A Comprehensive Survey of Artificial Intelligence (AI): Principles, Techniques, and Applications

. AI has emerged as a transformational technology with enormous potential to change a wide range of sectors. Its foundations are founded on robots' capacity to learn and do jobs that would normally need human intellect. AI techniques such as machine learning and deep learning have grown in sophistication, enabling for the development of strong AI applications in fields such as healthcare, finance, and transportation. Yet, the fast development and implementation of AI raises a slew of issues that must be addressed. Ethical issues, data privacy and security, transparency and explainability, legislation and policy, technological hurdles, adoption and acceptability, accessibility, and interaction with current systems are among these challenges. To address these issues, industry, government, and academia must work together to create ethical frameworks, invest in research and development, and encourage openness and accessibility. Notwithstanding these obstacles, the potential advantages of AI are enormous. AI has the ability to improve efficiency, production, and decision-making in a variety of industries. It also has the ability to enhance people's lives and find answers to some of the world's most urgent problems. Overall, the ideas, methodologies, and applications of AI provide great prospects for good change; nevertheless, addressing the issues is critical to ensuring that AI is created and utilised in an ethical and responsible manner.


Introduction
Understanding natural language, recognising images, and making decisions based on data are all examples of the types of tasks that are typically reserved for humans.Artificial Intelligence (AI) is the study of intelligent agents as well as the design and development of computer systems that are capable of performing these and other asks.Artificial intelligence (AI) is a fast developing science that has the potential to change many parts of modern civilization, including healthcare, banking, transportation, and manufacturing, to name a few.
The field of artificial intelligence is based on a set of core ideas known as AI.They are interested in figuring out what intelligence is and how it works so that they can build machines that have intelligent behaviour.The concept that intelligence is a computational process that can be mimicked by a machine is one of the fundamental ideas behind artificial intelligence (AI).This principle has led to the development of a number of different approaches to artificial intelligence, such as symbolic AI, which employs logic and knowledge representation to reason about the world, and connectionist AI, which employs neural networks to learn from data.Both of these approaches were made possible as a result of this principle.The notion of autonomy, which refers to the capacity of an agent to behave autonomously and make decisions based on its own thinking and goals, is another one of the fundamental tenets of artificial intelligence (AI).
Autonomy is a crucial notion in artificial intelligence (AI) due to the fact that it enables agents to adjust to different settings and make choices in difficult circumstances.Machine learning, deep learning, natural language processing, computer vision, robotics, and expert systems are some of the technologies that fall under this category.In the field of artificial intelligence (AI), one subfield known as machine learning focuses on teaching computers to detect patterns in data and to base their choices on those patterns.Deep learning is a subfield of machine learning that makes use of neural networks in order to acquire knowledge from significant volumes of data.
Artificial intelligence (AI) has spawned a number of subfields, one of which is natural language processing (NLP), which teaches computers to comprehend and produce human language.A subject of artificial intelligence known as computer vision focuses on training computers to detect and evaluate still pictures and moving videos.Robotics is a branch of artificial intelligence that focuses on the design and development of intelligent devices that are able to interact with the physical environment.Roboticists try to mimic human behaviour as closely as possible.
Expert systems are artificial intelligence (AI) systems that are able to execute activities that would ordinarily need the knowledge of a human being, such as offering financial advice or detecting medical issues.They are programmed with knowledge representation and reasoning methods, which enables them to reason about the world in a manner that is analogous to how humans think.
The domains of healthcare, finance, education, transportation, agriculture, manufacturing, gaming, and social media are just few of the areas that can benefit from the applications of artificial intelligence.In the field of medicine, artificial intelligence (AI) is being utilised to enhance patient outcomes by making it possible to make more accurate diagnoses and to treat patients in a more individualised manner.In the field of finance, artificial intelligence is being put to use to improve decision-making processes including portfolio management and the identification of fraudulent activity.
AI is now being applied in the field of education to tailor students' learning experiences and deliver feedback to them.In the transportation industry, artificial intelligence is being utilised to increase safety and optimise traffic flow.Artificial intelligence is being put to use in the agricultural industry to increase crop yields and decrease waste.Artificial intelligence is being applied in the manufacturing industry to automate operations and enhance quality control.
In the gaming industry, artificial intelligence is being employed to build worlds that are more realistic and difficult to play in.AI is being utilised within social media platforms to do user behaviour analysis and deliver individualised suggestions.
The many AI subfields-principles, methodologies, and applications-are inextricably linked to one another and mutually dependent on one another for further development.The application of AI is made possible by the development of AI techniques, which are made possible by the application of AI concepts.
The most significant obstacle that artificial intelligence must overcome is the building of computers that can think and learn like humans.The first obstacle that artificial intelligence approaches must overcome is ensuring The concept of accountability: Bostrom and Yudkowsky (2014) examine the possible concerns linked with the emergence of powerful AI systems in their paper.The authors believe that it is the responsibility of developers and politicians to ensure that AI is created in a way that maximises its advantages while reducing its hazards.
These publications demonstrate the significance of AI principles in ensuring that AI systems are created and utilised responsibly and ethically.We may create AI systems that benefit society while limiting possible hazards by addressing values like as openness, explainability, justice, privacy, and accountability.

AI Principle Research Key Findings
Transparency Buolamwini and Gebru Natural language processing algorithms exhibit biases based on gender and race, highlighting the need for fairness as a fundamental principle in AI development.

Yudkowsky (2014)
Developers and policymakers have a responsibility to ensure that advanced AI systems are developed in a way that maximizes benefits while minimizing risks.Koller and Friedman (2009) define Bayesian networks as a probabilistic approach for modelling uncertain connections between variables.This approach has been applied in a variety of applications, including healthcare diagnostics and decision-making.Koller and Friedman's research study emphasises the use of Bayesian networks in modelling complex systems and making educated decisions based on uncertain information.

III. AI Techniques
Overall, the table gives a rapid overview of some of the most important AI approaches and applications, based on research publications that illustrate their efficacy and promise.Ethical problems: AI poses a number of ethical concerns, including the possibility of AI systems being exploited to cause damage, the risk of employment displacement, and the issue of bias in AI systems.

Deep
Data privacy and security: AI is strongly reliant on data, and the usage of personal data for AI applications poses privacy and security problems.
Transparency and explainability: As AI systems improve, it becomes more difficult to comprehend how they make judgements.This lack of openness and explanation can lead to trust and accountability difficulties.
Regulation and policy: As AI advances, it is critical to ensure that suitable legislation and policies are in place to oversee its development and usage.

Turkish
Journal of Computer and Mathematics Education Vol.11 No.03 (2020), 1990-2000 DOI: https://doi.org/10.17762/turcomat.v11i3.135961991 Research Article AI techniques are the many instruments and procedures that are utilised in the development of AI systems.

Figure. 1
Figure.1 Understanding Artificial Intelligence is biased against darker-skinned individuals and women.Transparency is important to ensure AI algorithms are fair and provide clear and interpretable explanations for their decisions to build trust and ensure ethical use.Privacy Wang et al. (2018) AI systems in healthcare must protect patient privacy, and patients should have control over how their data is used.Fairness Caliskan et al.
Learning: LeCun et al. (2015) -Deep learning is a powerful image recognition, audio recognition, and natural language processing approach.LeCun et alstudy .'s report emphasises the usefulness of deep learning in various applications and how it has led to substantial improvements in AI.Turkish Journal of Computer and Mathematics Education Vol.11 No.03 (2020), 1990-2000 DOI: https://doi.org/10.17762/turcomat.v11i3.13596Research Article Mnih et al. (2015) -Reinforcement learning is a strong approach for training agents to make decisions in complicated situations, and it has been used to construct effective AI systems for games like Go and chess.Mnih et alstudy .'s report highlights the usefulness of reinforcement learning in various applications.Pan and Yang (2010) define transfer learning as the ability of AI models to use information learnt from one task to improve performance on another.This method has proven to be effective in a variety of applications, including computer vision and natural language processing.Pan and Yang's study article emphasises the potential of transfer learning and its applications in a variety of sectors.Deb et al. (2002) define evolutionary algorithms as "a collection of optimisation approaches influenced by the process of natural selection."These strategies have been used effectively to a wide range of AI optimisation challenges, including neural network construction and robot control.Deb et alstudy .'s report highlights the efficiency of evolutionary algorithms in optimising complicated systems.
a powerful technique for image recognition, speech recognition, and natural language processing.ReinforcementLearningMnih et al. (2015)Reinforcement learning is a powerful technique for training agents to make decisions in complex environments, and has been used to develop successful AI systems for playing games such as Go and chess.learning allows AI models to leverage knowledge learned from one task to improve performance on another task.This technique has been successful in a wide range of applications, including computer vision and natural language processing.a family of optimization techniques that are inspired by the process of natural selection.These techniques have been successfully applied to a wide range of optimization problems in AI, including neural network design and robot control.a probabilistic technique for modeling uncertain relationships between variables.This technique has been used in a wide range of applications, including diagnosis and decision-making in healthcare.

Table . 4 A Comparison of AI Principles, Techniques, and Applications VI. Key Challenges to AI Principles, Techniques, and Applications AI
, like any other technology, confronts a slew of problems in its development and implementation.The following are some of the major difficulties facing AI concepts, methodologies, and applications: