artificial intelligence is to imagine them as umbrella categories. Artificial intelligence is the overarching term that covers a wide variety of specific approaches and algorithms. Machine learning sits under that umbrella, but so do other major subfields, such as deep learning, robotics, expert systems, and natural language processing.
Now that you understand how they are connected let's understand the difference between AI & ML.
While artificial intelligence encompasses the idea of a machine that can mimic human intelligence, machine learning does not. Machine learning aims to teach a machine how to perform a specific task and provide accurate results by identifying patterns.
Let’s say you ask your Smartphone, “How long is my commute today?” In this case, you ask a machine a question and receive an answer about the estimated time it will take you to drive to your office. Here, the overall goal is for the device to perform a task successfully—a task that you would generally have to do yourself in a real-world environment (for example, research your commute time).
In the context of this example, the goal of using ML in the overall system is not to enable it to perform a task. For instance, you might train algorithms to analyze live transit and traffic data to forecast the volume and density of traffic flow. However, the scope is limited to identifying patterns, how accurate the prediction was, and learning from the data to maximize performance for that specific task.
AI allows a machine to simulate human intelligence to solve problems
The goal is to develop an intelligent system that can perform complex tasks
We build systems that can solve complex tasks like a human
AI has a wide scope of applications
AI uses technologies in a system so that it mimics human decision-making
AI works with all types of data: structured, semi-structured, and unstructured
AI systems use logic and decision trees to learn, reason, and self-correct
On the other hand, Machine Learning allows a machine to learn autonomously from past data.
The goal is to build machines that can learn from data to increase the accuracy of the output
We train machines with data to perform specific tasks and deliver accurate results
Machine learning has a limited scope of applications
ML uses self-learning algorithms to produce predictive models
ML can only use structured and semi-structured data
ML systems rely on statistical models to learn and can self-correct when provided with new data
Hope it clears your doubts...
Want to understand difference between Data Science & Data Analytics - Read this out
https://bhageshpant.medium.com/data-science-vs-data-analytics-ee2f3d5b2799
Should you need further guidance, please feel free to reach out.
Best,
Bhagesh
Bhagesh recommends the following next steps:
Updated
Sumitra’s Answer
Hi Jay,
You’re right! The terms Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably, but they’re not the same. Think of AI as the overall goal: building systems that behave intelligently, like detecting fraud calls. To actually make that goal work, we need specific techniques that allow the system to learn from data and improve over time ; those are Machine Learning models. ML provides the “How,” while AI is the “What.”
So in simple words: AI is the destination, ML is one of the main roads to reach it. There are also AI approaches that don’t use ML at all, like rule-based systems, but today ML is the most common path because it’s powerful and adaptable.
I hope this clears the confusion ☺️
You’re right! The terms Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably, but they’re not the same. Think of AI as the overall goal: building systems that behave intelligently, like detecting fraud calls. To actually make that goal work, we need specific techniques that allow the system to learn from data and improve over time ; those are Machine Learning models. ML provides the “How,” while AI is the “What.”
So in simple words: AI is the destination, ML is one of the main roads to reach it. There are also AI approaches that don’t use ML at all, like rule-based systems, but today ML is the most common path because it’s powerful and adaptable.
I hope this clears the confusion ☺️
Updated
Prajwal’s Answer
AI is the big goal (making machines smart), and ML is one way to achieve it (learning from data).
Updated
Sandeep’s Answer
Hello Jay,
The simplest way to understand the difference is that AI is the large goal, and ML is the primary technique used today to achieve that goal.
AI is the broad field of computer science dedicated to creating systems that can mimic things like reasoning, problem solving, learning, and decision making.
Think of AI as the circle, and ML as a slice within it. All ML systems are indeed considered a type of AI. ML is a specific method where a system uses statistical techniques to enable computers to learn from data without being explicitly programmed. Instead of writing a fixed set of rules, you feed the machine a large amount of data, and the machine learns the pattern to solve a problem, like recognizing a cat in an image or predicting stock prices etc.
Hope this helps!
The simplest way to understand the difference is that AI is the large goal, and ML is the primary technique used today to achieve that goal.
AI is the broad field of computer science dedicated to creating systems that can mimic things like reasoning, problem solving, learning, and decision making.
Think of AI as the circle, and ML as a slice within it. All ML systems are indeed considered a type of AI. ML is a specific method where a system uses statistical techniques to enable computers to learn from data without being explicitly programmed. Instead of writing a fixed set of rules, you feed the machine a large amount of data, and the machine learns the pattern to solve a problem, like recognizing a cat in an image or predicting stock prices etc.
Hope this helps!
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