Generative Artificial intelligence (generative AI) is a type of AI systems that create new data or content based on patterns from existing data. The algorithms can generate images, text, sounds, and even programming code. They use various techniques, such as language models, GANs (generative adversarial networks) neural networks, and VAEs (variational autoencoders), to produce realistic and creative results. Generative AI is widely used in creative industries, such as design, film, and music, as well as in business process optimisation.
Generative Artificial Intelligence
Type of technology
Description of the technology
Basic elements
- Generative algorithms, such as GAN, VAE, and GPT (generative pre-trained transformer).
- Training data sets: Data from which models learn patterns.
- Neural networks: A basic mechanism for processing and generating content.
- Optimisation algorithms: Techniques for adjusting the model to generate more realistic results.
- Validation process: Mechanisms for evaluating the quality of generated content, such as comparing it with actual data.
Industry usage
- Marketing: Creating personalised ads, graphics, and texts.
- Art and entertainment: Generating music, images, film scripts, and graphics in video games.
- Medicine: Simulation of molecules for new drug development.
- E-commerce: Creating product descriptions automatically and personalising offers for customers.
- Research and development: Generating simulations and prototypes based on collected data.
Importance for the economy
Generative Artificial intelligence is revolutionising many sectors of the economy, enabling the automatic generation of content, personalisation of products, and simulation of business scenarios. It is widely used in marketing, design, and video games as well as in film and music production. Generative AI models enable companies to create new and unique content with less effort, speeding up product and service development while lowering costs.
Related technologies
Cloud Computing
It is a key component of AI in its broader sense, supporting the automation of creative processes.
Big Data
It can be used to track the authenticity of generated content and its intellectual property.
Mechanism of action
- Generative Artificial intelligence is based on training models on data sets that enable models to understand data patterns. In models such as GAN, there are two neural networks: a generator, which creates new data, and a discriminator, which evaluates its authenticity. The process is iterative – the generator tries to fool the discriminator, and the discriminator learns to distinguish real data from generated data. Other models, such as GPT, generate content from strings of input data, using previously understood language patterns. Through this interaction, AI systems can create increasingly realistic and complex content.
Advantages
- Creating unique content: AI generates images, texts, and sounds that are unique and customised.
- Automation of creative processes: Reducing the time needed to design and create new products.
- Personalisation: It enables customisation of content according to individual user preferences.
- Cost optimisation: Reducing the production costs associated with manual content creation.
- Simulations and testing: Facilitating simulations of different scenarios, which is useful for research and product development.
Disadvantages
- Distortion of reality: Generative systems can be used to create fake content, such as deepfakes.
- Copyright: Problems regarding intellectual property of generated content.
- Ethics: Misuse of generative AI to create disinformation content or invade privacy.
- Dependence on data: The quality of the content generated depends on the quality of the training data.
- Training costs: Generative models require considerable computing and energy resources.
Implementation of the technology
Required resources
- Large data sets: Training data that reflects the actual patterns from which new content is generated.
- Computing power: High computing power, especially in GPU applications.
- Software: Tools for training generative models, such as GAN, GPT, and VAE.
- Team of specialists: AI engineers, data analytics specialists, and creative developers.
- IT infrastructure: Extensive server resources, either local or in the cloud, to support intensive computing operations.
Required competences
- Knowledge of AI algorithms: Ability to create and train generative models.
- Data analysis: Ability to work with large training data sets.
- Creativity: Ability to turn AI results into valuable content.
- Programming: Knowledge of AI frameworks, such as TensorFlow and PyTorch.
- Model optimisation: Ability to customise and optimise models for specific applications.
Environmental aspects
- Energy consumption: Training large generative models requires considerable energy resources.
- Raw material consumption: Maintaining computing infrastructure requires raw materials, such as rare earth metals.
- Recycling: The electronic waste generated by AI development must be recycled.
- Emissions of pollutants: Data centres generate emissions from intensive data processing.
- Waste generated: Upgrading computer equipment generates electronic waste.
Legal conditions
- Legislation governing the implementation of solutions, such as AI Act (example: regulations for transparency of algorithms).
- Environmental standards: Regulations for data centre sustainability (example: energy efficiency regulations).
- Intellectual property: Rules for the protection of AI-generated content (example: copyright on generated images or music).
- Data security: Protection of data used to train generative models (example: GDPR regulations on data privacy).
- Export regulations: Restrictions on the export of advanced AI algorithms (example: AI technology export regulations).