Artificial intelligence (AI) has become one of the most discussed topics in the scientific community and among the general public. In Google Trends, there has been a sharp increase in the number of searches for “artificial intelligence” and related topics since the end of 2022. This coincides with the introduction of ChatGPT in November 2022. This large language model (LLM) offered the public a firsthand experience of the capabilities of modern conversational AI: a fully functional interactive dialogue-based system capable of natural linguistic interactions with users. Its introduction demonstrated not only the practical utility of AI but also its ability to enhance productivity, creativity, and knowledge sharing.

For AI experts, the unveiling of ChatGPT was not quite as surprising, but rather the culmination of decades of continuous development in the field. The roots of AI can be traced back to the 1980s when expert systems, built on predefined rule-based logic, were among the first practical implementations of artificial intelligence. These systems were capable of solving specific tasks within constrained domains but lacked adaptability and flexibility. Very broadly, the term "artificial intelligence" (AI) is used to describe computer systems that are capable of performing tasks that would normally require human intelligence. These tasks include reasoning, learning, and understanding language. Whereas traditional computational and statistical approaches concentrate on the application of fixed formulas, rules, and models for the analysis of data, AI is able to transcend these methods.

In the 1990s, the paradigm shifted with the advent of machine learning (ML), which introduced algorithms capable of learning from data. Unlike rule-based systems, these algorithms could adapt and improve their performance through experience, a significant leap forward. ML marked the beginning of a transition towards more autonomous and data-driven systems. At its core, an algorithm is a system designed to solve tasks efficiently, and ML enabled these systems to become more dynamic and responsive (Cormen et al. 2022).

The next transformative wave arrived in the 2010s with the rise of deep learning, an advanced subset of ML. Deep learning leverages artificial neural networks, which are computational structures inspired by the human brain's synaptic connections. These networks excel in processing and analyzing large, multidimensional datasets. The simultaneous growth of big data organized in structured datasets fueled the rapid development of deep learning methods. This synergy of deep learning and big data found critical applications in diverse fields, including medicine, where neural networks have demonstrated remarkable success in analyzing medical images, predicting disease outcomes, and tailoring personalized treatment strategies.

The evolution from expert systems to deep learning represents a journey of increasing complexity and capability in AI. What once relied on hardcoded rules has now transitioned into systems that not only learn from data but also demonstrate nuanced reasoning, problem-solving, and creativity. The introduction of ChatGPT epitomized this leap, showing how far AI has come in enabling meaningful human-like interactions. This journey also underscores the broader potential of AI across industries, from healthcare and education to entertainment and beyond.