Before an AI system can answer a query, write a paragraph of text, recommend a movie, or drive a car, it must first learn - and that process is called AI training.
AI training involves teaching an artificial intelligence model to make accurate predictions or decisions. It learns by analysing large volumes of training data, identifying patterns, and adjusting its internal settings like numerical weights to improve its predictions.
Throughout training the AI compares its predictions to known correct answers, refining itself over millions of cycles to reduce errors. However it’s important to remember: AI doesn’t understand its tasks the way a human would. It is simply refining its pattern recognition.
Training AI models is a resource-intensive process, demanding significant computing power, energy and time. These demans introduce operational, environmental and strategic risks that organisations must now factor into their technology strategies. As adoption accelerates, researchers are racing to make AI training more efficient - but in the meantime, the risks associated with cost, scaleability and sustainability remain front of mind.
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