What is generative AI?

A computer generates a unique image or an entire text at the push of a button – almost entirely without human assistance. This is not a vision of the future but already a reality, thanks to generative artificial intelligence (AI). But what exactly is generative AI, and how does the technology work? In this guide, you'll learn everything you need to know about generative AI – from its definition and early beginnings to how it works and the different models. We’ll also provide examples of how generative AI is used in practice and take a look at the challenges this technology presents.

• Generative AI independently creates new content such as text, images or music based on large datasets and complex machine learning models

• The technology is used in areas such as art, medicine, investment, the automotive industry and marketing, where it accelerates and optimises creative processes

• Challenges include ethical concerns, high resource requirements and potential data protection violations, particularly with large models such as ChatGPT

• Despite the risks, generative AI offers huge opportunities to automate workflows and create innovative solutions across various industries

Definition: what is generative AI?

Generative AI is a form of artificial intelligence that generates new content, such as text, images or even music, rather than simply analysing existing data. The term "generative" comes from the Latin word "generare," which means "to create" or "to produce." When asking, "What is generative AI?" the concept refers to a machine's ability to learn patterns from large datasets and independently generate new, creative outputs.

To better understand generative AI, it's helpful to distinguish it from other forms of artificial intelligence. Here are the key differences:

Analytical AI

This form of AI analyses existing data to identify patterns, make predictions or support decision-making. However, it does not generate new content but focuses on evaluating existing information.

Reactive AI

Reactive systems operate based on rules and predefined scenarios without learning from past experiences. They cannot create new information or content but simply respond to specific inputs.

Machine learning

This system forms the basis of many AI types, including generative AI. It encompasses various learning methods, such as supervised learning, where models are trained on labelled data to make predictions or classifications. In generative AI, unsupervised learning and deep learning are also commonly used.

Unlike these forms, generative AI creates new content by recognising patterns in data and independently generating creative outputs based on these insights. It is often used in conversational AI to generate dynamic, context-based responses, employing advanced language models (LLMs – Large Language Models) such as ChatGPT.

How long has generative AI existed?

Developed as an early model of neural networks, generative artificial intelligence has existed since the 1980s. However, the breakthrough came in 2014 with the introduction of Generative Adversarial Networks (GANs), which enabled the creation of realistic content. By the late 2010s, transformer models like ChatGPT had driven the further development of generative AI.

Historical development of generative AI

• 1980s/1990s – Early neural networks: The foundations of today's generative AI were laid with the development of neural networks and early machine learning methods

• 2006 – Deep learning revolution: Computer scientist Geoffrey Hinton and his colleagues developed the concept of deep learning, improving the performance of neural networks and the recognition of patterns in large datasets

• 2014 – Introduction of Generative Adversarial Networks (GANs): During his time at the University of Montreal, Ian Goodfellow introduced GANs, which pit two neural networks against each other to generate realistic content

• Late 2010s – Transformer models and GPT: The introduction of transformer-based models such as GPT-2 and GPT-3 took generative AI to a new level, particularly in language generation and complex applications

Why is generative AI important?

Generative AI is highly significant because it fundamentally changes the way content is created. It allows machines to generate creative content such as text, images and even music independently, without being given exact instructions beforehand. This capability is particularly valuable in fields like art, design, medicine and research, where new solutions and innovations are in demand.

The importance of generative AI is especially evident in its diverse applications and innovative possibilities across various industries

• New content: Generative AI opens up entirely new creative possibilities by generating content such as images, music or text from existing data but in a completely new way

• Wide range of applications: It is also used in highly specialised fields such as architecture and fashion to develop customised designs or prototypes faster and more efficiently

• Business significance: Generative AI helps process large amounts of data and develop new products or ideas from them

• Efficiency and automation: It automates creative tasks and accelerates workflows, for example, in game development or film production

• Medical innovations: In medicine, it contributes to the development of new drugs by generating previously unknown chemical compounds

How does generative AI work?

Generative AI is based on machine learning models trained on massive datasets to independently create new content. These models, often referred to as foundation models (FMs), identify patterns and relationships in training data to generate new data instances that resemble the input data. Common applications include language models (LLMs) and specialised neural networks capable of generating text, images and other media.

Step-by-step process of generative AI

1. Data collection

A large dataset is first gathered, containing examples of the content to be generated. This could be a collection of texts for text generation or a set of images for image generation.

2. Training foundation models (FMs)

The models are trained on unlabelled, generalised data. They recognise patterns and relationships in the data to predict and generate content. A classic example is image generation, where the model analyses an image and produces an improved, clearer version based on patterns.

3. Large language models (LLMs)

For language-based tasks, LLMs such as GPT models are often used. These models learn from vast amounts of text data sourced from the internet and can perform complex language tasks such as text generation, summarisation or information extraction. They use billions of parameters to create contextually relevant content with minimal input.

4. Content generation

Once trained, the model can independently generate new content. This is done through predictions in latent space (an abstract mathematical space) or networks such as generator networks. As a result, models can generate text sentence by sentence or create images pixel by pixel.

5. Refinement and optimisation

The generated content is refined or adjusted as needed to improve quality. In many cases, this is done through additional training steps or manual post-processing.

This structured process enables generative AI to create high-quality, realistic content across various domains, transforming industries and creative workflows alike.

Types of generative AI

There are different types of generative AI, each using specific technologies and models to create content. The three most significant model types are transformer-based models, Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Each of these technologies has its own strengths and is used for different content generation applications.

Transformer-based models

Transformer-based models are neural networks specifically designed to process sequential data, such as text, efficiently. These models use the self-attention mechanism to analyse the context of words within a sentence and understand their relationships to other words. This allows them to generate meaningful and coherent content, such as entire texts from just a few inputs.

A major advantage of transformer models is their ability to process vast amounts of data and capture context more effectively than traditional neural networks. This makes them particularly well suited for applications such as large language models (LLMs), including GPT-3, which is based on this technology. Transformer-based models are widely used in text generation, translation services and virtual assistants and are also applied to process complex image data.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) consist of two competing neural networks – the generator and the discriminator. The generator creates new content, while the discriminator attempts to determine whether the content is real or artificially generated. This ongoing competition drives the generator to produce increasingly realistic content that is difficult to distinguish from genuine data.

GANs are particularly effective in image generation and the creation of realistic visual content, such as faces that do not belong to real people. This technology makes it possible to generate highly detailed and lifelike images. Additionally, GANs are used in video generation, music production and synthetic dataset creation, for example, to enhance low-resolution images into high-resolution versions.

Variational Autoencoders (VAEs)

Variational Autoencoders (VAEs) are generative models that operate with an encoding-decoding structure. They compress input data into a latent representation, which serves as a compact version of the data. From this latent space, new, similar content is generated.

The strength of VAEs lies in their ability to create continuous variations of data, making them particularly well suited for generating multiple, slightly different images. VAEs are frequently used in image generation and 3D modelling, as they can create different variations of a subject. They also play a key role in medical research, where they help generate new image data that resembles existing datasets. Additionally, VAEs are valuable for data compression, as they allow large datasets to be processed and reconstructed efficiently.

Diffusion models

Diffusion models are a newer type of generative AI designed to gradually transform noisy data into high-quality content. The process begins by adding noise to the input data, and the model then learns to reverse this noise process to restore the original data. This method enables the generation of realistic images and other content by optimising the transformation process.

Diffusion models are particularly well suited for image generation and other visual applications, as they provide precise control over the generation process and produce high-resolution, realistic results. They are increasingly being used in art, medicine and film production, where high-quality visual content is essential.

Examples of generative AI applications

Here are some examples of how generative AI is being used:

• Creative applications: Generative AI creates original artworks, composes music and writes screenplays based on minimal input

• Natural language processing: Tools like ChatGPT generate human-like text for chatbots and virtual assistants to facilitate natural conversations

• Product and spatial design: Architects and designers use generative AI to develop new designs and floor plans more efficiently

• Medical research: Generative AI assists in developing new drugs and generating synthetic medical images for AI training

• Marketing and e-commerce: Businesses leverage generative AI to create realistic 3D models and personalised marketing content

Challenges in using generative AI

The use of generative AI presents several challenges, including ethical concerns such as the creation of misinformation and the difficulty of distinguishing between real and generated content. Additionally, it requires immense computing power and vast amounts of data, making it costly and resource-intensive for many businesses. Issues related to data protection and control over generated content also remain significant concerns.

The most common risks associated with generative AI

Ethical concerns

Generative AI can be used to create misinformation, deepfakes and manipulated content, which could threaten the credibility of media and information. As it becomes increasingly difficult to distinguish between real and artificially created content, the potential for misuse grows.

Computing power and resource requirements

Developing and running generative AI models, particularly large language models like ChatGPT, requires enormous computing capacity. For many businesses, the necessary hardware infrastructure is expensive and often difficult to access.

Data protection

The use of large datasets carries the risk of data privacy breaches, especially when sensitive or personal information is included in training data. It is often unclear how and whether such data is adequately protected.

Copyright and control

Controlling generated content is another major issue. Who is responsible for the publication or misuse of AI-generated content? How can copyright protection be enforced in such cases? These questions remain largely unresolved.

Bias and discrimination

Generative AI models can develop bias (AI bias) based on the datasets used for training, leading to discriminatory or unethical results. This is a major challenge, as AI can unintentionally reinforce societal stereotypes.

Conclusion: is generative AI the future?

Generative AI is already being successfully used in many areas and continues to have the potential to become a key technology of the future. In industries such as investment, energy and the automotive sector, it is opening up entirely new possibilities.

• Investment: Generative AI could develop tailored investment strategies and automate complex market analyses

• Energy: It may help optimise energy distribution and support the development of efficient energy storage systems

• Other sectors: In medicine, education and research, generative AI could play an even greater role by enabling new medical treatments, personalised learning content and complex research simulations

Despite challenges such as data protection and ethical concerns, generative AI offers huge opportunities to optimise workflows and create innovative solutions.

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