How Does AI Work? Basics to Know

Artificial intelligence (AI) enables machines to learn from data and recognize patterns in it in order to do tasks more efficiently and effectively. It powers a wide range of products and services like Netflix’s algorithm that recommends TV shows and movies based on your preferences or Waymo's fleet of self-driving cars.

But what goes on behind the scenes? How does AI actually work? Read on to learn more about the basics of artificial intelligence. 

What is artificial intelligence (AI)?

Artificial intelligence (AI) is the theory and discipline of programming computer systems to learn from and spot patterns in data sets. These advanced algorithms and models perform human tasks, like recognizing speech or images and making decisions. AI relies on machine learning and neural networks, as well as more complicated concepts like deep learning and natural language processing. 

AI is a complex technology with hundreds, if not thousands, of possibilities for creating solutions for businesses across industries. It enables machine learning algorithms that make our lives easier or better by doing things like automating tasks, powering virtual assistants, and generating transcripts of Zoom calls. With generative AI, we can create prompts to request content needs from processors like ChatGPT or Google Gemini. 

Read more: What Is Artificial Intelligence? Definition, Uses, and Types

How does AI work?

In order to create AI, you need to: define the problem, determine the outcomes, organize the data set, choose the appropriate technology, and then test solutions. If the intended solution does not work, you can continue experimenting to reach the desired outcome.

Below, we’ll go through five steps that illustrate how AI works: inputs, processing, outcomes, adjustments, and assessments.

Input

Data is first collected from various sources in the form of text, audio, videos, and more. It is sorted into categories, such as those that can be read by the algorithms and those that cannot. You would then create the protocol and criteria for which data will be processed and used for specific outcomes.

Processing

Once data is gathered and inputted, the next step is to allow AI to decide what to do with the data. The AI sorts and deciphers the data using patterns it has been programmed to learn until it recognizes similar patterns in the data that is being filtered into the system. 

Outcomes

After the processing step, the AI can use those complex patterns to predict outcomes in customer behavior and market trends. In this step, the AI is programmed to decide whether specific data is a “pass” or “fail”—in other words, does it match previous patterns? That determines outcomes that can be used to make decisions.

Adjustments

When data sets are considered a “fail”, AI learns from that mistake, and the process is repeated again under different conditions. It may be that the algorithm’s rules must be adjusted to suit the data set in question or that the algorithm needs slight alteration. In this step, you might return to the outcomes step to better align with the current data set’s conditions.

Assessments

The final step for AI completing an assigned task is assessment. Here, the AI technology synthesizes insights gained from the data set to make predictions based on the outcomes and adjustments. Feedback generated from the adjustments can be incorporated into the algorithm before moving forward.

How does generative AI work?

Generative AI is powered by large language models (LLMs), which are complex machine-learning models created from algorithms trained on massive datasets with deep learning. This allows generative AI programs, such as ChatGPT or Microsoft Copilot, to produce or generate new content based on their training sets rather than just predict patterns in them.

While the applications and technology used to power generative AI are new, many of their core concepts and processes have existed for much longer.

Read more: Generative AI Examples and How the Technology Works

Four main types of AI

While learning in AI can fall under categories of “narrow intelligence,” “artificial general intelligence,” and “super intelligence,” each classification demonstrates AI’s capabilities as it evolves–much of which has not yet been seen. In fact, artificial general intelligence is still to come.

Here are the four main types of AI in their current form.

1. Reactive machines: AI systems that have no memory and are task-specific. An input always delivers the same output.

2. Limited memory machines: This algorithm imitates how our brains’ neurons work together, so it gets smarter as it collects more data to train on.

3. Theory of mind: This type of AI does not yet exist, but it has the potential to understand how other entities have thoughts and emotions, allowing the AI to behave differently in relation to those around them.

4. Self-awareness: Self-aware AI also does not yet exist, but it goes beyond the theory of mind to understand that they exist as an entity, realize their state of being, and predict others’ feelings.

Read more: 4 Types of AI: Getting to Know Artificial Intelligence

Disciplines that make up AI

It can be confusing to differentiate between AI and machine learning and between all of the subfields within artificial intelligence. Here’s a brief look at some of those disciplines:

• Machine learning: Machine learning is a subset of AI that incorporates computer science, mathematics, and coding. Machine learning focuses on developing algorithms that help machines learn from data and predict trends without human assistance.

• Deep learning: Deep learning is a discipline of AI that imitates the human brain by learning from how it structures and processes information to make decisions. This subset of machine learning can learn from unstructured data without supervision, instead of being programmed to perform a specific task.

• Neural networks: A neural network is a deep learning technique designed to resemble the human brain’s structure. Neural networks perform calculations and create outputs on large data sets.

• Natural language processing: Natural language processing (NLP) is AI that enables computers to understand spoken and written human language. NLP enables text and speech recognition on devices.

• Computer vision: Computer vision is an interdisciplinary field that focuses on how computers can gain understanding from images and videos. In AI, computer vision enables automating activities that the human visual system typically performs.

Read more: Artificial Intelligence (AI) Terms: A to Z Glossary

Learn AI for your career 

Anyone can learn AI, and learning AI can be beneficial whether or not you are directly involved in developing AI. To discover how AI works behind the scenes, consider enrolling in DeepLearning.AI’s AI For Everyone to learn the basics in 10 hours.

If you're interested in learning how to unlock your AI-powered efficiency and innovation, consider enrolling in the Microsoft Copilot: Your Everyday AI Companion Specialization. Harness the power generative AI and Copilot across Microsoft's productivity suite, including in Word, Excel, Teams, and Powerpoint.