- AI systems learn patterns from data rather than following explicit instructions. Neural networks process information through connected layers to detect complex patterns.
- Modern AI chatbots like ChatGPT predicts the next word based on statistical probability.
- Unlike humans, AI systems don't know what they are saying and lacks true understanding of the world.
Artificial Intelligence is no longer science fiction so understanding this technology is crucial if you want to understand the future. So, in this article we have explained how does AI work, what actually happens behind the scenes, and answer whether machines truly think like humans. On that note, let’s go ahead and learn how AI really works.
How Does AI Work?
Before we learn how AI works, we must understand that Artificial Intelligence is not just chatbots like ChatGPT and Gemini, it’s much more than that. AI is a broad term and it encapsulates different systems, but nearly all of them share a common quality: the ability to perform tasks that typically require human intelligence.
So from weather prediction systems to Netflix recommendations, recognizing speech, translating languages, autocorrect on your phone — all are powered by AI systems. But how exactly does AI work?
To give you a simple explanation, AI systems learn patterns from data and make predictions based on the data they have been trained on. It’s called Machine Learning. Unlike traditional software programs which are rule-based and defined for each scenario, AI relies on machine learning, a method where computers learn from experience. AI systems are not explicitly programmed for each scenario.
For example, instead of telling a computer that if you see four legs and fur, it’s probably a dog, we show thousands of dog images so that AI systems understand the pattern and figure out on its own. So AI systems improve through exposure to more data. The more examples they see, the better they become at various tasks. And that’s the reason AI companies want to collect so much data to train their AI systems.
Neural Networks: AI’s Brain
Modern AI systems use neural networks, which are basically modeled on the human brain. A neural network has layers of interconnected nodes (like neurons) and they process information. For example, to recognize an image of a cat, the first layer might detect basic details like colors and edges. The middle layers may gather these information and detect complex features like ears and eyes.
The final layers combine all these information together and calls it a cat. Each connection between neurons has something called a “weight” that decides how much influence it has. While training the AI system, these weights are adjusted several times so the neural network gets better at the task.
Now, Deep Learning uses hundreds of these neural networks to create a “deep” network. The aim is to create an incredibly abstract network where complex patterns emerge. These layers extract abstract features from the data, which help in accurate detection of the object.
How AI Systems are Trained?
To train an AI system, there are three key steps. First, you feed a massive amount of data. For example, if you are training an AI system to create a spam filter for emails, you feed millions of emails labeled as “spam” or “not spam”. Now, the AI attempts to learn the pattern like identifying which emails are spam and which ones are not.
Initially, the AI makes some mistakes and that is where learning from error comes in. The AI system compares its prediction with the correct answer to improve its accuracy. It adjusts the internal parameters to reduce errors and this process repeats millions of times until accuracy gets better.
For instance, when you teach a child to identify animals, you show them pictures and they make guesses. If they get it wrong, you correct them and they gradually improve. AI training works exactly like that.
Here, training on labeled data is called “Supervised Learning” and finding patterns on unlabeled data is called “Unsupervised Learning”. Currently, Reinforcement Learning (RL) is gaining ground which learns through trial and error with rewards. Basically, with RL, you teach the AI system to learn through experiences.
If it does something good, you give it a reward, and when it does something wrong, you withhold the reward. This way, the AI system learns which behaviors lead to rewards. As a result, you get an AI system that is optimized for the best possible outcome.
How AI Chatbots like ChatGPT Work?
If you are wondering how AI chatbots like ChatGPT and Gemini work, well, they are also sophisticated probability engines. AI chatbots basically calculate the probability of what word should come next based on all the words that appeared before. While generating text, it doesn’t know what it’s saying like humans, but simply generating statistically plausible text, based on the training data.
That’s the reason AI chatbots sometimes hallucinate and generate incorrect information. For training large language models (LLMs) that power ChatGPT and the likes, billions of web pages, books, and documents are fed into the system. It learns grammar, facts, reasoning patterns, and even some common sense.
Now, the AI models are fine-tuned using human feedback. Human trainers provide examples of helpful responses and tech the model to follow instructions carefully. This alignment process makes the AI chatbot more helpful to users.
So that was our explainer on how does AI work and its underlying mechanism. The key insight is that AI is an incredibly powerful pattern-matching system and it learns by examples and experiences.
