How Does AI Actually Work?

AI entered the mainstream in a big way when tools like ChatGPT by OpenAI and Google’s Gemini captured the world’s attention. Since then, it has reshaped how we think about work, decision-making, and what’s possible across industries.

And while the technology has advanced rapidly in just a few years, we’re still in the early stages of what AI can do and who’s equipped to use it. Understanding how AI works can help you make better decisions about how to use it, where to trust it, and how it may shape your work going forward.

That’s where this overview comes in.

In the sections that follow, we’ll break down what artificial intelligence is, how it learns, the technologies that power it, and how it’s applied in real-world settings.

For those looking to build practical skills in this space, CSU Global offers two flexible pathways designed for working adults at different stages in their tech journey:

• A master’s degree in artificial intelligence and machine learning
• An undergraduate certificate in Azure AI automation

“At CSU Global, we understand that students enter our AI program with a wide range of backgrounds, some come from non-technical fields, while others have advanced programming and data experience. It was important for us to design a single, unified program that is accessible to all learners but still rigorous enough to challenge those with more advanced technical skills,” said Matthew Brown, PhD., Lead Program Director. “The program scaffolds learning in a way that builds confidence and competence, ensuring students are progressively exposed to both foundational concepts and more complex, hands-on applications. Our goal is to prepare every student, regardless of starting point, to be job-ready in the evolving AI workforce.”

To understand where AI is headed, it helps to start with how it works and what’s behind it.

What Is Artificial Intelligence?

Artificial intelligence refers to a set of technologies that enable machines and software systems to perform tasks that typically require human intelligence. These systems learn from data, identify patterns, and make decisions at a speed and scale that would be difficult to achieve manually.

In practice, AI is used to automate tasks, surface insights, and support decision-making across a wide range of industries.

What Fields Make Up Artificial Intelligence?

Artificial intelligence draws on several related disciplines that work together to process information, identify patterns, and produce useful outputs.

Many of these show up in ways you’ve likely already experienced:

Machine Learning: When Amazon or Netflix suggests what you might want next, it’s using machine learning. These systems learn from past behavior, recognizing patterns in what you’ve clicked, watched, or purchased, and improving their recommendations over time without being explicitly programmed.

Neural Networks: When a credit card company like Visa flags a potentially fraudulent transaction, it’s often using neural networks. These systems analyze large volumes of data to detect patterns and identify activity that doesn’t fit, even when the signals are subtle.

Deep Learning: Tools like ChatGPT or advanced image recognition systems rely on deep learning. This approach builds on neural networks, using multiple layers to process complex data, making it possible to generate responses, interpret language, or recognize images with a high degree of accuracy.

Natural Language Processing (NLP): Voice assistants like Siri or tools that summarize emails and generate content rely on natural language processing. NLP allows computers to understand and respond to human language, whether written or spoken, making interactions feel more conversational and intuitive.

Computer Vision: Features like photo tagging on Meta platforms or autonomous driving systems developed by Tesla are powered by computer vision. This field enables AI to interpret visual information, identifying objects, patterns, and features within images or video.

What Technology Powers AI Today?

Although the ubiquity of the term makes it feel newly invented, AI isn’t actually new. (Think back to spam filters from the early days of email or GPS devices like Garmin units that could guide you from point A to point B but didn’t adjust for traffic in real time.) At the time, they were just built-in features.

Older systems relied on smaller data sets, simpler models, and far less computing power. They could perform specific tasks well, but only within narrow boundaries.

What’s changed is the technology behind them. AI systems now operate with far more data, significantly greater processing power, and more advanced models, all of which allow them to analyze information more accurately and at scale. At the same time, AI has moved into the tools people use every day, rather than existing as standalone systems.

What Powers AI Behind the Scenes?

As AI adoption grows, so does the need for computing power and the infrastructure to support it. The Google searches of yesterday retrieved information. The AI tools of today generate it, interpret it, and apply it, from drafting content to writing and debugging code. That requires significantly more processing behind the scenes.

Data centers are large-scale facilities filled with servers that store data and run the systems behind AI. When you ask a question, generate content, or receive a recommendation, that request is processed in a data center in a matter of seconds.

The scale is substantial. Data centers can span dozens of acres, often the size of multiple football fields, and are typically built in areas with access to reliable power and connectivity. Once built, they limit what else can be developed nearby.

They also place heavy demands on local infrastructure. Data centers require large amounts of electricity, expanded transmission capacity, and, in many cases, significant water use for cooling, resources that are already in demand from households, transportation, and other industries. Data centers already account for an estimated 1.5% of global electricity use, and demand is expected to rise as AI adoption grows, according to the International Energy Agency.

The equipment inside these facilities generates heat as it runs. To prevent overheating, data centers use cooling systems that circulate air or water to remove that heat and keep servers operating. In larger facilities, this often involves industrial-scale systems that rely on water to absorb and dissipate heat. At the same time, data centers typically do not add significant long-term employment relative to their size.

The benefits of AI are widely distributed. The costs are often local.

This is the physical infrastructure behind AI and one of the reasons its growth is tied as much to resources as it is to technology.

How Does AI Learn?

At the core of most AI systems is machine learning, which allows programs to improve over time without being explicitly programmed.

These systems are trained on large data sets, which are essentially collections of examples the system learns from. Algorithms process that data repeatedly, identifying patterns, testing different outcomes, and refining their outputs based on feedback.

For example, in a medical setting, an AI system might be trained on thousands of X-rays to identify early signs of disease. Over time, it learns to recognize subtle patterns that may not be immediately obvious, helping clinicians flag potential issues more quickly and consistently.

Training a model requires significant computing power and time, depending on the complexity of the task and the size of the data. Once trained, the system can apply what it has learned in real time, generating outputs, making predictions, or assisting with decisions.

Because these systems rely on patterns in data, they can still produce incorrect or biased results. If the data is incomplete, outdated, or not representative, the model may misinterpret what it sees. Errors can also occur when a situation falls outside what the model was trained on, or when it identifies patterns that are statistically likely but not accurate in a specific case.

AI can recognize patterns, but it does not understand context or meaning in the way a person does. It identifies patterns and generates outputs based on those patterns.

Even when a question has a clear, factual answer, AI systems can still get it wrong. These models generate responses based on patterns rather than verified sources, do not fact-check in real time, and can misstate precise details like numbers or dates, especially when training data is inconsistent or incomplete.

What is Generative AI?

Generative AI refers to a category of artificial intelligence that creates new content—text, images, code, audio, and more—based on patterns learned from existing data. Unlike traditional machine learning models, which primarily identify patterns and make predictions, generative AI systems use those patterns to produce new outputs.

Tools like ChatGPT and Gemini are well-known examples. These platforms use large language models (LLMs) and deep learning to respond to prompts, summarize documents, generate creative writing, or even debug code. Generative AI tools are becoming essential for professionals across a range of fields.

Professionals looking to build these skills can explore CSU Global’s AI programs.

How CSU Global Can Help You Build Real-World AI Skills

CSU Global offers two AI programs designed to help students build practical, real-world skills. Whichever path students choose, the focus is on graduating ready to apply AI in meaningful ways.

“We want every CSU Global student to graduate from the program feeling confident in their ability to apply AI in real-world settings,” said Matthew Brown, PhD., Lead Program Director. “This means being able to analyze data, work with AI tools, understand ethical implications, and contribute to innovation within their organizations.”

The programs go beyond technical training, emphasizing applied knowledge, adaptability, and the critical thinking needed to succeed in a workforce that increasingly relies on AI-literate professionals.

Master’s Degree in Artificial Intelligence and Machine Learning

CSU Global’s online master’s degree in artificial intelligence and machine learning is designed for professionals interested in advanced concepts in programming, data science, and automation. The program emphasizes applied skills in software development, AI modeling, and machine learning implementation.

Students in this program:

• Apply software development techniques to AI and machine learning projects.
• Use AI and machine learning to address real-world problems.
• Build systems that simulate human behavior.
• Evaluate the performance and effectiveness of AI-driven solutions.

The program is intended for students with a strong foundation in math or programming who want to deepen their expertise.

Undergraduate Certificate in Azure AI Automation

CSU Global’s undergraduate certificate in Azure AI automation is an 18-credit program that introduces students to core concepts in artificial intelligence, machine learning, and cloud-based automation. The certificate is aligned with Microsoft’s Azure Data Engineering certifications and can be applied toward CSU Global’s Bachelor of Science in computer science.

Students in this program:

• Learn the fundamentals of machine learning.
• Work with core Azure services to build AI solutions.
• Use Azure Cognitive Services to automate tasks.
• Develop bots using Azure Bot Services.
• Explore advanced features in cloud-based AI platforms.

This certificate is designed for students looking to build job-ready skills in cloud and AI technologies. It is fully online and accessible to learners with a general familiarity with technology.

To learn more about CSU Global’s online AI programs, contact an enrollment counselor at enroll@csuglobal.edu or call 800-462-7845. You can apply here.