The three primary input domains discussed in this module are:
Computer vision uses visual inputs, such as images and video, to interpret meaningful information in artificial intelligence, computer science, and engineering.
Its goal is to emulate human vision, allowing machines to identify, process, and analyze visual information; make predictions and recommendations; and take actions.
Example: Face detection in phone cameras
A phone camera identifies faces in the frame and adjusts its focus and exposure accordingly. The system scans pixel patterns, detects features such as the arrangement of eyes and mouths, and draws a bounding box around each face in real time.
Essentially, computer vision converts image or video input into recognition judgments. It mimics tasks that the human visual system performs automatically, but assembles them piece by piece and without the broader understanding humans use to connect visual information.
Assigns a category label to an entire image. In practice, the result is often a probability distribution, such as 50% husky, 12% wolf, and 3% Inuit dog.
Common examples include animal-species classification and facial recognition.
Identifies and locates objects from different categories within an image.
Examples include traffic-sign detection and person detection.
Divides an image into regions based on its content.
Examples include tumor detection in a brain MRI and lane detection for self-driving cars.
Generates a natural-language description of the content of an image.
Answers natural-language questions about the content of an image.
Identifies and classifies actions taking place in a video.
Examples include sports analytics and detecting suspicious human behavior in security footage.
Estimates the position and orientation of objects or people in an image or video, such as representing the human body as a skeleton.
Examples include:
Improves image quality by removing artifacts, noise, or blur; restoring missing content; or increasing visual quality.
Example: Google Pixel’s Magic Eraser.
Natural Language Processing (NLP) is a field of AI concerned with interactions between computers and human languages. It enables computers to understand, manipulate, and generate languages spoken or written by humans.
NLP is used to build machines that understand text or voice data and respond with text or speech. It helps bridge the gap between natural human communication and the ways computers process information.
Assigns a category label to a piece of text.
Example: Gmail labeling an incoming email as spam or not spam.
Determines whether the emotional tone of a text is positive, negative, or neutral. Sentiment analysis is a specialized form of text classification.
Example: Classifying the product review “This product broke in two days” as negative.
Automatically converts text from one language to another.
Example: Google Translate converting “Bonjour, comment ça va?” to “Hello, how are you?”
Condenses a long document into a shorter version that preserves its key information.
Example: Generating a three-sentence abstract from a 20-page research paper.
Produce new, coherent text in response to an input, often in a conversational format.
Example: ChatGPT or Claude answering a question or drafting an email from a prompt.
Detects and corrects spelling or typing errors using language models to predict the intended words.
Example: A phone changing “teh meetign” to “the meeting.”
Converts spoken audio into a form that a machine can understand. It is the broad field concerned with voice input.
Example: Siri understanding the command “Set a timer for 10 minutes.”
Converts written text into spoken audio.
Examples include:
Transcribes spoken audio into written text. It describes the transcript-producing output of speech recognition.
Examples include:
Note: Speech recognition and speech-to-text are often used interchangeably. The distinction is that STT specifically refers to producing a written transcript.
Statistical data is information collected, organized, and analyzed to describe, understand, and draw conclusions about a phenomenon or population.
It often begins as raw data and is transformed into structured data, such as tables, for statistical analysis.
When a task predicts a category, such as a movie genre or flower species, it is called a classification problem. Its output is a categorical label.
When a task predicts a numerical value, such as a housing price or stock price, it is called a regression problem. Its output is a numerical label.
| Data source | Example features | Prediction or use |
|---|---|---|
| Medical records | Blood pressure, cholesterol, age, BMI | Disease-risk prediction |
| Credit data | Income, debt ratio, payment history | Credit scoring |
| Weather measurements | Temperature, humidity, pressure readings | Weather forecasting |
| Sales figures | Historical monthly revenue, seasonality statistics | Demand prediction |
| Machine sensors | Vibration and temperature averages | Predictive maintenance |
The common thread is the use of structured numerical data—such as counts, averages, rates, and measurements—as input for models including regression models, decision trees, and neural networks.