Generative Artificial Intelligence: The Future of Content Creation
Generative Artificial Intelligence (short: Generative AI) refers to a type of Artificial Intelligence that is capable of autonomously creating content such as text, images, or videos. Unlike other forms of Artificial Intelligence designed to perform specific tasks like classification or prediction, the focus of generative AI is on producing new content. This content should be barely distinguishable or indistinguishable from human-generated content. In short, generative AI models create synthetic text, images, and videos from existing data.
A particularly well-known example of generative AI is GPT (“Generative Pre-trained Transformer”). The various GPT versions and ChatGPT belong to a newer generation of AI models and were developed by OpenAI, founded in 2015. The technology is based on large neural networks that have been around since the 1950s but are only able to generate human-like content at a high level due to today’s available computing power. ChatGPT is trained with several hundred billion words from books, online texts, Wikipedia articles, and code libraries, and then further refined using human feedback for dialogue capabilities.
Generative AI can use both structured and unstructured datasets to identify complex relationships and patterns in the data and generate human-like content based on these patterns, which are virtually indistinguishable from manually created content. This is done on a probabilistic basis, determining the probabilities of sequences and relying on machine learning, particularly neural networks.
Content creation is based on sampling, selecting elements according to calculated probabilities. It is important to note that these models do not generate word by word but token by token instead. A token is a single unit or element in a sequence used as an input for an AI model. A token generally corresponds to a single word, but it can also include partial words, punctuation, or other characters. Processing tokens is a fundamental step in generating content – and therefore a crucial building block of generative AI.
Determining the probabilities of word sequences with generative AI
Tokens in English and Finnish as basis for generative AI
To generate content, such AI models require a so-called prompt, i.e., a directive as a starting point. The quality of the prompt and the ability of the generative AI to extract context and meaning from structured and unstructured data are crucial for the quality of the generated content.
The prompt is the input to the model (task, examples, etc.)
Applications of Generative AI
Generative AI is used in a wide range of applications across various industries, including:
1. Text generation:
Generative AI can be used to create articles, blog posts, product descriptions, emails, and other texts to create human-like writing styles and content.
2. Image and graphic generation:
Generative AI create new images and graphics based on existing styles and patterns, for example, in digital commerce, art, graphic design, or fashion.
3. Music and audio generation:
Production of music pieces and audio files based on existing melodies, rhythms, and sound structures thanks to generative AI.
4. Video and animation generation:
Generative AI can be used to create new video and animation content based on existing videos or animations.
5. Product design:
Development of new product designs and concepts based on current design styles and principles with generative AI.
Advantages of generative AI
Generative AI offers a wide range of benefits, such as:
1. Efficiency:
Generative AI enables the rapid and automated creation of content and data, saving time, and resources.
2. Scalability:
Production of large volumes of content and data with generative AI, which may be impossible or difficult manual processes.
3. Personalisation:
Generative AI can create personalised content and data based on the individual needs and preferences of users.
4. Creativity:
Creation of new and unique content that goes beyond what humans might be able to create thanks to generative AI.
Challenges in Generative AI
At the same time, the risks, and challenges of using generative AI should not be underestimated. The aspect of verifying the origin and sources of information becomes increasingly important. The use of verifiable and transparently trusted sources will take on a high importance. With automatically generated content, it is difficult or sometimes impossible to trace the sources of the information used and to determine if the statements are factually correct.
The use of generative AI ultimately carries the risk of amplifying the spread of fake news. AI systems can easily create vast amounts of convincing but false or misleading information in a short amount of time. The distinction between truth and disinformation becomes much more difficult for users. Therefore, it is essential to take appropriate measures to combat AI-generated fake news and ensure the responsible and transparent use of AI. In the future, human control for fact-checking will play a central role in text creation (“human-in-the-loop”).
One possibility for businesses and organisations could be automated text generation solutions that combine large language models with a data-driven approach. Here, dynamic and static content from generative AI systems such as the current GPT versions is used within predefined text templates to create flawless and compliance texts. The text templates act as guardrails within which text generation takes place.
Conclusion on Generative AI
Overall, generative AI offers a powerful way to create content and data in an innovative and efficient manner. Despite the challenges associated with this technology, businesses, and organisations can benefit from the advantages of generating new and unique content based on existing data and information.
Further information:
https://en.wikipedia.org/wiki/Generative_artificial_intelligence
https://www.techtarget.com/searchenterpriseai/definition/generative-AI