Getting to know the basic concepts of artificial intelligence is very important, so that it allows you to get to know the basic concepts and principles of artificial intelligence and use it to solve different issues and problems. Some basic concepts of artificial intelligence are:

 

neural network

 

Neural networks are one of the most important artificial intelligence algorithms that are built on the basis of the human brain. These networks consist of multiple layers of processing nodes that work together in parallel and allow computers to automatically recognize different patterns.

 

A neural network usually consists of three layers: input layer, hidden layer and output layer. In the input layer, the input data is given to the neural network and in the output layer, the output of the network is obtained. The hidden layer is located between these two layers and is used as a stage for data processing and feature extraction.

 

In the neural network, each node or nerve has weights that indicate the importance of each input in the processing. Then, these inputs are added with their respective weights in each node and the activation function of that node is applied. The activation function has a role similar to how nerve cells work in the human brain, and with its help, the neural network can identify different patterns.

 

Neural networks are used in many fields such as pattern recognition, imaging, natural language processing and recommender systems due to their learning ability, ability to process complex data and high predictability.

 

machine learning

 

Machine learning is a subset of artificial intelligence that allows computers to automatically learn from their input data and use them to perform tasks such as prediction, classification, pattern recognition and many other tasks related to the world of artificial intelligence. give

 

In machine learning, special algorithms are used to learn from input data such as images, text, audio, video, etc. These algorithms automatically learn from new data in a stable and improveable manner without the need for manual programming, and their accuracy and efficiency increase over time. Some machine learning algorithms are:

 

 Reinforcement learning: In this algorithm, an agent in an environment interacts with the environment and by receiving different rewards, tries to find an optimal policy for the best performance in the environment.

 Supervised learning: In this algorithm, input data is given to the computer along with their corresponding output, and the computer tries to build a model to predict new outputs.

 Unsupervised learning: In this algorithm, unlabeled input data is processed by the computer and the computer tries to discover patterns and categorize the data automatically.

Machine learning algorithms are used in many fields such as face recognition, pattern recognition, natural language processing and many other applications of artificial intelligence and are very important due to their predictive ability and high accuracy.

 

Natural Language Processing

 

 

Natural language processing is a process that allows computers to extract information from natural language (such as English, Farsi, etc.) and understand and analyze them using different algorithms and models.

 

In natural language processing, algorithms such as grammar analysis, semantic analysis, sentiment analysis, and topic analysis are used to process texts. These algorithms allow the computer to automatically and improve, process and analyze different texts.

 

Applications of natural language processing can be used for machine translation, sentiment analysis, spam detection, automatic document processing and many other useful applications.

 

One of the important challenges in natural language processing is the many differences that exist in natural language; For example, there are differences that can be found in the use of words and sentences in different languages, different dialects of the same language, everyday and official language. For this reason, to process natural language, special and strong algorithms are needed, as well as a lot of data to train these algorithms.

 

data analysis

 

 

Data analysis is a process in which data is collected, organized, analyzed and the results obtained from them are used to provide decisions and support business and scientific processes.

 

In data analysis, data is collected from various sources such as databases, Excel files, logs, payment systems, etc. After the data is collected, it must be organized and put into a logical and usable form in analytical programs.

 

In the next step, using different data analysis algorithms, the information in the data is extracted and analyzed. These analyzes can include data detail analysis, comparative analysis, factor analysis and many others.

 

Finally, the results obtained from data analysis using graphic and visual software can be used to present to users and decision-makers in the form of reports and charts.

 

Data analysis is used as one of the main elements in the decision-making and productivity process in companies and organizations, in many industries and economic sectors such as marketing, finance, science, health, etc.

 

Recommender systems

 

 

Recommendation systems are intelligent systems that use data analysis to give users suggestions for shopping, content, reading, or anything else they're looking for. These systems are used in many websites, online stores, video and music streaming systems, social networks and many other communication platforms.

 

In recommender systems, by analyzing data collected about users' interests, tastes, and behavior, users are provided with suggestions for products, services, and content they may be looking for. These offers can be determined based on purchase history, ratings, user reviews and ratings, favorite content, and many other factors.

 

In recommender systems, different algorithms are used, such as content-based filters, procedure-based filters, and deep learning models. These algorithms help users to achieve a better experience in using the system and increase user satisfaction with the system.

 

The use of recommender systems is beneficial for businesses for two important reasons: First, these systems help businesses to more easily access their products and services and interact with other users. Secondly, by using recommender systems, it is possible to improve the user experience and increase their satisfaction with the system.

 

black box

 

 

Black box in artificial intelligence refers to models whose behavior and internal functions are not clear and understandable for humans. In other words, in such models, the process of making decisions and learning is done by complex and usually multi-layered algorithms (Deep Learning), and we cannot clearly understand how these algorithms make their decisions and how they respond to learning data.

 

These models are usually trained using a set of data and deep learning algorithms to solve a specific problem. One of the challenges of black boxes is to understand and accurately interpret their results and outputs. For example, in the field of image recognition, a model may respond with high accuracy to images of animals and objects, but we may not be able to understand exactly which image features were influential and how decisions were made.

 

While black boxes are useful for many artificial intelligence problems due to their high power and efficiency, for some other problems such as health and medicine and cyber security, transparency and the ability to accurately interpret and prove the reactions of models are of great importance. . For this reason, trying to develop models with high interpretability is one of the main challenges in the field of artificial intelligence.

 

What is the LLM big language model?

 

 

Large Language Model refers to models of deep neural networks that have been trained for different languages ​​in order to process and understand texts and linguistic varieties. These models, using a set of very large linguistic data, learn words, sentences, and language structure, and can answer users' questions or requests very precisely and semantically.

 

LLM is one of the most popular major language models. These models use deep neural networks with a special structure called Transformer and increase their knowledge by training on very large linguistic data sets, such as Wikipedia texts. This model, with high accuracy and speed, is capable of producing continuous texts, answering questions, automatic translation and many other linguistic applications.

 

LLM is used in various fields such as natural language processing, text generation, question and answer, automatic translation and many other fields of application, and by improving and developing it, we hope to be able to look for smarter and more effective solutions in the language field.

 

What is a GAN?

 

 

GAN stands for "Generative Adversarial Networks", which is one of the most widely used deep learning algorithms in the field of data and image generation. This algorithm was introduced by "Ian Goodfellow" in 2014 and quickly became one of the most effective and widely used data generation methods.

 

In GAN, two neural networks are placed against each other. One of these networks, called the "generator network" (Generator), tries to create new data, and the other network, called the "discriminator network" (Discriminator), tries to distinguish between the new generated data and the real data. . Over time, the generator network challenges the detector network by progressing in generating new data, making it harder to distinguish between the newly generated data and the real data.

 

As a result, with this method, the generator network can generate new data that closely resembles the real data. This algorithm is used in the field of image and video production, music production, automatic translation, text production and many other applications of deep learning.

 

to calculate

 

 

Computing is one of the main activities of artificial intelligence. In fact, artificial intelligence is usually used to solve complex mathematical and computer science problems and perform complex calculations with high accuracy and speed.

 

Different algorithms are used to perform calculations in artificial intelligence. Algorithms are a series of mathematical rules and instructions that allow a computer to automatically solve problems and perform calculations. For example, algorithms such as search algorithm, optimization algorithms, machine learning algorithms and many other algorithms are used in artificial intelligence.

 

Also, to perform calculations in artificial intelligence, deep neural networks and other models trained using a set of data are used. These models learn and process data using different algorithms and are usually used to solve complex problems such as image recognition, automatic translation, question and answer and many other applications of artificial intelligence.

 

Since artificial intelligence performs calculations with high accuracy and speed, it is used as a powerful tool in many fields such as computer science, mathematics, data science and many other applied fields.

 

Alignment or alignment

 

 

Alignment in artificial intelligence means obtaining relationships between two data sets or two different types of information. In other words, alignment helps us compare two sets of data and understand the relationship between them.

 

An example of alignment in artificial intelligence is automatic translation. In automatic translation, our goal is to translate a text from one language to another. For this, we must first create an alignment between the sentences of the original text and the translated text. In other words, we must establish a relationship between the words and sentences of these two texts in order to be able to translate the text correctly and automatically.

 

Alignment in artificial intelligence is not only used in automatic translation but also in many other fields. For example, in natural language processing, alignment between different sentences of a text is very important. In the field of machine vision, alignment between different images is also used. In general, alignment helps us to automatically compare different data and understand the relationships between them.

 

last words

 

 

These basic concepts of artificial intelligence are only a small part of the concepts that exist in the world of artificial intelligence. However, familiarity with these concepts can help you better understand how AI works and how you can use it to solve your problems.