top of page


How does AI work?

how does ai work

It feels like artificial intelligence (AI) is everywhere—cropping up in news stories, appearing in just about everyone’s business blog (guilty as charged) and sliding into conversations at family gatherings—and for good reason. AI is changing the way we move through the world.

For small business owners and entrepreneurs, the benefits of AI are nearly limitless. You can use it to turbo-charge your marketing efforts, optimize operations and even making a website from beginning to end. But, how does AI work? Join us as we take a glimpse behind the proverbial curtain to demystify this fascinating technology.

Want to use an AI website builder? Get started with Wix today.

What is AI?

AI is a machine’s ability to mimic the way our brains process information. It harnesses algorithms and rules to analyze extensive datasets in order to identify patterns that serve as the foundation for its decision-making models.

AI is not a single technology but rather a diverse collection of technologies and methodologies that collaborate to enhance the intelligence of machines. As you continue to integrate AI into your business processes and workflows, understanding these components can help you leverage AI more effectively.

The different fields of AI, explained in plain English

AI is a general concept, an umbrella term that encompasses a wide range of specialized fields and techniques. It's important to note that these fields are not entirely separate but often intersect and complement each other. Here is a sampling of the different fields of AI:

Basic components of AI: machine learning, deep learning, cognitive computing, computer vision neural network

Machine learning (ML)

The heart and (not-quite) soul of AI, ML refers to machines being able to learn on their own and improve performance over time without the need for human intervention. ML uses data as the foundation for learning. ML is how your favorite streaming service learns what you like, suggesting Rage Against The Machine when you’ve been obsessively listening to Tool all week.

Deep learning (DL)

A subset of ML, deep learning uses multi-layered neural networks to analyze data and perform complex tasks. Where ML is broader and refers to the broad techniques that allow machines to learn on their own, DL is a specific approach within machine learning that makes use of neural networks to complete a human-like task (like seeing, writing or creating art).

Neural network

Neural networks, or “artificial neural networks (ANNs),” is a component of deep learning that mimics the human brain’s data processing functionality. A neural network is made up of multiple layers of artificial neurons (called “nodes”), which are used to process all that data we keep talking about. Neural networks are complex, so we’ll break them down further, below.

Natural language processing (NLP)

NLP is a branch of AI that empowers machines to understand, process and mimic human language. We see this most commonly in voice assistants like Amazon’s Alexa and Google’s Assistant, which respond appropriately when we bark orders at them (e.g., “Play ‘The Pot’ by Tool on Amazon Music”). NLP is what tells Alexa to play a song as opposed to purchasing a $400 Dutch oven. The point is this—NLP is how machines understand human commands and respond in a way that sounds natural.

Computer vision (CV)

Computers that can understand visual data like photos and videos are somewhat Terminatoresque but also kind of important when it comes to mimicking how humans process information. CV interprets visual data like photos and videos to make decisions. When your favorite social platform automatically tags you in your mother’s family Christmas photo, this is CV at work.

Reasoning and problem solving

AI systems simulate human reasoning to solve problems. The keyword here is “simulate.” As of now, computers can’t reason the way people do. Rather, they use data, algorithms and probability to make their decisions. This can manifest as a chess-playing computer strategizing its next move or ChatGPT answering a question based on how it has answered questions in the past.


Perception is a function of AI that’s all about interpretation. AI systems use sensors to perceive real-world scenarios and communicate insights and information to humans. It’s how your car knows to alert you when an object is too close. Autonomous cars take this to the next level, using sensors and perception to navigate and avoid obstacles.

Cognitive computing

Think of cognitive computing (CC) as the brainy cousin of AI. It mimics human thought processes by using algorithms and data, making decisions, predictions and providing insights. CC systems can’t replicate human thought perfectly (at least, not yet), but they’re trained to understand context in addition to content. For example, if you're chatting about "apple" in the context of tech, a cognitive system won't start rambling about fruit salad.

Strong AI vs. weak AI vs. superintelligent AI

Artificial Intelligence has evolved significantly since humans laid the groundwork for it in the early 1900s (yes, it goes back that far), but we still only have what is commonly known as Weak AI or Narrow AI. Weak AI refers to AI systems designed and trained for specific tasks or domains. These systems excel within their predefined context but lack the broad, general intelligence seen in humans. For instance, AI website builders is a form of Weak AI, as its only purpose is to help you launch a new website. ChatGPT is also a form of Weak AI because it’s only task is to generate text-based responses in natural language.

On the other hand, the AI research and development community are still striving for Strong AI (a.k.a., General AI). Their expectation is that they will eventually be able to replicate human-like general intelligence with the ability to understand, learn and apply knowledge across a wide range of tasks and domains.

Superintelligent AI is the vision of AI that tends to give people the heebie-jeebies. The term represents a hypothetical level of AI intelligence surpassing human capabilities across all domains. It is more advanced and capable than Strong AI, and its development is purely theoretical at this point.

The different levels of AI

Artificial Intelligence (AI) can be categorized into various levels based on its capabilities and characteristics. Here are the typical levels of AI:

Reactive AI

Operates solely on present data, focusing on specific tasks without the ability to learn from experience. Reactive AI excels in domains like recommendation engines and spam filters but has limited capabilities. It lacks foresight and can’t adjust for or predict unexpected events.

Limited memory AI

This type of AI can temporarily store data from past experiences, which allows it to learn and improve over time. An example of limited memory AI in action is AlphaStar by Google. Alphastar got good at playing the strategy game StarCraft 2 by playing against itself repeatedly. A 2019 study found that it was a better chess player than 99.8% of officially ranked players. As the name suggests, limited memory AI has some, well, limitations. It requires vast amounts of data to learn simple tasks, and any changes to its training environment necessitates complete retraining.

Theory of Mind AI

A novel type of AI which is currently under development, Theory of Mind AI refers to a machine's ability to understand human emotions, beliefs and ways of thinking. Theory of Mind AI may one day be able to recognize human emotions which will enable it to respond and adjust to them appropriately. Sound creepy? Wait until you read about self-aware AI.

Self-aware AI

Self-aware AI, or conscious machines, are aware of their own internal state and the internal state of others. This AI type would be on par with human intelligence, mimicking human emotions, desires or needs. At this point, self-aware AI is fully the domain of science fiction movies and dystopian timelines.

How are businesses using AI?

Today, businesses actively use AI to expand their existing capabilities and streamline everything from customer service to product development. Specifically, here are ways you can leverage AI:

Generative AI

This subset of AI focuses on creating or generating new content, data or information rather than simply analyzing or processing existing data. Generative AI models are designed to produce novel and contextually relevant outputs, often in the form of text, images, audio or other types of content. These models can be trained to generate content that mimics human creativity and can be used in various applications. Wix's artificial design intelligence, for example, is a form of generative AI that helps to streamline the website development process.

Learn more about Wix’s AI website builder.

wix ai website builder


AI chatbots use natural language processing (NLP) to understand and respond to user queries. Remember how we mentioned voice assistants like Alexa? Chatbots work in a similar way by interpreting human language queries and providing relevant answers. You can integrate customer service chatbots into your professional website, social media accounts and activated via SMS to assist your customers in real-time. In a survey of 600 business owners, customer service was the most popular application for AI, with 56% saying they currently use or plan to use the tech.

Product recommendations

Primarily the domain of eCommerce and retail websites, AI-generated product recommendations are an easy way to personalize your customers’ online shopping experience. These ML tools analyze the shopper’s data including user behavior, preferences and purchase history—to provide them with relevant recommendations as they browse your website or app.

Predictive analytics

When you give an AI lots of data like weather patterns, shopping trends and purchase behavior to process, categorize and analyze, you have a recipe for predicting trends and making forecasts. Online store owners can use predictive AI to forecast which products will be hot next season and manufacturers can predict when machinery is likely to break down.

Marketing automation

Marketing automation tools can analyze customer behavior, segment audiences, then use this data to make recommendations or provide insights about how to allocate your marketing spend more effectively. For example, the Wix Analytics benchmarks report offers an in-depth analysis of your site, comparing it to similar sites and providing valuable insights for improvement.

Wix Benchmarks report

Operational automation

Operational automation is a set of technologies you can use to streamline and optimize repetitive, manual and time-consuming tasks within an organization. Its primary goal is to improve efficiency, reduce errors, cut costs and free up human resources for more strategic and creative work.

Accessibility enhancement

Accessibility enhancement AI tools are designed to improve the usability of digital content for individuals with disabilities. For instance, Wix's Accessibility Wizard provides guidance for how to make a website accessible. Wix built the tool to ensure that its users could easily create websites that are inclusive and compliant with accessibility standards.

Was this article helpful?

bottom of page