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.