What is artificial intelligence?

Artificial intelligence, or in other words Artificial intelligence, which is also known as artificial intelligence AI today, is a new way to make smart tools by modeling human intelligence. A tool that thinks like a human and makes decisions for him. In fact, this technology is the same machine programmed by human hands, which is designed with the aim of making everyday tasks easier.
Many people think of artificial intelligence as a robot that is physically visible. While in most cases, this concept is presented in the form of a response to human behavior and derived from his interests and tendencies. Artificial intelligence (AI) is a broad branch of computer science and is considered one of the interdisciplinary sciences. This concept means a machine that thinks like a human and has the ability to imitate human behavior. Such a machine can perform tasks that require human intelligence.

Can machines think?

During World War II, German forces used the Enigma machine to encrypt and send messages securely. At that time, Alan Turing, an English mathematician and scientist, was trying to break these codes. Less than a decade later, for the second time, Turing changed history with a simple question: “Can machines think?”
Turing’s paper “Machine Computing and Intelligence” in 1950, followed by the Turing Experiment, set the fundamental goal and outlook of the field. Artificial intelligence, in fact, is a branch of computer science that tries to give a positive answer to Turing’s question; This is an attempt to replicate or simulate human intelligence in machines.

Artificial intelligence approaches
Computer scientists Stuart Russell and Peter Norvig examine four different approaches that have historically defined the field of artificial intelligence. These approaches include:
• Humane thinking
• Thinking logically
• Acting humanely
• Act rationally
The first two ideas, human thinking and logical thinking, are related to thinking and reasoning processes; While the next two (acting humanely and acting rationally) deal with behavior. In these approaches, Norvig and Russell focus on the logical factors to achieve the best result.

What are the advantages and disadvantages of artificial intelligence?

Artificial intelligence (AI) is one of the widely used technologies that help simplify many processes.
The applications of this technology, from ranking web pages to designing clothes based on users’ tastes, are very different and wide. Artificial intelligence means a machine that thinks like a human and has the ability to imitate human behavior. There are different views about artificial intelligence. Some people are very optimistic about this technology and consider it a blessing to improve human life. On the other hand, there are also groups who believe that the use of artificial intelligence can be disastrous for humans. To understand the reasons for the existence of these two types of views, it is necessary to know the advantages and disadvantages of this technology. In this article, we examine some of the disadvantages and disadvantages of artificial intelligence technology.
• Less room for mistakes

Since the decisions made by machines are based on previous data records and a set of algorithms, the possibility of error in this type of decision is reduced. This issue is considered an important achievement; Because it makes complex problems that require difficult calculations to be solved without any errors. Less room for error Advanced business organizations use digital assistants to interact with users. This saves time and provides better and faster services to users.

• Making the right decision
The fact that machines lack any emotion makes them more efficient; Because they can make the right decision in a short period of time. The best example of this feature is the use of machines in medical care. The integration of artificial intelligence tools in the medical care sector improves the efficiency of treatment procedures by minimizing the risk of misdiagnosis.

• Application of artificial intelligence in risky situations
In some special situations where human safety is at risk, machines equipped with predefined algorithms can be used. Today, scientists use sophisticated machines to study certain conditions, such as the ocean floor. This is one of the biggest limitations that artificial intelligence helps humans to overcome.

• Ability to work continuously
Machines, unlike humans, do not get tired; Even if they have to work for hours on end. This feature of machines is considered an important advantage over humans, who need rest from time to time to maintain their efficiency. If the efficiency of the machines is not affected by any external factors and nothing prevents their continuous work.
The disadvantages of artificial intelligence are as follows:
• High cost of using artificial intelligence ai
When you factor in the costs of installing, maintaining, and repairing AI systems, this technology is an expensive proposition. So that only individuals and groups with huge budgets can implement it, and businesses and industries that don’t have enough budgets find it difficult to implement this technology in their processes or strategies.

• Dependence on machines
With the increasing dependence of humans on machines, we are reaching a period when it becomes difficult for humans to work without the help of machines. As we have seen in the past, human dependence on machines will definitely increase. Therefore, over time, human mental and intellectual abilities decrease.

• Replacement of low-skilled jobs
This issue, until now, has been the main concern of technology supporters. Artificial intelligence is likely to replace many low-skilled jobs. Since machines can work 24 hours a day, seven days a week without interruption, industrial owners prefer to invest in machines instead of humans. As we move into an automated world (where almost all jobs are done by machines), unemployment on a large scale becomes more likely. An example of this is the concept of driverless cars. If these types of cars take off, millions of drivers will be out of a job in the future.

• Limited work
Artificial intelligence machines perform certain tasks based on their training and programming. Relying on machines to adapt to new environments, be creative and think outside the box would be a big mistake. Such a thing is not possible; Because the field of thinking of machines is limited only to the algorithms that are trained for it.

Types of artificial intelligence

Artificial intelligence is divided into four general models: reactive machines, limited memory, theory of mind, and self-awareness. Each of these models are used in many fields according to their purpose and capabilities.
• Reactive Machines
One of the oldest models of artificial intelligence is reactive machines that are designed only to perform specialized tasks. These machines are not able to store information. As a result, it is not possible to make decisions based on past experiences and they are made only to meet the needs of people. Google search engine is a good example of this feature.

Limited memory
In this model, with the help of artificial intelligence, it is possible to store information and make decisions based on previous data. In fact, the basis of a machine’s behavior is the cues that have been provided in the past. Authentication and identification of people in different systems are of this type.
Authentication by Internet of Things – Types of Artificial Intelligence Artificial intelligence is divided into four general models of reactive machines, limited memory, theory of mind and self-awareness.

Theory of Mind
The theory of mind means that artificial intelligence can better understand the feelings, emotions and beliefs of humans and then use this information to make their own decisions. This branch of science is still developing and if it succeeds, a huge change will occur in human life.

• Self-aware
The purpose of designing a self-aware model is to simulate the human brain. In a way that the level of understanding of a machine is equal to the awareness and understanding of a human. In this hypothesis, a robot will have the power to understand the feelings and needs of others and communicate with them like a human.

What is the role of artificial intelligence in business?
With the growth of computers, smartphones and social networks, fewer businesses still operate in a traditional way. Looking around us, we will realize the effect of this importance on people’s behavior and lifestyle. In today’s world, people wake up with their smart watch alarm. They refer to their work calendar that is set in the smart software. They use social networks on the way to work and by the end of the day they make many choices based on the offer of different platforms. This is the undeniable power of a comprehensive product.
In fact, it can be said that the application of artificial intelligence in business has played an important role in speeding up and simplifying all daily events. It’s the same in business, businesses are already using this achievement to succeed in three main areas:
• Smartening of products and services
• Smartening processes through data analysis
• Interaction with customers and employees
• Smartening of products and services
The strategy of artificial intelligence in business is based on the optimal use of a product with the aim of satisfying users and increasing sales. Providing services based on artificial intelligence also means providing a targeted service to customers that will lead to the formation of customer interactions and their loyalty to an organization. Benefiting from this technology in both product or service topics will increase the profit of any business.
For example, consider an online food ordering service compared to traditional ordering. When placing a food order in the traditional way, it is not possible to track the order, measure the level of customer satisfaction, wait time and other things, while relying on artificial intelligence technology and using the data received from each service, it is possible to understand the behavior of customers. Analyze and try to improve the business. Let’s not forget that in the current market, a business that tries to simplify people’s lives and respond to their needs faster by providing smart solutions will be more successful.

• Smartening processes through data analysis
One of the goals of artificial intelligence is to facilitate various processes for users. In every organization, with the help of this technology, different data can be collected and then analyzed at different stages, including strategy, production, product and service delivery. In this way, the strengths and weaknesses of an organization are identified and it will be possible to plan for its growth and development. In fact, artificial intelligence brings many opportunities for customization and optimization.

• Process intelligence and data analysis
The example of ordering food online is also used in this section. By knowing the level of customer dissatisfaction of an organization, its regulatory bodies will be able to provide solutions to solve this problem. In fact, the best way is to identify which areas of improvement are priority for your business and what is the application of artificial intelligence in your business? Do you need to know where it adds the most value to your company? Then decide what your business’s goal is and how artificial intelligence will help to achieve this goal. In this way, you can gain a true understanding of the smartness of your organization’s processes.

• Interaction with customers and employees
Customer relationship management is one of the main pillars of any business. Establishing proper interaction with customers and storing their information is very necessary to provide better services. With the help of artificial intelligence, it is possible to identify the interests, beliefs and desires of each user and provide attractive offers based on his needs. Email sending services or CRM automations are good examples of this. Categorizing users based on specific parameters and providing timely solutions when problems occur is the secret of success for large organizations. For example, by relying on artificial intelligence, users who were born on a certain date can be identified and by sending a smart email, special discounts can be presented to them as a gift.
But many employees have seen artificial intelligence as a threat to lose their jobs. While this technology is designed to manage the interactions of employees and customers.

Advantages and disadvantages of using artificial intelligence in business
As mentioned earlier, the use of artificial intelligence brings many concerns for people. Concerns about job security, the diminution of emotions, the control of behavior by machines and the rule of robots are all of these. Among the advantages of artificial intelligence, we can mention the ability to perform tasks with high accuracy, continuous work without the need to rest, making correct decisions and away from emotions in different situations.
But sometimes these positive points may also lead to problems. In other words, making a decision without considering the emotional aspects will be a great risk for humans. On the other hand, excessive dependence on machines, software, and intelligent robots have also reduced the efficiency of people’s activities. Imagine being alone in a room without internet or mobile phone for just one hour. Without this tool, people often feel confused and anxious, and this is due to their lack of resilience. Another point is the high cost of using each tool. A significant part of people’s monthly expenses is spent on using various technologies. In business, there may be many pros and cons to using artificial intelligence, but the advantages always outweigh the disadvantages of this technology.

Examples of artificial intelligence
Related advertising is one of the applications of artificial intelligence, which is done in order to target the main audience and deliver relevant messages to them. But this technology has many other uses. The ranking of web pages based on user interests, automatic response in messaging software, clothing design based on users’ tastes, and facial recognition technology are examples of the applications of this technology.
Artificial intelligence is a phenomenon in which a machine with the ability to understand, analyze and learn through special algorithms acts as an intelligent program. AI machines can remember human behavior patterns and adapt according to their preferences. Contrary to popular belief, artificial intelligence is not limited to information technology or the technology industry. This technology is widely used in other fields such as medicine, business, education, law and production. The following statistics show the state of artificial intelligence development:
• In 2014, more than 300 million dollars were invested in artificial intelligence startups, which was a 300% increase compared to the previous year.

• By 2018, 6 billion devices will request support by default.
• By the end of 2018, “customer digital assistants” will recognize customers by face and voice.

• Artificial intelligence will replace 16% of American jobs by the end of the decade.

• 15% of Apple phone users use Siri voice recognition.

We examine some examples of artificial intelligence applications that are currently widely used.

Siri

Siri is one of the most popular personal assistant apps offered by Apple on iPhone and iPad. This virtual assistant, with a friendly voice, communicates with the user on a daily basis. Siri helps the user in finding information, finding directions, sending messages, making voice calls, opening applications and adding events to the calendar.

Tesla

It’s not just smartphones that are turning to artificial intelligence; Cars have also taken steps in this direction. Tesla’s car has not only been able to attract a lot of praise, but it also has features such as self-driving, predictive capability and absolute technological innovation.

Cogito

Kagito is a powerful software that analyzes the voice of customers who call, for example, a company’s support unit. This software, based on the results of the surveys, simultaneously provides the necessary behavioral recommendations to the employees of the support unit.

Netflix
Netflix is ​​a very popular on-demand content service that uses predictive technology to make recommendations based on users’ reactions, interests, choices, and behavior. This technology, by checking past records, suggests movies based on users’ previous interest and reactions.

• Nest – Google (Nest, Google)
Nest, one of the most successful artificial intelligence startups, was acquired by Google in 2014. Nest smart thermostat uses behavioral algorithms based on users’ behavior to save energy. In the first week, the user adjusts the thermostat to provide basic data on their behavior. Nest then learns what temperature the user prefers at what times and manages all systems to achieve that temperature. This system turns off automatically when no one is home to save energy. In fact, it is a combination of artificial intelligence and Bluetooth Low Energy.

Flying Drones
Drones have already delivered products to customers’ homes. Although this tool was used experimentally. These birds have a kind of powerful machine learning system that can convert the environment into 3D models through sensors and video cameras.
Routing algorithms guide drones on how and where to move. Using a Wi-Fi system, drones can be controlled and used for specific purposes such as product delivery, video making, or news reporting.

“Limited” and “General” artificial intelligence
AI artificial intelligence can be divided into two categories: limited and general. Each of these categories help solve different problems based on their strengths and abilities.
Limited or weak artificial intelligence has the ability to solve more limited problems and is able to operate in certain sectors. In other words, limited artificial intelligence only performs properly in one specific area and its power decreases in other areas; For example, it can translate texts from one language to another, but its capacity in other areas such as image recognition or strategic planning is low.
In contrast, general or strong artificial intelligence is placed, which has the ability to solve problems in various fields and its performance is similar to humans in many tasks and activities. General artificial intelligence has the ability to work effectively in more than one field; For example, it can recognize faces and at the same time analyze individual parts of the face such as eyes, nose and mouth.

How to use artificial intelligence
Artificial Intelligence (AI) helps solve complex problems and simplify previously laborious tasks in a variety of ways. How to use it is as follows:
• Determination of the problem
First, you need to determine the specific problem that needs to be solved or the job that needs to be automated.

• Collecting data
Obtain the information needed to train the information system in question. This information must be appropriate, accurate and complete.

• Choosing a suitable algorithm
Choose the AI ​​program that best fits your topic. Various methods are available such as decision trees and neural networks.

• Artificial intelligence system training
Train the AI ​​system using the collected data. This requires sending data to the program and adjusting it to increase accuracy. After training, you should evaluate the AI ​​system to measure its accuracy and reliability.

• System deployment
After testing and proving the correctness, you should put it in the production stage. This may require integration with existing systems or development of new systems.

• Continuous management of artificial intelligence system
You need to continuously monitor and update the system to ensure proper performance and accurate predictions.

Branches of artificial intelligence

• robotic
• Pattern recognition
• Artificial Neural Networks
• Deep learning
• Speech recognition
• Natural Language Processing
• Machine vision
• Recurrent neural network
• Convolutional neural network
• Artificial intelligence and machine learning
• Reinforcement learning
• Fuzzy Logic

Different levels of artificial intelligence

Artificial intelligence technologies are categorized based on the following:
• The capacity to imitate human characteristics
• Technologies used to do this.
• Real world applications and theory of mind
Based on these characteristics, all artificial intelligence systems, both real and hypothetical, are divided into one of the following three types:
• Narrow Artificial Intelligence or ANI
• Artificial General Intelligence or AGI
• Cloud artificial intelligence or ASI
• ANI

ANI artificial intelligence, also called weak artificial intelligence or narrow artificial intelligence, is the only type of artificial intelligence that we have successfully achieved to date. ANI is goal-oriented and designed to perform unique tasks such as face recognition, speech recognition/voice assistants, driving a car or searching the Internet, and is highly intelligent in completing the specific task it is programmed to perform.
Although these machines may appear intelligent, they operate under a small set of constraints; This is why this type is commonly referred to as weak AI. ANI does not imitate or replicate human intelligence but simply simulates human behavior based on a limited range of parameters and contexts. Consider the speech and language recognition of the virtual assistant Siri on iPhones, or the vision recognition of self-driving cars that recommend products based on your purchase history. These systems only learn to complete specific tasks.

ANI’s artificial intelligence has seen many advances in the past decade, boosted by advances in machine learning and deep learning; For example, today artificial intelligence systems are used in medicine to diagnose cancer and other diseases by replicating human cognition and reasoning. ANI uses natural language processing or NLP to perform various tasks. NLP is evident in chatbots and similar AI technologies and interacts with humans in a natural and personalized way by understanding speech and text in natural language. Examples of narrow artificial intelligence are as follows:
• Google’s RankBrain algorithm
• Siri by Apple
• Alexa by Amazon
• Cortana by Microsoft
• Face recognition software
• Mapping tools
• Special disease prediction tools
• Production and robots for drones
• Email spam filters
• Social media monitoring tools
• Recommending different contents to the user based on his behavior
• AGI
Strong or deep artificial intelligence is a concept of a machine with general intelligence that mimics human intelligence or behaviors and has the ability to learn and use its intelligence to solve any problem. AGI can think, understand and act in a way that is indistinguishable from a human in any situation.
AI researchers and scientists have not yet achieved AGI. To succeed in this field, they must find a way to make machines aware and programmed with a full set of cognitive abilities. Machines must acquire the ability to apply empirical knowledge to a wider range of different problems.
“K computer” developed by Fujitsu and RIKEN Institute is one of the fastest supercomputers. K computer is the biggest effort to achieve AGI artificial intelligence, but considering that it took 40 minutes to simulate one second of neural activity; So it is difficult to determine whether AI will be strong or not.
• ASI
Super artificial intelligence or ASI is actually a hypothetical artificial intelligence that does not just imitate or understand human intelligence and behavior. ASI is where machines become self-aware and exceed the capacity of human intelligence and ability. Superintelligence has long inspired dystopian science fiction. In his stories, robots trample, overthrow, or enslave humanity.
ASI is theoretically better at everything we do, from math to science, sports, art, medicine, hobbies, relationships, and more. ASI has more memory and faster ability to process and analyze data and stimuli; As a result, its ability to make decisions and solve problems is much superior to humans. The potential of having such powerful machines may sound appealing, but the concept has many unknown implications.

What is an artificial intelligence algorithm?

Different artificial intelligence algorithms can be used to solve a group of problems. In the following section, we examine different types of algorithms together.
• Naive Bayes
This algorithm is based on “Bayes’ rule” and is used to estimate the probability of an event. This algorithm acts as a probabilistic classifier and is used to classify problems such as email spam detection or disease diagnosis.

• Decision Tree
In this algorithm, a decision tree is built for data classification. In each node of the tree, a condition is placed based on the characteristics of the data, and according to the condition, the data is divided into child nodes. This process continues until the end nodes are reached.

• Random Forest
This algorithm works based on the combination of several decision trees. Each tree in this algorithm is randomly constructed from available data and features; Then the classification result is determined using the majority voting of the trees.

• Logistic Regression
This algorithm is used for binary classification problems. The probability of an event occurring in each category is calculated using the logistic function, then, based on that, the data is divided into different categories.

• Support Vector Machines (SVM)
This algorithm is specific for linear and non-linear data classification. SVM divides the data into different classes using a plane (for linear data) or a hyperplane (for non-linear data).

• K Nearest Neighbors (KNN)
In this algorithm, to predict the label of a new sample, its nearest neighbors are found in the training data set and assign the most repeated label to the new sample. The working method of the KNN algorithm is that first the distance of the new sample with all the training samples is calculated; Then K nearest neighbors with the shortest distance to the new sample are selected. Finally, according to the labels of the selected neighbors, the label of the new sample is determined. The K number in the KNN algorithm indicates the number of neighbors that are considered. Choosing the right value of K for each problem may have a large impact on the accuracy of the algorithm.

• Linear regression
In the linear regression algorithm, a linear relationship between input and output is found. Using this relationship, the output value is predicted for new inputs.

• K-Means Clustering
In K-Means Clustering, the data is divided into K clusters so that the data of each cluster is close to each other and far from the data of other clusters.

• Gradient Boosting
This algorithm works based on the combination of several weak learner models. In each step, a weak model is added to the previous model and the weights of the samples are determined using the objective function.

• XGBoost
XGBoost is an improved version of Gradient Boosting and improves its performance and speed by using optimization and compression methods.

The position of artificial intelligence in Iran

In Iran, artificial intelligence is being developed and is being used in some fields; For example, Iranian companies in the field of face recognition were able to produce advanced face recognition systems that are used in attendance and security systems, or in the field of body recognition, projects are being carried out in Iranian universities and technology companies.
In addition, in the field of robotics, research and projects are carried out in universities and industry. Some artificial intelligence companies also produced intelligent robots that are able to recognize and respond to different environments and tasks. In the field of economics, artificial intelligence helps to analyze data and predict events. Some companies and research institutions in Iran were able to use machine learning algorithms to analyze economic and financial data. In general, artificial intelligence in Iran is still in the early stages of development and needs more investment and research.

Application of artificial intelligence
Object recognition
Face Recognition

Speech Recognition
• Deepfakes and generative networks (Deepfakes and Generative AI)

• Robotics and artificial intelligence
• Artificial intelligence in the field of business
• Artificial intelligence in the economy
• Artificial intelligence in the field of education
• In the field of production
• In establishing security
• Artificial intelligence and data interpretation
• Artificial intelligence in sports
• Artificial intelligence in social networks
• In legal services
• Radiology field
And…

Challenges of artificial intelligence

Although, since 2023, the field of artificial intelligence AI has witnessed significant developments and attracted wide attention, but among these developments, we must admit that the journey towards artificial intelligence is not without challenges. These challenges in artificial intelligence include countless complexities that require careful and strategic consideration. In this section, we are going to introduce you to the challenges and complexities that prevent the adoption of artificial intelligence.

• Lack of understanding
Artificial intelligence is still a relatively new technology and there is much that is not understood about how it works. This lack of understanding hinders the development of AI systems. To deal with these challenges, companies are trying to understand algorithms, models and artificial intelligence techniques.

• Privacy concerns
Artificial intelligence systems need a large amount of data to train and perform better. This data includes personal and sensitive information and raises concerns about privacy and data protection. To reduce these concerns, artificial intelligence companies should prioritize strong confidentiality measures such as data anonymization or secure data storage. Clear data use policies and obtaining informed consent from individuals also increase trust and reduce privacy concerns.

• Perks
These systems are computationally demanding and require significant processing power to perform complex tasks. This leads to high infrastructure costs. To overcome these challenges, companies must use advances in hardware technology such as specialized artificial intelligence chips and distributed computing systems.

• Lack of data
AI systems depend on large and diverse data to train and achieve optimal performance. However, not all industries have access to the required volume or quality of data. Companies are able to address these challenges in artificial intelligence by strengthening collaborations and partnerships to access relevant datasets or mitigate the problem of limited data access with techniques such as transfer learning, data augmentation, and artificial data generation.

• Unreliable results
AI systems produce unreliable results for various reasons, such as biased or incomplete datasets, algorithmic limitations, or task complexity. To deal with these challenges, companies must emphasize rigorous testing and validation processes during the development of AI systems. Continuous monitoring and improvement will be effective in solving this challenge.

• distrust
Some people may show hesitation or reluctance to trust AI systems, often due to a lack of understanding of how AI works. Building trust depends on transparency and explainability in AI algorithms and decision-making processes. Companies will increase trust by providing clear and accessible explanations of how AI achieves results. In addition, compliance with relevant standards and regulations strengthens the trust of users and stakeholders.

• Unclear goals
Sometimes companies are challenged in setting goals for implementing artificial intelligence in their organization. It is difficult to develop effective artificial intelligence systems without targeting. To overcome these challenges, companies must conduct comprehensive assessments of their business processes and identify specific areas where AI can create value.

• technical issues
Implementing AI involves overcoming technical challenges such as data storage, security, and scalability. Companies must invest in robust infrastructure to be able to manage AI-related data. Ensuring data security and privacy throughout the AI ​​lifecycle is critical to building user trust. Scalability must be considered from the start to meet the demands of AI systems.

• Bias in algorithms
Sometimes AI algorithms inherit biases in the data used and provide unfair or discriminatory results. This challenge is very critical; Because artificial intelligence systems play an important role in decision-making processes in various fields. To address these biases, companies need to implement strategies that promote fairness and inclusiveness.

• Implementation strategy
There is no one-size-fits-all approach to implementing AI. Every company has unique requirements, and an effective implementation strategy must be tailored to its specific needs. Conducting thorough assessments of existing infrastructure, data availability, and organizational readiness is a must. Companies should develop a clear roadmap that outlines the steps, resources, and timelines required to successfully integrate AI.

Will artificial intelligence replace experts?

In laboratory work, there is some interest in algorithms that support operational processes. For example, in inter-laboratory monitoring of diagnostic systems, this technology can detect problems before they occur. This allows for proactive maintenance programs. Clinically, algorithms are suitable for diagnostic decision-making in laboratory medicine. In addition, as in pathology, they are also suitable for predictive analysis based on complex biomarker patterns.
The role of radiologist, pathologist and laboratory doctor may be separated from each other in the future. Perhaps specialists will become “diagnostic information integrators” and put all the pieces of the diagnostic puzzle together as quickly as possible by working more closely together in integrated diagnostic departments.

Artificial intelligence programming languages ​​and techniques

Haskell is a lazy programming language, meaning it evaluates pieces of code as needed.

The problem of artificial intelligence control
The problem of artificial intelligence control means designing and determining methods and algorithms that can control artificial intelligence correctly. In this issue, the goal is for the artificial intelligence to perform its actions properly and accurately and achieve the desired tasks; For example, in the field of robotics, the issue of controlling artificial intelligence includes designing algorithms and methods that robots can recognize and respond to their environment and tasks.

Artificial intelligence arms race
An AI arms race is a competition between artificial intelligence systems. In this competition, artificial intelligence systems compete with each other to show which of them is better at solving a specific problem. This competition is held in various fields such as computer games, image recognition, machine translation, etc. The main goal of the AI ​​arms race is “AI research and development”.

Jobs related to artificial intelligence
Some of the most popular jobs related to AI include:
• Software Engineer
This group of engineers works in software development to create new products, from new and improved chatbots to AI shopping apps. They use programming languages ​​like Python and Java.

• Data scientist
Data scientists collect, organize, and analyze data used in artificial intelligence. They are responsible for tagging data to help improve AI for the future. These people work in technology companies or engineering companies.

• Machine learning engineer
Machine learning engineers use data and algorithms to improve AI tools. They want to help AI improve its accuracy and “think” like a human. The tasks of machine learning engineers include research, analysis and optimization of machine learning formulas.

• Data engineer
These engineers build the digital infrastructure to securely store the data that AI tools need to function properly.

• Natural language processing engineer
Natural Language Processing (NLP) engineers design NLP processing systems; For example, they might create tools that allow AI to recognize speech patterns, much like Alexa that follows your commands. In addition to developing new tools, natural language processing engineers may improve existing tools to improve the user experience.

• Robotics engineer
Robotics engineers use tools such as automation and artificial intelligence (AI) to develop robotic systems. These systems may perform laborious tasks previously performed by humans, from picking warehouse items to cleaning floors.

• Developer of business intelligence (BI)
Business intelligence developers help bridge the gap between AI data and the people who work with it, including product managers, analysts, and executives. They organize and report data in an understandable way.

• Deep learning engineer
Deep learning is a type of machine learning that deals with artificial neural networks. Deep learning engineers seek to improve artificial intelligence so that it can better mimic the way knowledge is acquired.

• Computer vision engineer
Computer vision engineers help AI-based tools see like a human. They create programs that can create and interpret visual information similar to the human brain, such as scanning a QR code to see a restaurant menu.

What is the difference between artificial intelligence and machine learning?
Many people mistakenly think that artificial intelligence and machine learning are synonymous, when they are completely different. If we want to explain this topic in a simpler way, it should be said that artificial intelligence is computer software that uses creative techniques with mathematical numbers rather than all the things that humans do to perform complex tasks, such as analysis, reasoning and learning. imitate what they do On the other hand, machine learning is a subset of artificial intelligence that implements trained algorithms on data to perform complex human tasks well.
Today, technology is rapidly developing and creating amazing things. For example, artificial intelligence, using machine learning, performs and presents every digital request that a human has with maximum quality. Therefore, artificial intelligence can often have more comprehensive applications in addition to machine learning. Another point is that artificial intelligence tries to imitate human behavior and actions by using a computer using code, techniques or mathematical figures. While machine learning has nothing to do with understanding deep and coded concepts.
The scope of artificial intelligence activity is very wide, but machine learning tries to teach a specific task to a machine or devices and helps to automate them. Considering the future of artificial intelligence and its continuous development, this technology is slowly learning to think and make decisions completely like a human. In contrast, machine learning is only able to perform tasks for which it has already received relevant data.