What's Everyone Talking About? Generative AI, GPT, and ChatGPT Explained
For those of us who want to get “up close and personal” with every acronym that comes our way, let’s deconstruct three of the terms getting the most buzz at the moment: GenAI, GPT, and ChatGPT.
GenAI stands for generative artificial intelligence, and GPT stands for Generative Pre-trained Transformer (a mouthful, I know).
The Chat in ChatGPT refers to the chatbot front-end that OpenAI has built for its GPT language model.
GP tells us that this model was created using generative pre-training consisting of huge amounts of text to predict the next word in a given sequence and understand the context and relationships between words in a sentence, thus enabling more coherent and contextually relevant language generation.
The T or transformer architecture refers to the type of neural network ChatGPT is based on, the same technology first developed by Google researchers in 2017 for its search engine.
Generative AI (GenAI)
Originally inspired by the way human brain neural networks function, GenAI combines enormous datasets with lighting fast speed and analysis acumen that far exceed any one person’s memory bank and processing capabilities. As a chatbot built on top of OpenAI's GPT-3.5 and GPT-4 foundational large language models (LLMs), ChatGPT takes advantage of huge compute power and is continuously being fine-tuned (to transfer learning) using both supervised and reinforcement learning techniques.
This AI language model works by using a very large dataset of text to learn patterns in language and generate responses based on those patterns.
When you enter a message or question, ChatGPT processes the text and uses its knowledge of language to understand what you're asking.
It then uses its machine learning (ML) algorithms to generate a response based on patterns it has observed from the vast amount of text it has been trained on.
The natural language processing (NLP) tool by OpenAI is constantly learning and adapting based on the interactions it has with users. Once training data has been ingested, the AI model looks for patterns and relationships to automatically deliver the requested response – be it information, an image, text, or a video. And as these responses are used, it continuously fine-tunes its parameters, improving its ability to emulate human-generated content. Because it is self-learning and continually refines its outputs, the more content the AI model generates and the more feedback it gets, the more sophisticated, accurate, and convincing its outputs become.
OpenAI was formed in 2015, with Elon Musk as one of its cofounders, and Microsoft as the first big investor. Now GenAI applications are moving artificial intelligence into the mainstream with new product introductions in areas like manufacturing, healthcare, transportation, robotics, graphic design, and customer experience … with much more to come.