Open AI is an American-based research laboratory, working on advancements in artificial intelligence. For years people have heard of AI advancements and especially Chatbots, as they intrigue curiosity. There has also been a long debate on the usefulness of these AI bots. However, after the launch of ChatGPT, people are overjoyed with its usefulness as a virtual assistant in solving complex problems and writing creative content for users.
The current talk of the town is about ChatGPT and GPT-3, are they the same thing? or are they different from each other? Both of them are powerful language models belonging to the same GPT family. However, they are quite different from each other in terms of their training, work, and usage. We will explore the differences between ChatGPT and GPT-3 while highlighting their unique strengths and weaknesses.
Whether you’re a technology and AI enthusiast or just curious about the advancement and emergence of new tools and technologies, this comparison will provide you with valuable insights into the world of AI language models.
Overview of OpenAI’s GPT-3 and ChatGPT
Both ChatGPT and GPT 3 are large language models by OpenAI. However, their purpose is different from each other.
GPT-3: GPT-3 was introduced by OpenAI back in 2020. It is the largest neural network-trained generalized AI processing model at present. It belongs to the GPT (generative pre-trained transformer) family. It is trained to produce human-like text and hold a conversation that makes you feel like you are chatting with a person and not an AI. Researchers suggest that it’s not just close to the human-like text. Instead, it is going beyond that and producing results that a domain prodigy can produce.
ChatGPT: ChatGPT was launched in November 2022 as a prototype project. It is based on OpenAI’s large language models in GPT-3 family and further finetuned with reinforcement and supervised-based learning techniques. Due to its accurate and detailed human-like answers, it quickly got recognition and attention from every sector of life. ChatGPT has more practical applications in daily life, such as helping students with their studies, making drafts for office work, and writing sample codes to understand a complex problem without surfing the whole internet to find a solution.
Differences between GPT-3 and ChatGPT
ChatGPT and GPT-3 both sound the same as they belong to the same family. However, they are a bit different from each other. Their difference comes from their inputs, outputs, training size, architecture, and applications. Let’s first understand their basic differences.
GPT-3 is a large language model based on Natural Language Processing and Neural Networks, and it works like an architecture where many products are powered by GPT-3. Examples are ChatGPT, Jasper.aI, Debuild, characterGPT, and InstructGPT.
While GPT-3 is used as a building block for developing applications, ChatGPT is an application of the GPT-3 family which is based on the GPT-3.5 model of Reinforced Neural Networks. GPT-3 covers a wide range of text-related jobs which include, chatbot features of question answering, language translation, and text summarization.
Input and Output
Both applications have the same input and output procedures, they take a small input of text, and AI will generate a reply with the appropriate answer. However, the major difference lies in the type of results a user gets.
The GPT-3 generates sophisticated larger volumes of data. However, there are times when it also produces data that is toxic. This is because it mimics the data it is trained on (covered in the next section). Compared to their predecessors, GPT-1 and GPT-2, most data is now refined and less toxic.
ChatGPT, which is specially designed as a chatbot, also follows the same process flow. The difference is in the conversation layer of its algorithm. Because of that, ChatGPT gives an optimized and concise result, and it reduces harmful responses. Another feature that gives ChatGPT an edge is that it gives answers to counterfactual questions.
Another major difference is the training data on which these models are trained. Training data is the most important part of building a neural network model, and more data will mean a longer time to train the model. However, it will give more detailed results and have a larger repository of questions and answers.
GPT-3 is trained on 175 billion parameters and 499 billion tokens which are byte pair-encoded. GPT -3 is currently the largest trained AI data set ever, and these 175 billion parameters include web-crawled data, Wikipedia, data from letter images, and books. Due to the large training data size, the GPT-3 doesn’t require any further training. It can generate any type of textual answer with maximum accuracy.
Compared to GPT-3, the ChatGPT is way smaller in training size. It only consists of 20 billion refined parameters and is trained in a supervised environment. Where trainers have provided the input and output data for training, as it is a chatbot with a small training data makes it responds faster than GPT-3.
ChatGPT and GPT-3 are both based on the same Generative pre-trained transformers. GPT-3 architecture is based on Decoder-only transformers, with a limit of 2048 tokens output. It is trained on a generative-based pre-training model, which works as a prediction mechanism to predict the new token based on the results of the previous token.
A simple way to understand it is that it predicts the next word in the sentence based on the earlier words and the context of the sentence. This model also works with zero-shot learning, finding answers within and outside of the training data, and few-shot learning, where the model is trained on thousands of samples of specific data to classify one object. GPT is trained in an unsupervised learning mechanism where human interaction is not needed to train the model.
ChatGPT Architecture is similar to GPT-3 however, to use it as a chatbot, it was further fine-tuned with a transfer learning approach. This approach stores the answers to one problem and uses those answers to solve similar problems. On top of using transfer learning GPT-3 model was further enhanced with reinforced and supervised learning techniques.
This increased human involvement in training and increased the model’s performance to GPT-3.5. In the model training process, the trainers played both the part of the User and AI to assist with conversational data. A reward-based model is also created to rank the responses generated by AI. Users can also leave comments and suggestions about the generated results. This helps ChatGPT to grow based on the responses it gets.
Applications of GPT-3 and ChatGPT
Applications of these two are varied based on their uses, as GPT-3 is a largely trained model and has way more capacity to be used in different programs ChatGPT itself needs to catch up because of its limited uses.
Applications of GPT-3:
- Computer Code generation and Syntex completion
- Translate natural language into computer code
- Generation of SQL code based on query
- Chatbot capabilities to generate text in contextual form with emotional intelligence
- Provide summarised text with contextual meaning
- Building blocks for Applications like ChatGPT, Jasper AI, and more
Applications of ChatGPT are:
- Human-like conversation
- Emotional intelligence of learning context
- Debug and Write code for computer programs
- Generate Music compositions
- Write fairy tails, stories, essays, drama scripts, test questions, poetry, and lyrics.
Natural Language Processing
Natural language processing combines the rules of Human language and computational language rules by using deep learning and machine learning models to produce human-like text. It takes in a large amount of natural language data and converts it into computer code so that the computer can understand it. It can also take input in the form of text and audio better to understand the intent and sentiments of the writer.
A chatbot uses made by using technologies such as NLP and machine learning. They receive the input in the form of text or audio and convert it into computer code to create an understanding of what the user is asking. Commonly chatbots can hold a one-sided conversation. However, in some cases, they are programmed to ask preprogrammed questions like “How may I help you” and “I don’t understand; can you please repeat it?”
The human advent of chatbots started in 1966 with the text-based chatbot named Eliza, and it was based on keyword-based preprogrammed output solutions. A user can input a question, and Eliza will always reply with the same answer. If the user changes the question just a little while keeping the subject matter (main focus) and keywords the same.
After Eliza, another famous text-based AI was “The Winter of AI” Rector, the short form of the French word recontour, which means storyteller. In 1984 Rector was used to publishing a book named “The policeman’s beard is half constructed”.
With advancements in NLP and machine learning, the next wave of chatbots was seen when chatbots were able to take voice-based inputs and start to hold a conversation with humans. Chatbots were used in the retail industry in the form of audio calls, guiding maps and destinations, and doing tasks like finding a number and playing music: Alexa, Siri, and Cortana. They can take any type of input, process it, and provide a result. All these options are attached to internet devices and have greater access to media.
While GPT-3 and ChatGPT are powerful language models, they have some key differences. The ChatGPT is specifically designed as a chatbot for conversational AI applications, and it is trained on a smaller dataset compared to GPT-3.
On the other hand, GPT-3 is a much larger and more versatile language model trained over a huge dataset, and it can generate a wide variety of responses.
Ultimately, users can choose any of these models based on their specific needs and end goals.