It’s easy to mix up artificial intelligence (AI) and machine learning (ML). Despite the fact that machine learning is an element of artificial intelligence, these two phrases refer to two separate concepts that can be difficult to distinguish between.
Put simply, machine learning is a subset of artificial intelligence, which is a large field of study.
But there’s a lot more to it. On a broad level, we can distinguish AI and ML as follows:
Machine learning is an application of AI that allows machines to learn from data without being explicitly programmed. AI is a larger idea that aims to produce intelligent machines that can replicate human thinking capabilities and behavior.
Let’s get into the details and break down the core differences between artificial intelligence and machine learning.
Artificial Intelligence
Artificial intelligence is a branch of computer science that aims to create a computer system that can think like a human. It is made from the words “artificial” and “intelligence,” which together signify “human-made thinking ability.” As a result, we can define it as a technology that allows us to build intelligent systems that mimic human intelligence.
Artificial intelligence systems do not need to be pre-programmed. Instead, they employ algorithms that work in conjunction with their own intellect. Reinforcement learning algorithms and deep learning neural networks are examples of machine learning algorithms.
AI can be divided into three categories based on its capabilities:
- Weak AI (Narrow AI)
- General AI
- Strong AI
Most AI we interact with in our daily lives is “weak AI”. Don’t let the name fool you. “Weak AI” is anything but weak. Weak AI is defined by its narrow scope of application. Alexa, Amazon shopping suggestions, and smart chatbots are powerful examples of weak AI.
Machine Learning
The goal of machine learning is to extract knowledge from data.
Machine learning allows machines to learn without being explicitly taught from past data or experiences.
Without being explicitly coded, machine learning allows a computer system to generate predictions or make decisions based on past data. Machine learning makes use of a large amount of structured and semi-structured data in order for a machine learning model to produce reliable results or make predictions based on it.
Machine learning uses algorithms that learn on their own with the use of historical data.
It only works for specific domains. For example, if we create a machine learning model to detect dog pictures, it will only return results for dog pictures. If we provide new data, such as a cat picture, it will become unresponsive. The datasets used to train the machine are pivotal to ensure that the model built works.
Machine learning is utilized in a variety of applications, including online recommender systems, Google search engines, email spam filters, and Facebook auto-friend tagging suggestions, among others.
It can be classified into four types:
- Supervised learning
- Semi-supervised learning
- Unsupervised learning
- Reinforcement learning
These four types of machine learning can be further broken down into their subtypes. We wrote a guide with more information about machine learning and the four types of machine learning that goes into more detail.
Major Differences Between AI and ML
Artificial Intelligence (AI) and Machine Learning (ML) have some key differences:
| Artificial Intelligence | Machine Learning | |
| Definition | Artificial intelligence (AI) is a technology that allows machines to mimic human behavior. | Machine learning is a subset of artificial intelligence that allows a machine to learn from prior data without having to design it explicitly. |
| Goal | The goal of AI is to create a clever computer system that can solve complicated problems in the same way that people can. | The goal of machine learning is to allow machines to learn from data and produce reliable results. |
| How It Works | AI is working to develop an intelligent system capable of performing a variety of complex tasks. | Machine learning aims to construct machines that can only accomplish the tasks for which they have been programmed. |
| Subsets | Machine Learning Deep Learning | Deep Learning |
| Types | Weak AI (Narrow AI) General AI Strong AI | Supervised learning Semi-supervised learning Unsupervised learning Reinforced learning |
| Application Scope | Wide scope | Limited, narrow scope |
| Data Structures | Structured data Semi-structured data Unstructured data | Structured data Semi-structured data |
| Data Use | Learning, thinking, and self-correction are all part of it. | When presented with new data, it incorporates learning and self-correction. |
| Examples | Siri/Alexa Smart Chatbots Online gameplay Humanoid robots | Google search algorithms Facebook auto-friend tagging/suggestions Image recognition |
