Artificial Intelligence: A Multidisciplinary Approach towards Teaching and Learning E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Fachliteratur
- Sprache: Englisch
Artificial Intelligence: A Multidisciplinary Approach towards Teaching and Learning explores the evolving role of AI in education, covering applications in fields such as bioinformatics, environmental science, physics, chemistry, economics, and language learning. Written by experts, this book provides a comprehensive overview of AI's integration into diverse subjects, offering insights into the future of AI in education and its potential to enhance academic research and pedagogy.
Targeted at faculty, students, and professionals, the book addresses AI's role in blended learning environments and offers practical tools for educators seeking to incorporate AI into their teaching practices.
Key Features:
- Multidisciplinary exploration of AI in teaching and learning.
- Practical tools and methodologies for educators.
- Insights into AI-driven innovations in research.
- Relevant to a broad audience, from students to professionals.
Readership:
Undergraduate/Graduate students, academics, and professionals interested in AI applications in education.
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Seitenzahl: 485
Veröffentlichungsjahr: 2024
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FOREWORD I
I feel immense pleasure to write a Foreword to the book titled “Artificial Intelligence: A Multidisciplinary Approach towards Teaching and Learning.” As we move further into the 21st century, the role of artificial intelligence (AI) in education is becoming increasingly important. In this book, the authors explore the ways in which AI can be used to enhance and support the teaching and learning process. They provide a comprehensive overview of the latest research and developments in this field and offer practical advice for educators looking to incorporate AI into their teaching practice. The authors are experts in their discipline and are full of bright ideas on how the field of AI can be infused into their discipline, and their insights are invaluable for anyone interested in this topic. They provide a clear and concise overview of the ways in which AI can be used to support individualized learning, provide diagnostic feedback, and improve teaching practice. They also address the philosophical perspectives associated with the use of AI in education.
This book is an important contribution to the field of education, and I am confident that it will be of great interest to educators, policymakers, and researchers alike. It provides a timely and insightful analysis of the ways in which AI is transforming the teaching and learning process, and offers practical guidance for those looking to incorporate AI into their own practice. I highly recommend this book to anyone interested in the future of education and the role that AI will play in shaping it.
In the dynamic intersection of Artificial Intelligence (AI) and education, the field of chemical sciences is undergoing a remarkable transformation. This book provides an insightful exploration of how AI is reshaping both pedagogy and research in different disciplines. It delves into AI's role in enhancing learning experiences, accelerating research, and presenting new methodologies for understanding complex phenomena.
The authors bring to light the profound implications of AI applications, from personalized education paths to innovative solutions in different fields. They present a nuanced discussion on the potential and challenges of integrating AI, emphasizing the need for ethical considerations and the continued role of educators in guiding learning.
As AI becomes increasingly embedded in educational practices, its potential to enrich and transform learning is immense. This book invites readers to reflect on the future of education, the ethical deployment of technology, and the exciting possibilities at the nexus of AI and various educational streams. Let this be a starting point for educators, students, and researchers to navigate and contribute to the evolving landscape of AI in education.
PREFACE
As we stand on the brink of the Fourth Industrial Revolution, AI has revolutionized and acted as a transformative force in redefining and reshaping industries, economies, and perhaps most significantly, education. The landscape of education is positioned for a paradigm shift with the infusion of AI. This book delves into the absorbing connection of AI and the teaching-learning process, exploring this constantly evolving association that has the potential to impact educators and learners in a way that is productive for them. The book is a compilation of 11 chapters from the contribution of different experts in their areas and therefore covers a rich account of insights, new frontiers, and collaboration across disciplines. Each chapter is constructed to be self-contained, permitting readers to dive in and out as per their own understanding.
The book begins with an introduction to AI, its roots in philosophy, its application in different disciplines, and most importantly an analysis of AI from the perspective of philosophy. The subsequent chapters will cover a spectrum of topics, which are constructed in a way that each chapter draws upon insights from various fields including biological science, physical sciences, mathematics, languages, environmental science, bio-informatics, chemical science, education, and research. The book examines the theoretical underpinnings of AI-assisted teaching-learning in different disciplines, explores the latest technological advancements, and offers practical strategies for integrating AI into the classroom. The chapters delve deeper, delivering a comprehensive in-depth analysis of the multi-faceted connection between AI and the teaching-learning process of different disciplines.
Our aim is to provide a rich tapestry of insights to educators, researchers, policymakers, and students, while encouraging cross-disciplinary dialogue and collaboration. We hope to empower stakeholders to harness the potential of technology while addressing the challenges it presents by fostering a deeper understanding of AI's impact on teaching and learning. This book would be useful for students, teachers, researchers, and academicians who look forward to the amalgamation of AI and education.
As the editors of this multidisciplinary book, we would like to thank the contributing authors for their time and expertise. We also want to thank the readers whose curiosity and commitment to advancing education through technology drive our ongoing investigation of this fascinating intersection.
List of Contributors
The Evolution of Artificial Intelligence from Philosophy to New Frontier
Manisha Singh1,*,Arbind K. Jha2,Tahmeena Khan3,Saman Raza4Abstract
In an era characterized by significant technical advancements in the field of Artificial Intelligence (AI), it is crucial to comprehend AI by considering its origins and future prospects. This chapter examines the historical origins of artificial intelligence (AI) and explores its relationship with philosophy. It also delves into the significant inquiries that philosophy poses regarding AI, encompassing its metaphysical, epistemological, and axiological dimensions. The chapter additionally provides an overview of the historical context of artificial intelligence (AI), its various manifestations, its theoretical underpinnings, and a framework that establishes a correlation between humans and machines, referred to as “Human-machine Teamwork.” The chapter also explores the importance of AI in several fields and illuminates emerging areas where artificial intelligence is also examined, giving rise to significant inquiries. The objective of this chapter is to offer comprehensive knowledge and a fresh viewpoint on the examination of AI by its users, producers, and designers.
INTRODUCTION
AI is often thought of as “a system's ability to interpret external data correctly, to learn from such data, and to use that learning to achieve specific goals and tasks through flexible adaptation” [1]. Artificial Intelligence (AI) refers to the field of study and development focused on creating intelligent computers. Intelligence, in this context, is the ability of an entity to operate effectively and with anticipation within its surroundings [2]. AI, or artificial intelligence, refers to the intelligence
exhibited by machines, as opposed to the natural intellect exhibited by people. The term AI is commonly employed to refer to machines that imitate human cognitive abilities, including learning, comprehension, logical thinking, and problem-solving [3].
The history of artificial intelligence (AI)
The history of AI extends further than commonly acknowledged, encompassing various disciplines such as science and philosophy dating back to ancient Greece [4]. However, the term “artificial intelligence” was formally coined by John McCarthy in 1956 at the first academic meeting dedicated to the advancement of intelligent machines. Russel and Norvig [5] described it as the “genesis of artificial intelligence.” However, the quest to determine if machines may genuinely exhibit cognitive abilities commenced long before that. In his influential publication, Vannevar Bush presented a concept that enhances individuals' knowledge and comprehension [6]. Five years later, Alan Turing authored a paper discussing the concept of robots being capable of emulating human beings and exhibiting intelligent behaviors, such as playing Chess [7].
“Is it possible for machines to possess the ability to think?” Alan Turing posed this problem in his renowned article “Computing Machinery and Intelligence” [8]. In order to address this question, he believes it is necessary to provide a clear definition of thinking. Nevertheless, due to the arbitrary nature of thought, it proves challenging to precisely define or describe it. Turing subsequently introduced the Turing Indirect Method, which is an approach for assessing the capacity of a machine to engage in thinking. This method examines whether a machine can exhibit intellect that is indistinguishable from that of a human. When a machine successfully completes a test, it is classified as possessing artificial intelligence (AI). In the 1980s, the revival of artificial intelligence (AI) was propelled by the development of systems by multiple research institutes and universities. These systems were able to generate a set of essential rules based on expert knowledge, which in turn helped non-experts in making precise decisions. They are referred to as “expert systems.” Stanford University's MYCIN and Carnegie Mellon University's XCON are two prominent instances. The expert system utilized expert knowledge to generate logical rules, facilitating its ability to tackle practical issues for the initial instance. The comprehension that enhanced the intelligence of machines served as the foundation for AI research throughout this period. However, as time passed, the expert system became apparent with several disadvantages, such as privacy concerns, limited flexibility, limited variety, expensive maintenance expenses, and other issues. Concurrently, the Japanese government allocated substantial financial resources to the Fifth Generation Computer Project ultimately fell short of accomplishing the majority of its initial objectives. Simultaneously, the Japanese government devoted significant financial resources towards the Fifth Generation Computer Project, which eventually fell short of attaining the majority of its initial objectives.
In 2006, Geoffrey Hinton and his colleagues achieved significant advancements in the field of artificial intelligence (AI) by introducing an innovative method for building neural networks with increased depth and a solution to address the problem of gradient vanishing during the training process. Consequently, there has been a resurgence in AI research, leading to the emergence of deep learning (DL) algorithms as a very active field within the realm of AI studies. Deep learning (DL) is a distinct subfield within the broader domain of machine learning (ML) that employs neural networks with multiple layers and places emphasis on the acquisition of representation knowledge. On the other hand, ML is a broader field within artificial intelligence (AI) where computers or programs may learn and acquire intelligence without the need for human interaction [9].
Langley emphasizes that one of the first concepts of AI was centered on “high-level cognition” [10]. AI lacks the ability to recognize concepts, perceive objects, or perform complex motor skills like most animals. However, it is designed with the capacity to engage in multi-step reasoning, comprehend natural language, create innovative artifacts, generate new plans to achieve goals, and even reason about its own reasoning. The term “strong AI” [11] is used to describe a form of artificial intelligence that exhibits a level of intelligence comparable to that of a human being. Another branch of AI, known as weak AI, differs in its approach to rule adherence. This pertains to how robots interact with rules. Rule-based decision-making is associated with narrow or weak artificial intelligence (AI), while rule-following decision-making is associated with general or strong AI. Wolfe argues that Strong AI entails computers creating and adhering to their own set of rules, a capability that is currently unattainable [12]. The main methodology focused on strong artificial intelligence (AI) is symbolic reasoning, which posits that computers are not merely arithmetic calculators but rather versatile symbol manipulators. According to Newell and Simon's physical symbol system concept, intelligent behavior seems to necessitate the capacity to understand and alter symbolic structures [13]. Although this technique initially displayed potential, numerous disciplines of AI have subsequently abandoned it because of its inherent complexity and the limited advancements achieved in the 21st century. The timeline and feasibility of achieving strong AI are yet uncertain [14]. AI is described by two dimensions: one pertains to the process and reasoning part, while the other focuses on the behavior aspect. Both components of AI, namely thinking, problem-solving, and understanding, as well as behavioral changes, have equal significance. Table 1 illustrates four categorizations of the definition of AI [3].
AI can be categorized into analytical, human-inspired, and humanized AI based on the specific forms of intelligence it demonstrates, such as cognitive, emotional, and social intelligence. Alternatively, it can be defined as Artificial Narrow, General, or Super Intelligence, depending on its level of advancement.
Philosophy and AI: A Philosophical Journey
AI is widely employed across various domains, with a particular emphasis on its application in the field of Education, where nearly every discipline leverages AI. AI is inherently multi-disciplinary in its nature. Nilsson's narrative provides a comprehensive overview of the diverse disciplines that have contributed to advancements in AI, encompassing biology, languages, psychology and cognitive sciences, neuroscience, mathematics, philosophy, and logic, as well as engineering and computer science [24]. The field of AI is closely connected to the discipline of Science, which has evolved from philosophy. Historically, philosophy, specifically its branches of Natural and Moral philosophy, evolved over time into what is now known as “science”. Science was then further divided into sub-disciplines such as biological sciences and physical sciences, which eventually led to the development of fields like engineering and technology. These advancements ultimately gave rise to the concept of AI. Fig. (1) illustrates the progression of the fields that contributed to the emergence of AI.
Fig. (1)) Evolution Journey of AI.While AI is a result of multiple disciplines, it is essential to address the philosophical aspects of AI since its origins may be traced back to philosophy. Philosophy is divided into three primary branches: Metaphysics, Epistemology, and Axiology. Each branch raises a distinct question with AI.
PHILOSOPHICAL CONSIDERATION OF AI
Metaphysics and AI
Metaphysics is a philosophical discipline that explores fundamental inquiries regarding the essence of reality, the state of being, and the interconnection between consciousness and physical substance. Aristotle's work on metaphysics was commonly referred to as “First Philosophy,” a branch of philosophy that encompassed other subjects, including what we now classify as scientific fields such as physics, astronomy, and biology [25]. Metaphysics is the discipline that examines the essence of existence and investigates the fundamental nature of reality [26]. According to Aristotle, metaphysics is considered the fundamental branch of philosophy because it examines reality in its entirety, rather than focusing on specific aspects. It is the most comprehensive field of study, aiming to uncover the underlying structure of reality and understand the ultimate causes of things. Metaphysics is the investigation of concepts that are eternal and unchanging. Its role as the basis of scientific knowledge persisted until the 17th century, which coincided with the scientific revolution. During the medieval era, philosophers like Duns Scotus and Thomas Aquinas described it as the examination of “Being Qua Being”. Thus, in terms of metaphysics, the two most crucial inquiries regarding AI are frequently posed. Metaphysical inquiries into the “essence of consciousness and mental states” play a vital role in AI discussions. Can AI attain awareness or mental states, or is it just computational without actual subjective experience? This pertains to the renowned “hard problem of consciousness” presented by David Chalmers. Metaphysics is concerned with the veracity of existence, encompassing the study of what exists and the essence of being. In the field of artificial intelligence, there is a significant debate on whether AI is merely a collection of tools without its own existence, or if it has its own essence. When constructing and designing AI, it is crucial to always consider this question as it encourages discussions.
Epistemology and AI
Epistemology is a branch of philosophy that examines the nature, origin, and acquisition of knowledge and belief. Epistemology is the branch of philosophy that focuses on the investigation of the origins, acquisition, and most importantly, the creation of knowledge. Epistemology is a philosophical discipline that explores the origins, extent, essence, and constraints of knowing [27]. Epistemology is a method that allows us to analyze the levels of certainty regarding human knowledge and its accuracy. Epistemology illuminates the concepts of “Know,” “Knowing,” and “the Knowledge.” In order to obtain knowledge, it is crucial to possess justified beliefs and understand that knowledge is comprised of justified beliefs. Artificial intelligence (AI) is a branch of computer science that specifically deals with the development of machines capable of carrying out tasks that usually necessitate human intelligence. The examination of the theory of knowledge in the context of artificial intelligence (AI) revolves around comprehending the processes by which knowledge is produced, acquired, and advanced through the utilization of AI technology. The domain of AI epistemology centers its attention on the subsequent facets:
♦ How knowledge is originated through AI?
♦ What are the different sources of knowledge in AI?
♦ How do we reach different sources of Knowledge generated through AI?
♦ How to test the reliability of knowledge and its sources?
♦ What is the transparency and opacity of AI?
♦ How is the knowledge constructed in AI and what are the different ways to construct that knowledge?
There is substantial evidence supporting the idea that analytical epistemology and artificial intelligence are mutually reinforcing fields. In their work “Epistemology and Artificial Intelligence,” Wheeler and Pereira argue that both fields explore epistemic relationships. Artificial Intelligence (AI) primarily centers on the understanding of the formal and computational attributes of frameworks that seek to depict diverse epistemic relationships. Conversely, conventional epistemology investigates the characteristics of epistemic relationships with respect to their conceptual attributes [28]. The argument posits that the execution of these two procedures should not be conducted in isolation. In order to illustrate this concept, we will examine the techniques used to display a specific group of logical deductions that are frequently seen in conventional statistical reasoning. The fascinating nature of this particular category of reasoning patterns stems from the presence of two traits commonly observed in epistemic connections: defeasibility and para-consistency. The utilization of results from both logical artificial intelligence and analytical epistemology is integral to the construction of standard inferential statistical arguments. This statement underscores the notion that the approach employed to address this modeling issue has the potential to be extended to a more comprehensive multidisciplinary examination of epistemic relationships [28]. Epistemology is a crucial branch of philosophy for comprehending AI. Artificial intelligence (AI) is now being utilized across various industries, including education, where it plays a multifaceted and crucial role in the work of all those involved in the educational process. AI plays a crucial role in constructing knowledge and providing answers to user queries. In this context, epistemology becomes essential as it pertains to the study of knowledge. Analytical epistemology and AI are disciplines that investigate and examine the connections between knowledge and artificial intelligence. Hence, comprehending the theoretical and practical dimensions of AI necessitates a grasp of the various facets of AI's epistemology. Therefore, it is imperative for all significant parties participating in any domain, especially in the realm of education, to take into account the epistemological implications of artificial intelligence when researching and utilizing AI.
Axiology and AI
Axiology is a philosophical discipline that specifically examines values and ethics. Axiology is a branch of philosophy that focuses on the study of value. It explores topics related to the nature and categorization of values, as well as the determination of what things possess value. The axiological examination of Artificial Intelligence centers around determining whether the principles, procedures, and outcomes of AI adhere to ethical values, and whether the knowledge it provides is grounded in value-based principles. Axiology focuses on evaluating the influence of the researcher's values across the entire research process, with the goal of clarifying the study objectives and the values that shape them. The axiology analysis will concentrate on the essence, classifications, and standards of values and value assessments that AI encompasses, particularly in the realm of ethics and the impact of values on the knowledge-building process.
Examining the philosophical aspects of AI, such as metaphysics, epistemology, and axiology, is crucial for comprehending AI as a whole. It is also important for evaluating its knowledge-building process, critically assessing the knowledge it offers, and analyzing the concept of AI in a critical manner. Fig. (2) provides a concise overview of the philosophical aspects related to artificial intelligence.
Fig. (2)) Aspects of Philosophical Consideration of AI.Examining artificial intelligence (AI) via a philosophical lens is of utmost importance due to two key factors. First and foremost, this facilitates the comprehension of developers, designers, programmers, and users of artificial intelligence (AI), furnishing them with vital perspectives for subsequent enhancements and progress in the domain. Moreover, engaging in philosophical contemplation will function as a catalyst for ethical discourse throughout the process of developing and implementing artificial intelligence. The domain of artificial intelligence (AI) is poised to flourish in tandem with advancements in technology. However, it is imperative to comprehend the philosophical ramifications that emerge alongside each novel development.
Framework of AI
The architecture of AI consists of three layers. The three layers are the Perception layer, the Cognitive Layer, and the Decision-making layer. The perceptual layer emphasizes Perceptual Intelligence, this concept relates to the manner in which individuals see and make meaning of the information they receive. In the realm of artificial intelligence, perceptual intelligence pertains to a machine's capacity to recognize and understand sensory data from its surroundings, encompassing auditory, tactile, gustatory, visual, and olfactory stimuli. In order for machines to understand and respond to their environment in a suitable manner, machine perception is a crucial initial stage. AI researchers utilize machine perception to develop algorithms that convert gathered real-world data into a raw perception model. The ultimate objective of machine perception is to equip robots with sensory motor capabilities that allow them to imitate human experience using a technical framework [29]. Machine perception is essential in various domains such as autonomous systems research, intelligent robot development, voice recognition, translation, and artificial intelligence. Machines must possess perceptual intelligence in order for AI to function effectively.
The second layer, referred to as the Cognitive layer, is around Cognitive Intelligence, which emphasizes an individual's development of mental capacity to comprehend their nature and environment through thinking, sensory perception, and learning experiences. Cognitive intelligence, within the domain of artificial intelligence (AI), pertains to the capacity of a machine to emulate human cognitive processes with the aim of offering solutions to intricate situations. Artificial intelligence (AI) utilizes digital models that strive to replicate the operations of the human brain and simulate various cognitive functions, such as perception, representation, comprehension, and introspection. Cognitive computing is a theoretical framework that seeks to replicate the functioning of the human brain and produce suitable reactions. Machine cognitive intelligence is utilized in several domains such as robots, speech recognition systems, and virtual realities. The third layer, referred to as the decision-making layer, centers around decision-making intelligence. This layer explores how individuals make best decisions in response to diverse and intricate situations they encounter. In the realm of artificial intelligence, decision-making intelligence refers to the ability of a computer to process information in a manner that allows it to make decisions similar to those made by people when faced with issues or situations. The capacity for decision-making in AI is a prominent characteristic that enables it to mimic human behavior. Fig. (3) illustrates the structure of AI. The objective of AI is not to rival people, but rather to form a collaborative team where humans can focus on more complex jobs while working efficiently with AI. Hence, a “Human Machine Teaching Framework” is currently imperative.
Fig. (3)) Theoretical Framework of AI.HUMAN-MACHINE TEAMING FRAMEWORK
The advent of AI has necessitated a shift in the level of human engagement required in the realm of AI. The use of AI and its diverse tools should neither be regarded as a means to replace humans nor should humans and AI be seen as adversaries. The “Human-Machine Teaming Framework” is based on the collaboration between humans and machines, with a particular emphasis on the importance of trust between them. As per the research team, AI is increasingly responsible for distributing work between humans and robots by advancing in the hierarchy of activities it can accomplish. Moreover, as AI systems increasingly participate in human-machine collaboration, they are becoming more cooperative as they take on additional responsibilities in this collaborative effort. The human-machine teaming structure illustrated in Fig. (4) is referenced in the study conducted by Petraki et al. (2016) [30]. The importance of human-AI collaboration becomes evident as AI advances and various agents engage in interactions, necessitating a focus on both human-AI collaboration and AI-to-AI cooperation.
Fig. (4)) Human-Machine Teaming Framework.FORMS OF AI
The progression of AI has been dynamic, as each advancement contributes a novel characteristic to its capabilities. Since the inception of AI, various iterations of the technology have emerged over time. The two primary kinds can be categorized into two groups, namely ‘Based on capacities’ and ‘Based on Functionalities’.
1. AI can be categorized into three sorts based on its capabilities.
2. Categorization according to their functionalities.
Based on Capabilities
Artificial Narrow Intelligence
AI systems that are specifically designed to carry out highly specialized tasks or execute particular commands are commonly known as artificial narrow intelligence (ANI), also referred to as narrow AI or weak AI. ANI technologies are purposefully engineered to concentrate on and demonstrate exceptional proficiency in a singular cognitive function. Individuals lack the ability to acquire talents that are beyond their intended design. In order to achieve these goals, they frequently utilize machine learning and neural network techniques. Natural language processing artificial intelligence (AI), as exemplified by the aforementioned example, can be classified as a form of narrow intelligence due to its capacity to recognize and respond to voice commands while lacking the ability to perform additional tasks. The domain of artificial narrow intelligence spans various applications, including image recognition software, autonomous vehicles, and AI virtual assistants like Siri.
Artificial General Intelligence
Artificial general intelligence (AGI), alternatively referred to as general AI or strong AI, pertains to a form of artificial intelligence that exhibits the capacity to acquire knowledge, engage in logical thinking, and perform a wide range of tasks in a way akin to that of human beings. The system has the capacity to engage in logical thinking, strategic planning, problem-solving, abstract thinking, comprehend complex concepts, acquire knowledge quickly, and learn from previous experiences [31]. The goal of developing artificial general intelligence is to design computers capable of executing a wide range of tasks and acting as highly intelligent assistants to individuals in their everyday routines. Although the development of artificial general intelligence is still ongoing, it is possible to lay the groundwork for this field by leveraging cutting-edge technology such as supercomputers, quantum hardware, and generative AI models like ChatGPT.
Artificial Super Intelligence
Artificial super-intelligence (ASI), often known as super AI, is a concept commonly found in science fiction literature and movies. It is hypothesized that once artificial intelligence (AI) achieves general intelligence, it will rapidly acquire knowledge and capabilities at a rate beyond that of humans, ultimately surpassing human powers. ASI would serve as the fundamental technology for fully self-aware AI and other autonomous robots. The concept of AI takeovers, as depicted in films like Ex Machina or I, Robot, is fueled by the same idea.
However, now, all statements made are based on conjecture and not on concrete evidence.
Generative AI
Generative AI is a branch of AI that specifically deals with the creation of new data that closely resembles current data which makes it different from AI as Artificial Intelligence (AI) is an expansive domain within computer science that concentrates on developing computers with the ability to carry out tasks that usually necessitate human intelligence. The tasks encompassed in this category involve cognitive processes such as reasoning, learning, problem-solving, perception, language comprehension, and decision-making. This entails producing text, images, audio, and other types of material that imitate the patterns and structures seen in the training data. Generative AI predominantly usesmethodologies such as generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models. These strategies facilitate the generation of fresh data by acquiring knowledge about the fundamental pattern of the training data. Generative AI has a narrower focus than general AI. The focus of this topic is on models and algorithms that have the ability to create new content. Examples of such models include generative adversarial networks (GANs), variational autoencoders (VAEs), and language models like GPT (Generative Pre-trained Transformer).
Based on Functionality Artificial Intelligence
Reactive Machines
Artificial intelligence originated with the development of reactive machines, which serve as the foundational type of AI. Reactive machines possess an intrinsic quality of being responsive and reactive. Although they possess the capacity to rapidly attend to current demands and tasks, they exhibit a deficiency in memory retention and the acquisition of knowledge from previous experiences [32].
Reactive machines possess the capacity to promptly perceive and respond to external stimuli. As a result, they have the ability to perform basic autonomous functions, such as organizing undesirable emails from your inbox or recommending movies based on your recent Netflix searches. In 1997, IBM's AI engine 'Deep Blue' showcased its capacity to analyze real-time hints and achieved victory against Russian chess grandmaster Garry Kasparov during a chess competition. However, the construction of reactive artificial intelligence is not feasible. Furthermore, reactive AI is deficient in its capacity to utilize existing.
Limited AI
This technology is also often known as Limited Memory Machines. The next step in the progress of artificial intelligence entails developing the ability to retain knowledge. It possesses the capacity to briefly retain information from past interactions. It is an integral part of the significant revolution in deep learning. The researchers developed a revolutionary algorithm that utilizes our understanding of the complex mechanisms of the brain, allowing it to replicate the complicated interconnections among neurons. One fundamental characteristic of deep learning is its capacity to augment its cognitive abilities through the accumulation of extensive training data. The utilization of deep learning has greatly augmented the capacity of artificial intelligence to accurately identify images, so paving the way for the emergence of further AI techniques, including deep reinforcement learning. The AI models showed a notable ability to effectively incorporate the characteristics of their training data, and more importantly, they exhibited the capability to improve their performance as time progressed. Google's AlphaStar project is a notable example of limited artificial intelligence since it successfully outperformed very proficient professional gamers in the real-time strategy game StarCraft II. The models were specifically engineered to function with limited information, and the AI actively participated in repetitive self-play to acquire novel techniques and enhance its decision-making process. Initial choices made in StarCraft can have substantial consequences in the future. Therefore, the AI required the ability to predict the outcomes of its actions with significant foresight.
Theory of Mind AI
The concept of theory of mind capability pertains to the capacity of an AI computer to ascribe mental states to entities other than itself. The phrase originates from the field of psychology and necessitates artificial intelligence (AI) to deduce the motives and intentions of entities, such as their beliefs, emotions, and goals. These AI systems have not yet been created. Emotion AI, presently in the developmental stage, seeks to identify, replicate, observe, and react suitably to human emotion through the analysis of speech, images, and other forms of data. However, despite its potential use in various domains including healthcare, customer service, advertising, and others, the concept of AI possessing a theory of mind is still distant. The latter possesses the capacity to not only adapt its treatment of individuals in accordance with its capacity to perceive their emotional condition but also to comprehend them. Comprehension, as commonly defined, poses a significant obstacle for AI. The AI capable of producing a masterpiece portrait remains unaware of its creation. It has the ability to produce lengthy writings without comprehending any of the content. An artificial intelligence that has achieved the state of theory of mind would have successfully surmounted this constraint.
Self-aware AI
The AI point of singularity is the stage where artificial intelligence achieves self-awareness, surpassing the theory of mind. It is believed that once this threshold is reached, AI robots will surpass our ability to govern them, as they will possess not just the capability to perceive the emotions of others but also a sense of self. The aforementioned types of AI serve as antecedents to self-aware or conscious computers are systems that possess the ability to perceive their own internal state and the status of external things. This refers to an artificial intelligence that possesses a level of intelligence comparable to that of a human and is capable of imitating the same emotions, desires, and requirements. This aim is both ambitious and challenging, as we currently lack both the necessary algorithms and hardware to do it. It is uncertain whether there is a correlation between artificial general intelligence (AGI) and self-aware AI, and this will only become clear in the far future. Our current understanding of the human brain is insufficient to construct an artificial counterpart that possesses a comparable level of intelligence to humans. The invention of the humanoid robot Sophia, which incorporates advanced AI technology, offers a glimpse into the potential future of self-aware AI.
Some other forms of AI
As the capabilities of AI progress, a few new types of AI have been developed. Artificial intelligence, in simple terms, refers to machines performing tasks in areas traditionally associated with human intelligence, such as problem-solving, decision-making, and providing answers to questions posed to them. It is an artificially intelligent device designed by humans that has the potential to replace humans in various domains. Various iterations of Artificial Intelligence have emerged over time (Table 2).
Machine Learning
This entails the process of instructing machines to acquire knowledge from data and enhance their performance progressively. Machine learning algorithms can be categorized into three types: supervised, unsupervised, or semi-supervised. These algorithms are employed for many tasks including image identification, natural language processing, and predictive analytics.
Deep Learning
Deep Learning refers to a specific branch of machine learning where artificial neural networks are trained to acquire knowledge from vast quantities of data. Deep learning methods are applicable to applications such as speech recognition, image and video analysis, and natural language processing.
Expert Systems
These computer programs are designed to replicate the cognitive abilities of a human expert in a certain domain. Expert systems are frequently characterized by their reliance on rules and can be utilized for various tasks, including but not limited to medical diagnosis, financial analysis, and legal decision-making.
Robotics
This is the utilization of robots to do tasks that often necessitate human intervention. Robotics has applications in several areas such as manufacturing, healthcare, and others, where it is employed to automate operations that are either repetitive or pose a risk to human safety.
AI and New Frontiers
The dynamic field of artificial intelligence has had a profound and significant influence on human existence. Undoubtedly, the scope of AI is extensive and highly varied. The impact of AI has extended from scientific exploration to individual comprehension and the efficient execution of tasks in a shorter duration. The wide range of AI applications has had an impact on every aspect of human existence, including the subject of Education. AI enhances the study of nature in disciplines like Science by providing a more lucid depiction and offering diverse approaches to comprehending the surrounding natural world. AI offers numerous instruments to facilitate technological revolutions when it comes to transforming scientific breakthroughs into usable entities for people in disciplines like technology. AI offers diverse methods for understanding the learner's knowledge in fields like language and social sciences. Artificial Intelligence (AI) has revolutionized the field of Education and has had a significant impact on nearly every discipline. Below, we will discuss the impact of AI in several disciplines, categorized by distinct topics.
