This much-used and often-misunderstood flagship of the AI world has a technical meaning behind the catchy name. Learning what GPT stands for will give you a greater understanding of the technology and how it works. So, what does GPT stand for in ChatGPT?
What does GPT stand for in ChatGPT?
GPT is an acronym that stands for Generative Pre-Trained Transformer. A GPT can take simple prompts in natural human language as input, and answer questions, write poems, translations, blog posts, or any other style of human-like text. GPT-4 is the latest version under the hood of Chat GPT.
The power of an LLM can be observed quantitatively in the number of parameters, sometimes called weights. In artificial intelligence, a parameter is like a connection made between nodes in the neural network. The more connections that lead to the same node, the more important that node must be – just like frequently used neurons in the human brain.
The original GPT-1 had 117 million parameters – compare this to the 175 billion parameters of GPT-3.5 just over 4 years later, and the escalation of power becomes clear. The fine-tuning of GPT models has enabled emergent capabilities never before seen from such nuanced instructions. The text generation tech has capabilities including acting as virtual assistants or an AI chatbot for internet-based customer service applications. The tone and accuracy of its human-like responses to user queries and language translation are so impressive that it can often be indistinguishable from a human customer service professional.
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Is GPT a kind of AI?
Yes, GPTs are one kind of LLM (Large Language Model) and therefore a text-based artificial intelligence. A Large Language Model uses an extremely large dataset of examples of high-quality human-like text data to predict the next word it should write in a sequence, and thereby write similarly to its training data.
Invented by OpenAI, a GPT technology is a neural network that uses transformer architecture. This is an AI concept wherein the model has contextualized what data in a dataset is most important, and how it relates to the other data in the set. Ultimately, that’s how a large language model can mimic the complexities of human-like writing.
