Having explored the technical foundations, architectures, and modalities of generative AI, a critical question remains regarding its real-world impact and long-term viability. The unprecedented technological progress has been met with equally unprecedented public and investor attention, leading to a debate about whether the current boom is a sustainable reality or
Beyond their underlying architectures, another crucial way to classify generative systems is by the type of data—or modality—they process and generate. Previously, such models were typically limited to a single modality (unimodal), meaning they were designed to process and generate only one type of content. A prominent example
Generative models represent a fundamental paradigm in machine learning, enabling computers to create new data samples that closely mirror real-world examples. These models have become indispensable tools across diverse fields including image creation, natural language processing, and scientific research. Three principal architectures have emerged as dominant approaches: Generative Adversarial Networks
While the concept of generative models has existed for decades, the field began to accelerate in the mid-to-late 2010s, building upon foundational advances in deep learning. According to Bill Gates, founder of Microsoft, these tools represent the most important technological advance since the advent of the graphical user interface in
Generative AI relies on a specialised branch of machine learning (ML), namely deep learning (DL) algorithms, which employ neural networks to detect and exploit patterns embedded within data. By processing vast volumes of information, these algorithms are capable of synthesising existing knowledge and applying it creatively. As a result, generative
