If you have heard the terms AI and Machine learning, you probably want to know the major differences between AI and Machine Learning. In this blog, we will explain these two terms and describe how they are different from each other. So, read till the end.
Overview
Artificial intelligence (AI) and machine learning (ML) are the two most trending terms that most people use interchangeably. Although they are related, they are not the same. They are the latest technologies that have an impact on almost every industry. They have the potential to improve the performance and accuracy of many sectors. But, to integrate these technologies into the system, you must know what these terms exactly are and what their major differences are.
What is Artificial Intelligence?
Artificial intelligence in simple terms is the intelligence of a machine that mimics human intelligence. In this technology, machines are developed that can recognize speech and respond accordingly, make decisions based on facts, and solve real-life problems. Based on the capability, AI has two categories: Narrow or Weak Artificial Intelligence and Strong or General Artificial Intelligence.
Artificial Intelligence has been used for many years. However, its capability was limited to just giving suggestions and curating the content. But, with time, more emphasis is given to developing intelligence that can take actions based on the situation or command. Narrow AI has a very limited scope like language translation and face recognition. But the Advanced or strong AI is capable of doing complicated tasks like writing, drawing, playing chess, etc.
To learn about the differences between AI and Machine Learning, first, we explore what machine learning is.
What is Machine Learning?
Machine learning is a subfield of Artificial Intelligence. It involves teaching machines to find patterns and conclusions from data to make better decisions and accurate results without being explicitly programmed. It uses self-predictive models. However, it has limited scope. There are three types of machine learning: Supervised, Unsupervised, and Reinforcement learning.
In supervised learning, you train the machine models on labeled data. It involves the data point being related to a target value or label. The main objective here is to train the model to accurately predict the value of unseen data based on past data.
On the other hand, unsupervised learning is related to teaching machines how to find patterns and structures in data. Here the main motive is to train the model to work with the unlabeled data which is uncategorized.
Reinforcement learning develops machines capable of making decisions in an environment. The decision-making is based on the results in the form of rewards and penalties.
Major Differences between AI and Machine Learning
You have learned about both Artificial Intelligence and Machine Learning. Both of them are associated with developing smart machines capable of doing tasks that are earlier only humans can do. Now, let us look at some key AI and Machine Learning differences.
#1. Scope
The first AI and ML difference is in terms of scope. Artificial Intelligence is a broad term. It includes many sciences including Machine Learning, Robotics, Natural Language Processing, Computer Visioning, etc. Machine Learning is a subfield of AI and has a narrow scope. It is mainly related to the processing of data without being explicitly programmed.
#2. Objective
The way of pursuing things is also different in AI and Machine Learning. The former is associated with developing machines that are capable of thinking like a human. They can process language and make logical decisions. The latter mainly focuses on analyzing the data and giving accurate results to enhance productivity and performance.
#3. Learning
Both AI and ML are designed with an attribute to learn. They can improve their results with every mistake. However, they have a distinction between how they learn. Artificial Intelligence is pre-programmed with various rules and algorithms. So, it works based on that to do a certain task. Machine learning, on the other hand, learns from data. It improves the accuracy and performance of data. The more data it has, the better the answer will be.
#4. Applications
As Artificial Intelligence is a broader term, it has various applications which involve creating intelligent robots that can think like a human. They can perform complicated tasks. The applications of Machine Learning are mainly associated with evaluating data and delivering accurate predictions. Also, it can find patterns and structures in unseen data. Apart from that, both these technologies have applications in various sectors like healthcare, finance, transport, etc.
