At its core, AI uses data to make predictions. This ability can feed recommendations such as “you may also like” in streaming services, but it is also behind chatbots capable of understanding queries in natural language and predicting the correct response, as well as applications that use facial recognition to identify who appears in a photo.
Arriving at these predictions requires training these AI models, and newer applications that rely on AI may demand slightly different approaches to learning.
This article aims to explain, in a simple way, what AI training is, how it works and why it is essential for these tools to be effective and accurate.
What is AI training?
Training an AI is the process by which machines are taught to recognize patterns and make predictions or decisions based on large amounts of data.
To understand it better, let’s think about how human beings learn to walk. Toddlers don’t know how to walk, and will fall hundreds of times on their first attempts.
Over time they learn to associate sensations (input data) with certain muscular commands (outputs) to avoid falling. Because walking is that; move avoiding falling.
This process is similar to training an AI. In this case, the “subject” of the training is a machine that must learn from examples, from large volumes of data, to be able to perform specific tasks.
The data provided to the AI contains examples that the machine must process to understand how it should react in different situations. Throughout the process, the machine adjusts its algorithms to improve its responses, until it can accurately predict or make decisions.
Training vs. Inference
Although the terms “training” and “inference” are often used in the context of artificial intelligence, they represent two distinct but complementary phases of the model development process.
As we have said before, during training, the model receives a set of input data (for example, mathematical problems, images, texts, etc.) and is provided with the correct answers or labels associated with that data.
As you process more examples, the model adjusts its parameters and improves its ability to make accurate predictions. Training is typically carried out in a controlled environment and with significant computational resources, as it involves performing multiple calculations, tuning, and testing to optimize the model.
Inference is the phase in which a pre-trained model makes predictions or decisions based on new data that it has not seen before. In other words, inference is the practical use of the AI model to solve problems in real-world situations.
Once the model has been trained, it can be used in a production environment to make predictions with data arriving in real time.
Inference is typically much faster and requires fewer computational resources than training, since no complex calculations are being performed to modify internal parameters.
How is an AI trained?
Training an AI model is not a simple process and requires several stages. Below, we explain each of them to understand how to have effective AI:
Data preparation
For an AI model to be efficient, it needs high-quality data that represents real situations. This data can come from various sources: images, texts, sounds or numbers.
However, it is not enough to accumulate large amounts of data; These must be carefully selected and processed to ensure that they are accurate, relevant and representative of the scenarios in which the AI will be used.
Model selection
Once we have the data ready, the next step is to choose what type of AI model to use. There are different types of AI models, and the choice of one or another depends on the objectives of the project.
Some models are better suited for simple tasks, while others are better for more complex problems.
For example, if we need AI to identify patterns in very complex data, as in the case of speech recognition, we can opt for a deep neural network model.
If what we want is a simpler task, such as predicting a trend in the prices of a product, we could choose a more basic model, such as linear regression.
Initial training or pre-training
Initial training is the phase in which the model begins to process the data. At first, the model has no idea how to make accurate predictions, but it begins to learn as it is shown more data.
During this phase, algorithms within the model adjust their parameters to improve predictions.
It is important that the training is not too complex at the beginning. If given a very large data set or a very complicated model, the model could fail in its task.
During initial training, the goal is to get the model to start making predictions within an acceptable range of error.
Training validation
Once the model has gone through initial training, it is validated to check that it is working correctly.
In this phase, additional data sets that have not been used during training are used, with the goal of evaluating how well the model generalizes to new cases.
In other words, the model’s ability to apply what it has learned to data it has not seen before is tested.
If the model makes correct predictions with this new data, we can say that the training has been successful. If not, you need to adjust the model and repeat the process.
Real world testing
The last step before launching the AI model is to test it in a real environment. This is where the model begins to interact with data that comes from real situations.
If the AI shows good performance with this data, it means that it is ready for deployment. After security testing, which we will surely cover in another article, the model can be published.
However, even after this step, the training is not over. AI can continue to learn and improve over time as it interacts with more data and receives corrections.
Why is AI training so important?
AI training is crucial for several reasons, from the obvious (such as ensuring the reliability of inferences and predictions) to the more complex (such as reducing the use of computational resources).
Accuracy and reliability
If an AI is not properly trained, its predictions or decisions may be incorrect. Training the AI ensures that the model is accurate and reliable enough to make inferences that are correct.
Adaptability and continuous improvement
Situations in real life are very diverse and changing. Continuous training of the AI allows it to adapt to these changes, improving its ability to offer accurate predictions even in the face of new circumstances.
Furthermore, an AI is never “perfect” from the start. Just like a person, it needs practice to improve. With each set of data it processes, AI has the opportunity to adjust its parameters, make more accurate predictions, and avoid making the same mistakes.
Resource optimization
Proper training of an AI allows you to optimize resources and improve the efficiency of the model. Running some models to solve certain types of problems can be incredibly energy intensive.
Finding “shortcuts” to reduce inference time, or the amount of processing needed, may require additional training.
In short, training AI is vital not only to its performance, but also its ability to adapt and evolve over time. It is a fundamental part of how machines learn to “think”, and understanding this process helps us better understand the technologies that are changing the world.
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