AI vs Machine Learning

In this page, We will learn about AI vs Machine Learning, What is the difference between Artificial intelligence (AI) and Machine learning?, Artificial Intelligence, Machine learning, and What is key differences between Artificial Intelligence (AI) and Machine learning (ML)?

What is the difference between Artificial intelligence (AI) and Machine learning?

Artificial intelligence and machine learning are two aspects of computer science that are linked. These two technologies are the most popular when it comes to developing intelligent systems.

Despite the fact that these are two related technologies that are sometimes used interchangeably, they are nonetheless two distinct names in some situations.

On a broad level, we can distinguish AI and ML as follows:

“AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.”

ai vs machine learning

The following are some key distinctions between AI and machine learning, as well as an overview of AI and machine learning.

Artificial Intelligence

Artificial intelligence is a branch of computer science that aims to create a computer system that can think like a human. It is made from of the words "artificial" and "intelligence," which together signify "human-made thinking ability." As a result, we can define it as,

“Artificial intelligence is a technology using which we can create intelligent systems that can simulate human intelligence.”

Artificial intelligence systems do not need to be pre-programmed; instead, they employ algorithms that function in conjunction with their own intellect. Reinforcement learning algorithms and deep learning neural networks are examples of machine learning algorithms. Siri, Google's AlphaGo, AI in chess, and other applications of AI are all examples.

AI can be divided into three categories based on its capabilities:

  • Weak AI
  • General AI
  • Strong AI

We are now dealing with both weak and general AI. Strong AI is the AI of the future, and it is predicted that it will be more intelligent than humans.

Machine learning

Machine learning is about extracting knowledge from the data. It can be defined as,

“Machine learning is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed.”

Without being explicitly coded, machine learning allows a computer system to generate predictions or make decisions based on historical data. Machine learning makes use of a large amount of structured and semi-structured data in order for a machine learning model to produce reliable results or make predictions based on it.

Machine learning is based on an algorithm that learns on its own with the use of previous data. It only works for restricted domains; for example, if we create a machine learning model to detect dog pictures, it will only return results for dog pictures; however, if we add fresh data, such as a cat picture, it will become unresponsive.

It can be divided into three types:

  • Supervised learning
  • Reinforcement learning
  • Unsupervised learning

What is key differences between Artificial Intelligence (AI) and Machine learning (ML)?

Artificial Intelligence Machine learning
Artificial intelligence (AI) is a technology that allows machines to mimic human behavior. Machine learning is a subset of AI that allows a machine to learn from previous data without having to design it explicitly.
The goal of AI is to create a clever computer system that can solve complicated problems in the same way that people can. The purpose of ML is to allow machines to learn from data and produce reliable results.
In AI, we create intelligent computers that can execute any task in the same way that a human can. In ML, we use data to train machines how to do a task and produce reliable results.
The two primary subgroups of AI are machine learning and deep learning. Deep learning is a main subset of machine learning
AI offers a wide range of applications. Machine learning is limited in its use.
AI is attempting to develop an intelligent system capable of performing a variety of complex tasks. Machine learning aims to construct machines that can only accomplish the tasks for which they have been programmed.
The AI system is focused with increasing the likelihood of success. The key concerns of machine learning are accuracy and patterns.
Siri, customer service via catboats, Expert Systems, online game play, intelligent humanoid robots, and other AI applications are among the most common. Machine learning is used in a variety of ways, including online recommender systems, Google search algorithms, and Facebook auto friend tagging suggestions, among others.
Weak AI, General AI, and Strong AI are the three forms of AI that can be classified based on their capabilities. Supervised learning, Unsupervised learning, and Reinforcement learning are the three main categories of machine learning.
Learning, thinking, and self-correction are all part of it. When presented with new data, it incorporates learning and self-correction.
Structured, semi-structured, and unstructured data are all dealt with by AI. Machine learning is concerned with Data that is structured and semi-structured.