What is Generative AI?

Nils Knäpper 11/3/2023

Creating something new out of nothing - that's what generative AI can do. We'll show you what's behind it.

Table of contents
  1. Definition: Generative AI
  2. Basics of generative AI
  3. Key technologies and models
  4. Applications of generative AI
  5. Generative AI - blessing or curse?
  6. AI Software on OMR Reviews

At least since the hype about in 2022, artificial intelligence has been on everyone's lips and above all arrived in the everyday consciousness of many people. But AI is not the same as AI. Especially in connection with tools like , or , one often speaks of a generative AI - an artificial intelligence that is capable of creating new content on its own. Learn how exactly this works and what you can do with generative AI in this article.

Definition: Generative AI

Generative AI refers to systems that not only process data, but independently create new content that is hard to distinguish from human works - or even surpasses them.

This technology uses extensive data sets to learn patterns, styles and structures and to generate original results from them. From automating routine tasks to creating entirely new works of art: generative AI systems like language models and diffusion models have reached a point where they can not only assist, but act autonomously and be creative.

Basics of generative AI

At its core, generative AI involves systems that are trained to analyze data and independently generate content that is not only new, but also meaningful and valuable.

The foundation of these systems is machine learning, a subfield of AI that enables machines to learn from experience and improve. Generative AI systems use special algorithms to recognize and internalize patterns in large amounts of data. These patterns then serve as the basis for generating new data that corresponds to the learned structures.

A key element of generative AI is the ability to create data that is similar to, but not identical to, the inputs on which it was trained. This means that if a generative model is trained with thousands of images of dogs, for example, it should subsequently be able to generate an image that looks like a dog, but does not copy an existing dog.

A central distinction between generative AI and “classic” AI is that the latter is mainly used for pattern recognition and predictions. Generative AI, on the other hand, is creative; it creates new things. This opens up a world of possibilities where AI not only solves problems, but also collaborates creatively with us.

Crucially, the size of the datasets on which the AI was trained. The larger and more diverse the training data, the better the model can imitate reality, and the more creative and accurate the results can be.

 

Key technologies and models

The world of generative artificial intelligence is driven by a variety of key technologies and models. The most important innovations in this area are Transformer models and Diffusion Models:

Transformer Models 

Transformer Models are a type of neural networks that are particularly well suited for tasks that require an understanding of sequences, such as natural language. They differ from their predecessors by their ability to understand context over long distances within a text. This is facilitated by the so-called "Attention" mechanism, which allows the model to weigh and combine information from different parts of the input text. This leads to remarkable accuracy and flexibility in processing language, making them ideal for creating Large Language Models (LLMs). KI text generators like OpenAI's GPT series is a prominent example of a Transformer model that is capable of generating coherent and context-related texts through processing large amounts of text, which are hard to distinguish from human writing. If you want to try it out for yourself, take a look at our list for free AI text generators.

Diffusion Models

Diffusion Models are a more recent advancement in generative AI, which takes a completely different approach. They work by gradually converting noise into structured data. The process starts with a pattern that is little more than statistical noise, and over many steps leads to a structured and recognizable image or other type of data. This process is somewhat similar to a chemical diffusion, in which particles transition from a state of imbalance to a state of equilibrium. In the world of AI, diffusion models can generate impressive images. If you want to see what is possible with this, check out our article on free AI image generators.

Applications of generative AI

Generative AI has a variety of use cases that span diverse areas, ranging from text creation to video generation. In each of these use cases, generative AI uses its ability to learn from large data sets and create new, original content based on these data.

Text Generation

The text generation by generative AI uses models like Transformers to create new texts based on context and learned speech patterns. These models are trained with large amounts of text and can then independently write content that ranges from creative stories and news articles to poetry. The generative AI can adapt topics, style and complexity to be useful for a variety of purposes, such as composing emails or generating blog posts.

Code Generation

Generative AI can also be used in the field of software development by generating code snippets or even entire programs. With the help of LLMs, which are trained on databases with code examples, the AI can recognize patterns and structures that are common in software development. It can help developers work more efficiently by making suggestions for code completion or even detecting and correcting errors in the code.

Image Generation

In image generation, diffusion models or GANs (Generative Adversarial Networks) are used to create new images from an initial noise. These can be used to design graphics, create artwork, or generate training data for other AI systems. The models learn the structure of real images and can then generate new images that exhibit similar patterns.

Video Generation

The creation of videos by generative AI is a relatively new, but rapidly growing field. Here, AI models learn how objects and scenes move and change over time, and then generate their own video clips. This technology can be used to create animations, for training purposes, or even to generate footage for the entertainment industry.

Chatbots

Generative AI is also used in chatbots, which are capable of conducting natural and fluid conversations through the combination of NLP and machine learning. These bots are trained with data that reflect real conversations, and can then respond to user queries in a way that is close to human interaction. They are used in customer service, personal assistants, and as part of interactive entertainment offers.

Speech synthesis

In speech synthesis, generative AI is used to generate spoken language that is hardly distinguishable from human speech. Models like neural TTS (Text-to-Speech) learn how words are pronounced and what pitch and intonation naturally sound like. This technology enables the creation of synthetic voices for virtual assistants, announcements, and even voiceovers for characters in video games and movies.

Generative AI - blessing or curse?

Due to the amazing results that can be generated with generative AI in a short period of time, this type of artificial intelligence is often criticized. This mainly concerns ethical and data protection aspects as well as societal impacts:

Ethics

The use of generative AI raises fundamental ethical questions. For example, the ability of AI to generate convincing texts and images can be used to create deepfakes or disseminate disinformation. Concerns arise about authenticity and trust in digital content. In addition, ethical considerations touch upon authorship and copyright when AI creates works that resemble or mimic those of humans. Questions arise about the attribution of copyright and the fair distribution of revenue.

Data Protection

Data protection is another significant issue in the discussion about generative AI. Since these systems are trained on extensive datasets, there is a risk that they may inadvertently reproduce personal information that was contained in the training data. These concerns are particularly relevant if it comes to personal texts, images, or speech recordings that have been included in the datasets.

Societal Impact

The societal impacts of generative AI are far-reaching. On the one hand, these technologies have the potential to create new jobs and promote creativity, but on the other hand, they could replace existing jobs, especially in creative professions. The ability of AI to penetrate into areas traditionally considered purely human domains raises questions about the future role of humans in the world of work. The potential disparity between those who have access to AI technologies and those who are excluded from them could also exacerbate existing social and economic inequalities.

AI Software on OMR Reviews

Interested in diving into the world of generative Artificial Intelligence? Take a look at our categories for AI Images and AI Text Generators on OMR Reviews. We've brought a few softwares along for you already:

Generative AI Image Generators


Generative AI Text Generators

Nils Knäpper
Author
Nils Knäpper

Nils ist SEO-Texter bei OMR Reviews und darüber hinaus ein echter Content-Suchti. Egal, ob Grafik, Foto, Video oder Audio – wenn es um digitale Medien geht, ist Nils immer ganz vorne mit dabei. Vor seinem Wechsel zu OMR war er fast 5 Jahre lang als Content-Manager und -Creator in einem Immobilienunternehmen tätig und hat zudem eine klassische Ausbildung als Werbetexter.

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