Artificial intelligence (AI) and machine learning (ML) are two buzzwords these days. People frequently use the terms interchangeably when referring to intelligent software or systems.
Despite the fact that they are both based on statistics and mathematics, AI and machine learning are not the same.
In this post, you’ll learn the differences between AI and machine learning, as well as some practical examples to help you understand them.
What is AI, or Artificial Intelligence?
The ability of a computer or machine to copy or reproduce human intelligent behavior and accomplish human-like activities is known as artificial intelligence or AI.
Artificial intelligence can think, reason, learn from experience, and, most crucially, make its own judgments, all of which require human intelligence.
“Artificial intelligence (AI) is the science and engineering of creating intelligent machines.” — McCarthy, John
Artificial intelligence can do a lot of things, but it hasn’t yet mastered the capacity to communicate with people on an emotional level.
Let’s look at some examples of artificial intelligence in action to learn more about it.
Personal assistant tools, which are Human-AI interaction gadgets, are another kind of AI. Google Home, Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana are the most popular personal assistants.
Users can use these personal assistants to look up information, book hotels, add events to calendars, answer queries, plan meetings, and send messages or emails, among other things.
A good example of AI is an industrial robot. To avoid costly downtime, industrial robots can check their own accuracy and performance and sense or detect when the repair is required. It can also act in a new or unfamiliar setting.
What is ML, or Machine Learning?
Machine learning, or ML, is a subset of artificial intelligence that can learn from data without being explicitly programmed or aided by domain knowledge.
In machine learning, learning refers to a machine’s ability to learn from data, as well as an ML algorithm’s ability to train a model, assess its performance or accuracy, and then generate predictions.
For example, supervised machine learning techniques like Random Forest and Decision Trees can be used to train a system.
The goal of machine learning is to allow machines to learn from their own data and make accurate predictions.
Let’s look at some Machine Learning examples to learn more.
Email Spam and Malware Filtering
Spam (unwanted commercial bulk email) has become a major issue for internet users. Machine learning algorithms are being used by the majority of email service providers to automatically learn and identify spam emails and phishing messages.
Gmail and Yahoo mail spam filters, for example, do more than just looking for spam emails based on pre-defined algorithms. As they continue their spam filtering activities, they build new rules based on what they’ve learned.
Machine learning tools are available on most e-commerce platforms, and they provide product recommendations based on previous data.
For example, if you search for machine learning books on Amazon and then purchase one when you return after a set length of time, Amazon’s home page will display a list of machine learning books.
It also provides suggestions based on what you’ve liked, what you’ve placed to your cart and other similar actions.
The major difference between AI and ML are:
|ARTIFICIAL INTELLIGENCE||MACHINE LEARNING|
|Artificial intelligence (AI) is described as the ability to learn and apply knowledge, while intelligence is defined as the acquisition of knowledge.||Machine Learning (ML) is described as the acquisition of knowledge or expertise by a computer.|
|The goal is to enhance the likelihood of success rather than accuracy.||Its goal is to improve accuracy, but it is unconcerned about success.|
|It functions like a computer program that performs intelligent tasks.||It’s a simple concept: a machine gathers information and learns from it.|
|The idea is to create a computer model of natural intelligence that can solve complex problems.||The idea is to learn from data on a specific task in order to maximize the machine’s performance on that task.|
|AI is decision-making.||Machine learning (ML) is a technique that allows a computer system to learn new things from data.|
|It leads to the creation of a system that can imitate human behavior in a certain context.||It entails the development of self-learning algorithms.|
|AI will go for finding the optimal solution.||ML will choose the best answer regardless of whether it is optimal.|
|AI leads to intelligence or wisdom.||ML leads to knowledge.|
To summarise, AI is a subset of artificial intelligence that solves specific tasks by learning from data and generating predictions, whereas ML is a subset of artificial intelligence that solves problems that require human intellect.
This indicates that every AI is machine learning, but not all machine learning is AI.
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