Artificial Intelligence (AI)

Artificial intelligence is software designed to perform tasks that would normally require a human brain. That includes understanding language, recognising images, making decisions, and learning from experience.

Despite the name, AI is not intelligent the way a person is. It does not have feelings, opinions, or awareness. What it does have is the ability to process enormous amounts of data and find patterns within it — patterns that would take a human team months or years to spot.

How AI actually works

At its core, most modern AI is built on statistics and probability. When you ask an AI assistant a question, it is not "thinking" about the answer. It is calculating the most statistically likely useful response based on the patterns it learned during training. Training is the process where the AI analysed billions of examples of text, code, images, or other data to build an internal model of how things relate to each other.

There are different types of AI, and understanding the distinctions matters:

• Narrow AI (also called weak AI) is designed to do one thing well. Your email spam filter is narrow AI. So is the recommendation engine on Netflix. Every AI product you use today is narrow AI.
• General AI (AGI) would match human-level reasoning across all domains — conversation, science, creativity, physical tasks. This does not exist yet. It is a research goal, not a product you can buy.
• Super AI would exceed human intelligence in every area. This is entirely theoretical and the subject of much debate about whether it is possible or desirable.

AI is not new — but the recent leap is

The concept of artificial intelligence dates back to the 1950s, when Alan Turing asked whether machines could think. For decades, progress was slow. AI went through multiple "winters" — periods where funding dried up because results did not match expectations.

What changed is the combination of three things arriving at once: vastly more computing power (especially GPUs), vastly more training data (the entire internet), and a breakthrough architecture called the transformer (introduced in 2017). This combination produced large language models like ChatGPT and Claude, which crossed a threshold of usefulness that made AI accessible to everyone, not just researchers.

Common misconceptions

AI does not understand things the way you do. When Claude writes a paragraph about climate change, it is not drawing on beliefs or experiences. It is generating text that statistically follows from the patterns in its training data. This distinction matters because it explains both AI's remarkable capabilities and its limitations — including hallucinations, where AI produces confident but incorrect information.

AI also does not replace entire jobs in most cases. It replaces specific tasks within jobs. A marketing manager who uses AI to draft first versions of email campaigns, analyse customer data, and summarise competitor research is not being replaced — they are being amplified.