ChatGPT is an artificial intelligence-based chatbot that uses various technologies such as generative pre-trained transformer and natural language processing to communicate with humans. Users can use ChatGPT for various tasks like customer service, digital marketing, code evaluation, etc. ChatGPT can understand natural language input and provide human-like responses. This issue has made the above platform a powerful tool for businesses that seek to improve interaction and support their customers.

A pre-trained model

ChatGPT is a pre-trained natural language processing model developed by OpenAI. This model is built on Transformer architecture and responds to user conversations by extracting information from pre-prepared databases and websites such as Wikipedia, social platforms and similar examples. One of the main reasons for ChatGPT's success is providing understandable answers to users.

Using natural language generation tools such as Multi-Head Attention and Dropout, ChatGPT is able to automatically generate sentences and answer users' questions without human intervention.

OpenAI has designed ChatGPT in such a way that any expert or non-expert user can use it. To be more precise, users should enter their question in the corresponding field and click on the send button so that the chatbot will provide them with an accurate and short answer. With this description, ChatGPT should be described as a virtual assistant that provides natural and intelligent answers based on various sources that exist on the Internet.

What technologies is ChatGPT built on?

OpenAI has used various technologies in the development of ChatGPT, the most important of which are Transformer architecture, multi-head attention, extrapolation, neural networks, natural language processing, and deep learning algorithms.

Converter architecture

Transformer is one of the main technologies used to develop ChatGPT. This architecture has the ability to sequence and scatter input data. This algorithm continuously learns new points according to the increase of received inputs at different times, similar to a child who has just started the learning process.

The Transformer model, or to be more precise, the transfer model, is a deep learning model that uses the Attention Mechanism. This model was introduced for the first time in 2017. The Transformer model is commonly used in the field of natural language processing and has become very popular in recent years. Today, machine translation engines like Google Translate widely use the above architecture to better translate languages ​​to each other. Because the above architecture is based on tabular networks, it performs better than other neural network architectures in solving natural language processing problems. Figure 1 shows a view of this architecture.

As you can see in Figure 1, this model has a group of encoders and decoders that are connected to each other. Normally, the number of encoders and decoders is equal. The structure of all encoders is similar to each other and the structure of decoders is also similar.

Each encoder has two separate sub-layers, the first layer is the self-attention layer and the second layer is a feedforward neural network (Figure 2). The input to the encoder first passes through an attention layer that helps the encoder pay attention to the sentence structure while decoding one word to another. The output of the attention layer is fed into a feedforward neural network layer.

Decoder structure in Transformer model

Each decoder has two layers of attention and feedforward neural network, with the difference that in the decoders there is another layer of attention called Encoder-Decoder Attention, which helps the decoder to focus on related words. In natural language processing, the first step is to convert the input words into vectors so that the model has the ability to understand the meaning of the words. This work is done by word embedding algorithms. Embedding words in the transformer model is done only in the first encoder. All encoders receive a list of vectors of a given size.

An important point to mention in this section is that in the transformer model, the order of the input words is done with positional encoding. In this way, another vector is added to each input vector in the transformer model. This vector is actually a specific pattern that the model learns from and also helps the model to recognize the position of each word or the space between different words in the input sequence.

 In the transform architecture, not only layers are used to process information, but combinational blocks are used to perform the required processing. It is necessary to explain that the above architecture is included in the group of semi-supervised learning algorithms. Among the advantages of the transformative architecture index, it should be mentioned that the training process becomes more accurate, double reversibility or better receiving feedback, the possibility of non-autoregressive dynamics and architecture based on generative networks.

The converter works by dividing the input received from users into several dialogs, generating a weight matrix for each dialog, and performing the process of going from layer to layer to optimize the dialog. That's why ChatGPT is a little late in responding to users' questions.

To process textual data, instead of using a single input-output function, a number of small input-output functions are used, each of which processes a specific dimension of the input in a specialized way. Therefore, instead of processing textual data in an integrated manner similar to what machine translations do, the input is examined and analyzed from different angles to determine a weight for each input.

This information processing architecture has significant advantages, including the ability to process diverse and wider information, the ability of the algorithm to remove unnecessary or unused words in the input fields, improving the results of machine processing, more accurate processing of texts and providing a detailed summary to users. did

Multi-head attention technique

Multi-head attention technique is used to improve natural language processing performance in ChatGPT to communicate with users in a more friendly way. Based on this technology, the model is able to focus on an important part of the input data at a particular time.

In general, the multihead attention technique is a way to improve the performance of the base self-attention layer, because it only focuses on one or a limited number of points. To achieve this goal, several heads are designed, each of which is trained with different initial conditions during learning. Figure 3 shows a view of the performance of the multi-head architecture.

In multi-head attention architecture, we are connected with three different entities named Key, Value and Query. These components help us determine the degree of connection between each component of the output sentence with all parts of the input sentence. In the above architecture, the key and value are specified for each component of the input string and dialog for the components of the output sentence. For a better understanding of the issue, pay attention to the following example.

Suppose you are looking for a specific movie on a movie streaming site. To find this movie, you enter a specific phrase in the search section of the site. This phrase has a role like dialogue or question in the mechanism of multi-headed attention. To find the video you want, the site compares the phrase you searched with things like the title and description of the available videos and after finding the most similar title, plays the video related to it for you. The title and description of each movie play a role like a key and the movie itself plays a role like a value in the mechanism of multi-headed attention.

Projection technique

Another technology that ChatGPT uses is dropout, which is a technique to reduce overfitting and underfitting in neural networks. At each stage of network training, the extrapolation randomly selects and deactivates a part of the neurons, and double activates the neurons that were deactivated in the previous steps, and deactivates the function of another part of the neurons. This is done with the aim of more detailed analysis of the dialogues entered by users.

In the extrapolation technique, instead of trying to get the network to arrive at one correct answer for all training data, the goal is for the network to achieve more structure and reach a range of correct answers for the training and testing data. In this way, by improving its architecture, the network achieves better efficiency in response to new data. Overall, outsourcing is one of the effective overfitting reduction techniques that ChatGPT uses to improve its performance in responding to new data.

What features does ChatGPT have?

ChatGPT is one of the advanced models based on neural networks and natural language processing, which performs better compared to competitors. ChatGPT is developed based on the GPT-2 model, which is designed to answer user questions. As an advanced model with natural language processing capability, ChatGPT has the following key features:

 Convolutional neural network: In ChatGPT, convolutional neural networks are used to process and analyze the structure of sentences, which increases the stability of the model in processing different data.

 Pre-training: Before answering the questions, ChatGPT examines the closest data in the pre-training so that it can process the new data in a better and more accurate way.

 Ability to interact with the user: ChatGPT has the ability to interact with the user, so it can answer users' questions.

 Text prediction: ChatGPT has the ability to predict text, understand the exact meaning of the input data in terms of grammar and semantics, and provide the best answer based on previous learning.

Overall, ChatGPT allows users to ask their questions using natural language processing and get the best answer.

What is the future of ChatGPT?

It seems that in the near future, ChatGPT will be used in many products, especially artificial intelligence-based devices such as robots, automated systems, chat bots, question and answer systems, etc. Due to ChatGPT's ability to process user conversations, this model can act like a colleague for humans and help them learn better about different topics. Due to the continuous progress in the field of neural networks and deep learning, ChatGPT seems to provide us with more advanced and practical capabilities in the coming years. For example, it can be used to translate different languages ​​or as an aid in the diagnosis of diseases in the medical field.

In general, the progress in the field of artificial intelligence and deep learning will make ChatGPT and similar tools in the field of maintaining the stability of business activities, predicting changes and developing business processes to be noticed by companies and organizations in the future.

In the medical field, chatGPT can help patients and doctors to help diagnose diseases and provide remote medical care, and in some cases, prescribe adjuvant drugs. This model can help retrieve information about diseases and their treatment by using hospital data and medical records. For example, ChatGPT can be used to talk to patients and gather information about specific signs and symptoms that lead to various diseases. This model can help doctors to prescribe specific drugs for each patient and treat patients more precisely. Using hospital data, the neural network of this model can analyze different data and suggest better patterns to improve treatment. Overall, ChGPT can play an important role in the medical field and minimize the negative effects of drugs on the body or the wrong administration of drugs. Interestingly, ChatGPT will also be useful in diagnosing diseases. This model can identify the symptoms of some diseases and prevent epidemics by using data stored in databases and medical records and analyzing them. For example, once the signs and symptoms of a disease are given to the model, it is able to diagnose the disease related to a person or list a possible list of diseases based on the symptoms.

In addition, ChatGPT can help predict diseases that we may face in the future. Using population data and diseases that are already known, the neural network can discover patterns that help predict new diseases or outbreaks of epidemics.

Due to the fact that ChatGPT has the ability to learn and use complex patterns, in the coming years it will play a key role in diagnosing complex diseases that are difficult for doctors to diagnose. In general, the application of ChatGPT in the diagnosis of diseases can be an inspiration for doctors and help to treat patients better. ChatGPT can help patients gain more knowledge about their diseases, and this interaction with the virtual assistant will make the communication between the patient and the doctor more accurate, which will play an effective role in better treatment of diseases. Also, it can help patients to automatically diagnose some symptoms and diseases so that they can regain their health. Interestingly, with the development of new technologies, ChatGPT can also help medical students in better education and learning of medical concepts. The combination of these factors will make ChatGPT become an important tool in the future in various fields such as medicine, information technology, digital marketing, etc.