When ChatGPT made its debut in November 2022, it did not take too long for the world to freak out about it. Both in good ways and bad ways. It was all over Instagram, TikTok, YouTube, Twitter and even made it to mainstream news in just days after its release. Safe to say, it spread like wildfire.
Many were excited and impressed at the utility that ChatGPT came with. As a matter of fact, it made the lives of many people slightly easier in terms of generating ideas, reports and generally to have their questions answered. Then there were, in fact still are, the skeptics. Coming up with all the ways that ChatGPT is “too scary” or how it’s “stealing your job”.
In the midst of all the noise, there is a good chance that a majority of those involved in the conversation are not even aware of what the “GPT” in ChatGPT actually stands for. So while everyone is busy taking a stand on whether ChatGPT is going to take over the world or not, it does beg the simple question of, what does the GPT in ChatGPT stand for?
What is the “GPT” in ChatGPT stands for?
As anyone who was not living under a rock for the past few months would do, instead of just Google-ing, I keyed in this exact question into ChatGPT. This is what it had to say 👇
So let us break down what that means. As mentioned by ChatGPT itself, the GPT in ChatGPT stands for “Generative Pre-trained Transformer.” What does that mean?
“Generative Pre-trained Transformer” (GPT) is a kind of artificial intelligence (AI) language model intended to comprehend and produce text that resembles that of a person. To analyze text data, the GPT model employs a deep learning architecture dubbed as “Transformer”.
The words in the phrase “Generative Pre-trained Transformer” are broken down as follows:
- “Generative” refers to the model’s ability to create fresh text that resembles human feedback or what a human would write. This indicates that the model can generate text that are logical and grammatically sound in response to a prompt. In other words, it is capable of producing text that appears to have been authored by a person.
- Pre-trained refers to a model that has been trained on a substantial quantity of data prior to application to a particular job. In the instance of GPT, a sizable library of text from the internet, books, as well as other sources served as the model’s training data. With the help of this pre-training, the model can acquire linguistic structures and patterns that can be used for a myriad of text-based tasks.
- The deep learning architecture utilized by the GPT model is known as “Transformer”. This architecture was first presented in a study published in 2017 by Google researchers, and since then it has gained popularity for natural language processing (NLP) problems. As text input is processed by the Transformer architecture, long-term dependencies and relationships between words can be learned by the model.
This GPT model, for instance, can be utilized as a chatbot. For instance, it can generate responses to customer service inquiries online 24/7. One of the first few ChatGPT use cases that many users were baffled by is the ability for ChatGPT to produce original creative written content like poetry or songs.
Not to mention, ChatGPT is now frequently used as a search engine as users prefer having precise and customized answers to their questions without having to spend so much time sifting through Google or other conventional search engine results.
Development Process of ChatGPT
ChatGPT was not built overnight. There is a whole process behind the development of this language model to have it churn out results that are not only above satisfactory in quality, but also fine tuned to the specific prompts or user questions entered.
Without getting into too much technical detail, there are 5 main steps in training language models like ChatGPT. Those are:
- Compiling and Data Cleansing: This step starts with collecting large amounts of data from variety of sources that include books, websites, publications and many other sources. Then, a process called data cleansing will be carried out to polish out the data. Essentially, data cleansing is the process of recognising and removing irrelevant, inaccurate or duplicate data.
- Data Preparation: Data needs to be preprocessed in order it to be ready for training. This step includes a few tinier steps like tokenization (the process of breaking up text into smaller units), encoding (the representation of words as numerical values), and constructing input-output pairings.
- Model Training: The GPT model is trained using pre-processed data with something called the Transformer architecture (explained more below). From this training, the model learns how to produce new text that may be similar to the input text that it was trained on. The training is done elaborately and the model’s parameters are adjusted based on the error rate as the output are calculated.
- Improvements: After a period of elaborate training, the model undergoes an improvement stage. This stage involves retraining the model on smaller sets of data that are more pertinent to specific tasks in order to enhance its performance.
- Deployment: Eventually, the trained model was put to use in a variety of applications, such as chatbots, customer support, and other jobs requiring natural language processing.
NLP
ChatGPT and other models of similar nature require substantial computational resources, as well as deep learning and natural-language processing (NLP) knowledge, to be developed. To get ideal performance, it takes a lot of trial and error and fine-tuning. The accuracy and efficiency of the model are directly related to the caliber of the data utilized for training and fine-tuning.
Depending on the version or variant of the model, the ChatGPT model has a different amount of parameters that it is trained. In 2018, OpenAI published the original GPT-1 model, which has almost 117 million parameters. In contrast, later iterations of the model, such GPT-2 and GPT-3, have a substantially higher number of parameters—up to 175 billion parameters in GPT-3.
History of GPT Models
Elon Musk, Sam Altman, the CEO of OpenAI, and numerous other tech titans launched the San Francisco-based artificial intelligence research organization OpenAI in December 2015. The major objective of OpenAI was to responsibly and safely progress the field of artificial intelligence in order to develop potent AI systems that can benefit humanity in a variety of ways.
An important development in the field of natural language processing occurred in 2018 when OpenAI released GPT-1, its first language model. A vast amount of text data from the internet, notably web pages and books, was used to train the machine learning algorithms that went into the development of GPT-1. GPT-1 contained 117 million parameters in all, which enabled it to produce text responses that were both grammatically correct and semantically appropriate.
GPT-2 debuted in 2019. GPT-2 was a vast improvement when compared to the previous model as it came with its staggering 1.5 billion parameters. This GPT model was trained to be able to predict the next word in a given string of words. Using reinforcement learning from human feedback, GPT-2 was enhanced of its precision.
GPT-3, OpenAI’s third-generation language model, was launched in June 2020 and represents a considerable advancement over GPT-2. The largest language model ever constructed, GPT-3 contained an astounding 175 billion parameters. A vast amount of data, including books, websites, and other text sources, were used to train GPT-3. Unlike its predecessors, GPT-3 was able to produce text that was not only intelligible and grammatically correct but also capable of accurately performing a number of NLP tasks, including question answering, translation, and summarization.
In addition, GPT-3 featured a breakthrough method of few-shot learning that allowed it to learn new tasks from a limited number of examples, greatly increasing its flexibility and adaptability. The incredible performance and adaptability of GPT-3 highlighted the potential future of AI and the enormous potential of natural language processing.
Conclusion
The way we engage with artificial intelligence has completely changed because of this cutting-edge Generative Pre-trained Transformer (GPT) in the field of natural language processing. The language model ChatGPT, which has several uses, including as chatbots, is one of the most well-known instances of GPT.
In conclusion, GPT and ChatGPT constitute a significant development in the field of natural language processing, with a variety of applications that could enhance our daily life. Although the development of these models requires substantial computer resources and knowledge in deep learning and natural language processing, the advantages of these models make the investment worthwhile for a promising future of AI.
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Shojeni is a tech enthusiast who is a web 3 writer and a computer science major. She has a deep interest in emerging technology and is very informed of developments happening within the tech niche. Her writing career began as she was lured into the world of NFTs about a year ago. She started writing project analyses as a way to contribute to the NFT communities that she was part of, but soon found her passion for writing about tech in general.
Being a computer science student, she is familiar with AI concepts and the technicalities of it. She is an advocate of AI and is determined to make technical and complicated AI information much more accessible and digestible to the masses alongside providing helpful resources through Ava Machina.
