Artificial Intelligence and Machine Learning- Relation, Differences, & More

Artificial intelligence and machine learning are essential parts of computer science and they are correlated with each other. People often use the two while performing the same function but they are both different terms. Artificial intelligence is a concept and a huge one in itself while machine learning is artificial intelligence’s subfield. 

The computer program that can perform tasks that specifically require human intelligence is known as Artificial Intelligence (AI). The tasks include decision-making, speech recognition, visual perception, and language translation. The development of algorithms and models that enable computers to learn from and make decisions based on data is known as Machine Learning (ML). This function usually creates models that identify the patterns in data and uses that information to take action.

In this blog, we will learn about artificial intelligence and machine learning, the difference between AI and machine learning in-depth, and go through its functions and uses.

What is Artificial Intelligence?

A program that helps the computer perform tasks that usually require human assistance is known as Artificial Intelligence. It has made many tasks easier and is building its grip in the field of technology rapidly. This program is built by creating certain algorithms and statistical models that allow the computer to learn and make decisions based on the data. 

Artificial Intelligence can be classified into three types, below are those types:

• Weak Artificial Intelligence: This system of Artificial Intelligence is specifically designed to perform tasks to a limited amount, or a limited set of tasks. This range of Artificial Intelligence is not designed to understand the broader concept of data or perform advanced functions. It does not have the capabilities of broad human intelligence but still plays a significant role in a wide variety of industries such as healthcare, finance, and transportation. Examples of weak artificial intelligence include virtual assistants, recommendation engines, image and speech recognition software, and chatbots.

• General Artificial Intelligence: This category of artificial intelligence falls between the weak and strong spectrum of artificial intelligence. It is known for adapting and being flexible hence it can be used in a wide range of domains. It has abilities such as reasoning, problem-solving, and learning.

• Strong Artificial Intelligence: This system still exists in concepts and is being worked upon to create a program that has all the abilities to perform any intellectual task that a human being can do. As scary as it may sound, there are possibilities that artificial intelligence might be able to replace human brains someday, but there is a long way before we get to witness that day. 

The main reason to work on creating such a system is to create machines and systems that do not just process the information but can also understand and reason with it.

Artificial Intelligence has a wide range of applications across various industries such as healthcare, finance, transportation, and manufacturing, and it will likely continue to grow its importance in the coming years. However, there are also concerns about the potential ethical and societal implications of AI, particularly around issues such as privacy, bias, and job displacement because it can only process the information as of now and can not understand it. 

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What is Machine Learning?

A subfield of artificial intelligence that builds algorithms and models which enable computers to learn and improve performance on a specific task is known as Machine Learning. This system is a process of training a machine to understand patterns in data and make decisions based on it. The computer uses data to create its own rules and models in this program.

Machine learning has three main types, they are listed below:

• Supervised Machine Learning: A type of machine learning in which the algorithm learns from the labeled data and the answers and correct outputs are already known is known as supervised machine learning. The algorithm usually adjusts the parameters to minimize the differences between the predicted and actual outputs. Supervised learning is used in a wide range of tasks like linear regression, logistic regression, and support vector machines.

• Unsupervised Machine Learning: It is the opposite of supervised machine learning as the algorithm learns from the unlabeled data where neither outputs nor any categories are known. The usual task of unsupervised machine learning is to find patterns in the data without any prior knowledge of the correct labels or answers. It is used in fields such as image and speech recognition, natural language processing, and anomaly detection.

• Reinforced Machine Learning: A type of machine learning where an agent learns to make the decisions through trial and error method is known as Reinforcement Machine learning. Its goal is to learn a policy, or set of rules that tells how an agent should behave to maximize the rewards. This learning also involves the process of exploitation and exploration, where the agent tries different methods and chooses what is best. It is used in various fields such as robotics, game playing, and autonomous vehicles.

Machine learning has a wide variety of applications, including image recognition,  recommendation systems,  fraud detection, natural language processing, and more. As the data eventually becomes increasingly abundant, the potential uses of machine learning are expanding along with it.

Uses of Artificial Intelligence

A field encompassing various subfields has been used as an alternative for human-required tasks in the technological field for quite some time now. Following below are the uses of artificial intelligence: 

• Virtual Assistants: They are also known as chatbots and help assist big brands and industries with customer servicing requirements.
• Recommendations: The AI algorithm detects user data and after analyzing that, it gives the users personalized recommendations about products and services.
• Fraud Detection: It is a very beneficial tool of AI as it can detect fraud and other malicious activities in the areas where fraud is a huge concern.
• Healthcare: AI has been proven efficiently helpful in the healthcare sector as it can detect the pattern of medical issues through data and provide personalized treatments.
• Smart Homes: AI devices can learn a person’s habits and preferences to adjust lighting, temperature, and other settings automatically, and work efficiently in smart appliances.
• Natural Language Processing: AI algorithms analyze and understand human language, making it possible for chatbots and virtual assistants to communicate with users.
• Maintenance: The AI algorithm analyzes sensor data from equipment and predicts when maintenance is required, which automatically minimizes downtime and reduces expenditure.
• Finance: The AI algorithm analyzes large amounts of financial data and identifies the patterns, which helps investors to make confidential decisions.
• Video And Image Processing: AI algorithm analyzes the videos and images to identify various objects, patterns, and people, and makes them useful for security purposes and surveillance applications.

Uses of Machine Learning

One of the subfields of Artificial Intelligence that works on improving and learning by training computers with explicit programming is known as Machine Learning. Here are some of its uses: 

• Image And Speech Recognition: Machine learning algorithms are used to identify and classify objects, people, and speech patterns, which is useful for facial recognition, and speech-to-text conversion.
• Predictive Modeling: Models that can predict future events, weather, and outcome of some specific events are built using the machine learning algorithm.
• Autonomous Vehicles: The technology behind driving cars in auto mode, machine learning enables the vehicle to learn from its surroundings and make decisions based on that data.
• Social Media: A machine learning algorithm uses social media data to identify trends, and patterns, that help companies to make informed decisions about marketing and customer service.
• Anomaly Detection: The detection of errors and identification of security threats is made possible by using machine learning algorithms as they can detect anomalies or outliers in data.

For advanced knowledge and a better understanding of these innovative applications, you can opt for a professional machine learning course. 

Difference between AI and Machine Learning

Artificial Intelligence (AI) refers to the field of computer science that aims to create machines that can perform tasks that typically require human intelligence such as perception, reasoning, learning, and decision-making. Meanwhile, Machine Learning (ML) is a subset of AI that focuses on the ability of machines to learn from data and improve their performance on a task without being explicitly programmed.

In simple words, artificial intelligence’s goal is to create machines that can perform tasks like humans and machine learning is a set of techniques that enables machines to learn from data. Below is a table that explains the difference between AI and machine learning. 

Artificial Intelligence	Machine Learning
A technology that enables the machine to resonate with human behaviors and activities.	This subset of AI enables the machine to learn from past data.
Its goal is to make a smart computer system that can understand and solve complex issues like the human brain.	Its goal is to allow the machines to learn data so they can give the required output that is correct.
It has a wide range scope as various industries are using this technology.	The machine performs specific tasks and gives accurate results in machine learning.
It makes systems that are intelligent enough to perform tasks like humans.	It has a limited scope, unlike artificial intelligence.
It focuses on maximizing the chances of success.	The purpose of machine learning is to create machines that can perform the tasks that they are created for.

Conclusion

AI and machine learning are two interrelated fields of computer science. Both of these systems have a specific task to perform, artificial intelligence is on a mission to create machines that can work like a human brain. These fields are evolving rapidly with a variety of applications around the world carrying the potential to transform technology.