With the launch of ChatGPT at the end of 2022, the world will no longer be the same. It sparked the AI revolution, turning what once felt like science fiction into reality. If you’re still wondering what AI is and how it works, we have a good explanation for you. You can learn about artificial intelligence, its history, how it works under the hood and much more.
What is AI (artificial intelligence)?
Artificial intelligence or AI is a breakthrough technology that allows computer systems or machines to mimic human-like intelligence. It essentially allows machines to simulate many of the human qualities such as learning, understanding, decision making, problem solving, reasoning, etc. Like humans, AI can understand language, analyze data, generate content, sense the environment and even perform actions.
AI is a research area in computer science and compared to established sciences such as physics or mathematics, it is a relatively young discipline, having been formally established in 1956. However, AI has not only been shaped by computer science, but researchers from linguistics, neuroscience, psychology and even philosophy have contributed to the field.
All in all, AI is about developing a smart machine that is able to think, learn and perform actions like the human brain.
How does AI actually work?
First, AI doesn’t work like traditional software that is pre-programmed to follow rigid, rule-based instructions. For example, a calculator can only perform operations when given input in a certain syntax, and the output is completely deterministic.
However, AI systems can adapt to new inputs because they learn patterns from large data sets, allowing them to process new inputs for which they were never directly programmed. This makes AI systems probabilistic or non-deterministic.
How does modern AI actually work? Well, it’s a technique called machine learning, where systems learn patterns from data. AI systems are not explicitly programmed for every question or scenario, but are trained on massive amounts of data (text, images, videos, audio, code, etc.) allowing them to process new information and even generate meaningful responses even to new input.
To give you an example, suppose you want to teach a child to recognize dogs. Instead of describing every dog characteristic, now show them multiple pictures of dogs. Eventually they learn to identify dogs themselves, even dogs they have never seen before. And machine learning works exactly like this: it learns from examples (statistical patterns), and not from explicit rules.
Today, deep learning is the most powerful form of machine learning, using neural networks. Neural network is inspired by the human brain and consists of layers of interconnected nodes such as neurons to process information.
A brief history of AI development
It may come as a surprise, but the concept of AI appeared before computers existed. In 1950, British mathematician Alan Turing asked a question: can machines think? His famous Turing Test proposed that if a machine can carry on a conversation indistinguishable from a human, it can be considered intelligent. This idea laid the foundation for artificial intelligence (AI).
Now, the term “artificial intelligence” was officially coined in 1956 at a conference at Dartmouth College. AI researchers like John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon came together to explore the idea of AI and whether machines could think like humans. This conference established AI as a formal field of study.
Over the next few decades, there was intense optimism surrounding the development of AI, followed by disappointment. For years, progress in AI stagnated and funding dried up, leading to a period dubbed “AI winters.” Early AI systems couldn’t understand natural language or recognize objects in photos, but they were effective at solving well-defined mathematical problems.
In the 1980s, some expert AI systems were trained for specific tasks, but they could not adapt to new situations. Ultimately, three things happened in the 2010s. Massive amounts of data came from the Internet, powerful GPUs (originally designed for video games) became available, and a significantly better deep learning algorithm emerged.
In 2012, a neural network called AlexNet was released that outperformed all previous systems at recognizing objects in images. It started the AI Renaissance period, and in 2017, Google researchers published a seminal paper titled “Attention Is All You Need,” which introduced the Transformer architecture. Now the Transformer architecture supports almost all Large Language Models (LLMs), including GPT-5 and Gemini 3 Pro.
Types of AI
There are basically two types of AI: narrow and general. Narrow AI is designed for a specific task, such as a song recommendation engine on Spotify, an object detection AI for images, or AI chatbots like ChatGPT. It is excellent at what it was designed for, but cannot perform tasks outside its domain.
On the other hand, general AI or artificial general intelligence (AGI) refers to AI systems with a human-like ability to understand, learn and apply knowledge in any domain. Essentially, AGI systems can match or exceed human capabilities. Although AGI is still a fictional concept in the field of AI, many labs, including OpenAI, Google DeepMind, and Anthropic, are working to achieve AGI.
Limitations and challenges in AI
First, since AI systems are trained on massive data sets, if biases are present in the data, AI systems will also perpetuate those biases. For example, if a dataset contains historical hiring data with gender discrimination, the AI system also learns to discriminate. AI companies are working to reduce bias in LLMs by cleaning the data before training.
Beyond that, current AI systems don’t understand the world the way humans do. While an AI system can beat world champions at chess or Go, it can’t match a child’s ability to understand the real world. In short, AI models lack common sense and real understanding of the world.
Finally, the black box problem, another challenge in the field of AI. AI researchers say they don’t fully understand how AI thinks internally and reaches a particular conclusion. Understanding the internal behavior of AI is a key challenge in AI as we give AI systems more responsibility and decision-making power.
