With all the buzz around generative AI, it’s easy to get swept up in the excitement without stopping to ask: what exactly is AI, and how does it work? 

If you’ve ever tried Googling this question, you probably came across answers that sound something like, “AI is a computer system designed to mimic human reasoning processes.” But what does that really mean? To understand AI, we first need to understand intelligence itself—starting with how humans reason and make sense of the world. 

What is intelligence? 

Intelligence isn’t one thing. You’ve likely heard of different types: logical intelligence, emotional intelligence, spatial intelligence, and so on. For this post, let’s zoom in on emotional intelligence—the kind that helps us navigate social situations. 

Imagine this scenario: your coworker asks for help completing a task. At that moment, you’re bombarded with a wealth of data: 

  • Their tone of voice: rushed, serious. 
  • Their facial expressions: a wrinkled forehead, no smile. 
  • Their body language: tense, hurried. 
  • Their words: “urgent,” “please,” “overwhelmed.” 

This is your data collection point—gathering millions of data points every second. Making Sense of the Data 

Our brains don’t just process raw data point by point. Instead, we group it into meaningful patterns or features. This process, called feature engineering, allows us to make sense of the world quickly and efficiently. 

In this example, you focus on relevant features like your coworker’s tone, words, and body language, while ignoring irrelevant ones: the color of their sweater, the pen in their hand, or the sound of coffee brewing in the kitchen. This ability to select features—focusing on what matters and filtering out the rest—is critical to intelligence. 

From data to decisions 

Once you’ve selected the right features, your brain connects them to draw a conclusion. This is reasoning. But how do we know which connections to make? 

Think back to childhood, when adults taught you to interpret emotions: 

  • “Look at Jim—he’s crying. Do you think he’s happy or sad?” 
  • “Kelly told Pam she doesn’t like her shirt, and now Pam is upset. Why do you think that is?”

Over time, we learned to associate certain signals (e.g., crying, frowns) with emotions and outcomes. We don’t just learn these connections once—we refine them constantly, through experience and feedback. This is our training process. The more times we’re exposed to 

certain situations and data, the more we learn. We can use new information to correct our previous assumptions. 

And here’s the clever part: once we’ve trained our brains, we don’t need to see every possible scenario to make sense of new ones. For instance, we can infer that criticising someone’s appearance is likely to upset them, even if we’ve never seen it happen with the exact same people or context before. Inference is the process of drawing a conclusion or making a decision based on available information. This ability to apply past knowledge to new situations is called generalisation. 

There’s a lot more to artificial intelligence than what we’ve covered so far, but understanding these core concepts can help us answer some important questions about AI. Here are a few: 

Why the hype now? 

The concept of artificial intelligence isn’t new at all. In fact, it’s been around for decades. We’ve not only theorised about AI but also built the mathematical foundations for it long ago. Edmund Callis Berkeley compared computers to human brains in his book “Giant Brains, or Machines That Think” back in 1949. Alan Turing published “Computing Machinery and Intelligence” in 1950, where he posed the famous question, “Can machines think?” 

Even before generative AI, we’ve been using basic AI systems in everyday tools. Spell check, for example, learns to recognise letters, form them into words, detect patterns, and make suggestions. Similarly, Google has been refining its algorithms for years, helping us find the information we need with just a simple search phrase. 

So why has AI become such a hot topic now? The main reason is the rise of generative AI and large language models like ChatGPT. These systems are fundamentally similar to earlier AI but are much, much bigger. They’re trained on vast amounts of data and powered by computing capabilities that simply didn’t exist before. At their core, these models collect data, transform it, select important features, learn patterns, and make predictions—but on an unprecedented scale. 

Should I trust AI? 

This is a tricky question.AI is a tool, just like any other appliance or software. It’s designed to perform a specific task: to take the data it has seen, recognise patterns, and predict the most probable solution to your question. 

In many ways, AI works like humans. When we make decisions, we rely on our knowledge and past experiences to predict what action will yield the best outcome. But just like humans, AI isn’t always right. And unlike humans, AI is limited by the data it was trained on and the task it was designed to perform.

For this reason, it’s essential to treat AI with caution—especially when you don’t know the specifics of its training or purpose. Think of AI as a powerful assistant to help you process information, not as a decision-maker. Use it as a tool to inform your choices, but don’t let it make them for you. 

What is the future of AI? 

I don’t know. But I’m excited for it. Yes, it’s a little scary—any tool, in the wrong hands, can be used for harm. But think about the possibilities. AI could support doctors in making faster, more accurate diagnoses, potentially saving lives. It could help children learn in ways tailored to their unique learning styles, even in places where access to teachers is limited. It could offer better protection against fraud, optimised waste management systems, accelerated drug development, faster natural disaster prediction and so much more. 

The future of AI is full of potential to solve problems that feel insurmountable today. From advancing scientific discovery to creating more inclusive and accessible technologies, we have the opportunity to use the power of AI to reshape industries and improve lives. The key, however, will be ensuring that this technology is developed and used responsibly, with ethical considerations and transparency at the forefront. AI’s future is ultimately what we make of it.