What is Machine Learning and Artificial Intelligence?

In recent years, companies in this commercial world are using advanced technologies for innovative purposes. They are building intelligent machines along with a diverse range of applications. All around the world, businesses are dominating by using AI and machine learning. But what is Machine Learning and Artificial Intelligence? 

Read this till the end to know more astonishing aspects.

Machine learning and artificial intelligence are not only used by companies or organizations. Moreover, they are used by many computer programmers and software developers to analyze data or solve problems. There is a big difference between machine learning and artificial intelligence.

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Difference between Machine Learning and Artificial Intelligence:

Difference between Machine Learning and Artificial Intelligence

But before describing the difference, you must know what is artificial intelligence and machine learning. Read this till the end!

What is artificial intelligence?

Artificial intelligence is a technology that simulates human intelligence processes through computer systems. This AI is made of two words ‘Artificial’ and ‘Intelligence’. These words mean ‘a human-made thinking power.’’

In other words, the ability of computer-controlled robots to do a human being’s task intelligently. The tasks done through artificial intelligence are impeccable. These are used for reasoning, getting know-how from past datasets, and much more beyond your imagination.

Undoubtedly, artificial intelligence is the best way to get professional work better than human beings. For instance, a person needs plenty of time to do thorough research on past datasets. But AI does thorough extensive research in a few seconds and gives impeccable results.

Artificial intelligence is the best approach for analyzing massive amounts of data. As a result, it gives precise conclusions in less time. In recent years, artificial intelligence has taken organizations to the next level.

History of Artificial Intelligence:

In 1950, Alan Turing proposed a logical framework in his paper known as ‘Computing Machinery and Intelligence’. He explored the mathematical possibility of artificial intelligence. He suggested that ‘Computers Can Think’’ and it astonished everyone.

The first conference on Artificial intelligence was held in 1956 and it was hosted by Marvin Minsky and John McCarthy. This was presented at the DSRPAI (Dartmouth Summer Research Project on Artificial Intelligence).

John McCarthy brought the top searchers from different fields for an open-ended discussion. As a result, they failed to agree on standard techniques for artificial intelligence.

Furthermore, in the 1980s there were two sources of AI strength. These were advancements in the algorithm toolkit along with massive funds. David Rumelhart and John Hopfield proposed the concept of ‘deep learning’ methods that allowed computers to learn by using past datasets.

But later on, scientists, mathematicians, and others worked together to develop the impossible thing possible. They created computer-controlled machines that work the same as human beings.

Working of AI:

Many AI systems use natural intelligence to solve complex issues. The algorithms are developed by constraints that support models through loops for thinking, insight, and actions altogether. It includes machine learning algorithms like reinforcement learning algorithms, deep learning neural datasets, and many others. They permeate programs and machines with machine learning.

Types of Artificial Intelligence

Types of Artificial Intelligence:

There are 7 total types of Artificial Intelligence. But they are categorized based on their capabilities and functionalities. They all are described in the below section one by one:

Types Due to Capabilities:

  • Weak AI
  • General AI
  • Super AI

Types Due to Functionalities:

  • Reactive Machines
  • Limited Memory  
  • Mind Theory
  • Self-Awareness

Types Due to Capabilities:

  • Weak AI

It includes machines that can’t think and performs according to pre-defined instructions. That’s why they are known as weak AI. It’s also known as artificial narrow intelligence (ANI).


  • Alexa
  • Alpha-Go
  • Siri and others

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  • General AI

In this, machines will think like humans and they will be smart as human beings. This stage is in the working process to achieve AI machines. Moreover, they will succeed in developing a human-like machine in the upcoming years.

  • Super AI

This is known as Artificial Super Intelligence (ASI). Recently, it’s a hypothetical approach including thinking, decision making, judging abilities, and much more like human beings. The machines developed by ASI will not possess needs, emotions, and other human characteristics.

Types Due to Functionalities:

  • Reactive Machines

These machines perform fewer pre-defined tasks. They cannot do inferences from datasets to predict their future movements. Operations done by these machines are based on the present situation.


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  • Limited Memory

This is the same as its name suggests that it has limited memory. They can develop improved results by researching past datasets from their memory. The memory it uses is a temporary one. These are capable of evaluating future operations by storing past experiences.


Self-driving cars are an example of Limited Memory. It has sensors to recognize traffic signals, civilians, and others for diving purposes.


  • Mind Theory 

Mind Theory AI is the most advanced AI type that will play a big contributor in the psychology field. It will focus on emotions, thoughts, and other human characteristics. Yet, it’s in the working process of conducting thorough research.

  • Self-Awareness

As you read, its name is self-awareness so it means that these machines will possess their thinking abilities. They will be self-aware on their own without human assistance. In the future, it can achieve its possible real state.

Applications of Artificial Intelligence:

  • Artificial Intelligence helps manufacturing companies automate their business procedures by implementing data analytics.
  • AI-powered assistants used NLP (Natural Language Processing) to develop conversation sounds like human beings.
  • It diminishes the possibility of credit cards frauds by identifying fake accounts on the internet.
  • Artificial Intelligence assists in carrying out administrative tasks in the education sector through automation.
  • This includes Self-driving cars that have sensors to recognize traffic signals, civilians, and others for diving purposes.
  • Furthermore, it has NLP (Natural Language Processing) chatbots to produce human sounds like Siri, Alexa, and many other machines.
  • It helps to generate unique original content through its AI writing tools for the better development of websites.


Read this till the end to know aspects!

 What is Machine Learning?

To solve existing world problems, scientists have developed machine learning in which machines can interpret, process, and analyze data. In other words, it’s the subset of artificial intelligence. This focuses on learning machines from their experience and does future predictions.

This is the best way to develop machines and they are specifically designed to carry out a single pre-defined instruction. For instance, a machine learning model for detecting cat photos. So, it will only provide results about the cat’s photos nothing else.

Machine learning algorithms enable a machine to learn and evolve into a better-advanced machine. So, it’s also known as the evolution of machines. Because of machine learning, programmers can test how they can improve the insights, cognition, and working of a machine.

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History of Machine Learning:

A model of brain cell interaction was created by Donald Hebb in 1949 in his book known as ‘The Organization of Behaviour’. The book was based on neuron excitement theories along with communications between them. This was a basic concept for developing machine learning.

In 1952, Arthur Samuel was the first one to come by the name of ‘machine learning.’ After that, Frank Rosenblatt combined Donald Hebb’s model with Arthur Samuel’s machine learning and created a perceptron. This perceptron software was developed for IBM 704 to put inside Mark 1 perceptron customized machine. It made the software’s algorithms more effective for other machines.

In the late 1970s, it was considered a part of AI evolution. After that, machine learning branched off to evolve on its own. In 1997, IBM supercomputer Deep Blue beat the chess champion, Garry Kasparov. A machine had beaten a world-class player. This was done through machine learning.

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As time passes by, the algorithms of machine learning get developed. These algorithms are capable of improving accuracy and efficiency in interpreting data. In 2002, Geoffrey Hinton, Andrew Ng, and Pedro Domingos developed Torch. This Torch is library software for machine learning. The torch has more than 1 million install on Github and it’s one of the best machine-learning libraries.

Two Kinds of Data for Machine Learning:

There are two types of data for machine learning. These data types are ingested by the machines. They are described as given below:

  • Labeled Data

In a complete machine-readable pattern, it has input and output constraints as well. This requires human labor to label each data for initiating a machine.

  • Unlabeled Data

This has only one or no constraints in a machine-readable pattern. It doesn’t require human labor for data labeling. Furthermore, unlabeled data needs more complicated solutions.

 Types of Machine Learning:

In the recent era, there are three different fundamental methods for carrying out machine learning. They are discussed one by one as follows:

  • Supervised Learning

The most fundamental type of machine learning is supervised learning. It uses labeled data to carry out its processing. But this method requires a lot of hard work of human labor to label the data accurately.

The machine learning algorithm gives fewer training datasets for supervised learning. It serves to provide ML algorithms with a concept of the problem along with their solution. Moreover, the data points are given with it.


In characteristics, the training dataset is similar to the final dataset. It provides labeled constraints needed for problem-solving. After that, the algorithms find a correlation between the given constraints. As a result, an algorithm has an idea of the working process along with a correlation between input and output.

  • Unsupervised Learning

This can work with unlabeled data. It allows the programs to use large datasets for their work. They create hidden structures because they lack work with labels.

This develops a correlation between data points accurately taken from algorithms. The hidden structures make the algorithms of unsupervised learning. Dynamically, they adapt to data through alterations of hidden structures.

  • Reinforcement Learning

Reinforcement learning features the algorithms to improve themselves on their own. It enables ML algorithms to learn from new circumstances. But it’s done by using the trial-and-error technique.

It encourages favorable results as ‘reinforced’ and non-favorable ones are considered as ‘punished.’ Reinforcement learning is based on psychological concepts.

Reinforcement learning puts the algorithm in a situation in which there is an interpreter and a rewarding system. In each iteration of the algorithms, the results are given to the interpreter to decide whether the result is favorable or not.

When the interpreter finds out the results as favorable then it rewards that algorithm. And if the result is unfavorable then it forces the algorithm to reiterate until it removes the errors. That’s why it uses the trial-and-error method and it improves the algorithm’s working efficiently.

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Applications of Machine Learning:

  • In big B2C companies, customer service executives are replaced by NLP through machine learning known as chatbots.
  • Netflix, Amazon, Google, and others use machine learning to provide unique content to their users.
  • Banking system utilizes machine learning to identify and prevent cyber-security attacks.
  • Machine learning is being used to diagnose and treat ailments by recognizing the patterns and correlations in data.
  • Machine learning is being used in robotics for different purposes like classification, clustering, regression, and anomaly detection.
  • To find solutions to complex problems, many companies use machine learning to extract information and future insights as well.
  • It handles massive amounts of data to provide accurate results in a short time with efficient working.
  • The advancements in machine learning will lead to astonishing quantum computing.

 In a nutshell:

Artificial intelligence is the technology that simulates human intelligence processes through computer systems. Furthermore, machine learning is a subset of artificial intelligence. They both are very beneficial for human beings all around the world.

These are advancing in every field of life day by day. Like in the healthcare field, both artificial intelligence and machine learning helps to diagnose ailments along with their treatment.

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