What is Generative AI?

Generative AI, short for generative artificial intelligence, is capable of generating content similar to the data it was trained on—from texts to images to music. The potential is impressive, but generative AI also brings challenges and ethical concerns, particularly regarding the authenticity and potential misuse of generated content.

The definition of Generative AI

Generative AI stands for generative artificial intelligence. The term refers to AI models and algorithms like ChatGPT, which can generate new content or data similar to what they were trained on. This can involve various data types such as text, images, music, etc. The technology today mainly relies on so-called transformer models. Transformers are specialised neural networks developed to handle large amounts of text data. This is a form of machine learning.

How does Generative AI work?

Generative artificial intelligence typically works through the use of Neural Networks. For creating images, CNNs (Convolutional Neural Networks) are often used, whereas transformers are increasingly used for text.

• Initially, large amounts of training data are collected and processed to serve as the basis for training the generative model. This can include, for example, texts, images, or videos.
• The neural network consists of multiple layers. The exact architecture depends on the type of data to be generated. For texts, a model with recurrent neural networks (RNNs) or the previously mentioned transformers can be used, while CNNs are used for images.
• The AI model is applied to the training data to learn how to generate data similar to the training data. This is done by adjusting the weights and parameters of its neurons to minimise errors between the generated data and the actual training data.

Once the model is trained, it can generate new data. This process begins by providing the model with a starting sequence or value, known as a prompt, which can take the form of text, images, videos, or drawings. In response, the Generative AI creates new content. The generated output is then evaluated for quality and relevance. The model can be further fine-tuned by training it with new data to improve its performance.

What is the difference between machine learning and artificial intelligence?

As a broad field of research, artificial intelligence (AI) aims to develop machines that can perform tasks typically requiring human intelligence. Chatbots and voice assistants like Google Home or Amazon Echo are examples based on artificial intelligence.

Machine Learning (ML) is a subfield of AI focused on developing algorithms that can learn from data. Instead of receiving specific instructions for a task, an ML model learns from sample data and then makes predictions or decisions without being explicitly programmed for the task. The volume and complexity of data have increased the potential of machine learning.

What Generative AI models are there?

Generative AI models use a specific neural network to create new content. Depending on the application, these include:

• Generative Adversarial Networks (GANs): GANs consist of a generator and a discriminator and are often used to create realistic images.
• Recurrent Neural Networks (RNNs): RNNs are specifically designed for processing sequential data like text and are used for generating text or music.
• Transformer-based models: Models like GPT (Generative Pretrained Transformer) from OpenAI are transformer-based models used for text generation.
• Flow-based models: Used in advanced applications to generate images or other data.
• Variational Autoencoders (VAEs): VAEs are frequently used in image and text generation.
• Diffusion models: Models like DALL-E or Stable Diffusion are diffusion models. They generate data by progressively removing noise from a random input. They are mainly used in image generation and achieve very realistic results.

Different methods of machine learning

In machine learning, there are different types of models chosen based on the task type and available data. A fundamental distinction is made between supervised learning and unsupervised learning. Systems based on unsupervised learning are often implemented in neural networks.

In addition to these two main categories, there is also semi-supervised learning, reinforcement learning, and active learning. All three methods fall under supervised learning and differ in the type and extent of user involvement.

In addition, deep learning is widely used today. Unlike simple machine learning with few layers, it uses deeper neural network architectures to identify more complex features and patterns in large datasets. Fundamentally, machine learning and deep learning are subfields of artificial intelligence.

What are ChatGPT, DALL-E, Gemini, and Co.?

Solutions like ChatGPT, DALL-E, and Gemini are AI interfaces that enable users to create new content using generative artificial intelligence.

ChatGPT

ChatGPT is one of the most popular text generators. This AI chatbot is powered by OpenAI’s GPT-4 language prediction model and can provide human-like text responses in a chat format. Like other GPT models, ChatGPT is trained on large amounts of text data, allowing it to cover a wide range of topics and offer detailed explanations. By considering the conversation history with the user, ChatGPT simulates a more natural and dynamic conversation.

DALL-E

DALL-E is a multimodal AI application for generating images based on text descriptions. The generative artificial intelligence was developed using OpenAI’s GPT implementation in 2021 and, like ChatGPT, was trained on a large dataset of images and corresponding text descriptions. This allows the image AI website to connect the meaning of words with visual elements. The latest and most powerful version is DALL-E 3. It was released in October 2023 and allows users to create images in various styles controlled by user prompts and also to render text within images.

Gemini

Gemini is a generative AI chatbot developed by Google. The generative artificial intelligence is powered by the Large Language Model Gemini 1.5. Like ChatGPT, Gemini can answer questions, program, solve mathematical problems, and assist with writing tasks. It also uses techniques of Natural Language Processing (NLP). Although the AI operates independently from Google Search, it draws its information from the internet. Users can actively contribute to improving the data through their feedback.

Claude

Claude is an AI chatbot from the US company Anthropic, founded by former OpenAI researchers. The current version, Claude 4, released in May 2025, consists of multiple models differing in computational power and capability. Claude is known for its particularly secure, dialogue-oriented design and is frequently used in sensitive areas such as education or businesses. The focus is on transparency, clarity, and responsible AI usage. Claude models are accessible via API connections and in the ChatGPT-like app ‘Claude.ai’.

Mistral

Mistral is a French AI startup focused on creating efficient, high-performance open-source models. Unlike proprietary models such as GPT or Claude, Mistral emphasises openness and modularity. The models they release are lightweight yet powerful, making them popular in open-source projects and self-hosted AI applications. In Europe, Mistral is seen as a promising solution for privacy-compliant AI applications.

LLaMA

LLaMA is the latest language model from Meta. The most recent version available in Europe, LLaMA 3.1, was released in 2024 and stands out for its high efficiency and performance in open-source scenarios. Various versions are freely available and well-suited for custom AI applications, chatbots, or research. The models are designed to run on commercial hardware, making them particularly appealing to developers and companies that wish to avoid proprietary providers.

What can generative artificial intelligence be used for?

Generative AI can be used in a wide variety of fields to create practically any type of content. Thanks to groundbreaking developments like GPT and the user-friendliness of the technology, it is becoming increasingly accessible. Application areas of generative artificial intelligence include, for example:

• Text creation: News articles, creative writing, emails, CVs, etc.
• Image and graphic creation: Logos, designs, artworks, etc.
• Music and sound: Composing, sound effects, etc.
• Video game development: Generation of game levels, characters, storylines, or dialogues
• Film and animation: Creation of CGI characters or scenes, generation of animations or video content, etc.
• Pharmacy and chemistry: Discovery of new molecular structures or drugs, optimisation of chemical compounds
• Chatbots: Customer service or technical support
• Educational content: Product demonstration videos and tutorials in various languages
• Architecture and urban planning: Designing buildings, interiors, or city plans, optimising space or infrastructure use, etc.

What are the benefits of generative artificial intelligence?

Due to its wide range of applications, generative AI offers a variety of benefits for diverse fields. Besides creating new content, it can also facilitate the interpretation and understanding of existing content. The benefits of implementing generative artificial intelligence include:

✓ Automation of manual processes

✓ Summary and preparation of complex information

✓ Easier content creation

✓ Answering specific technical questions

✓ Responding to emails

What are the limitations of generative AI?

The limitations of generative artificial intelligence often arise from the specific approaches used to implement certain use cases. While the generated content often sounds very convincing, the underlying information can be incorrect and manipulated. Other limitations in the use of generative AI include:

• Source of information is not always identifiable
• Bias of original sources is hard to assess
• Realistic-sounding content makes detecting false information more difficult
• Generated content can include bias and prejudice

What are the concerns regarding generative AI?

There are a number of concerns associated with the use of generative AI. These include not only the quality of the generated content but also the potential for misuse.

• Misuse and disinformation: The ability of generative AI to create realistic content can be exploited, e.g., for deepfakes, fake news, fabricated documents, and other forms of misinformation.
• Copyright and intellectual property: Generated content raises questions about copyright and intellectual property, as it is often unclear who holds the rights to the generated content and how it is permitted to be used.
• Bias and discrimination: If generative artificial intelligence has been trained on biased data, this may be reflected in the generated content.
• Ethics: The generation of false content and manipulated information can raise ethical questions.
• Legal and regulatory issues: The rapid development of generative AI has led to an unclear legal situation; there is uncertainty about how the technology should be regulated.
• Data protection and privacy: The use of generative AI to generate personal data or identify individuals in images is questionable in terms of data protection and privacy.
• Security: Generative AI can be used for social engineering attacks that are more effective than human-led attacks.

Examples of generative AI tools

Depending on the type of content to be generated, there are various generative AI tools. Among the best AI text generators are:

• ChatGPT by OpenAI
• Jasper
• Writesonic
• Frase
• CopyAI

Some of the best AI image generators include:

• Midjourney
• DALL-E 3
• Neuroflash
• Jasper Art
• Craiyon

Some of the best AI video generators include:

• Pictory
• Synthesys
• Synthesia
• HeyGen
• Veed

Generative AI vs. AI

The difference between generative AI and artificial intelligence in general lies mainly in application rather than the underlying technology. While the main goal of artificial intelligence is to automate or enhance tasks that typically require human intelligence, generative artificial intelligence produces new content such as chat responses, designs, synthetic data, or deepfakes. Generative AI requires a prompt, where the user inputs an initial query or dataset. Traditional AI, on the other hand, focuses on pattern recognition, decision-making, refined analysis, data classification, and fraud detection.

Best practices for using generative artificial intelligence

The use of generative AI presents both opportunities and risks. For users who employ generative AI models or work with their outputs, there are some best practices to achieve better results while avoiding potential risks:

• Validate results: Always check the generated content for plausibility and quality.
• Understand the tool: You should know how the particular generative AI tool works and what its strengths and weaknesses are. The key term here is Explainable AI (XAI)
• Critically engage with sources: When working with content as sources created by generative AI, you should verify them.
• Clear labelling: Generative AI content should be labelled as such for others.
• Ethics: Use generative AI responsibly, meaning you should not create or distribute misleading, inaccurate, or manipulative content.
• Continuous learning: Generative artificial intelligence is evolving quickly, so you should stay informed about new technologies, techniques, and best practices.