Difference between Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are two of the most trending technologies now. Nowadays, hardly any smart devices or systems are designed without complete or partial application of AI or ML. Though the term AI and ML are commonly used together, they are different from each other. Here, we will discuss the difference between AI and ML.

Difference between Artificial Intelligence and Machine Learning – Table

In the following table, we have pointed out some key differences between AI and ML.

Artificial Intelligence Machine Learning
Artificial intelligence is a technology that intends to give a system human-like intelligence to perform complex tasks. Machine learning is a subset of AI that is used to train a system using data from past experiences to perform a specific task.
AI includes the processes – learning, reasoning, problem-solving, and self-correction. ML includes the processes – learning and self-correction to reduce the error.
AI has a wide range of applications. The scope of applications of ML is limited.
AI can be divided into 3 categories – Narrow AI, General AI, and Strong AI. ML can be classified into 3 categories – Supervised Learning, Unsupervised Learning, and Reinforced Learning.
The target of AI is to create intelligent systems to solve complex problems. The target of ML is to train a system with data to perform a task with minimum error.

What is Artificial Intelligence?

Artificial intelligence is a term that is used to describe the ability of a computer system, robot, or software to act intelligently as a human. Artificial intelligence can be classified into the following three categories:

1. Narrow AI

When the system performs some specific tasks using AI then the AI technique or scheme used is called Narrow AI. Sometimes it can perform better than a human.

2. General AI

When an AI system is more general purpose and can perform multiple tasks with an accuracy level comparable with a human, then the AI scheme used is called general AI.

3. Strong AI

When an AI system can perform multiple tasks better than a human, then the AI system is called a strong AI system.

AI has many applications in modern society. Amazon Alexa, Google home, computer games such as chess, intelligent humanoid robots (ex: Sophia Robot) are examples of AI.

What is Machine Learning?

Machine learning is a subset of AI which is used to train a system using collected data to perform a task and act intelligently to reduce the error of a task. Machine learning is nothing but a set of algorithms that are used to analyze the dataset and train a system to do a specific task. Machine learning is of three types:

1. Supervised learning

The machine learning process in which the user gives well-labeled, refined data to the system to train it i.e., supervise the training process himself, is called supervised learning.

2. Unsupervised learning

The machine learning process in which the system is trained without any labeled or classified data or any guidance, is called unsupervised learning.

3. Reinforced learning

Reinforced learning is a type of machine learning which is used in solving tasks that can be solved in multiple ways. We associate some reward functions with each way and its target is to maximize the reward.

Machine learning techniques are more common and are widely used in computer science, robotics, biotechnology, data science, internet of things (IoT), etc.

Conclusion

AI is a vast subject that requires multi-disciplinary knowledge. Though ML is a subset of AI, it also includes a strong understanding of mathematics, algorithms, programming, and problem-solving. AI and ML have a huge scope of applications in various areas of science and technology. Many researchers are working on the possible application of AI and ML in the future.

Author
Subhrajyoti Choudhury

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