Difference between AI and ML

Artificial Intelligence (AI) and Machine Learning (ML) are translated into Vietnamese as: Artificial intelligence and machine learning. These are two common terms and are often mentioned today. And people often use them interchangeably to describe an intelligent software or system.

But although both AI and ML are based on statistics and math, they are not the same thing. In this article, you will learn the difference between AI and ML with some real life examples to help you understand between AI and ML.

What is AI or Artificial Intelligence?

Artificial Intelligence, or WHOis the ability of a computer or machine to mimic human behavior and perform human-like tasks.

Artificial intelligence performs jobs that require human intelligence such as thinking, reasoning, learning from experience and most importantly, making decisions on its own.

“AI is the science and engineering of creating intelligent machines.” – John McCarthy

Artificial intelligences can perform tasks exceptionally well, but they have not yet reached the point of being able to interact with humans on a true emotional level.

To learn more about AI, let’s take a look at some examples of artificial intelligence in action.

Robots

Industrial robots are a prime example of AI. Industrial robots have the ability to monitor their own accuracy and performance, and sense or detect when maintenance is needed to avoid “downtime” that wastes productivity. It can also operate in a new or unknown environment.

Virtual assistant

Another example of AI is virtual assistant tools which are interactive utilities between Human and AI. The most popular personal assistants are Google’s Google Home, Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana.

These virtual assistants allow users to find out information, book hotel rooms, add events to the calendar, answer questions, schedule meetings, send messages or emails, etc.

What is ML or Machine Learning?

ML can be called Machine Learning or machine learning, which is a “subset” of AI that is capable of automatically learning from given data.

Learning/Learning in ML refers to the ability to learn based on available data and the ability of an ML algorithm to train a model, evaluate its performance or accuracy, and then make predictions.

For example, you can train a system with machine learning algorithms like Random Forest and Decision Trees.

The purpose of ML is to allow machines to learn on their own using data and ultimately make accurate predictions.

To learn more, check out some Machine Learning examples.

Recommend Products

Most e-commerce websites have ML tools that provide recommendations on various products based on historical data of user visits.

For example: if you search for the product “Tiki Water Toothpick”, the next time you visit Tiki you will be prompted for “Water Toothpick” products of different brands, even discount codes related to the product. this or related products such as toothpaste, oral hygiene products, etc.

ML will make recommendations based on what you liked, added to cart, and other related behaviors like commenting, viewing comments, checking prices, etc.

Email Spam and Malware Filtering

Email Spam (spam) has become a big problem for internet users. Today most email service providers use machine learning tools to automatically learn and identify spam emails and phishing messages.

For example: Gmail and Yahoo’s spam filters don’t just check for spam emails using pre-existing rules. It creates new rules on its own based on what it has learned as it continues to perform its spam filtering activities from user reports or user behavior.

Difference between AI and ML

• Artificial intelligence is a branch of computer science that aims to make computers mimic human intelligence.
• Come to Machine learning, ML is a branch of artificial intelligence. It studies data and algorithms to teach computers how to make decisions like humans.

As defined above, AI is a broader field with larger applications. ML is a subfield of AI that teaches machines to make data-driven decisions.

In summary, all ML models are AI models, but the converse is not necessarily true. Both paradigms are geared towards creating machines that can learn things without being explicitly programmed.

Another difference between AI and ML is applicability. ML trains computers to make data-driven choices and decisions, and improves over time as more data is fed to them.

On the other hand, the AI ​​model has more implementations. So, the key difference between AI and ML is the scope of application. While ML is quite specific, AI targets broader implementations with more functionality.

For example, Google Assistant is an example of artificial intelligence AI. It listens to your voice and delivers specific results. However, the speech recognition feature that allows the Google Assistant to listen to you is an ML model.

So, in essence, AI aims to train machines to perform human-like intelligence, and ML trains machines to make intelligent data-driven decisions.

Apart from the differences, there are a lot of similarities between AI and ML. For starters, both are fields of computer science, and it’s impossible to jump into one without studying the other.

In a nutshell, AI solves tasks that require human intelligence while ML is a subset of artificial intelligence that solves specific tasks by learning from data and making predictions.

This means that all ML is AI, but not all AI is ML.