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This content has been automatically translated from Ukrainian.
Artificial Intelligence (AI) is becoming an increasingly significant part of our everyday lives. From voice assistants to autonomous vehicles and complex algorithms for data analysis — all these technologies utilize powerful AI models. A few years ago, children copied from Google search results, and now it is quite common to hear about the use of new models like ChatGPT and other AI systems. But how do these models actually work? In this post, we will explore the fundamental principles (in simplified form) that underlie the operation of artificial intelligence.
What is an artificial intelligence model?
An AI model is a mathematical structure or algorithm capable of learning from data and making predictions or decisions without explicit programming for each specific task. Models can vary: from simple linear regressions to complex neural networks that mimic the workings of the human brain.
Main stages of AI model operation
The process of creating and using an artificial intelligence model typically consists of several main stages:
Data collection. AI models require large volumes of data for training. Data can be structured (e.g., tables with numerical values) or unstructured (texts, images, audio). The quality and quantity of data are key factors that affect the model's effectiveness. For example, millions of photographs are needed to train image processing models, while large text corpora are required for text models.
Data preparation. The data obtained for training the model is rarely perfect. It may contain gaps, errors, or irrelevant information. Therefore, at this stage, the data is cleaned, normalized, and transformed into a format suitable for further analysis. For instance, text data can be converted into numerical vectors, and images can be changed into pixel arrays.
Model selection. There are many types of AI models suitable for different tasks. For example, linear regression may be used for predicting numerical values, while convolutional neural networks (CNN, ConvNet) may be used for image recognition. The choice of model depends on the type of data and the specific task.
Model training. Training a model involves finding the optimal parameter values that allow it to make accurate predictions. This is done using learning algorithms, such as gradient descent, which minimizes the model's errors on the training data. During training, the model receives data samples and gradually "learns" to find patterns. The more data and the more complex the model, the longer the training process takes.
Model evaluation. After the model is trained, it needs to be evaluated. This is done using test data that was not used during the training process. This allows checking how well the model generalizes new information and how effectively it performs in real-world conditions. There are several metrics for assessing model quality, such as accuracy, precision, recall, and F1-score (a measure that combines two important characteristics of the model: precision and recall into one overall measure).
Model deployment. If the model shows good results, it can be deployed in real applications. For example, this could be a recommendation system that suggests new movies to users, or a speech recognition system that converts voice into text. For instance, on the platform tseivo.com, AI attempts to categorize user content to create the most interesting collections of posts (categories). At the end of this post, you will be able to see the categories that I added myself and the categories that AI will add (it adds them for new posts every night).
Types of artificial intelligence models
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Machine Learning (ML): Machine learning models use algorithms that allow computers to "learn" from data and make predictions or decisions. They are divided into several categories:
- Supervised learning: The model is trained on examples where the correct answers are known. For example, in a classification task, the model may learn from labels "cat" or "dog" for images of animals.
- Unsupervised learning: The model works with data without explicit labels. The task of such models is to discover hidden patterns or structures in the data, such as clusters of similar objects.
- Reinforcement learning: The model "learns" through trial and error, receiving rewards for correct actions. This approach is often used in games and robotics.
- Neural Networks: This is one of the most popular approaches to building AI models, which mimics the workings of the human brain. Neural networks consist of a large number of "neurons" that are grouped into layers. Neural networks can be simple (feedforward) or complex (e.g., convolutional networks for images or recurrent networks for working with sequences).
- Deep Learning: This is a subset of neural networks that uses a large number of layers to process complex data. Deep neural networks excel at tasks such as image, speech, and text recognition. They have become the foundation for many modern AI technologies, such as autopilots or translation systems.
- Natural Language Processing (NLP): NLP models work with textual data and language (human). They are used for speech recognition, translation, sentiment analysis of text, and other tasks related to textual data. GPT (Generative Pre-trained Transformer) models are one of the most well-known examples of natural language processing technologies.
How does AI make decisions?
Decisions in AI models are made based on probabilities. When the model receives a new data sample, it processes it through its layers (if it is a neural network), and at the output, it obtains a set of probabilities for each possible outcome. For example, in the case of image recognition, the model may assess the probability that the image contains a cat or a dog. The result is the category with the highest probability.
Problems and limitations of AI models
Despite all the achievements in the field of artificial intelligence, there are many problems and limitations:
The need for large amounts of data: Effective training of complex models requires vast amounts of data. This can be a problem, as collecting, processing, and storing data is an expensive and time-consuming process.
Black box: Complex AI models, especially deep neural networks, are difficult to interpret. It is often unclear how the model arrived at a particular decision, complicating their use in critical fields such as medicine or law.
Generalization: Models may be well-trained on a specific dataset but perform poorly in new or unexpected situations.
That is, AI must have access to a large amount of content in Ukrainian to improve language knowledge. Data arrays must be verified for AI to make correct conclusions. All of this is a complex process.
Artificial intelligence models have become a key technology of our time, constantly evolving and improving. They find applications in many fields, from business to science and medicine. Anyone can use a particular AI model to obtain some result in response to an prompt. This can be done either for free or for a small fee (depending on the volume of the task and the model).
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