Introduction to AI: The basics you need to know

Artificial Intelligence (AI) is one of those terms that everyone hears but few truly understand. We see it in headlines, in apps, and in new products, yet when you ask people what AI actually is, the answers are often vague.

Let’s change that.

This post is a simple, no-jargon introduction to AI, what it means, where it came from, and why data is the secret ingredient that makes everything work. It’s designed for anyone who wants to get started with AI, whether you’re completely new to the topic or already familiar with it and want to gain a clearer understanding of the fundamentals.

We’ll build a solid foundation first, the key concepts, a bit of history, and what makes AI possible. In future posts, we’ll go deeper into how AI actually learns, makes decisions, and can even be used in real applications.

What exactly is AI?

At its core, AI is about making machines think and learn like humans. It’s the ability for a computer to recognise patterns, make decisions, or understand language, tasks that normally require human intelligence.

If your phone recognises your face, if your email filters spam, or if Netflix knows what you want to watch next, that’s AI quietly working behind the scenes.

A quick trip through AI history

AI isn’t new at all. It’s been around for almost 70 years, evolving through different stages.

  • 1956: The term “Artificial Intelligence” was born at the Dartmouth Conference, the official start of the field.
  • 1970s–1980s: The era of expert systems, computers that followed human-written rules (“if this, then that”). They worked, but couldn’t learn on their own.
  • 2000s: The rise of the internet brought big data; suddenly, machines had tons of information to learn from.
  • 2012: The deep learning boom. Neural networks powered by GPUs began to outperform humans in image and speech recognition.
  • Today: The age of Generative AI, systems like ChatGPT, Gemini, and Stable Diffusion, can now create text, code, and images from scratch.

AI, Machine Learning, and Deep Learning

You’ve probably heard these terms: AI, ML, and DL used interchangeably. They’re closely related, but not the same. Here’s how to tell them apart:

  • AI is the big idea, making machines intelligent.
  • Machine Learning (ML) is a branch of AI that learns patterns from data.
  • Deep Learning (DL) is a more advanced type of ML that uses neural networks to process massive amounts of data (like images, audio, or text).

AI is the goal → ML is the method → DL is the breakthrough.

Let’s make it crystal clear and easy to explain!

Artificial Intelligence (AI)

AI is the broad concept; anything that allows a computer to think, reason, or make decisions in a way that seems intelligent falls under AI.

Examples
– A chess program that makes moves strategically.
– A spam filter that recognises unwanted emails.
– Siri or Alexa understands your voice and answers questions.

All of that is AI, even if it’s based on simple logic rules.

Machine Learning (ML)

Machine Learning is a subset of AI; a specific way to make machines intelligent. Instead of giving the machine every rule manually, we feed it data and let it find patterns by itself.

In other words, we teach the machine by example, not by instructions.

Example
If we want to detect spam, we give the system thousands of emails labelled “spam” or “not spam.” It learns patterns: words, phrases, or structures that are common in spam, and then uses those to predict new emails.

So, ML is how machines learn from data.

Deep Learning (DL)

Deep Learning is a special type of machine learning that uses a structure called a neural network, inspired by how the human brain works. It can handle huge amounts of complex data, like images, sound, or natural language.

Example
If you show a deep learning model millions of pictures of cats and dogs, it automatically learns which visual patterns define a cat vs a dog, without anyone telling it “look for whiskers” or “look for fur.”

That’s how technologies like facial recognition, voice assistants, and ChatGPT are possible, all thanks to deep learning.

So, DL is how machines learn complex things automatically.

Keep in mind the following:

  • With AI, we tell the computer what to do.
  • With ML, we show it examples so it can figure it out.
  • With DL, we give it raw data, and it discovers patterns by itself.

Where you already see AI

AI isn’t the future, it’s everywhere:

  • At home: Face unlock, Alexa, Siri.
  • At work: Email spam filters, document search, customer service bots.
  • Entertainment: Netflix or Spotify recommendations, TikTok’s algorithm.
  • Healthcare: Detecting diseases earlier with image analysis.
  • Transport: Smart GPS routes that adapt in real time.

We use AI dozens of times a day, often without realising it.

Data is the Fuel of AI

Now here’s the secret: AI doesn’t run on magic, it runs on data. Algorithms are the brains, but data is the fuel that keeps them alive.

There’s a saying in AI: “Garbage in, garbage out.” If you feed an AI bad or biased data, you’ll get bad or biased results.

For example, imagine training a facial recognition system, but your dataset only includes photos of adults. The system will likely fail when it sees a child’s face.

It’s like a car, you can have the best engine in the world, but if you fill it with bad fuel, it’s not going anywhere.

That’s why quality, quantity, and diversity of data matter so much. Good data makes AI fairer, smarter, and more accurate. In fact, many breakthroughs in AI didn’t come from smarter algorithms; they came from more and better data.

Types of Data

Data comes in many forms, and AI systems need to handle all of them.

  • Structured data is the easiest to work with. It’s clean, organised, and stored in tables, like databases or spreadsheets. For example: sales numbers, sensor readings, or bank transactions.
  • Unstructured data is everything else: text, images, videos, and sounds. This is what most modern AI systems use today. For example, ChatGPT is trained on text, and Stable Diffusion on images.
  • Then there’s semi-structured data: things like JSON or XML files, where data has some organisation but isn’t fully tabular. Log files or API responses are examples of this data.

Deep learning especially thrives on unstructured data, because it reflects how humans experience the world.

Data Quality Issues

Not all data is good data, and bad data can destroy your AI model’s performance.

Let’s look at a few common problems:

  • Missing data: Imagine a patient database where half the blood pressure values are empty. This way, your model can’t learn what it doesn’t see.
  • Noise: This means random errors or irrelevant information, like typos in text or background noise in audio.
  • Bias: When your dataset doesn’t represent reality. For instance, if you train only on data from one country or one demographic, the model will make unfair predictions for others.
  • Duplicates: Repeated records make some patterns look more important than they really are.
  • Imbalanced data: When one class dominates, for example, 95% of patients are healthy and 5% are sick, the model just learns to always predict “healthy.”

Many of these problems go unnoticed until you test the model, and by then, it’s too late.

Cleaning and preparing data are often 80% of the total AI work (it is the most time-consuming part)

The Data Pipeline

What is the Data pipeline?

It’s the process of how data moves from the real world into an AI system.

Let’s look at this process:

  • Collect: We gather data from different sources, sensors, websites, databases, user interactions, or log files.
  • Clean: We remove duplicates, fix missing values, and filter out bad data.
  • Transform: Here we format the data — normalise it, scale it, or convert it into a form the model can understand (like embeddings for text).
  • Use: Finally, the cleaned and transformed data is fed into a Machine Learning or Deep Learning model for training and prediction.

Once the model starts producing results, the cycle continues. We collect more data, clean it, retrain, and improve. The data pipeline never ends; it’s a continuous loop of collecting, cleaning, and improving.

Data Privacy and Ethics

This is a critical topic. AI systems are only as trustworthy as the data they’re trained on, and since most data today comes from real people, their messages, voices, faces, and behaviour, we must handle it carefully.

There are two aspects to consider:

  • Privacy means protecting personal information, making sure data isn’t shared, exposed, or used without consent.
  • Ethics goes a bit further; it’s about using AI fairly and transparently.

Many countries now have laws, such as the GDPR (Europe) and CCPA (California), that enforce these principles.

For example:

  • Avoid using private data for purposes for which users have not agreed.
  • Make sure models don’t discriminate against groups of people.
  • Be honest about when an AI is being used; people should know when they’re talking to a bot, not a person.

The Human Side of AI

Beyond algorithms and data, AI is about people, how we work, learn, and create. It’s changing how we approach problems, not by replacing humans, but by extending our abilities.

The most exciting part of AI isn’t machines being smarter, it’s humans becoming smarter with machines.

Final Thoughts

Artificial Intelligence isn’t about replacing humans; it’s about amplifying what we can do. But the success of AI doesn’t depend on having the smartest algorithm; it depends on having the right data.

AI’s future will be defined not just by technical progress, but by how responsibly we collect, use, and protect the data that powers it. As AI becomes more embedded in our lives and applications, understanding these foundations will help us build systems that are both smart and trustworthy.

If this topic sparks your curiosity or you’re interested in exploring how AI could be integrated into your APEX applications, we’d love to help.
Whether you’re starting from scratch or improving an existing project, feel free to reach out at Novoshore.com. Let’s turn your ideas into intelligent solutions.