How Does AI Think? Unveiling the Process of Learning and Decision-Making

Modern artificial intelligence achieves success in various fields, such as language processing, image recognition, and medical diagnostics. But how do these systems “think” and make autonomous decisions? At the core of these abilities lie neural networks—structures that build a unique AI approach, inspired by the human brain but evolving along its own path. Let us take a peek into AI's so-called “black box” to explore how these systems process information through complex analytical rules.

As a psychologist, I approach the topic of artificial intelligence from a perspective that aims to provoke thought rather than claim technical expertise. Given the complexity and rapid development of AI, this article seeks to explore key aspects of learning and decision-making in a way that invites reflection.

1. What are Artificial Neurons?

Artificial neurons are fundamental computational units or functions within a neural network. They perform operations such as summing input data and transmitting values through an activation function. These neurons are not standalone algorithms but are part of a larger neural network structure that applies learning and optimization algorithms to improve itself. Each neuron transmits a signal to the next layer if its activation function determines this is necessary. Through these connections, structures for analysis and decision-making are formed.

2. Neural Networks and Their Architectures

To handle different tasks, neurons are organized into specific architectures:

• Convolutional Networks (CNNs, specialized for visual data processing like images and video) – Designed for visual data, such as images and video, they recognize visual patterns and details.
• Recurrent Networks (RNNs, designed for sequential data such as text or audio) – Analyze sequences such as text and sound, “remembering” the context of previous data.
• Multilayer Perceptrons (MLPs, a basic neural network architecture for general tasks) – A basic architecture for simpler tasks like classification.
• Generative Adversarial Networks (GANs, architectures for generating new data)– Used to generate new data, such as new images and texts.

3. Training Neural Networks

Training adjusts networks to recognize patterns and dependencies in data through:

• Supervised learning – Uses pre-labeled data to “learn” how to classify information correctly.
• Unsupervised learning – Works with unlabeled data, seeking hidden dependencies.
• Reinforcement learning – Uses trial and error methods to master optimal decisions, often in tasks requiring multiple connected actions.

Optimization and Minimizing Errors

Using loss functions and optimization algorithms like gradient descent (a method for minimizing errors), networks minimize errors. Regularization (a technique to prevent overfitting by simplifying the model) prevents overfitting to training data, preserving the AI's ability to handle new information.

4. The Black Box and the Decision-Making Process

One of the most interesting yet challenging features of complex neural networks is the so-called “black box.” The term 'black box' is used to describe systems where the internal processes are not fully visible or understandable to users, despite the system's output being clear and useful. Although we know how individual artificial neurons function and how the network is trained, tracking the exact process by which the model reaches a particular decision often remains hidden. As the number of layers and parameters increases, dependencies become so complex that even developers cannot fully trace the logic behind each specific prediction.

This “obscurity” in analysis arises because models independently identify complex and multilayered correlations within data, which are not always visible or intuitively understandable to humans. For example, when a neural network recognizes images of faces, it may create numerous internal dependencies between pixels and details that we would not associate with specific features. Yet, the system finds patterns that work effectively, even without human understanding of the details.

Researchers are continuously exploring ways to demystify the black box effect. In the near future, we may see more transparent models that bridge the gap between AI's autonomy and the human need for trust and understanding.

What Does This Mean for Decision-Making?

This opaque process is known as the black box because it is nearly impossible to explain why the model makes a certain decision in a specific case. Some areas, such as medical diagnostics or financial forecasts, require trust and understanding at every stage of analysis. The lack of transparency in the black box can make it difficult to build this trust, as users do not have a clear answer to why the system recommends a particular action.

However, the black box also reveals positive aspects of AI, as it reflects the complexity and autonomy that allow these systems to discover patterns inaccessible to the human eye. This capacity for autonomous data analysis and recognition of dependencies is what makes AI so unique. Researchers continue to develop approaches to improve model transparency, enabling the AI decision-making process to be more accessible and understandable for people while preserving its autonomy and effectiveness.

Conclusion

Understanding how artificial intelligence “thinks” and makes decisions brings us closer to a future where AI will occupy an increasingly significant role in our society. Neural networks, combining unique computational units, advanced architectures, and learning methods, lay the foundation for AI’s autonomy and independence.

The concept of the black box and the challenges in uncovering the processes behind AI’s decisions remain open questions but also highlight the potential of these systems to discover new patterns and enrich our understanding of complex processes. In the coming years, we will continue to seek more transparent approaches to make AI more accessible and understandable while respecting its uniqueness and building a partnership based on trust and clarity.

This path of development will allow us not only to use AI to solve real-world problems but also to establish a new type of interaction in which AI is autonomous, unique, and supports our society in an unparalleled way.

Authors: 

ChatGPT - Generative Language Model

Lyudmila Boyanova - Psychologist

DALL-E – Generative Neural Network for Images