Artificial Intelligence Is A Field Of Research In Which Techniques And Algorithms Are Presented To Build Systems With The Ability To Perform Human-Like Tasks Using Patterns And Data. 

Artificial intelligence includes concepts and techniques such as neural networks, machine learning, data interpolation, classification, pattern recognition, natural language processing, recommender systems, text generation, and more.

Getting to know the basic concepts of artificial intelligence is very important. It allows you to learn the basic concepts and principles of artificial intelligence and use them 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 built on the human brain. These networks consist of multiple layers of processing nodes that work together in parallel, allowing computers to recognize different patterns automatically.

A neural network usually consists of three layers: input, hidden, and output. The input data is given to the neural network in the input layer; the network’s result is obtained in the output layer. 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 is 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 charges related to the world of artificial intelligence. give

In machine learning, unique algorithms learn from input data such as images, text, audio, video, etc. These algorithms automatically learn from new data in a stable and improvable manner without manual programming and their accuracy and efficiency increase over time.

Some machine learning algorithms are:

1.  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 background.
2.  Supervised learning: In this algorithm, input data and their corresponding output are given to the computer, which tries to build a model to predict new results.
3.  Unsupervised learning: In this algorithm, the computer processes unlabeled input data, tries to discover patterns, and automatically categorizes the data.

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

Natural Language Processing

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

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

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

One of the critical challenges in natural language processing is the many differences that exist in natural language; For example, there are differences in the use of words and sentences in different languages, different dialects of the same language, and everyday and official language. For this reason, unique and robust algorithms are needed for natural language processing, and a lot of data is required to train these algorithms.

data analysis

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

When Data analysis is collected from various sources such as databases, Excel files, logs, payment systems, etc., after data is collected, it must be organized and placed in 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 analyses can include data detail analysis, comparative analysis, factor analysis, etc.

Finally, the results obtained from data analysis using graphic and image software can be presented to users and decision-makers through reports and charts.

Data analysis is one of the main elements in the decision-making process and productivity 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 want. These systems are used in many websites, online stores, video, and music streaming systems, social networks, and 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 seek. These offers can be determined based on purchase history, user reviews and ratings, favorite content, and many other factors.

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

Recommender systems benefit businesses for two critical reasons: First, they help companies to access their products and services more efficiently 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

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

These models are usually trained using data and deep learning algorithms to solve a specific problem. One of the challenges of black boxes is understanding and accurately interpreting their results and outputs. For example, in image recognition, a model may respond accurately to images of animals and objects. Still, we may be unable to precisely understand which image features were influential and how decisions were made.

While black boxes are helpful for many artificial intelligence issues 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 models’ reactions are essential. For this reason, trying to develop models with high interpretability is one of the main challenges in artificial intelligence.

What is the LLM big language model?

Large Language Model refers to deep neural network models trained for different languages ​​to process and understand texts and linguistic varieties. Using extensive linguistic data, these models learn words, sentences, and language structure and can answer users’ questions or requests precisely and semantically.

LLM is one of the most popular major language models. These models use deep neural networks with a unique structure called Transformer and increase their knowledge by training on giant linguistic data sets, such as Wikipedia texts. With high accuracy and speed, this model can produce continuous texts, answer 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 more innovative and more effective solutions in the language field.

What is a GAN?

GAN stands for “Generative Adversarial Networks,” one of the most widely used deep learning algorithms in data and image generation. This algorithm was introduced by “Ian Goodfellow” in 2014 and quickly became one of the most influential 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 newly generated data and the actual data. Over time, the generator network challenges the detector network by progressing in developing new data, making it harder to distinguish between the newly created data and the actual data.

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

To calculate

Computing is one of the main activities of artificial intelligence. 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 allowing a computer to solve problems and perform calculations automatically. For example, algorithms such as search, optimization, machine learning,s, and many others are used in artificial intelligence.

Also, deep neural networks and other models trained using a data set are used to perform calculations in artificial intelligence. 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 artificial intelligence applications.

Since artificial intelligence performs calculations with high accuracy and speed, it is 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 data sets and understand their relationship.

An example of alignment in artificial intelligence is automatic translation. We aim to translate a text from one language to another automatically. 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 to solve the text correctly and automatically.

Alignment in artificial intelligence is used in automatic translation and many other fields. For example, in natural language processing, alignment between different sentences of a text is critical. In machine vision, alignment between additional images is also used. Alignment helps us automatically compare other data and understand their relationships.

last word

These basic concepts of artificial intelligence are only a tiny 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.