from Michael Reber

ChatGPT - A large language model

Artificial Intelligence

ChatGPT is a LLM (Large language model)which is being developed by OpenAI and on the technology of the generative Pre-trained Transformer (GPT) based.

It is specifically designed to process and generate natural language and can be used to perform a variety of tasks such as text generation, text summarisation, text completion and much more. It can also be used to create chatbots that can respond to natural language.

ChatGPT was created using machine learning and neural networks. It was then trained with a large amount of text to gain the ability to understand and generate natural language.

Training is carried out using machine learning algorithms based on neuronal synapses. These algorithms use the large amount of text to recognise and learn patterns in the respective language.

The transformer-based approach is used to understand the meaning of words in relation to context.

A network of neurons makes sense

ChatGPT uses several layers of neurons that are connected to each other. Each layer processes the input and forwards the results to the next layer. This process is repeated until the model is able to understand the meaning of the text.

It then uses these comprehension patterns to generate new texts that mimic natural language. The model uses its understanding of the context and meaning of words to generate new sentences and texts that sound natural and make sense.

Ongoing improvement

One of the most important features of ChatGPT is its Capabilityconstantly and also to improve independently. The model utilises feedback from usersto improve his understanding of the language and his ability to "generate natural texts".

It can also be practised with new texts to expand your knowledge of the language and improve your skills.


In terms of resources, ChatGPT requires powerful hardware to function effectively. A GPU with high performance and a lot of RAM is used to train the model quickly and efficiently. Today, the latest ChatGPT model itself consists of only 180 GB of neural data.

In terms of the size of the models in general, OpenAI offers different versions of GPT, which have different sizes and capacities. The larger the model, the better its performance is usually, but it also requires more resources and training can take longer.

What does the future hold for ChatGPT?

The future of ChatGPT could develop in several ways. Some possible developments are:

  • Further improvements in performanceChatGPT will likely continue to be improved to achieve even higher accuracy and performance in natural language processing and generation. This could be achieved by developing larger and more powerful models that are trained on even larger amounts of data.
  • Expansion of the application areasChatGPT could be used in even more areas in future applications. For example, it could be used in medicine, education, finance and customer service.
  • Interactive systemsChatGPT could be integrated into interactive systems such as voice assistants and chatbots to provide an even more natural and engaging user experience.
  • More personalised resultsChatGPT could deliver personalised results in the future by being trained on the behaviour and preferences of a particular user.
  • Increased securityChatGPT could also be used in the future to recognise and prevent security problems such as phishing and fraudulent activities in real time.

It is important to emphasise that the future of ChatGPT depends on the ongoing development and application of machine learning and neural networks, and that the above developments may not all occur, or may occur in a completely different way than listed here.

To summarise, ChatGPT is a powerful tool for processing and generating natural language. It offers a wide range of application possibilities and can help to improve the interaction between humans and machines. Its ability to constantly improve makes it a powerful tool for developers and companies who want to integrate natural language processing into their applications.

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Michael Reber

Years of experience in Linux, security, SIEM and private cloud

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