What is Artificial Intelligence (AI)?

1. Machine Learning (ML)

Machine Learning is a subset of AI where computers learn from data and make decisions without being explicitly programmed for every task. Machine Learning is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions based on data. Instead of being explicitly programmed to perform a task, ML systems use statistical techniques to identify patterns and improve their performance over time as they are exposed to more data. Example: Email platforms like Gmail use machine learning to automatically detect spam emails. They observe the characteristics of millions of spam emails and learn to block them from reaching your inbox.

2. Deep Learning

Deep Learning is a more advanced type of Machine Learning that uses structures called neural networks (inspired by the human brain) to process data in complex ways. Deep Learning is a specialized area within Machine Learning that employs neural networks with many layers (hence “deep”) to analyze various forms of data. It is particularly effective in handling large datasets and has been instrumental in advancements in areas such as image and speech recognition. Example: Self-driving cars rely on deep learning to recognize pedestrians, street signs, and other vehicles by processing data from cameras and sensors.

3. Neural Networks

Neural Networks are algorithms designed to recognize patterns. They consist of layers of interconnected nodes (neurons) that transform data inputs into useful outputs. Neural Networks are computational models inspired by the human brain’s structure and function. They consist of interconnected nodes (neurons) that process data in layers. Neural networks are the backbone of Deep Learning and are used to identify patterns and make decisions based on input data. Example: When Facebook tags you in a photo automatically, it’s because a neural network has learned how your face looks based on previous pictures.

4. Natural Language Processing (NLP)

NLP is the ability of machines to understand, interpret, and respond in human language.Natural Language Processing is a field of AI that focuses on the interaction between computers and humans through natural language. It involves the ability of machines to understand, interpret, and generate human language in a way that is both meaningful and useful. Applications of NLP include chatbots, translation services, and sentiment analysis. Example: When you ask Siri, “What’s the weather like today?” and Siri replies with the forecast — that’s NLP at work!

5. Computer Vision

Computer Vision enables machines to interpret and understand visual information, like photos and videos.Computer Vision is an interdisciplinary field that enables machines to interpret and make decisions based on visual data from the world. It involves the use of algorithms to process and analyze images and videos, allowing applications such as facial recognition, object detection, and autonomous vehicles. Example: Tesla‘s autonomous vehicles use computer vision to detect traffic signals, obstacles, and lane markings, allowing them to drive without human assistance.

6. Supervised Learning

Supervised Learning is when a model is trained on a labeled dataset — meaning the input and output are both known. Supervised Learning is a type of Machine Learning where the model is trained on a labeled dataset, meaning that each training example is paired with an output label. The goal is for the model to learn to predict the output from the input data. Common applications include classification and regression tasks. Example: If you train a model with a dataset where pictures are labeled “dog” or “cat,” the AI learns to predict whether a new image is a dog or cat based on what it learned.

7. Unsupervised Learning

Unsupervised Learning is when a machine tries to find patterns in data without any labels. Unsupervised Learning, in contrast to Supervised Learning, involves training a model on data without labeled responses. The model attempts to identify patterns and structures within the data on its own. This approach is often used for clustering and association tasks, such as customer segmentation and anomaly detection.  Example: A music streaming app might group songs into new playlists by recognizing that people often listen to them together, even if no one labeled the songs “party music” or “relaxing music.”