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AI vs Machine Learning – Understanding Differences

A comprehension of the distinction between AI and Machine Learning
In this context, I will share my understanding of AI and machine learning and their differences. The term artificial intelligence was coined in 1956 by John McCarthy, who defines it as "the science and engineering of making intelligent machines, especially intelligent computer programs."
The history of AI is a long one. We can trace back its origins to the 1800s when Ada Lovelace wrote the first algorithm for a computer program. In 1936, Alan Turing published the first paper that proposed a test for machine intelligence. The first computers were built in 1945 and they were used to break codes during World War II.
In 1956, John McCarthy coined the term artificial intelligence and defined it as "the science and engineering of making intelligent machines." In 1966, Marvin Minsky created his first neural network model for pattern recognition. In 1973, Joseph Weizenbaum created ELIZA which was an early chatbot.
However, it's common to mix up and confuse the phrases artificial intelligence and machine learning. But as we learn more about both ideas, we see that they are distinct from one another. Let's first understand some key facts about each using typical instances before diving into the differences.
AI vs Machine Learning – Understanding Differences
Fig1.1 Artificial Intelligence()
From the graph above, we can know deep learning is a type of advanced machine learning, whereas artificial intelligence (AI) in the form of machine learning enables software applications to become better at predicting outcomes without being explicitly designed to do so.


• What is Deep Learning?
• What is Artificial Intelligence?
• What is Machine Learning?
• AI uses cases
• Summary

Before we learn the differences between Artificial Intelligence and Machine Learning, I will introduce the circle at the center of Artificial Intelligence and machine learning–-deep learning.

AI vs Machine Learning – Understanding Differences
Fig1.2 Deep Learning
Deep learning is a subset of machine learning. It is a new way for computers to learn from data and make predictions.

Deep Learning uses neural networks with many layers. The first layer receives input, the last layer provides output. Between these layers are hidden layers that have neurons that connect to each other and form connections between the input and output layers.

Deep learning algorithms are trained using supervised learning methods like backpropagation or reinforcement learning methods like Q-learning, TD(0), etc.

The main advantage of deep learning is that it can learn more complex representations, which are more difficult to learn with other machine learning algorithms.

Deep Learning has been used to classify images, recognize speech, translate languages, and do many other tasks.

What is Artificial Intelligence?
Artificial Intelligence is the intelligence of machines that are designed to solve complex problems. AI can be categorized into three different types: weak AI, strong AI, and artificial general intelligence.

• Weak AI is artificial intelligence that can only solve a specific set of problems.
• Strong AI solves issues on the same level as humans but cannot learn new tasks or solve new problems. Artificial General Intelligence (AGI) is a form of strong AI that has the ability to learn from its environment and from other agents in order to solve complex problems.

AI has been used in many industries for decades, but recent advancements have led to an increase in its use by companies around the world. Some companies use it for marketing purposes such as generating content for social media posts or emails. Other industries like healthcare also used it.

The goal of AI is to create an intelligent machine, which simulates natural intelligence that can handle a variety of challenging tasks.

What is Machine Learning?
Machine Learning is a subset of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. The three broad categories of ML are supervised unsupervised learning and reinforcement learning.

• Supervised machine learning involves training a model on a set of data and then using the model to make predictions on new data.
• Unsupervised machine learning involves analyzing data without any labels or a training set and finding patterns in the dataset.
• Reinforcement learning is a type of machine learning that is based on the idea that an agent can learn to perform a task by exploring the environment, trying different actions, and receiving feedback from the environment. The agent’s behavior is shaped by rewards or penalties associated with these different actions. The agent uses this information to make decisions about which action to take in the future.

Machine learning is the process of enabling machines to learn with data, without being explicitly programmed. This can be done by giving the machine access to large data sets, so it can learn from them and develop its own understanding of concepts like speech recognition or language translation.

The goal of Machine Learning is to build a model that can make accurate predictions about new data, based on information learned from past experience and maximize the performance on that task.

AI uses cases
The main focus is on improving the customer experience and increasing productivity, but there are other benefits as well. For example, the company may be able to reduce costs by automating certain tasks or increase revenue by selling data they have collected from customers.

Companies should make sure they have a plan in place before implementing AI into their business. It is important that they know what goals they want to achieve with AI and how it will impact employees. Here are some examples in which AI has been utilized across different industries:

Marketers have always been innovators and now AI(ex. Google Analytics) is giving them the ability to be even more creative. Marketing campaigns are becoming more personalized as marketers are using AI to target their audience in a more precise way.

AI also helps marketers with their data-driven decision-making process, by giving them insights into what content is working and what content needs to be improved.

AI has helped marketers in so many different ways that it’s hard to name them all, but we can say for sure that AI is going to continue changing the marketing industry as we know it.

Finance and Banking
Banks are using AI in order to provide better customer service and make more accurate predictions. One of the most popular examples is the chatbot that helps customers with their banking needs. They can answer questions about their accounts, give advice on how to manage their finances, and even make appointments for them.

The future of banking will be dictated by AI. It will be a mix of humans and machines working together to offer the best possible service to customers.

AI is being used in healthcare to diagnose and treat patients. It is also used in medical research, drug discovery, and medical education. AI can be applied to a wide range of tasks in healthcare, including imaging analysis, patient triage and scheduling, predictive analytics for diseases or treatment outcomes, and even personalized medicine based on the DNA of the patient.

Besides, AI can provide automated feedback when a doctor or nurse performs an operation. It can also help doctors make decisions about treatments by analyzing data from medical records and scans. Other than that, we can use it to predict which patients are at risk of developing certain conditions or it may be able to identify unusual symptoms that might require further investigation.

Currently, the Fourth Industrial Revolution is continuing(4IR). Every sector is being revolutionized by artificial intelligence, machine learning, data analytics, automation, and deep learning technologies, which also present amazing business potential. Universities, tech behemoths, and governments have already entered the race. Utilizing the new technologies allows businesses to improve their bottom line, obtain significant competitive advantages, and even become market leaders in their respective industries.


Now, we know there are two main differences between artificial intelligence and machine learning:

- Artificial Intelligence is a general term, whereas machine learning has more specific applications.
- Artificial Intelligence is about replicating human-like behavior, whereas machine learning focuses on solving problems with data.
- AI is a branch of computer science that deals with intelligent behavior in machines, while machine learning is a subset of AI that focuses on getting computers to behave intelligently without explicit programming.

Written by: Xiao Tong Loh

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