How Does AI Work? Step-by-Step Guide in Simple Terms

Artificial intelligence (AI) is a growing technology that aims to mimic the performance of human intelligence. It helps computers replicate the human brain’s approaches to reasoning, learning, and decision-making.

AI basics: What is artificial intelligence?

AI is a subset of computer science that uses techniques from machine learning (ML), cognitive science, and even robotics to create useful technology. The field relies on AI models, which are frameworks that use math to mimic cognitive functions like problem-solving.

Types of AI

If you read about AI online and in scientific publications, you may encounter three different types of AI: narrow (or weak) AI, artificial general intelligence (AGI), and superintelligent AI.

• Narrow AI. Every AI tool or machine learning algorithm that you may encounter today is a form of narrow AI. This means that the AI is programmed to execute a specific task—like the algorithm that curates videos on the TikTok For You Page.
• Artificial general intelligence (AGI). This is a hypothetical form of AI that doesn’t exist. In theory, AGI could take input data and then actually reason or think through a series of possible results. It would essentially be equal to real human intelligence. Some futurists and AI experts say that AGI is the future of AI, but we aren’t there yet.
• Superintelligent AI. Like AGI, this is purely hypothetical—it’s a concept in which AI could become self-aware and surpass human cognitive abilities. But superintelligent AI is still the stuff of science fiction.

How AI works

AI works through a combination of large amounts of data, human guidance, and mathematical probability. A multi-step AI development and training process is required in order to create AI systems that are useful.

1. Data collection

The foundation of every AI tool lies in its training data. The team of engineers building and training an AI model must carefully select large data sets that will guide how the model works. Sometimes, this data is broad and covers many topics (such as in the case of ChatGPT). Other times, the data sets are focused on one very specific field or industry—like healthcare data from hospitals within a specific region.

2. Data preprocessing

Next, the data is cleaned, evaluated, corrected, and standardized. Sometimes the data is annotated, or labeled, as well. By reviewing and improving the data before feeding it into an AI model, engineers can reduce the chances of their new AI hallucinating and giving users incorrect responses.

3. Model selection and training

Once the data is ready to use, the AI engineers must select an AI model to train. There are many different AI models available, including:

• Supervised learning models. This model relies on human-labeled data. ML engineers must clearly indicate what each data point is for the AI model to “learn” and use the information to predict an output.  
• Unsupervised learning models. This model uses unlabeled data. In this case, the AI model’s programming enables it to identify patterns in the data, which then influence its ability to predict the next outcome.
• Reinforcement learning models. This model allows the AI to interact with its environment. Rules and dependencies in the AI’s makeup enable it to collect data around how its outputs perform. This information is then used to further refine the model’s future performance.
• Deep learning models. A deep learning model uses an artificial neural network made up of layers of neurons that process information. As data passes through each layer, the AI model makes calculations, identifies relationships, and creates connections.

4. Training the model

After model selection is complete, the training process can begin. Typically, the data is split into two sets: one for training, and one for testing.

The AI trainers start by entering the training data into their model. As the training process advances, the AI model executes calculations and identifies patterns that will power its future predictions.

The length of time it takes to train an AI model varies based on the type of model used and the amount of data collected.

5. Testing and evaluation

Once the initial training data has passed through the AI model, it’s time to test the outcome. At this point, machine learning engineers will take their testing, or validation, data set and run it through their newly trained AI model.

The trainers will evaluate the model’s accuracy, precision, and recall ability to determine how well it’s working.

6. Model optimization

Sometimes, the AI model’s testing outputs aren’t quite right. The trainers may notice:

• Poor data. Inaccurate data means the model isn’t producing good results
• Underfitting. This means the AI can’t capture data patterns, and the model is too simple
• Biases. AI bias occurs when the data leans in one direction, and can mirror human biases captured in the training data

If any of the above happen, then the training team needs to work on optimizing their model. This can involve machine learning techniques like adjusting a deep learning model’s neural layers and nodes, updating the AI algorithms, and regularizing the data.

7. Deployment

Once the AI engineers are happy with their model’s outputs, they can begin to deploy the model. This means releasing the model to the public, integrating it into existing tools, or building the software that will use the model.

8. Continued learning

AI models aren’t something that you train one time and then forget about. The teams of engineers behind AI-powered tools are continuously training their AI models on new information.

This ongoing training happens in several ways—such as by continuing to fine-tune the original AI model with new data, or by giving the system human feedback based on its continued outputs.

For example, when you use a tool like Claude, you may see the option to give a “thumbs up” or “thumbs down” based on how accurate the chatbot’s responses are. This feedback is then used by the AI training team to improve the model.

Natural language processing (NLP)

Natural language processing is another important part of how AI works, especially when we talk about conversational chatbots like ChatGPT. It involves feeding AI models data related to the structure of human speech, including syntax and grammar.

ChatGPT and similar tools don’t think of new statements and sentences on the fly like people do. Instead, they’re using probability—based on training data about language—to predict what the most natural-sounding series of words will be.

This same technology can also be applied to figure out if text was or was not generated by an AI model. While the process isn’t foolproof, several AI text generators online can help you reverse-engineer content and figure out if it may have been an AI output.

We tested one of these tools, made by Content at Scale, using AI text, a mix of AI and human text, and 100% human copy.

Test 1: AI-generated text

The Content at Scale tool quickly identified this paragraph of text from ChatGPT as AI-generated. The more red and orange highlighting you see in the text, the more likely that the use of AI was involved.

Test 2: AI-generated text edited by a human

Our manual edits to the text began to swing the results a little closer to the green (non-AI) end of the spectrum, but several sentences still read as if they were probabilistically generated by an AI model.

Test 3: 100% human copy

Our completely human rewrite passes the test: it uses combinations of words that aren’t likely to be predicted and reproduced by a computer.

This just goes to show that while outputs produced with NLP are indeed good at mimicking human speech patterns, it still all boils down to probability—not true human creativity.

Real-world AI examples and applications

You can see AI at work in many applications and industries—and you may even rely on AI in your day-to-day life more than you think! AI is an increasingly important technology for:

• Chatbots. ChatGPT is a well-known example of AI chat technology, but businesses can use AI to train custom chatbots on company data, too. These bots can then be deployed across the business apps and websites to act as a first layer of customer support. Here’s an example: If a financial services company creates a secure chatbot that can help its customers with problems and questions related to account access, they can then offer 24/7 customer support—even if some queries must be referred to a human agent at the start of the next business day.
• Virtual assistants. Tools like Siri, Google Assistant, and Amazon Alexa all use AI to deliver information to users in a conversational, natural-sounding way. Recent strides in generative AI technology mean that these tools will soon become even more natural and helpful. Microsoft Copilot and Google Gemini are now available natively on select PCs and Chromebooks, while Apple Intelligence brings text and image generation to macOS and iOS.
• Translation. Advancements in NLP and computing power mean that AI can now act as a real-time translator between two languages. This can include translation of text and audio thanks to speech recognition and computer vision technology, which helps with image recognition. These tools can make it easier for patients to communicate with doctors speaking another language, aid families in international travel, and open up more content to accurate translation and dubbing.
• Search engines. By integrating AI into search results, companies like Google and DuckDuckGo can offer their users faster and at times easier to understand results—especially on complex search queries, where someone might normally have to click through several results to get the full picture.
• Automation. The speed at which a deep neural network can process large volumes of data goes beyond human capability, making AI a valuable partner when automating repetitive actions or building repeatable workflows to automate complex tasks. For example, researchers can now use AI to process and analyze critical public health data faster, which allows them to move more quickly on treatments, therapies, and other beneficial medical developments.

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Upwork does not control, operate, or sponsor the tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.