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Difference Between Artificial Intelligence and Machine Learning

by Lottar

Artificial Intelligence and Machine Learning are the two most popular terms in computer science. To build intelligent systems, these two technologies are the most well-known. Sometimes people use these terms interchangeably. Some people think both are the same. But in reality they are nevertheless two different concepts in different contexts. Yes, they are connected and related to computer science. So people have confusion related to them.

In the blog we will discuss the difference between artificial intelligence and machine learning. Let us try to understand each term separately. We start with artificial intelligence.

Artificial Intelligence (AI)

Artificial intelligence is a part of computer science that can create a computer system and simulate human intelligence. It is made of the words “Artificially” and “intelligence“which means”a thinking ability created by humans.

The artificial intelligence system can function with its intelligence rather than being pre-programmed using some algorithms. Machine learning techniques such as the reinforcement learning algorithm and deep learning neural networks are used by artificial intelligence. Artificial intelligence is used in various applications including playing chess, Siri, Google’s AlphaGo, etc.

Read more: Artificial Intelligence vs. Machine learning vs. Deep learning: Know the difference

Artificial intelligence is divided into three categories according to its functionality:

  • Poor AI
  • General AI
  • Strong AI

Right now we are experimenting with weak and general AI. Strong AI is a future AI predicted to be more intelligent than humans.

Top examples of artificial intelligence in 2022

Let’s look at some examples of artificial intelligence.

1. Robotics

An industrial robot is a prime example of artificial intelligence. Industrial robots can monitor their accuracy and performance and can sense or detect when the repair is needed to save costly downtime. It can also act in an unfamiliar or new situation.

2. Personal Assistant

Tools for human-AI interaction, such as personal assistants, are another example of artificial intelligence. Google Home, Microsoft’s Cortana, Apple’s Siri and Amazon’s Alexa are the most famous personal assistants.

These personal assistants allow customers to perform various tasks, including researching information, making hotel reservations, adding events to calendars, answering inquiries, setting up meetings, and more.

Self-driving cars

Tesla now offers four electric car models with autonomous driving features on the road. The company develops and improves the hardware and software that allows its vehicles to brake, change lanes, park and stop using artificial intelligence.

AI in Healthcare

The most important investments are in reducing costs and improving patient outcomes. Businesses use machine learning to diagnose problems faster and more correctly than humans. One of the most famous healthcare technologies is IBM Watson.

Read More: 12+ Best Celebrity Lookalike Apps: What Do I Look Like?

Let’s look at the types of artificial intelligence.

Source: DreamSoft4u

Types of Artificial Intelligence (AI) Technology in 2022

There are four types of artificial intelligence:-

1. Reactive AI

It uses algorithms that optimize outputs in response to a collection of inputs. The most famous example of this AI is chess.

2. Limited memory AI

It can update itself based on fresh observations or data or adapt to past experience. For example, driverless vehicles can adapt to unusual circumstances, read the road and learn from past experiences.

3. Theory-of-mind AI

It is dynamic and has a wide range of learning and memory capabilities. The computer system would be socially intelligent enough to understand emotions. This kind of AI will be able to predict behavior and infer human intent, a capability needed for AI systems to become essential members of human teams. Examples – Chatbots.

4. Self-aware AI

It develops awareness and becomes aware of its existence. There is currently no such AI.

Now it’s time to see machine learning.

What is Machine Learning (ML)

Machine learning is a form of artificial intelligence where software programs can more accurately predict outcomes without being explicitly instructed. Machine learning services use a massive amount of structured and semi-structured data to provide accurate findings or make predictions based on that data.

It only works for limited domains. For example, if we build a machine learning model to find photos of a bus, it will only provide results for bus images; however, if we add new data, such as a car image, the model will stop working. Machine learning is used in various applications, including Facebook’s automatic friend suggestion feature, Google’s search engines, email spam filters and online recommendation systems.

Let’s look at the importance of machine learning.

Importance of Machine Learning in 2022

Machine learning is significant because it helps develop new goods and provides businesses with a picture of trends in consumer behavior and organizations’ operating practices.

A significant part of the operations of many of today’s top businesses revolve around machine learning, including Uber, Google, Facebook and many more. For many businesses, machine learning has emerged as a critical marketing success.

Let’s look at the different types of machine learning.

Types of Machine Learning in 2022

There are four types of machine learning. They are-

1. Supervised Machine Learning

Data scientists explain the variables they want the computer to look for correlations between and give the algorithms labeled training data for this type of machine learning. The algorithm’s input and output are both described.

The following task is ideal for supervised machine learning-

  • Binary classification
  • Multi-class classification
  • Ensemble
  • Regression modeling

2. Unsupervised Machine Learning

Under this machine learning, the algorithms used are based on unlabeled data. The algorithm searches through datasets looking for any significant relationships. The input data that algorithms use to train and the predictions or suggestions they produce are predefined.

Following are the ideal tasks for unsupervised machine learning-

  • Grouping
  • Anomaly detection
  • Association mining
  • Dimensionality reduction

3. Semi-supervised machine learning

This kind of machine learning is a combination of supervised and unsupervised learning. Data scientists can provide an algorithm with mostly labeled training data, but the algorithm is allowed to independently examine the data and reach its conclusions about the dataset.

The following are the ideal tasks for semi-supervised machine learning-

  • Machine translation
  • Fraud detection
  • Labeling of data

4. Reinforcement Machine Learning

Data scientists frequently use reinforcement learning to train a machine to perform a multi-step procedure with well-defined criteria. Data scientists teach an algorithm to do a task and provide it with positive or negative cues as it figures out how to complete a task. However, the algorithm generally chooses the course of action on its own.

The following tasks are ideal for reinforcement machine learning-

  • Robotics
  • Video game
  • Resource management

Let’s look at the application of machine learning.

Application of Machine Learning (ML)

The following are the applications of machine learning-

1. Customer Relationship Management

CRM software can analyze email and remind the sales staff to respond to the most critical conversations using machine learning models. More advanced programs may also offer potential solutions.

2. Business Intelligence

To recognize potentially significant data points, trends of data points, and anomalies, BI and analytics vendors are incorporating machine learning into their software.

3. Human resource information systems

To sift through applications and find the best prospects for a job, human resource information systems can apply machine learning models.

4. Self-driving cars

Even a semi-autonomous car might be able to distinguish a partially visible item and notify the driver, thanks to machine learning algorithms.

5. Virtual assistants

Smart assistants often use a combination of supervised and unsupervised machine learning models to understand spoken language and provide context.

Now it’s time to see the difference between artificial intelligence and machine learning.

Artificial intelligence and machine learning
Source: DreamSoft4u

Difference between artificial intelligence and machine learning

The following are the differences between artificial intelligence and machine learning-

1. Artificial intelligence is a technology where a computer system can imitate human behavior. And machine learning is a branch of artificial intelligence that enables a machine to automatically learn from primary data without explicit programming.

2. Making a smart computer system similar to humans with artificial intelligence is intended to help tackle challenging problems. And allowing machines to learn from data to provide accurate output is the goal of Machine learning (ML).

3. We create smart systems in artificial intelligence to perform any task just like a human. In machine learning, we train computers that use data to perform specific tasks to produce correct results.

4. The two primary subdivisions of AI are machine learning and deep learning. But in machine learning only deep learning is a significant division of machine learning.

5. The application of artificial intelligence is extremely wide. And the scope of machine learning is limited.

6. The goal of artificial intelligence is to develop an intelligent system that can handle a variety of challenging jobs. And the goal of machine learning is to develop tools that can only perform the exact tasks they are specifically programmed to do.

7. Artificial intelligence systems aim to increase their chance of success. And precision and patterns are the fundamental concerns of machine learning.

8. The most common uses of artificial intelligence are expert systems, Siri, online games, humanoid robots, catbots, customer services, and many more. And some of the key applications of machine learning are Google search algorithms, Facebook car friend tagging suggestions, online recommendation systems, etc.

9. Weak AI, General AI and Strong AI are the categories into which artificial intelligence can be separated based on capabilities. And the three main categories of machine learning are reinforcement learning, unsupervised learning and supervised learning.

10. Artificial intelligence involves learning, thinking and self-improvement. And machine learning includes education and self-correction when presented with new information.

11. Structured, semi-structured and unstructured data are all handled entirely by artificial intelligence. And machine learning addresses data that is structured and semi-structured.


In the blog we discussed the differences between artificial intelligence and machine learning. We have discussed each topic separately with its applications. Also mentioned their types. We hope that the blog will be useful for you.

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