{"id":4864,"date":"2024-04-01T22:35:52","date_gmt":"2024-04-01T20:35:52","guid":{"rendered":"https:\/\/rock-the-prototype.com\/uncategorized\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/"},"modified":"2024-04-01T22:39:39","modified_gmt":"2024-04-01T20:39:39","slug":"generative-pretrained-transformer-gpt-ai-tech-ai-learning-models","status":"publish","type":"encyclopedia","link":"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/","title":{"rendered":"Generative pretrained transformer (GPT) &#8211; AI tech &amp; AI learning models"},"content":{"rendered":"<p><\/p><div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\"><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1144px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:0px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-text fusion-text-1\"><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_86 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Inhaltsverzeichnis<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ffffff;color:#ffffff\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ffffff;color:#ffffff\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#What_is_a_generative_pretrained_transformer_GPT\" >What is a generative pretrained transformer (GPT)?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#What_are_the_main_features_of_GPT_technology\" >What are the main features of GPT technology?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#How_does_a_Generative_Pre-Trained_Transformer_%E2%80%93_GPT_work\" >How does a Generative Pre-Trained Transformer &#8211; GPT work?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Basic_principles_of_the_Transformer_architecture\" >Basic principles of the Transformer architecture<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Self-attention_mechanism\" >Self-attention mechanism<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Positional_Encoding\" >Positional Encoding<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Pre-training_process\" >Pre-training process<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Unsupervised_learning\" >Unsupervised learning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Tokenization_and_embeddings\" >Tokenization and embeddings<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Fine_tuning\" >Fine tuning<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Adaptation_to_specific_tasks\" >Adaptation to specific tasks<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Example_of_the_fine-tuning_process\" >Example of the fine-tuning process<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Generation_of_texts\" >Generation of texts<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Selecting_the_next_word\" >Selecting the next word<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Diversity_and_creativity\" >Diversity and creativity<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#GPT_sample_application\" >GPT sample application<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Challenges_and_solutions\" >Challenges and solutions<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Context_limitations\" >Context limitations<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Solution_approaches\" >Solution approaches:<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Distortions_and_ethics\" >Distortions and ethics<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Solution_approaches-2\" >Solution approaches:<\/a><\/li><\/ul><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#History_of_generative_pre-trained_transformers_GPT_and_artificial_intelligence_AI\" >History of generative pre-trained transformers (GPT) and artificial intelligence (AI)<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#The_beginnings_of_AI\" >The beginnings of AI<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Developments_before_GPT\" >Developments before GPT<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#The_era_of_GPT_and_OpenAI\" >The era of GPT and OpenAI<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/generative-pretrained-transformer-gpt-ai-tech-ai-learning-models\/#Influence_and_significance\" >Influence and significance<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_is_a_generative_pretrained_transformer_GPT\"><\/span>What is a generative pretrained transformer (GPT)?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A <strong>Generative Pre-Trained Transform <\/strong>&ndash; <strong>GPT<\/strong> for short <strong>&ndash;<\/strong> is an advanced<strong> <a href=\"https:\/\/rock-the-prototype.com\/en\/artificial-intelligence-ai\/machine-learning\/\" target=\"_blank\" title=\"Machine learning is a branch of artificial intelligence (AI) that deals with the development of algorithms and models that enable computers to learn from experience and data, recognize patterns and make predictions without being explicitly programmed. The aim of machine learning is to enable computers to learn independently from data and extract information in order to master complex tasks. We explain which concepts are used to do this and how predictions can be made by adapting models to existing data.\" class=\"encyclopedia\">machine learning<\/a> AI model<\/strong> based on the <strong>Transformer architecture<\/strong>.<\/p>\n<p>This <strong>AI technology<\/strong> was developed by OpenAI and is known for its ability to generate high-quality texts that are similar to those of a human being. The core of <strong>GPT technology<\/strong> lies in the use of large amounts of data to pre-train the model before it is fine-tuned for specific tasks.<\/p>\n\n<p>Thus, a <strong>Generative Pre-trained Transformer (GPT)<\/strong> is a sophisticated <strong>AI model<\/strong> that is located in the world of artificial intelligence (AI) and machine learning (ML).<\/p>\n<p>Developed on the basis of the Transformer architecture, which was originally introduced in 2017, GPT has the ability to generate human-like text and handle complex language understanding tasks.<\/p>\n<p>At its core, GPT is designed to analyze large amounts of text data, learn from it and use this knowledge to generate text based on a given input prompt or to answer questions. This capability makes it a powerful <strong>AI tool<\/strong> for a wide range of <strong>AI applications<\/strong>, from automatic text generation and translation to advanced <strong>chatbot systems<\/strong>.<\/p>\n<\/div><div class=\"fusion-text fusion-text-2\"><div class=\"w-full text-token-text-primary\" data-testid=\"conversation-turn-3\">\n<div class=\"px-4 py-2 justify-center text-base md:gap-6 m-auto\">\n<div class=\"flex flex-1 text-base mx-auto gap-3 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem] group\">\n<div class=\"relative flex w-full flex-col agent-turn\">\n<div class=\"flex-col gap-1 md:gap-3\">\n<div class=\"flex flex-grow flex-col max-w-full\">\n<div class=\"min-h-[20px] text-message flex flex-col items-start gap-3 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto\" data-message-author-role=\"assistant\" data-message-id=\"1e3f6f58-7471-4e7a-a920-5df4333b8a6d\">\n<div class=\"markdown prose w-full break-words dark:prose-invert dark\">\n<h2><span class=\"ez-toc-section\" id=\"What_are_the_main_features_of_GPT_technology\"><\/span>What are the main features of GPT technology?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ol>\n<li><strong>Transformer architecture<\/strong>: This architecture was originally described in the paper &ldquo;Attention is All You Need&rdquo; by Vaswani et al. (2017) and is particularly effective in the processing of sequences, such as sentences or paragraphs in texts. It uses mechanisms such as self-attention to understand correlations within the data.<\/li>\n<li><strong>Generative capabilities<\/strong>: GPT models are able to generate texts based on a given input text. You can write creative texts, answer questions, create summaries and much more.<\/li>\n<li><strong>Pre-training and fine-tuning<\/strong>: An essential part of GPT&rsquo;s success lies in its training process. First, the model is pre-trained on a broad base of text data to gain a basic understanding of the language. It can then be fine-tuned for specific tasks by training it on a smaller, task-specific data set.<\/li>\n<li><strong>Areas of application<\/strong>: GPT models are used in a variety of applications, including writing and editing text, creating artistic content, developing chatbots, automating customer service responses and much more.<\/li>\n<\/ol>\n<p>The latest versions of GPT, such as GPT-3 and any successors, have significantly expanded the AI&rsquo;s capabilities in text generation and language understanding, making it increasingly versatile.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"absolute\">\n<div class=\"flex w-full gap-2 items-center justify-center\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"w-full text-token-text-primary\" data-testid=\"conversation-turn-4\">\n<div class=\"px-4 py-2 justify-center text-base md:gap-6 m-auto\">\n<div class=\"flex flex-1 text-base mx-auto gap-3 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem] group\">\n<div class=\"flex-shrink-0 flex flex-col relative items-end\">\n<div>\n<div class=\"pt-0.5\">\n<div class=\"gizmo-shadow-stroke flex h-6 w-6 items-center justify-center overflow-hidden rounded-full\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><a class=\"fusion-modal-text-link\" data-toggle=\"modal\" data-target=\".fusion-modal.Spotify Podcast Folge 16 - Artificial Intelligence Act - Alle Fakten zum AI Act der EU Rock the Prototype - Softwareentwicklung &amp; Prototyping\" href=\"#\"><iframe class=\"lazyload\" style=\"border-radius: 12px;\" src=\"data:image\/svg+xml,%3Csvg%20xmlns%3D%27http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%27%20width%3D%27100%27%20height%3D%27352%27%20viewBox%3D%270%200%20100%20352%27%3E%3Crect%20width%3D%27100%27%20height%3D%27352%27%20fill-opacity%3D%220%22%2F%3E%3C%2Fsvg%3E\" data-orig-src=\"https:\/\/open.spotify.com\/embed\/episode\/59VHbKa3SKctBpGWXyIyK1?utm_source=generator\" width=\"100%\" height=\"352\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/a>\n<div class=\"fusion-text fusion-text-3\"><h2>How does a Generative Pre-Trained Transformer &ndash; GPT work?<\/h2>\n<h3><span class=\"ez-toc-section\" id=\"Basic_principles_of_the_Transformer_architecture\"><\/span>Basic principles of the Transformer architecture<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"Self-attention_mechanism\"><\/span>Self-attention mechanism<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>The core of the Transformer architecture, and therefore also of GPT, is the self-attention mechanism. This enables the model to understand the meaning of each individual word in a sentence in the context of all the other words. Instead of stepping sequentially from word to word, as previous model architectures did, the self-attention mechanism evaluates each word simultaneously in the context of the entire sentence. This parallel approach enables more efficient processing and a deeper understanding of linguistic contexts.<\/p>\n<p>An important aspect of the self-attention mechanism is its ability to recognize the relationship between widely spaced words in a text. For example, the model can understand that in the sentence &ldquo;The doctor gave the patient a medicine because he was sick&rdquo;, the word &ldquo;he&rdquo; refers to &ldquo;the patient&rdquo;, even if there are several words in between. By weighting the meaning of each word in relation to every other word in the text, GPT can capture subtle nuances and complex grammatical structures.<\/p>\n<h4><span class=\"ez-toc-section\" id=\"Positional_Encoding\"><\/span>Positional Encoding<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>Another key element of the Transformer architecture is positional encoding. Since the self-attention mechanism does not process the input words in any specific order, the model needs a method to take into account the order of the words in the text. Positional encoding solves this problem by adding additional information to each word about its position in the sentence.<\/p>\n<p>This is done by adding a position embedding to each word embedding before the data flows through the self-attention layers. The position embeddings have a unique pattern that allows the model to recognize the position of each word and take into account how far away it is from other words. This information is crucial for understanding language structures, especially in linguistic constructions where the word order strongly influences the meaning of the sentence.<\/p>\n<p>By combining self-attention and positional encoding, GPT can generate and interpret text with an understanding of both context and grammatical structure, resulting in astonishingly human-like performance in various language tasks.<\/p>\n<\/div><a class=\"fusion-modal-text-link\" data-toggle=\"modal\" data-target=\".fusion-modal.Apple Podcast Folge 16 - Artificial Intelligence Act - Alle Fakten zum AI Act der EU Rock the Prototype - Softwareentwicklung &amp; Prototyping\" href=\"#\"><iframe style=\"width: 100%; max-width: 660px; overflow: hidden; border-radius: 10px;\" src=\"https:\/\/embed.podcasts.apple.com\/us\/podcast\/folge-16-artificial-intelligence-act-alle-fakten-zum\/id1684107786?i=1000649820721\" height=\"175\" frameborder=\"0\" sandbox=\"allow-forms allow-popups allow-same-origin allow-scripts allow-storage-access-by-user-activation allow-top-navigation-by-user-activation\"><\/iframe><\/a>\n<div class=\"fusion-text fusion-text-4\"><h3><span class=\"ez-toc-section\" id=\"Pre-training_process\"><\/span>Pre-training process<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"Unsupervised_learning\"><\/span>Unsupervised learning<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>The pre-training process of a Generative Pre-Trained Transformer (GPT) is based on unsupervised learning, a method of machine learning in which the model is trained without explicitly labeled data. Instead of giving direct instructions on what exactly to learn, GPT analyzes an extensive corpus of text data that represents the broad spectrum of human language. This text corpus can consist of books, articles, websites and other written materials.<\/p>\n<p>During the pre-training process, the model tries to recognize the structure and patterns of the language on its own. A key aspect of this is the prediction of words based on their context. For example, the model learns to predict the next word in a sentence or to complete a missing part of the text. Through this type of training, GPT learns to identify and internalize complex language patterns, syntactic structures and semantic relationships within the text.<\/p>\n<h4><span class=\"ez-toc-section\" id=\"Tokenization_and_embeddings\"><\/span>Tokenization and embeddings<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>To process texts, GPT first breaks them down into so-called tokens. Tokenization is the process of converting continuous text into discrete units (tokens), which can be words, punctuation marks or even parts of words. These tokens are then converted into vectors, known as embeddings. Each embedding is a dense, high-dimensional representation that maps the semantic and syntactic properties of a token.<\/p>\n<p>Embeddings enable the model to capture meanings and relationships between words. During the pre-training process, these embeddings are continuously adapted and optimized so that the model learns to represent similar words with similar vectors. This process helps the model to develop a deep understanding of the language and its nuances that goes far beyond superficial matches.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Fine_tuning\"><\/span>Fine tuning<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"Adaptation_to_specific_tasks\"><\/span>Adaptation to specific tasks<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>After completing the pre-training process, GPT is able to cope with general language tasks. However, in order to optimize the model for specific applications, such as text generation in a specific style, translations or answering questions in a specific subject area, it is further fine-tuned. Fine-tuning is done by training the pre-trained model on a smaller, specific data set that is relevant to the desired task.<\/p>\n<p>Through this additional training phase, the model learns to apply its previously acquired general language skills to the specifics of the new task. This step enables a significant improvement in performance in specific use cases, as the model can now take into account specific terminologies, styles and requirements of the target application.<\/p>\n<h4><span class=\"ez-toc-section\" id=\"Example_of_the_fine-tuning_process\"><\/span>Example of the fine-tuning process<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>The fine-tuning of GPT for the creation of legal documents could serve as a concrete example. After the model has been pre-trained on a wide range of texts, it is additionally trained on a dataset of legal texts that includes laws, judgments and contracts. During this fine-tuning phase, GPT learns to understand and apply the specific language, style and structure of legal texts.<\/p>\n<p>Fine-tuning enables GPT to generate legal documents that are not only correct in terms of content, but also in line with legal spelling conventions. This adaptability makes GPT a valuable tool for a variety of specialized applications by tailoring the general model to the specific needs and requirements of each task.<\/p>\n<\/div><div class=\"fusion-text fusion-text-5\"><div id=\"attachment_4863\" style=\"width: 1802px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" aria-describedby=\"caption-attachment-4863\" class=\"size-full wp-image-4862\" src=\"https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT.jpg\" alt=\"Training process of a generative pre-trained transformer GPT\" width=\"1792\" height=\"1024\" srcset=\"https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT-200x114.jpg 200w, https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT-300x171.jpg 300w, https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT-400x229.jpg 400w, https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT-600x343.jpg 600w, https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT-768x439.jpg 768w, https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT-800x457.jpg 800w, https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT-1024x585.jpg 1024w, https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT-1200x686.jpg 1200w, https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT-1536x878.jpg 1536w, https:\/\/rock-the-prototype.com\/wp-content\/uploads\/2024\/04\/Trainingsprozess-einer-Generativ-vortrainierten-Transformers-GPT.jpg 1792w\" sizes=\"(max-width: 1792px) 100vw, 1792px\"><p id=\"caption-attachment-4863\" class=\"wp-caption-text\">Training process of a generative pre-trained transformer GPT<\/p><\/div>\n<h3><span class=\"ez-toc-section\" id=\"Generation_of_texts\"><\/span>Generation of texts<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"Selecting_the_next_word\"><\/span>Selecting the next word<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>The ability of Generative Pre-Trained Transformers (GPT) to generate text is essentially based on predicting the next word based on the given context. Once GPT receives an input text, it analyzes it and uses its comprehensive understanding of the language to determine the most likely next word. This prediction is based on the patterns, structures and relationships learned during pre-training and fine-tuning.<\/p>\n<p>The process of word selection is done by calculating the probabilities of all possible next words in the vocabulary. The model weighs up how well each potential word fits in context, based on the previous words in the text. The word with the highest probability is selected as the next word and added to the generated text. This process is repeated for each new word, with the model taking into account the growing context of the already generated text.<\/p>\n<h4><span class=\"ez-toc-section\" id=\"Diversity_and_creativity\"><\/span>Diversity and creativity<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>To ensure diversity in the generated text and to prevent GPT from always giving the same answer, techniques such as temperature and top-k sampling are used.<\/p>\n<ul>\n<li><strong>Temperature<\/strong>: This technique controls the &ldquo;creativity&rdquo; of the model when selecting words. A low temperature causes the model to choose safer, more probable words, while a higher temperature causes the model to choose riskier, less predictable words. This makes it possible to fine-tune the originality of the generated text.<\/li>\n<li><strong>Top-k Sampling<\/strong>: This method limits the selection of the next word to the k most probable options and then selects randomly from this reduced set. This increases the diversity of the generated texts by preventing the model from always resorting to the absolute most likely words and instead encouraging it to use more creative and varied language patterns.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"GPT_sample_application\"><\/span>GPT sample application<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Let&rsquo;s imagine we want to use GPT to generate a creative text on the topic of &ldquo;The future of AI&rdquo;. The input text (prompt) could be: &ldquo;Write a short story about the role of AI in society in 50 years&rsquo; time.&rdquo;<\/p>\n<p>Based on this prompt, GPT starts to generate the text by selecting the next most probable word after the other. It takes into account not only the direct request, but also its understanding of topics such as technology development, social trends and possible future scenarios.<\/p>\n<p>A generated text could begin like this: &ldquo;By 2070, artificial intelligence had transformed almost every aspect of human life. In cities, self-driving vehicles navigated safely through the streets, while intelligent assistants made everyday life easier at home. But the biggest change was seen in the way humans and AI worked together to tackle global challenges&hellip;&rdquo;<\/p>\n<p>By using techniques such as temperature and top-k sampling, GPT ensures that the story is creative and diverse, and avoids reproducing predictable or stereotypical narratives. The result is a unique and captivating text that transports the reader into a possible future.<\/p>\n<\/div><div class=\"fusion-text fusion-text-6\"><h3><span class=\"ez-toc-section\" id=\"Challenges_and_solutions\"><\/span>Challenges and solutions<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h4><span class=\"ez-toc-section\" id=\"Context_limitations\"><\/span>Context limitations<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>One of the main problems faced by Generative Pre-Trained Transformers (GPT) is the limitation of context length, i.e. the number of words or tokens that the model can consider when generating text. Earlier versions of GPT had a fixed limit on how many previous words could be included in the calculation of the next word. This limitation has a direct impact on the model&rsquo;s ability to produce long and coherent texts, as it can &ldquo;forget&rdquo; important contextual information from further back in time.<\/p>\n<h5><span class=\"ez-toc-section\" id=\"Solution_approaches\"><\/span>Solution approaches:<span class=\"ez-toc-section-end\"><\/span><\/h5>\n<ul>\n<li><strong>Increased context length<\/strong>: Technical improvements and more efficient training methods have made it possible to increase the maximum context length in newer GPT versions. This enables the model to take longer text passages into account and improves the coherence of the generated content.<\/li>\n<li><strong>Use of additional technologies<\/strong>: Approaches such as Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks can help overcome context length limitations by enabling information to be &ldquo;remembered&rdquo; over longer stretches of text.<\/li>\n<\/ul>\n<h4><span class=\"ez-toc-section\" id=\"Distortions_and_ethics\"><\/span>Distortions and ethics<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>Another significant problem with GPT and similar AI models are biases in the training data, which can lead to undesirable or unethical results in the generated texts. Since the training data comes from human language data, it may unintentionally reflect existing social, cultural or gender biases. This can lead to the model generating stereotypical, discriminatory or harmful content.<\/p>\n<h5><span class=\"ez-toc-section\" id=\"Solution_approaches-2\"><\/span>Solution approaches:<span class=\"ez-toc-section-end\"><\/span><\/h5>\n<ul>\n<li><strong>Active removal of distortions<\/strong>: Research teams are working to clean up the training data and develop mechanisms that minimize detectable biases. This can be achieved by selectively removing or neutralizing distorted content from the training set.<\/li>\n<li><strong>Ethical guidelines<\/strong>: Implementing ethical guidelines and having human moderators review generated content can help identify problematic content and prevent it from seeing the light of day.<\/li>\n<li><strong>Transparent use<\/strong>: Creating a transparent environment in which users are informed about the potential and limitations of the model can contribute to ethical use. Users should be informed about the origin of the training data and the possible distortions.<\/li>\n<li><strong>Customizable filters and policies<\/strong>: Developing systems that allow end users to filter or customize content based on individual or cultural preferences can also help mitigate ethical concerns.<\/li>\n<\/ul>\n<p>Overall, the challenges of dealing with context limitations and biases in AI models such as GPT require a combination of technological innovation and ethical considerations. Advances in AI research combined with responsible use can help to overcome these challenges and fully exploit the positive potential of generative AI models.<\/p>\n<\/div><div class=\"fusion-text fusion-text-7\"><h2><span class=\"ez-toc-section\" id=\"History_of_generative_pre-trained_transformers_GPT_and_artificial_intelligence_AI\"><\/span>History of generative pre-trained transformers (GPT) and artificial intelligence (AI)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Our compact overview of the history of Generative Pre-Trained Transformers (GPT) and artificial intelligence (AI) in general is rich in developments and breakthroughs.<\/p>\n<p>Here you will find our chronology of the most important milestones:<\/p>\n<h3><span class=\"ez-toc-section\" id=\"The_beginnings_of_AI\"><\/span>The beginnings of AI<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><strong>1950s<\/strong>: Alan Turing publishes the paper &ldquo;Computing Machinery and Intelligence&rdquo;, in which he poses the question &ldquo;Can machines think?&rdquo; and introduces the Turing test.<\/li>\n<li><strong>1956<\/strong>: The term &ldquo;artificial intelligence&rdquo; is introduced at the Dartmouth Conference, which is generally regarded as the birth of AI research.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Developments_before_GPT\"><\/span>Developments before GPT<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><strong>1980s<\/strong>: The development of neural networks begins with the backpropagation algorithm, which enables the training of deep neural networks.<\/li>\n<li><strong>1997<\/strong>: IBM&rsquo;s Deep Blue beats the reigning world chess champion Garry Kasparov.<\/li>\n<li><strong>2012<\/strong>: AlexNet, a deep neural network, wins the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), marking a turning point for deep learning and AI.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"The_era_of_GPT_and_OpenAI\"><\/span>The era of GPT and OpenAI<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><strong>December 2015<\/strong>: OpenAI is founded as a research company in the field of Artificial Intelligence with the aim of promoting and developing friendly AI to benefit humanity as a whole.<\/li>\n<li><strong>June 2018<\/strong>: OpenAI releases GPT (Generative Pretrained Transformer), the first version of its revolutionary model. GPT demonstrates impressive capabilities in text generation and sets new standards for language models.<\/li>\n<li><strong>February 2019<\/strong>: OpenAI presents GPT-2, an improved version with 1.5 billion parameters. Due to concerns about possible misuse, the full version will not be published for the time being. GPT-2 shows a clear improvement in text coherence and versatility.<\/li>\n<li><strong>June 2020<\/strong>: GPT-3 is released with an architecture of an impressive 175 billion parameters. This model sets new standards for natural language generation and is used in a wide range of applications, from automatic text generation to program code creation.<\/li>\n<li><strong>2021 and beyond<\/strong>: While GPT-3 continues to find widespread use, OpenAI is working on the next generation of AI models as well as refining their applications, ethical guidelines and accessibility issues. The exact date for the <a href=\"https:\/\/rock-the-prototype.com\/en\/learn-programming\/release\/\" target=\"_blank\" title=\"A release is a defined software version of an application. A software release is therefore a software version defined for users with a defined range of functions and maturity level. An initial software release generally represents the first generation of a new or improved software application.\" class=\"encyclopedia\">release<\/a> of a new GPT version is uncertain, but research and development is continuing.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Influence_and_significance\"><\/span>Influence and significance<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The development of GPT by OpenAI has revolutionized AI research and the application of artificial intelligence in many areas. By providing powerful, flexible and increasingly accessible models for natural language generation, OpenAI has helped to push the boundaries of what is possible with AI. The ongoing focus on ethical considerations and the safety of AI systems also underlines the responsibility that comes with advanced AI technology.<\/p>\n<p>Our chronology gives you a quick overview of the most important milestones in the development of GPT and AI in general, showing the rapid progress in this field and the role of research institutions such as OpenAI in shaping the future of artificial intelligence.<\/p>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-2 fusion-flex-container has-pattern-background has-mask-background nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\"><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1144px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-1 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:0px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><a class=\"fusion-modal-text-link\" data-toggle=\"modal\" data-target=\".fusion-modal.Rock the Prototype - Software development &amp; Prototyping Podcast iTunes\" href=\"#\"><iframe id=\"embedPlayer\" style=\"width: 100%; max-width: 660px; overflow: hidden; border-radius: 10px; transform: translateZ(0px); animation: 2s ease 0s 6 normal none running loading-indicator; background-color: #e4e4e4;\" src=\"https:\/\/embed.podcasts.apple.com\/us\/podcast\/rock-the-prototype-software-development-prototyping\/id1684835330?itsct=podcast_box_player&amp;itscg=30200&amp;ls=1&amp;theme=auto\" height=\"450px\" frameborder=\"0\" sandbox=\"allow-forms allow-popups allow-same-origin allow-scripts allow-top-navigation-by-user-activation\"><\/iframe><\/a><\/div><\/div><\/div><\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>What is a generative pretrained transformer (GPT)? A Generative Pre-Trained Transform &#8211; GPT for short &#8211; is an advanced machine learning AI model based on the Transformer architecture.<br \/>\nThis AI technology was developed by OpenAI and is known for its ability to generate high-quality texts that are similar to those of a human being. The core of GPT technology lies in the use of large amounts of data to pre-train the model before it is fine-tuned for specific tasks.<\/p>\n","protected":false},"author":1,"featured_media":4861,"template":"","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0},"categories":[1870],"tags":[2727,2455,1919,2723,1926,2718,2721,2722,1869,2719,2715,2716,2706,2705,2725,2726,2724,1806,1832,2708,2707,2712,2711,2709,2714,2717,2710,2720,2713,1888],"class_list":["post-4864","encyclopedia","type-encyclopedia","status-publish","has-post-thumbnail","hentry","category-artificial-intelligence-ai","tag-ai-en","tag-ai","tag-alan-turing-en","tag-alexnet-en","tag-backpropagation-en","tag-context-limitations","tag-dartmouth-conference-en","tag-deep-blue-en","tag-deep-learning-en","tag-distortions","tag-embeddings-en","tag-fine-tuning","tag-generative-pretrained-transformer-en","tag-gpt-en","tag-gpt-2-en","tag-gpt-3-en","tag-imagenet-en","tag-machine-learning","tag-neural-networks","tag-neural-networks-en","tag-openai-en","tag-positional-encoding-en","tag-self-attention","tag-text-generation","tag-tokenization","tag-top-k-sampling-en","tag-transformer-architecture","tag-turing-test-en","tag-unsupervised-learning-en-2","tag-unsupervised-learning-en"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Generative pretrained transformer (GPT) - AI tech &amp; AI learning models - Rock the Prototype - Softwareentwicklung &amp; Prototyping<\/title>\n<meta name=\"description\" content=\"What is a generative pretrained transformer (GPT)? 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