Difference between Artificial Intelligence and Machine Learning
AI and ML
happy patel

happy patel

14 November 2022
5 mn read


In computer science, terminologies like machine learning and artificial intelligence are closely related and interconnected. However, because of this interconnectedness, people get confused.

This article highlights a few factors that will clarify the differences between artificial intelligence and machine learning and these two terminologies and help us distinguish between these two terms.

What is Artificial intelligence?

Types of AI

Features of Artificial intelligence

What is Machine Learning?

Types of Machine Learning

Features of Machine Learning

AI Vs ML: Full Comparison


What is Artificial intelligence?

Artificial intelligence is the field of computer science that enables robots or computer programs to acquire knowledge and carry out tasks that normally need intellect and are done by humans.

It is all about the ability of a computer system to imitate human cognitive processes like learning and problem-solving. Moreover, there are numerous ways to define AI but the simple term is to accomplish tasks in a simple way.  

Artificial Intelligence is not limited to machine learning or deep learning. Apart from that it also consists of other domains like object detection, robotics, natural language processing, etc.

AI is becoming part of our day-to-day life because it is making our job easy. In the old days, people used to use printed maps and follow the way of doing things in traditional methods. Apart from this AI has made things easier and more accessible.

Types of AI

  1. Narrow AI

Narrow AI is a subset of AI that is focused on using AI for doing particular tasks. This field of narrow AI is the most prevalent and readily available to do a certain set of tasks. 

It is also known as weak AI as it does not accomplish tasks outside of its domain or set of constraints and is only taught for a single job.

Some small sets of predefined tasks like google assistant, Siri, google translate, recommendation systems, etc. are the perfect examples of narrow artificial intelligence.

Playing chess, receiving recommendations for purchases on an e-commerce site, self-driving automobiles, speech recognition, and picture identification are some examples of narrow AI.

  1. General AI

General artificial intelligence is capable of doing any intellectual job as effectively and it encompasses devices that can carry out mental functions that resemble those of human intelligence. 

The goal of general AI is to have the capacity for learning, and problem-solving as well as the need to create a system that is intelligent enough to think like a person.

AGI systems should have a number of qualities, including common sense, baseline knowledge, and transfer learning. However, some experts don’t believe that AGI will ever be feasible because we still don’t fully understand how our brains work.

Researchers from all over the world are currently concentrating on creating general AI devices. As generic AI systems are still being researched, it will take a long time and many resources to construct such systems.

  1. Super AI:

Super AI is a degree of system intelligence where computers are capable of outperforming people in any task thanks to their cognitive abilities. It is the product of general AI.

Strong AI is capable of understanding, reasoning, solving problems, making decisions, planning, learning, and communicating on its own, among other essential abilities.

Super AI is currently only an idealized idea in the world of artificial intelligence. Real-world implementation of such systems is still a challenging endeavor.

Features of Artificial intelligence

Automate Simple and Repetitive Tasks

At some point, all of us accomplished a certain task just because we do not enjoy doing it but AI has the ability to execute the same kind of task over and over again without any break. Artificial intelligence (AI) computers aim to act and behave in a similar way to how humans think, infer, and act. AI systems are reading human thought processes and finding solutions to many issues.

Data Ingestion

Data ingestion is one of the most important features of artificial intelligence. Artificially intelligent systems deal with huge amounts of data. One of the most crucial aspects of artificial intelligence is data ingestion. Systems that are artificially intelligent work with vast volumes of data.

A significant percentage of this data is analyzed by AI using neural networks, which aids in drawing a logical conclusion from it.

Cloud Computing

Cloud computing is one of the most extensive aspects of artificial intelligence. Physical storage is increasingly a major issue and every day massive amounts of data are produced. So with the introduction of cloud computing, organizations can now become more effective, perceptive, and strategic. A well-known cloud computing platform called Microsoft Azure makes it possible to apply ML models to server-stored data. AI capabilities are available with cloud computing.

Quantum Computing

Quantum neural networks are helping in solving complex quantum physics with the accuracy of supercomputers. So that day is not far, and this can lead to path-breaking developments in the near future. Building extremely sophisticated quantum algorithms is the focus of the interdisciplinary field of quantum computing, which aims to advance computational tasks.

What is Machine learning?

Machine learning is the study of statistical models and algorithms that computers use to carry out specific tasks without explicit instructions.

It is an application of AI that gives the system the capacity to automatically pick up new skills and get better with practice. By combining the program’s input and output in this case, we can create a new program.

The steps involved in machine learning, a subset of artificial intelligence, are as follows:

Data collection

Data preparation

Selection of Model

Model Training

Model Evaluation

Parameter tuning

Making predictions

Due to the impressive outcomes of their products, the majority of industries have realized the value of machine learning. These sectors include the government, healthcare, financial services, and transportation.

Types of ML

Supervised learning

The machine learning algorithm must then find out how to access the inputs and outputs once the operator provides it with a known dataset with the desired inputs and outputs. The operator is aware of the correct solutions to the issue, whereas the algorithm recognizes patterns in the data, gains knowledge from observations, and makes predictions. The operator adjusts the algorithm as the operator makes predictions, and this cycle continues until the method is effective and very accurate

Unsupervised learning:

Unsupervised learning is a type of machine learning where the goal is to discover patterns, relationships, and structure in data without the need for explicit labels or guidance from a teacher. The algorithm then attempts to identify patterns, group similar data points together, and create a representation of the underlying structure of the data.

The two main types of unsupervised learning are clustering and dimensionality reduction. In clustering, the algorithm identifies groups or clusters of data points that are similar to each other, while in dimensionality reduction, the algorithm reduces the number of features in the data by identifying the most important or relevant features.

Reinforcement learning: 

Machines learn by making mistakes, just like humans and it is helpful to know that there are consequences in terms of time, money, etc. It is comparable to learning by making mistakes. Machines learn by making mistakes, just like humans and it is helpful to know that there are consequences in terms of time, money, etc. As in the case of an algorithm learning to play a video game with various hurdles.

Features of Machine Learning

Machine learning is distinctive due to a number of reasons. Here are a few of them:

Automating repetitive tasks: Using machine learning, automating repetitive tasks has become simple, boosting productivity. Email automation is a prime illustration of this circumstance.

IoT compatibility: Machine learning is the best method for achieving efficiency in IoT-based products. Many businesses using IoT actively. With the combination of these two technologies, businesses may increase the quality of their industrial output.

Accuracy in data analysis: Traditional trial-and-error approaches for data analysis were very time-consuming for larger datasets, but machine learning (ML) has made it simple to quickly analyze a vast volume of data. With quick and effective algorithms that can process data, it can produce precise results.

Increasing business intelligence: The use of big data and machine learning can produce exceptional levels of business intelligence that support critical business decisions.


Both artificial intelligence and machine learning are widely used in a variety of contexts. Both technologies have a tonne of real-world examples. Thanks to AI and ML, our work is completed without our knowledge. In conclusion, ML solves problems after learning from data and making predictions, while AI handles problems that require human intellect.

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