Differences between AI, Machine Learning, and Deep Learning

Bytes & Pieces
4 min readNov 21, 2022

by Dustin Nguyen

When it comes to Artificial Intelligence, Machine Learning, and Deep Learning, they have become some of the most talked-about technologies in the tech industry now. However, many people still have difficulty differentiating between them and often use the terms interchangeably. Although all are very similar to each other, there are various differences that make each distinguishable from the other. If you want a quick and simple answer:

Artificial Intelligence is the concept of creating intelligent, human-like machines. Machine Learning is a subset of artificial intelligence that helps build artificial intelligence-based applications.

Deep Learning is a subset of machine learning that uses extensive amounts of data and algorithms to train a model.

What is Artificial Intelligence?

Artificial Intelligence (AI) is the process of giving data, information, and human intelligence to machines with the goal of developing sentient machines that can simulate and mimic human behaviors and perform tasks by learning and solving problems. AI does this through algorithms that analyze input data and produce an output. Chatbots, for example, take in inputs like a user’s inquiry about “What time do you close?” and output an answer to the user’s inquiry like “9 AM-5 PM.” However, a user could frame their query in various different ways such as “What are your hours?” or “What time do you open?”. How does a chatbot respond to all of these messages? They use AI that can pick up on specific keywords and through previous testing and training knows how to respond because of those keywords. Although only chatbots were used as an example, there are many other applications for AI, like

● Self-driving cars

● Manufacturing robots

● Data Analysis

● Language Translation

● Speech Recognition

● etc.

What is Machine Learning?

Machine learning (ML) is computer algorithms, analytics, processes, and supporting technology to build predictive models that can solve problems. it learns from the data by using multiple algorithms and techniques just as a human would. Although AI and machine learning are similar in that they predict and provide outcomes based on input data, AI still encompasses the idea of a sentient, human-like machine while machine learning does not support that idea. There are two types of machine learning: supervised and unsupervised. In supervised learning, machines learn to predict outcomes with help from data scientists. The data the AI reads are already structured which means a target is already predetermined. Using supervised learning, systems can predict future outcomes based on past data. In unsupervised learning, machines learn to predict outcomes on the fly by recognizing patterns in unstructured input data. The systems are able to identify hidden patterns from the input data provided, and once the data is structured, the patterns become more evident. Real-world applications of machine learning include

● Sales forecasting

● Stock prices prediction

● Consumer recommendations

● Fraud analysis

● etc.

What is Deep Learning?

Deep learning (DL) is a subset of machine learning that deals with algorithms inspired by the structure and function of the human brain like neurons. Neurons in computer science are equations that are adjustable that multiply input data with weights and add biases to input data. A group of neurons is called a layer and these layers can be stacked on each other to form a neural network. The neural network has an input layer that accepts inputs from the data. The hidden layer is used to find any hidden features in the data. The output layer then provides the expected output. However, when building a neural network, the neurons begin as random equations that output random results. Therefore, training and examples are needed. Data is passed into the neural network and is told how inaccurate its output is. Using high-level maths, like multivariable calculus, slight changes are made to the neurons and the network outputs a different result closer to the desired output.

The major difference between deep learning and machine learning is the format that data is presented to the machine. Machine learning algorithms are given input data and features from the data and classify new data based on the old data and features given to it. Deep learning

algorithms are given input data and then use neural networks to find features and patterns on their own and then make a classification.

Real-world applications of machine deep learning include

● Cancer Tumor Detection

● Image Coloring

● Automatic Handwriting Generation

● Object Detection

● Photo Descriptions

● Pixel Restoration

● etc.

Sources Used

https://www.geeksforgeeks.org/difference-between-artificial-intelligence-vs-machine-learning-vs- deep-learning/

https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks

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Bytes & Pieces

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