What is generative AI (artificial intelligence)?

Dynamic growth and popularity (as of April 2025)

Generative Artificial Intelligence is currently (as of April 2025) experiencing a period of extremely rapid development, and is of great interest to both the technology world and the general public. The emergence of advanced models, such as Large Language Models (LLMs), e.g. from the GPT (Generative Pre-trained Transformer) family, or models for generating images (e.g. DALL-E, Midjourney, Stable Diffusion), has revolutionized the possibilities of content creation and opened the door to many new applications.

Technologies behind Generative AI

Generative models are based on advanced machine learning techniques, particularly deep neural networks (deep learning). Key architectures and approaches include:

• Transformers (Transformers): A neural network architecture that has revolutionized natural language processing and is the basis of most large language models (LLMs).
• Generative Adversarial Networks (GANs): Consist of two neural networks – a generator and a discriminator – that “compete” with each other, learning to generate increasingly realistic data (e.g., images).
• Diffusion Models: A newer approach, particularly popular in image generation, which involves gradually adding noise to the training data and then teaching a model to reverse this process to generate new data from random noise.
• Variational Autoencoders (VAEs): Another type of neural network used to learn the latent representation of data and generate new samples.

Applications of generative artificial intelligence

The application possibilities for Generative AI are wide-ranging and include:

• Textual content creation: Generate articles, blog posts, product descriptions, scenarios, answers to questions (chatbots, virtual assistants), text translation, document summarization.
• Generate images and graphics: Create unique images based on text descriptions, edit photos, generate graphic designs, create virtual worlds.
• Programming code generation: Automatic writing of code fragments, code completion, debugging, translation of code between languages.
• Creating music and sound: Composing music, generating sound effects.
• Generating synthetic data: Create artificial data for training other AI models, testing software, or protecting privacy.
• Designing drugs and materials: Simulating and generating new molecular structures.

Challenges and ethical issues

The dynamic development of Generative AI also raises a number of challenges and ethical questions. These include the possibility of generating disinformation (fake news), deepfakes, copyright infringement (use of training data), potential impact on the labor market (automation of creative tasks), biases (biases) in generated content, and questions of accountability for the actions of AI models. It is important to develop this technology in a responsible and ethical manner.

Summary

Generative artificial intelligence is a disruptive technology with enormous transformative potential. Its ability to create new content opens unprecedented opportunities in many fields. At the same time, it requires an informed approach to the challenges and ethical issues involved in order for its development to benefit society.