AI ( Artificial Intelligence Technologies)

AI ( Artificial Intelligence Technologies)

Artificial intelligence is one of the latest and important technologies in these days of development, which has been used in various sciences in many cases and directions.

Artificial intelligence has several different branches and parts, some of the most important parts of which are mentioned below.

Inventions and discoveries in the last two to three decades have been almost double the total output in all previous years. This shows the importance of new technologies, especially artificial intelligence, in inventions for inventors. With the improvement of human intelligence every day, and with the emergence of artificial intelligence and data science on the horizon, modern artificial intelligence technologies will affect the world and change the entire future landscape, so far we have seen many of these changes among international inventions.

The concepts of artificial intelligence, machine learning and neural networks have been around for over a century. However, despite their brief initial success, they never had time to shine. The main reasons for their failure was because technological advances had not yet reached their full potential and people did not have enough and correct knowledge to understand this old but emerging technology, which caused the hard acceptance of this technology among the people. Also, there was not enough data for deep learning models and neural networks to make a significant impact, and it has been difficult to collect and access this data for use in designing new inventions.


Deep learning grew in popularity almost a decade ago. The resurgence of neural network hype was sparked by a 2012 incident in which a team led by George E. Dahl won the "Merck Molecular Activity Challenge" by using multitasking deep neural networks to predict the bimolecular target of a drug. After this incident, deep learning and neural networks have been continuously used in performing various tasks related to image segmentation, object recognition and other cases, but their special effects and importance are still related to molecular biology and medicine.


  • Natural language production

Natural language processing machines process and communicate in a different way than the human brain. Natural language generation is a common technology that converts structured data into native language. Machines are programmed with algorithms to convert the data into the desired format for the user. Natural language is a subset of artificial intelligence that helps content developers automate and present content in the desired format. Content developers can use automated content to advertise on various social media platforms and other media platforms to reach target audiences. Human intervention is significantly reduced as data is converted into desired formats. Data can be visualized in the form of graphs, charts, etc. This model of artificial intelligence tools provides the possibility for people, especially for inventors, to make their inventions much easier and using new technology and tools. expand and jump among other inventors and inventions.

How NLP works

Current NLP approaches are based on deep learning; DL is a type of artificial intelligence that examines patterns in data and uses them to improve understanding of complex structures. In deep learning models, a large amount of labeled data is needed to train on the data and identify the relationships in them, and collecting this large data set is one of the main obstacles of NLP.


Older approaches to NLP included a rules-based approach where simpler algorithms such as machine learning searched for words and phrases in text and returned the appropriate response if found; But deep learning is a flexible yet powerful approach in which algorithms learn the intent of speakers from many examples (much like a child learning human language).



  • Speech recognition

Speech recognition Speech recognition is another important subset of artificial intelligence that converts human speech into a useful format that can be understood by computers. Speech recognition is a bridge between human and computer interaction. This technology recognizes and converts human speech into several languages. The iPhone's Siri is a classic example of speech recognition.

Today, various inventions have been designed and built by world inventors around the world using this technology.

Speech recognition is also known as automatic speech recognition (ASR) and speech-to-text. This technology allows computers to convert human speech into text.

The important point here is that the terms Speech Recognition and Voice Recognition are often used interchangeably. However, these two are completely different. Speech recognition means recognizing the words used in a speech, but voice recognition means recognizing the voice of the person speaking.



  • Virtual agents

Growler virtual agents have become valuable tools for instructional designers. A virtual agent is a computer program that interacts with humans. Web and mobile applications offer chatbots as their customer service agents to interact with humans and answer their questions. Google Assistant helps organize meetings and Amazon's Alexia helps make your shopping easier. A virtual assistant also acts like a language assistant that picks up signs of your choice and preference. IBM Watson understands common customer service questions that are asked in a variety of ways. Virtual agents also act as software as a service.

The duties related to the virtual assistant are different according to the customer's needs and the terms of the contract. Some virtual assistants do clerical and bookkeeping tasks, while others may regularly engage in social media or write articles for a blog. Also, a complete and powerful virtual assistant can handle travel arrangements, appointment scheduling, data entry, and online file storage.

Statistics show that jobs such as secretarial, secretarial, and clerical jobs will decrease by about 9 percent from 2019 to 2029. Currently, job growth statistics are not available specifically for virtual assistants, but given that more and more companies are moving to remote work and hiring part-timers to do things that were previously done by full-time employees. There is a possibility of growth of virtual assistant job opportunities.


  •  Decision management

From this model, artificial intelligence tools are used to control different programs and projects, which can be used in easier management control of invention projects. Decision intelligence is a new academic concept that covers all the details of choosing between options. By bringing together applied sciences, social sciences, and management sciences and turning them into a single research field, this concept can help people improve their lives, businesses, and the world around them by using the information obtained. It is also considered a vital science in the field of artificial intelligence and covers all the skills needed to implement artificial intelligence projects at any scale.

Modern organizations are implementing decision management systems to transform and interpret data into predictive models. Enterprise-level applications implement decision management systems to obtain up-to-date information to perform business data analysis to aid in organizational decision-making. Decision management helps in quick decision making, risk avoidance and process automation. Decision management system is widely implemented in financial sector, healthcare sector, business, insurance sector, e-commerce etc.


  • Biometrics

Deep learning biometrics is another branch of artificial intelligence that works based on artificial neural networks. This branch is studied and used in many matters of artificial intelligence tools due to its breadth. This technique teaches computers and machines to learn by example the way humans do. The term "deep" was coined because it has hidden layers in neural networks. Typically, a neural network has 2-3 hidden layers and is usually used between multiple layers and can have up to 150 hidden layers. Deep learning is effective on massive data to train a model and a graphics processing unit. Today, many inventions are being designed that have used this method. Algorithms work in a hierarchy to automate predictive analytics. Deep learning has opened its wings in many fields such as aerospace and military to identify objects from satellites, by identifying dangerous incidents when a worker approaches a car, inventions in the fields of medicine, pharmaceuticals, automobiles and to identify cancer cells and etc. helps improve worker safety.


  • Machine learning

Machine learning is closely related to computational statistics (and often overlaps with it), the focus of this branch is computer prediction, and it has a strong link with mathematical optimization, which also brings methods, theories and applications into the field. he does. Machine learning is sometimes integrated with data mining; The focus of this subsection is on the exploratory analysis of data and is known as unsupervised learning. Machine learning can also be unsupervised and can be used to learn and recognize the basic form of behavior of various organisms and then find meaningful anomalies.

Machine learning Machine learning is a branch of artificial intelligence that empowers machines to make sense of data sets without actual programming. Machine learning techniques help businesses make informed decisions by analyzing data using algorithms and statistical models. Companies are investing a lot in machine learning to take advantage of its application in different fields. From this method, in a wide range of international inventions, especially in health care and the medical profession, machine learning techniques are needed to analyze patient data for disease prediction and effective treatment. The banking and finance sector needs machine learning to analyze customer data to identify and suggest investment options to customers and to prevent risk and fraud. Retailers use machine learning to predict changing customer preferences, consumer behavior, by analyzing customer data.

  • Robotic process automation

This model is useful and researched and used in robotic processes and has been used in many inventions in this field.

Robotic Process Automation (RPA) is a software technology that is easy for anyone to use to automate digital tasks.

With RPA, software users create software robots, or “bots,” that can learn, emulate, and then execute rules-based business processes. RPA automation enables users to create robots by observing digital human actions

Robotic-process-automation Robotic process automation is an application of artificial intelligence that configures a robot (software program) to interpret, communicate, and analyze data. This field of artificial intelligence helps automate partially or fully manual operations that are repetitive and rule-based.


  • Peer-to-peer network

Simply put, a peer-to-peer (P2P) network is when there are networks where two or more devices (usually computers) are connected and share resources. But what makes it real is that a Peer To Peer network, something different from normal networks, forms an ecosystem where computers are connected by a single computer system. machine-learning The peer-to-peer network helps to connect between different systems and computers for data sharing without the data transmitting via server. Peer-to-peer networks have the ability to solve the most complex problems. This technology is used in cryptocurrencies. The implementation is cost-effective as individual workstations are connected and servers are not installed.

Usually, in this regard, inventions are not examined and used directly, but they use this technology model partially and indirectly.


  • Deep learning platforms

Machine learning uses simpler concepts, but deep learning works with artificial neural networks (ANN). These networks are designed to imitate human thinking and learning. Until several years ago, neural networks (ANN) were limited due to insufficient computing power, but with the progress in the field of Big Data Analysis, it became possible to create larger and more complex neural networks. This allows computers to observe, learn and react to complex situations even faster than humans.

Artificial intelligence and machine learning are the cornerstones of the next revolution in computing. These technologies work by recognizing patterns in data and predicting output based on data they have already seen.

Deep learning has helped image classification, language translation, and speech recognition.

It has also contributed a lot to your invention

  • AL optimized hardware

ai-optimized-hardware artificial intelligence software is in high demand in the business world. The world of business is huge and important, and the use of innovation and inventions has always been important to facilitate business affairs. With increasing attention to software, the need for hardware that supports software also arises. An ordinary chip cannot support AI models. A new generation of artificial intelligence chips is being developed for neural networks, deep learning and computer vision. AL hardware includes processors to handle scalable workloads, internal special-purpose silicon for neural networks, neuromorphic chips, and more. Organizations like Nvidia, Qualcomm. AMD is building chips that can perform complex AI calculations. Healthcare and automotive may be industries that will benefit from these chips