What is AI?

  • 5 minutes

The term "Artificial Intelligence" (AI) covers a wide range of software capabilities that enable applications to exhibit human-like behavior. AI has been around for many years, and its definition has varied as the technology and use cases associated with it have evolved. In today's technological landscape, AI solutions are built on machine learning models that encapsulate semantic relationships found in huge quantities of data; enabling applications to appear to interpret input in various formats, reason over the input data, and generate appropriate responses and predictions.

Common AI capabilities that developers can integrate into a software application include:

Capability Description
Diagram of speech bubbles.
Generative AI		 The ability to generate original responses to natural language prompts. For example, software for a real estate business might be used to automatically generate property descriptions and advertising copy for a property listing.
Diagram of a human head with a cog for a brain.
Agents		 Generative AI applications that can respond to user input or assess situations autonomously, and take appropriate actions. For example, an "executive assistant" agent could provide details about the ___location of a meeting on your calendar, or even attach a map or automate the booking of a taxi or rideshare service to help you get there.
Diagram of an eye being scanned.
Computer vision		 The ability to accept, interpret, and process visual input from images, videos, and live camera streams. For example, an automated checkout in a grocery store might use computer vision to identify which products a customer has in their shopping basket, eliminating the need to scan a barcode or manually enter the product and quantity.
Diagram of a speech bubble and a sound wave.
Speech		 The ability to recognize and synthesize speech. For example, a digital assistant might enable users to ask questions or provide audible instructions by speaking into a microphone, and generate spoken output to provide answers or confirmations.
Diagram of a text document.
Natural language processing		 The ability to process natural language in written or spoken form, analyze it, identify key points, and generate summaries or categorizations. For example, a marketing application might analyze social media messages that mention a particular company, translate them to a specific language, and categorize them as positive or negative based on sentiment analysis.
Diagram of a form containing information.
Information extraction		 The ability to use computer vision, speech, and natural language processing to extract key information from documents, forms, images, recordings, and other kinds of content. For example, an automated expense claims processing application might extract purchase dates, individual line item details, and total costs from a scanned receipt.
Diagram of a chart showing an upward trend.
Decision support		 The ability to use historic data and learned correlations to make predictions that support business decision making. For example, analyzing demographic and economic factors in a city to predict real estate market trends that inform property pricing decisions.

Determining the specific AI capabilities you want to include in your application can help you identify the most appropriate AI services that you'll need to provision, configure, and use in your solution.

A closer look at generative AI

Generative AI represents the latest advance in artificial intelligence, and deserves some extra attention. Generative AI uses language models to respond to natural language prompts, enabling you to build conversational apps and agents that support research, content creation, and task automation in ways that were previously unimaginable.

Diagram of a prompt, a language model, and a response.

The language models used in generative AI solutions can be large language models (LLMs) that have been trained on huge volumes of data and include many millions of parameters; or they can be small language models (SLMs) that are optimized for specific scenarios with lower overhead. Language models commonly respond to text-based prompts with natural language text; though increasingly new multi-modal models are able to handle image or speech prompts and respond by generating text, code, speech, or images.