{"id":94597,"date":"2023-08-21T13:01:53","date_gmt":"2023-08-21T13:01:53","guid":{"rendered":"https:\/\/www.techopedia.com"},"modified":"2023-10-31T09:47:49","modified_gmt":"2023-10-31T09:47:49","slug":"insights-breaking-down-the-transformative-journey-of-gpt-models-in-ai-from-gpt-1-to-gpt-4","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/gpt-series-evolution-insights","title":{"rendered":"Insights: Breaking Down the Transformative Journey of GPT Models in AI, from GPT-1 to GPT-4"},"content":{"rendered":"

Artificial intelligence<\/span><\/a> (AI) has seen major changes since the <\/span>Chat Generative Pre-trained Transformer<\/span><\/a> (GPT) series started in 2018.<\/span><\/p>\n

Successive models brought enhancements, upgrades, and challenges, capturing the interest of enthusiasts, researchers, and users. From GPT-1’s basic text creation to GPT-4’s diverse skills, the progress is evident. Continuous studies examine these models’ actions, shedding light on their changing skills and possible issues.<\/span><\/p>\n

This article covers the growth and study of the chat generative pre-trained transformer models. It centers on their performance scores and insights from different tests.<\/span><\/p>\n

The Evolution of the Generative Pre-Trained Transformer Series<\/span><\/h2>\n

An essential aspect of understanding the advancements in the GPT series is the training computation, often gauged in total FLOP (floating-point operations). A FLOP represents basic math operations such as addition, subtraction, multiplication, or division performed with two decimal numbers.\u00a0<\/span><\/p>\n

When it comes to scale, one <\/span>petaFLOP<\/span><\/a> equals a staggering quadrillion (10^15) FLOP. This measure of computation showcases the vast resources invested in training these models.<\/span><\/p>\n\n

Launch of GPT in 2018<\/span><\/h3>\n

GPT-1, introduced in June 2018, marked the inception of the generative pre-trained transformer model series. This laid the groundwork for the ChatGPT of today. GPT-1 showcased the potential of unsupervised learning in language understanding, predicting the next word in sentences using books as training data.<\/span><\/p>\n

GPT was trained using 17,600 petaFLOPs.\u00a0<\/span><\/p>\n

The leap to GPT-2 in 2019<\/span><\/h3>\n

In February 2019, GPT-2 emerged as a significant upgrade to the generative pre-trained transformer series. It exhibited substantial improvements in text generation, producing coherent, multi-paragraph content. However, due to potential misuse concerns, GPT-2’s public release was initially withheld. It was eventually launched in November 2019 after <\/span>OpenAI<\/span><\/a>‘s careful risk assessment.<\/span><\/p>\n

GPT-2 was trained using 1.49 million petaFLOPs.\u00a0<\/span><\/p>\n

The revolutionary GPT-3 in 2020<\/span><\/h3>\n

GPT-3, a monumental leap in June 2020. Its <\/span>advanced text generation<\/span><\/a> found applications in email drafting, article writing, poetry creation, and even <\/span>programming<\/span><\/a> code generation. It demonstrated capabilities in answering factual queries and language translation.<\/span><\/p>\n

GPT-3 was trained using 314 million petaFLOPs.\u00a0<\/span><\/p>\n

GPT-3.5’s Impact\u00a0<\/span><\/h3>\n

GPT-3.5 is an improved version of GPT-3, released in 2022. This generative pre-trained transformer model has fewer parameters and uses fine-tuning for better <\/span>machine learning<\/span><\/a> (ML). This involves reinforcement learning with human feedback to make the algorithms more accurate and effective. GPT-3.5 is also designed to follow ethical values, making sure that the AI it powers is safe and reliable for humans to use.<\/span><\/p>\n

This model is offered for free use by OpenAI. The number of petaFLOPs used for training is not available.\u00a0<\/span><\/p>\n

Introduction of the multimodal GPT-4 in 2023<\/span><\/h3>\n

GPT-4, the most recent version, carries forward the trend of remarkable advancement, introducing enhancements such as:<\/span><\/p>\n