What Is AI? How It Works and Where It’s Used

The buzz of AI is everywhere. And why shouldn’t it be? 

It is transforming, if it hasn’t already, everything from how we write emails to how we design self-driving cars. 

Yet somewhere between the hype and the headlines, many are still oblivious to what is AI in essence and how it works.

That’s exactly what this guide covers without drowning you in technical jargon. 

By the end, you’ll understand the concept as well as the basics of how AI powers the tools you use every day.

Let’s start.

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Key Takeaways

• AI is the science of creating machines that can be trained and perform tasks we usually associate with human intelligence.

• AI works by taking in data, processing it through algorithms, making predictions, learning from mistakes, and improving over time.

• Most AI today is narrow AI, built for specific tasks, while general AI, or AGI, which can match human capabilities across fields, is still only a concept.

• Generative AI uses deep learning to create new text, images, audio, video, and even code in response to prompts.

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What Does AI Stand For?

AI is short for artificial intelligence and refers to a machine’s ability to do things we normally think require a human brain, such as:

• Perceiving 
• Reasoning 
• Learning
• Interacting with the world 
• Solving problems 
• Showing a spark of creativity

You’ve probably run into AI with such capabilities without realizing it. 

For instance, when you’re asking Siri questions or simply communicating with it, you’re chatting with AI.

Or when you chat with a website’s chatbot that somehow knows exactly what page you’re stuck on? That’s also AI.

But what makes AI able to recognize speech, identify images, and understand and respond to human language? 

This happens through plenty of algorithms, models, and some very advanced math. 

To give you an idea, some of the building blocks of AI are as follows: 

• Machine learning
• Neural networks
• Deep learning
• Natural language processing

Human Intelligence vs Machine Intelligence

AI is getting better, but it’s still not on par with us. 

We know that because no AI tool so far has passed the Turing test, which is a way of seeing if a machine can convincingly act like a human in conversation. Or to identify whether machines can think.

We’re still a long way from AI that truly understands context, nuance, and meaning the way people do.

Most experts believe we’re decades away from building an AI like that. And some say it may never happen. 

Yes, generative AI tools like ChatGPT and DALL·E can do some remarkable things, but under the hood, they’re essentially prediction machines. 

In other words, these tools have been trained on massive datasets based on which they can guess the most likely response to your request with impressive accuracy.

It’s useful, sometimes even uncanny, but it’s not the same as human intelligence.

A Brief History of AI

While the concept of “machines that think” can be traced back to ancient philosophy, the modern history of AI begins in the mid-20th century.

The table below summarizes some of the most important milestones in AI’s development:

Year	Milestone
1950	Alan Turing publishes Computing Machinery and Intelligence and proposes the Turing Test.
1956	John McCarthy coins the term “artificial intelligence” at the Dartmouth Conference.  The first AI program, Logic Theorist, is created.
1967	Frank Rosenblatt develops the Mark 1 Perceptron, an early neural network that demonstrated machine learning through trial and error.
1980	Backpropagation becomes widely used for training neural networks.
1997	IBM’s Deep Blue defeats chess champion Garry Kasparov and demonstrates the possibility of AI surpassing human expertise in a complex task.
2004	John McCarthy publishes What Is Artificial Intelligence?  This offers a widely cited definition of AI during the rise of big data and cloud computing.
2011	IBM Watson wins Jeopardy! against top champions.
2015	Baidu’s Minwa supercomputer achieves human-surpassing image recognition.
2016	DeepMind’s AlphaGo defeats Go champion Lee Sedol.
2022	Emergence of large language models like ChatGPT, which revolutionizes AI performance and expands generative AI applications.
2024	Growth of multimodal and smaller efficient AI models.
2025	Launch of GPT-5 with rapid adoption of agentic AI systems. Tech billionaires and governments invest billions in AI chips, infrastructure, and development.

How AI Works

So now that we’ve sorted out what is AI and how it compares to human intelligence, let’s give you a bird’s-eye view of how AI works.

The following are the five key stages of AI’s working:

• Inputs: Every AI system needs data to exist. This data can come from just about anywhere, such as text, audio, video, IoT sensors, you name it. 

• Processing: Once the data is made available in a format suitable for AI, the AI uses its programmed algorithms to identify patterns and relationships in it. This is called AI training and this training makes AI able to recognize similar patterns in new data.

• Outcomes: After analyzing the data, the AI makes its predictions or classifications. For example, it might decide whether a piece of data matches previous patterns (pass) or doesn’t (fail). 

• Adjustments: When the AI gets something wrong, it uses that failure as a learning point. After learning from failure, the system might loop back to the outcomes stage to recheck its decisions under the updated rules. This learning can be in any of the following forms:

• Tweaking the algorithm’s rules
• Changing how data is interpreted
• Refining the conditions under which it processes inputs

• Assessments: In the final stage, the AI evaluates its performance as a whole. It takes into account the results of previous adjustments, synthesizes new insights, and uses them to improve predictions going forward. 

Types of AI

The following are the main types of AI you’ve either seen or have yet to see:

Narrow AI vs General AI

When people first ask what is an AI, they’re often thinking of the version they’ve already seen in action: narrow AI. 

Narrow AI is also called weak AI, and it’s built to perform a very specific task or a defined set of tasks. 

This could be anything from spotting fraudulent transactions on a credit card network to powering voice assistants like Siri and Alexa or even running the natural language processing that helps a chatbot respond to your questions.

Most AI you interact with today is narrow AI.

On the other hand, we have Artificial General Intelligence (AGI) which is called strong AI or simply general AI. 

This kind of AI learns and applies knowledge across a wide variety of tasks and matches or surpasses human capabilities in performing those tasks.

Right now, AGI is still theoretical, as no AI system has reached that level.

Agentic AI

If you’ve been hearing the term and wondering what is agentic AI, the simplest way to put it is that it’s a system built from AI agents.

AI agents are autonomous programs that perform tasks and make decisions while needing minimum to no human assistance. The “agent” in their name refers to the agency these tools can exercise. 

Agentic AI builds on this by coordinating multiple AI agents to work together toward a bigger goal that no single agent could pull off alone.

Reactive Machines, Limited Memory, Theory of Mind

The types of AI we’ve already covered differed in scope and autonomy.

There’s another way to classify AIs based on whether or not they can retain information and how they interpret their environment. 

This perspective divides AI into three main categories:

• Reactive machines: These are the most basic form of AI. They respond only to the current inputs they receive, without storing any memory of what happened before. Their main limitation is that they operate without an internal state or representation of the environment. After processing an input, they discard its memory and move on to the next input with a blank memory.

• Limited memory machines: Limited memory machines  store past data internally so as to be able to recognize patterns and correlations in their environment over time and use that understanding to improve their responses in dynamic conditions. 

• Theory of Mind: This is an AI that can understand the existence of other agents, be they humans or other machines, and infer their internal states. This form of AI isn’t possible currently because achieving Theory of Mind requires the ability to recognize that actions often have unseen causes, which may be influenced by intentions, beliefs, or emotions.

Examples of AI

We’ve covered the different types of AI and how they process information, so it’s worth looking at how all of this plays out in the real world. 

Autonomous vehicles

Self-driving cars rely heavily on machine learning trained on enormous datasets that include everything from traffic patterns to road sign recognition. 

Developers often use artificial simulations to evaluate performance before vehicles ever hit the road. 

Black-box testing is common here, which is a method where testers don’t have direct access to the system’s inner workings but instead probe its behavior to identify weaknesses. 

Text Editors or Autocorrect

If you’ve ever used Grammarly to check an essay or relied on autocorrect while texting, you’ve interacted with AI. 

Just as you learned grammar rules in school, AI algorithms are trained to recognize proper language use and spot deviations.

When you misuse a comma or choose the wrong word, the editor can flag it and suggest an appropriate correction.

Virtual assistants

Virtual assistants like Amazon Alexa, Google Assistant, and Apple’s Siri help you with everyday tasks. 

They learn from your specific usage patterns and adapt to your preferences, as well as become better at anticipating your needs over time.

Search and Recommendation Algorithms

When you browse a streaming service and find a row of movie suggestions that feel surprisingly on point, or when an online store shows products that match your recent searches, you’re seeing AI-driven recommendation systems at work. 

These systems track your interactions over time and analyze them using machine learning and deep learning models to predict what you’ll want next.

What Is Generative AI?

There’s a particular category of AI that has gained significant attention for its ability to create entirely new content on demand. These systems go by the term generative AI. 

Let’s take a closer look at what is generative AI.

Generative AI or gen AI refers to deep learning models that produce original outputs in response to a user’s prompt. 

These systems can create: 

• Long-form text
• High-quality images
• Realistic video
• Lifelike audio
• Functional code 

The latest generative AI models can even create interactive simulations of a range of applications right in the chat.

The quality of generative AI’s output depends on the sophistication of the model and how closely the prompt aligns with its training. 

For example, ChatGPT can produce a clear, well-structured essay on theories of nationalism in seconds, while image-based systems like DALL·E 2 can create unusual but visually striking compositions, such as a Renaissance-style painting of a Madonna and a child eating pizza. 

For users interested in applying generative AI to writing, tools such as AI Humanizer, AI Essay Writer, and AI SEO Writer by Undetectable AI offer practical ways to create high-quality written content. 

At the same time, verifying the origin of digital work is becoming equally important, which is why tools like AI Image Detector, AI Detector, and Humanizer, also from Undetectable AI, can help ensure that content is authentic and trustworthy.

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FAQs About AI

Final Thoughts

Now that you know what is AI and how it works, you can spot it everywhere.

The more you understand it, the better equipped you’ll be to reap its benefits and avoid its pitfalls.

And when you need AI tools that work for you, Undetectable AI’s writing and detection tools have you covered.

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