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In today’s world, industrial development and technological advancement are improving because of the new generation’s updated communication technologies and information collectors such as AI (Artificial Intelligence), Blockchain technology, and IoT (INTERNET OF THINGS). Artificial Intelligence is recently becoming very popular and getting special attention from industries, academic research, and government. This report is going to give an overview of artificial intelligence and its techniques and types. In the year 1956, scholars at Dartmouth University proposed the term “Artificial Intelligence”. That was the beginning of the moment of studying further how machines can have the ability of human intelligence. In 2016, the chess champion of the world was defeated by Alpha Go, it is a computer program developed by DeepMind, a sister company of Google developed to play board games like humans.
Automation process and introduction to VR (virtual reality) enhanced global interest in AI (Artificial Intelligence) (Luger et al. 2022. Artificial intelligence development has not only brought economic benefits in the world but also in social development and almost all aspects of our life. It makes use of computers for simulating human intelligence acts and also it instructs computers to learn about human acts or behaviors like decision-making, in doing proper judgments, Artificial Intelligence is a project that uses knowledge as an object; it helps in analyzing the method and in studying the expression knowledge methods (Silva et al. 2022. Following these ways computers can achieve human behavior and intellectual activities. Artificial Intelligence is a combination of disciplines like biology, logic, philosophy, computer science, and many more. The three main viewpoints of Artificial Intelligence are Symbolism, Behaviorism, and Connectionism. These three viewpoints are the most important aspect of Artificial Intelligence and are the foundation stone for developing AI. A prerequisite of Artificial Intelligence is big data and it is the most important factor that helps in developing Artificial Intelligence.
Big Data improves accuracy and rate recognition. The number of data generation has increased proportionately with time because of the application of IoT (Internet of Things) there is an increase in growth rate annually (Trichina et al. 2022. Aside from increasing data numbers, data dimensionality has also increased. Many generations before when computer science started evolving, researchers like Alan Turing imagined the possibility that a computer could have the ability to play chess like humans. Therefore, in 1948 he published and edited “Intelligent Machinery” and in 1950 “Computing Machinery and Intelligence” both journals kept on inspiring new scientific researchers. Alan Turing first described a computing abstract machine that contains a scanner and a limitless memory, which moves front and backward through the memory and symbol (Christofi et al. 2022. The scanners used to act according to the program instructions, which were stored in the form of codes or symbols in the memory.
This concept is known as Alan Turing’s stored-program concept. All modern computers, which are now developed, are based on the concept given by Alan Turing. He also gave the concept of heuristic problem-solving concept through which computers are trained to learn from their experiences. Christopher Strachey wrote the first Artificial Intelligence program in 1951. The program was first run on the University of Manchester, England’s Ferranti Mark I computer (Taraba et al. 2022). Within 1952, the programmer was developed and it gained the ability to play chess like humans.
Experiments on prototypes Deployment of non-critical factors Critical deployment Deployment cascades
Dataset arrangement Requirements of desired outcome lead to problem mutation segregating the data Data silos, data labeled wrongly, improper management of dataset preprocessing for test and train Restriction and limited techniques to acquire data and even its large scale or small scale through non-stationary streams Data dependencies without segregation would increase the complexity
Model creation Data first are created when nonrepresentative data are used for the task Ineffective critical evaluation of the data set can lead to inappropriate results creating low accuracy not suitable for making decisions High scalable pipelines using machine learning would be difficult in preparing without data preprocessing Entanglements of data would create data isolation requiring improvements
Train and test for model evaluation Without laying out well-established queries and ground truth leads to ineffective output Model evaluation is inappropriate for business-centric measures Debugging results from reproducing models would increase complexity Integrated techniques required for sliced analysis before implementing the final model
Model deployment Nondeployment mechanism is m not suitable for data preprocessing Skewness in training servers which would lead to data overlapping and malicious activity Stringent behavior adherence would not serve requirements like latency and throughput Feedback loops which are hidden would make it impossible for consumers to model clarification
The company like Sony made their open-source Neural Network Libraries available that serve as the foundation of building deep programmer learning for Artificial Intelligence. The designers and the software engineers make use of core libraries without costing any charge to make development in deep learning coding and adding them to the products and services that need development (Vrontis et al. 2022. Deep learning is defined as the machine learning language form that may make use of modeled neural networks after the brains of humans. The opportunity that is seen due to deep learning use is that there is a huge development in voice and image recognition systems. It is seen that neural network design is a very important factor for deep learning programmer improvement. The programmers load the constructed neural networks, which are suitable for that product or maybe a service generally a voice or image recognizer (Pereira et al.
2022). After that, programmers optimize the performance of the network with the help of trial series.
The company named Cisco is processing a network intuitive for reinventing the network. It employs artificial intelligence or machine learning concepts so that a huge amount of data networks can be analyzed and anomalies and optimized network configurations could be understood. Cisco enabled networks of self-driving, intent-based, and self-healing. It is using Artificial Intelligence or Machine learning so that it can give support, can help in coordinating responses, and automation between different security elements. The opportunity that machine language offers detection abilities in IT environments for safeguarding the SaaS application by adapting user behavior (Luchini et al. 2022). Machine learning here also plays a very important role in analyzing data networks so that it can identify the indication of threats like ransomware.
The company Phillips used the concept of Artificial Intelligence in healthcare. They introduced the concept of digital pathology, which helped in implementing efficient workflows in pathology. This system also helps in connecting a team remotely, it can help in enhancing the use of resources, patient data are stored uniquely, and decision-making abilities are efficient. Phillips is building a deep-learning application by working with PathAI. Algorithms are being developed by analyzing big pathological data. Here, AI and machine learning make opportunities for adapting intelligence, which can address and support the needs of a patient (Scarpa et al. 2022). Earlier auditing patients' health manually was very time-consuming but now with the introduction of machine learning or artificial intelligence, it helps in saving time.
The company Microsoft uses the technology of Artificial Intelligence or machine learning for curing tumors and it helps in capturing 3D radiological pictures. This helps in enabling radiotherapy at a faster pace and maintaining surgery planning precisely. However, the technology of machine learning does not make a replacement for medical practitioners; rather it helps them to reduce the task timing. Through this, researchers can also perform an inspection of data and help in gene mutating, uploading, and testing data with the use of Bioinformatics tools (Pea et al. 2022). Through this data analysis process, researchers can now run the data in the cloud, which initiates tasks in much less time, and the process is cost-effective.
To stay prepared for the upcoming days where Artificial Intelligences or Machine learning is going to be more impactful in society in almost every aspect of our life, a clear understanding of Artificial Intelligence and human intelligence and their difference should be there. Biological intelligence and human intelligence can never be separated from the ongoing process of “Self-Replication “and because of this, a gap always exists between Artificial Intelligence and human intelligence. Humans have cognitive skills and social skills, which makes the difference between human intelligence and non-human intelligence. Maybe in the future, technology can advance a lot; still, Artificial Intelligence is going to remain a function of human activities. If someday Artificial Intelligence gets the ability to self-replicating and may become a life form made by humans, then the outcomes are uncertain. Now the challenge for Artificial technology is not to check the ability to solve problems or understand programming language or learning or making interfaces but to find out ways by which common experienced knowledge and senses can be enabled to people so that they could carry out activities of daily life like finding out their way to destination if lost. It can be hoped that in the future new machines can be developed which may have the ability to cope with the human’s complex thoughts.
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