Generative AI models are models that have the ability to generate new and creative data using machine learning algorithms. These models use the training data given to them in the training phase to generate new data.

Generative Adversarial Networks

One of the most famous models of generative artificial intelligence are adversarial generative networks. In this model, two networks are placed in front of each other, which is a generative network and a discriminative network, which is also called an observer. To better understand the operation of these networks, let us refer to a simple example.

Suppose we want to teach a 3-year-old child to draw faces. what do we do First, we draw some pictures of faces for him and ask him to imitate us and try to draw like us. This process may be repeated several times, meaning that we constantly draw pictures so that the child learns gradually; In the following, we find faults in his painting and ask him to paint better.

Ideally, we continue this process until the child can draw a picture exactly like our drawings; In such a way that if an "observer" comes from outside, he won't notice the difference between our drawing and a 3-year-old child. From this point on, the child can act like a facial model. In fact, in this idea, there are two artificial neural networks: the generating network of the 3-year-old child and the monitoring or detecting network.

The productive and monitoring networks are constantly interacting and confronting each other. The generator is constantly trying to produce more similar to the database so that the observer cannot distinguish the difference between his work and the original example. Over time, the observer learns more productive tricks and becomes stronger in distinguishing the original from the fake until the two reach a relative balance. Using the training data, the generative network tries to generate new and innovative data and provide it to the diagnostic network. The diagnostic network has the task to evaluate the data generated by the generative network based on the real data already trained and give feedback to the generative network so that this network can generate better data.

Applications of generative artificial intelligence models

Generative AI models are used in many fields, including music, image, text, video, and even 3D image generation. For example, in the field of music production, generative AI models can be used to generate new and creative music that would otherwise take months for composers to create. Also, in the field of image and video, these models can be used to produce new and creative images and videos.

Also, generative artificial intelligence models are used in fields such as machine translation, text generation, product design, customer behavior prediction, disease diagnosis, etc. In general, generative artificial intelligence models enable the generation of innovative and new high-quality data that can be used in many applied fields.

Types of artificial intelligence and how they work

Generative artificial intelligence is divided into different groups, the most important of which are generative neural networks and probabilistic generative models. Generative neural network models are used to generate new data. By receiving training data, these networks learn how to generate new data. Generative neural network models are usually used in the field of image and sound production. One of the most famous generative neural network models are adversarial generative networks that we mentioned. Probabilistic generating models are also used to generate new data, however, the above models have the ability to generate new data by using the probability distribution of training data. Probabilistic generative models are mainly used in the context of text and structured data.

One of the most famous probabilistic generator models is the Variational Autoencoder network. In this model, an encoder network and a decoder network, both of which are neural networks, are used. Encoder network is used to transform the input data into a hidden space with less dimensions, then the generative network transforms this hidden space into data similar to the training data. This model is used as an automatic text generation, image generation and structured data generation model.

In both generative AI models, new generated data are generated based on the training data given to them in the training phase. Also, using machine learning algorithms, these models try to generate new high-quality data that closely resembles the training data. For example, an image generating artificial intelligence model can generate new images with similar features to the training images using the training data. Also, a text generating artificial intelligence model can generate new text with similar style and topic to educational texts. In both cases, the model attempts to generate new, high-quality, creative data that closely resembles the training data.

What is the architecture of productive artificial intelligence models?

Generative AI models are models that transform input data into a series of meaningful outputs using machine learning algorithms. These models have various architectures, some of which are mentioned below.

Recurrent Neural Architecture (RNN)

It includes hidden layers of retrieval that transfer information from the past to the present with short-term memory. A prominent example in this field is the LSTM model. Recurrent neural architecture is a type of deep neural network used to process sequential data such as text and audio. In this model, the input data is received sequentially and in each step, the previous output of the network is used as a new input. This capability allows the network to retain information about previous data and act as a short-term memory.

Recurrent neural networks consist of two types of layers, which are called recurrent layer and fully connected layer. In the regression layer, each unit has an internal state that is updated at each time step. This internal state acts as a type of short-term memory that stores information about previous data. In the fully connected layer, the output from the recurrent layer is received as input and the final output is calculated.

Recurrent neural architecture is capable of processing sequential data and is used as one of the most popular neural network models for text, audio and image processing. Due to its ability to retain information about previous data, this network is well used in prediction and generation of long sequences, such as machine translation, poetry generation, and speech recognition. An important question that readers may have in this section is whether recurrent neural architecture is still popular compared to convolutional neural networks. Yes, the recurrent neural architecture remains one of the most popular neural network architectures and is used in many sequential data processing problems. However, with the expansion of convolutional neural networks and their ability to process images and videos, in many sequential data processing problems such as machine translation and text generation, convolutional neural networks are proposed as a worthy alternative to recurrent neural architecture.

Convolutional neural networks and recurrent architecture have different features and depending on the type of features needed in each specific problem, one of them works better. In general, recurrent neural architecture is used to process sequential data such as text and audio, while convolutional neural networks are more suitable for processing data such as images and videos. However, in many sequential data processing problems, these two architectures are used together for best results. For example, in the machine translation problem, recurrent architecture is used for text processing, and convolutional neural networks are used for image and word processing. Also, in more complex problems, such as audio and video recognition, architectures that include both types of networks can be used. In short, both neural network architectures should be used according to the needs of the given problem.

Convolutional Neural Network

These networks consist of convolutional layers that apply various filters to the input data to identify patterns. Inception and ResNet are two famous examples of these networks. Convolutional neural networks are a special type of deep neural networks used to process data such as images and videos. In this model, a convolutional layer is used, which has the ability to extract important features from the input data.

The convolutional layer is divided into three main parts: convolution layer, pooling layer and perceptron layers. In the convolutional layer, different features of the image are extracted using convolutional filters. These filters move on the image in the form of a concept called window and by reducing the size of the image and increasing the number of filters, they extract different features from the image. In the integration layer, by reducing the dimensions of the image and integrating the important features, the number of parameters is reduced and thus the processing speed is increased.

In the above architecture, after the convolution and integration layers, perceptron layers are used, which are used to classify and recognize different patterns from the features extracted from the image. Due to its high capabilities and better performance in image data processing, the above architecture is known as one of the most popular neural network architectures today. This model is used in many image and video processing problems, including object detection, face detection, vehicle performance detection, disease detection, etc. Also, convolutional neural architecture is used in other areas such as speech processing and text processing. Today, companies use this architecture in the following areas:

Object detection: these networks are successful in detecting objects in images and videos. Also, they are used in the field of car diagnosis, animal and plant diagnosis, and disease diagnosis.

Face recognition: convolutional neural networks are used in face recognition and identification of people in images and videos, security systems, attendance systems, identity recognition systems and surveillance systems.

Car performance detection: convolutional neural networks are used in car performance detection, identifying the strengths and weaknesses of cars, accident detection, and in general in the self-driving car manufacturing industry.

Speech processing: Normally, most of the tools used in the field of speech processing and recognition of audio features, such as recognition of speech signals, recognition of speech according to people's voices, and intelligentization of text-to-speech conversion systems, are designed based on convolutional neural networks.

Text processing: convolutional neural networks are used in text processing and recognition of language patterns such as recognition of emotions and the topic of text, recognition of text type, recognition of language type, etc.

Machine translation: In machine translation systems, this model is used to recognize language patterns and convert them into meaningful concepts.

Transformer architecture

Transformer architecture is based on a concept called Attention, and as its name suggests, it tries to extract and use only important and useful data from the data set. BERT and GPT models use this architecture. Transformer neural network architecture is one of the main architectures used in the fields of natural language processing and machine translation. This architecture was introduced in 2017 by Remihargo and his colleagues in an article, and in a short period of time, it became one of the most popular language processing architectures.

Compared to old models such as RNN and LSTM, Transformer is more accurate in natural language processing and machine translation by modeling the connections between words using the attention mechanism instead of learning time sequences. In this model, instead of using recurrent networks, Attention layers are used. The Attention layer allows the network to access more important words in the text and helps maintain accuracy in language processing. For example, in a 100-word sentence, words such as the main verb, common nouns, and proper nouns are important and should be carefully evaluated. In such a situation, the transformer model performs well.

In the transformer, the input data to the network is converted into a fixed-length vector, and then it is repeatedly displayed in the encoder layers of the network using Self-Attention layers to finally extract different features of the text. In these layers, each word is compared with all other words in the sentence and their importance is calculated using the Attention value.

Then, in the decoder layers, the translated text is produced by using the accuracy of the encoder and using the Masked Self-Attention mechanism. Transformer provides the best performance in many problems of natural language processing, machine translation and automatic text generation due to the ability to learn non-linear relationships as well as the ability to access more important words in the text. Also, this model is used in many natural language processing applications such as emotion recognition in text, automatic question and answer, and text summary generation. Other applications of this architecture in natural language processing include the following:

Machine translation: Transformer can be very effective in machine translation systems. Using this model, you can translate a sentence into another language.

Emotion detection: Transformer can help in detecting emotions in text. Using this model, you can recognize that a text is, for example, good, bad or neutral.

Automatic text generation: Transformer can be very effective in automatic text generation. Using this model, you can generate a new text based on an input text.

Automatic answering of questions: Transformer can be effective in answering questions automatically. Using this model, you can ask a question and get an answer automatically.

Text summarization: Transformer can be effective in text summarization. So that it is able to summarize the text and highlight the important information.

Production of poetry and stories: Transformer can be efficient in the production of poetry and stories. Using this model, you can generate a new poem or story based on an input text.

Text tagging: Transformer performs well in the field of text tagging. Using this model, you can automatically recognize text labels.

Graph Neural Network

A graph neural network is a type of neural network used to process graph data. In these networks, the network input is a graph that consists of nodes and communication edges between them, and the model tries to extract useful information from this graph and use them for a specific application.

These networks are based on graphs and relationships between nodes and are used for issues such as predicting social relationships. In traditional neural networks, the network input is a vector of numbers or images, but in a graph neural network, the input is a graph of nodes and edges of that graph. In a graph neural network, each node in the graph contains features that are usually located in a vector. The main purpose of graph neural network is to extract useful features from this graph. For this purpose, operations such as graph convolution and graph pooling are used in these networks. These operations allow the graph neural network to extract useful information from the graph structure and finally produce the result in an output layer using an activation function.

Graph neural network are used in many applied fields such as computational chemistry, natural language processing, social networks, telecommunication network analysis and many other fields. For example, in the field of computational chemistry, graph neural network is used to predict the chemical properties of molecules. Also, in the field of natural language processing, graph neural network is used to process texts that contain connections between words, such as social network texts and data related to questions and answers.

In general, graph neural network is a powerful approach for graph data processing that can be applied in many application areas. These networks make it possible to extract useful features from graphs and can be effective in solving complex and challenging problems related to graphs. What applications for graph neural network in the field

What is the future of productive artificial intelligence?

As we mentioned, Generative Artificial Intelligence refers to a group of artificial intelligence algorithms and systems that are capable of generating new and creative content, including images, music, text, video, etc. Generative artificial intelligence systems generate new content automatically without the need for human intervention using deep neural networks. In the future, generative artificial intelligence will be of great importance as one of the important technologies in the field of artificial intelligence. With the advancement of technologies and algorithms used in these systems, we can expect that their performance and efficiency will improve and they will be able to produce more creative and realistic content. These systems can be used in various fields, including advertising content production, music and film production, educational content production, etc.

With the increase in the use of productive artificial intelligence, issues such as ethics and legality of content production will receive more attention. For example, the production of fake and imitation content that may be used in advertisements, news and even political images can have serious negative effects on society. Therefore, there is a need to develop approaches to control and monitor the production of artificial intelligence content. For this reason, increasing awareness and attention to ethical and legal issues related to productive artificial intelligence will be very important in the future.