AI works by analysing large amounts of data, recognising patterns in it and generating predictions or output based on them. At its core, it is a mathematical optimisation process: the system constantly adjusts its internal settings until the output matches the desired result as closely as possible.
How does AI based on data and algorithms work?
An AI system needs three building blocks: data, algorithms and an optimisation goal. The data is the training material from which the system learns. The algorithms are the mathematical methods the system uses to recognise patterns in that data. The optimisation goal determines when the system performs “well enough”.
Unlike traditional software, which follows fixed rules built in by a programmer, an AI system learns its own rules from data. A spam filter is not programmed with a list of spam characteristics. It has learned what patterns in e-mails are associated with spam by analysing thousands of examples.
This distinction is important for professionals using AI tools. The output of an AI system is always a statistical prediction based on training data, not logical reasoning. When you understand how AI works, you also understand why the quality of your input has so much impact on the quality of the output. A broader explanation of what AI is and what forms it takes can be found in our article on what artificial intelligence is.
How does machine learning work to train AI?
Machine learning is the most common method used to train AI systems. The system is presented with large amounts of sample data and learns by repeatedly analysing that data. At each iteration, the system compares its output with the desired result and adjusts its internal settings to reduce the error.
This process is called training. After training, the model is applied to new, unseen data. This is called inference: the system applies what it has learned to new input. A language model like ChatGPT is trained on huge amounts of text and applies those learned patterns to generate answers to your questions.
Machine learning works best when sufficient training data is available and when the new situations for which the model is deployed resemble the situations in the training data. Outside that range, the reliability of the output decreases.
How do neural networks make AI possible?
Neural networks are the technology that powers modern AI systems. They are loosely based on the structure of the human brain: systems of interconnected nodes, organised in layers. Each layer further processes the data and learns to recognise increasingly abstract patterns.
A simple example: a neural network trained to recognise images learns basic patterns such as edges and colours in the first layers. In deeper layers, it combines those basic patterns into more complex shapes, until it makes a complete recognition in the last layer.
The more layers a neural network has, the more complex patterns it can learn. This is the essence of deep learning: working with networks of great depth to learn complex tasks, such as generating coherent text or analysing medical images.
How does deep learning work and what does it have to do with AI?
Deep learning is a subcategory of machine learning that uses deep neural networks. Most AI applications that professionals use every day, from ChatGPT to image generators, are based on deep learning.
Deep learning models learn hierarchically: simple patterns are combined into increasingly complex representations. In text generation, the model first learns which letters go together, then which words go together, then which sentences make sense in a given context, and finally what a coherent answer to a question looks like.
This layered learning process makes deep learning powerful, but also difficult to interpret. It is difficult to identify why a deep learning model produces a specific output, which limits transparency and accountability. If you want to know what types of AI exist and how they differ from each other, our article on the different types of AI a good overview.
How do AI systems learn and improve?
AI systems learn in two phases. During the training phase, the model is exposed to large amounts of data and adjusts its internal settings based on feedback. That feedback can come from humans indicating whether an output is correct, or from the system itself through mathematical evaluation methods.
The training phase is followed by the inference phase: the trained model is deployed on new data. The model does not learn in this phase unless it is trained again. This explains why AI tools have a knowledge date: ChatGPT knows nothing about events after its training cut-off date unless you provide that information yourself in your prompt.
Improving an AI system requires a new training process with additional or corrected data. That is why AI models are updated periodically and why newer versions tend to outperform older ones.
How does AI work in practice?
If you use a generative AI tool such as ChatGPT a question, the system processes your text through a mechanism called self-attention. Each word in your prompt is weighted against all other words, so that the system understands which words are relevant to each other in which context.
Based on that analysis, the model predicts token by token which word follows most logically. A token is roughly a word or part of a word. The model does not choose randomly, but calculates the probability of a logical continuation for each token based on everything it learned during training.
Important to understand: the system does not think. It has no concept of meaning, no opinion of its own and no consciousness. It recognises patterns and generates output that is statistically logical based on its training data. This explains why AI systems can sometimes produce convincing-sounding but factually incorrect information. You can read more about the risks of AI and how to weigh them up responsibly in our article on the risks of AI.
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