Prepare your organization forArtificial Intelligence

The Basics

AI, back to basics

Back to basics

You hear the term everywhere. In the news, at work, at the dinner table: "Artificial Intelligence". Often said as if it were something magical, or something that will take over your job tomorrow. Let's peel back the layers together!

In this chapter we go back to what AI really is. No science fiction, no marketing hype. Just: what does it actually mean?

Learning objectives

After this chapter you can explain in your own words what Artificial Intelligence is. And you understand the difference between a computer that calculates and a computer that learns. And that difference is bigger than you think!

Artificial Intelligence in brief

Artificial Intelligence is an umbrella term. It refers to systems or software that mimic human intelligence to perform tasks.

AI is not one device. It is not a robot or a computer; it is software that can do two things very well.

• First: recognise patterns. AI looks at enormous amounts of data and finds connections that we as humans often cannot see. A person may look at a thousand photos. An AI does ten million, and remembers every detail.

• Second: make predictions. Based on those patterns, the system makes a decision, or makes a guess about what comes next.

The difference from traditional software lies in the approach. Traditional software works like a cookbook. It follows fixed instructions step by step. If there's a mistake in the recipe, the dish fails every time. The computer doesn't notice.

AI works more like a trainee chef. Instead of just reading the recipe, the system tastes the result. Too salty? Less next time. Too bland? A bit more seasoning. It learns through training and feedback, not through pre-defined rules.

Narrow versus general AI

Time for an important distinction. Many people think of AI as the robots from films. Computers that are as smart as humans, with feelings and their own will. We're not there yet. Honestly, not even close.

In practice, we divide AI into two categories:

• Narrow AI is what we see everywhere around us today. These systems excel at one specific task. A chess computer beats the world champion, but can't fry an egg. A spam filter recognises unwanted mail, but can't hold a conversation about the weather. Narrow in expertise, strong in one thing.

• General AI is the theoretical form. An AI as versatile as a human. That can learn, reason, plan and understand in any situation. This doesn't exist yet.

Everything you use now, from ChatGPT to Google Translate, falls under narrow AI. Even if it sometimes feels enormously broad.

A real-world example

Think of something you probably use daily: Netflix or Spotify.

You open Netflix and immediately see a row of recommendations. "Because you watched The Crown." How does Netflix know you probably want to watch a historical series?

This is narrow AI in action, in three steps.

1. First the data. The system remembers everything. What you've watched, how long you watched, at what point you dropped off, where you gave a thumbs up. Together billions of data points.

2. Then the pattern. The system compares your behaviour with that of millions of other viewers. "People who liked The Crown often liked Downton Abbey too." No person draws that conclusion manually. An algorithm does, in milliseconds.

3. Finally the prediction. The AI pushes Downton Abbey to the front of your screen. And if you click on it, the system learns something new.

The system doesn't understand what love is, or drama, or history. It sees mathematical patterns in viewing behaviour, nothing more. Ask the same AI to do your tax return and you'll get nowhere.

In summary

• AI is software that mimics human intelligence by recognising patterns and making predictions.
• The fuel is algorithms and a lot of data.
• Today we use narrow AI: systems that excel at one task.
• General AI, with human versatility, doesn't exist yet and is still a thing of the future.