The A_Z of Artificial Intelligence Types and Principles_1687569150.pdf
- 1. A-Z 1 T h e of Artificial Intelligence T Y P E S A N D P R I N C I P L E S S H A I O M A R A L I 2 0 2 3
- 2. C O N T E N T Type | Principle ā¢ Adaptive AI ā¢ Ambient AI ā¢ Bayesian AI ā¢ Big Data AI ā¢ Creative AI ā¢ Conversational AI ā¢ Discriminative AI ā¢ Deep Learning ā¢ Evaluative AI ā¢ Ethical AI ā¢ Fraud Detection AI ā¢ Fuzzy Logic AI ā¢ General Artificial Intelligence ā¢ Generative AI ā¢ Human-in-the-Loop (HITL) ā¢ Hybrid AI ā¢ Immersive AI ā¢ Industrial AI ā¢ Journalism AI ā¢ Junk AI ā¢ Knowledge Graph AI ā¢ Limited Memory AI ā¢ Large Language Model ā¢ Machine Learning ā¢ Multimodal AI ā¢ Nanorobotics ā¢ Narrow Artificial Intelligence Page 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 12 12 13 13 14 14 Type | Principle ā¢ Optical Character Recognition ā¢ Organic AI ā¢ Personal AI ā¢ Predictive Processing AI ā¢ Quality Assurance Automation ā¢ Quantum AI ā¢ Reactive AI ā¢ Responsible AI ā¢ Self-aware AI ā¢ Superintelligence AI ā¢ Theory of Mind AI ā¢ Transhumanist AI ā¢ Unsupervised AI ā¢ User Interface AI ā¢ Virtual Agent AI ā¢ Visual AI ā¢ Weak AI ā¢ Wearable AI ā¢ XAI (eXplainable AI) ā¢ X-Ray AI ā¢ You Only Look Once (YOLO) ā¢ Young Artificial Intelligence ā¢ Young AI Researcher ā¢ Zero-Stop AI ā¢ Other AI types & jargon Page 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 25 26 27
- 3. Ambient AI is a type of AI that is designed to integrate seamlessly with the environment and become an invisible part of our daily lives. It involves creating AI systems that are always on and always listening, and that can respond to our needs and requests in a natural and intuitive way. Ambient AI systems can be used in a wide range of applications, such as home automation, healthcare, and entertainment. For example, an ambient AI system in a home might be able to recognize when someone enters a room and turn on the lights, adjust the temperature, and play their favorite music. Ambient AI systems typically use natural language processing (NLP) and speech recognition technologies to interpret and respond to user requests. They may also use machine learning algorithms to learn from user behavior and improve their performance over time. Ambient AI is a promising area of research and development, as it has the potential to create intelligent systems that are more integrated with our daily lives and that can provide more personalized and intuitive experiences. However, it also raises important questions about privacy, security, and the ethical implications of always-on AI systems. Adaptive AI is a type of AI that is capable of learning and adapting to changes in its environment over time. It involves designing AI systems that can automatically learn from data and improve their performance over time, without being explicitly programmed to do so. Adaptive AI systems can be used in a wide range of applications, such as recommendation systems, autonomous vehicles, and intelligent agents. They are particularly useful in situations where the environment is complex or dynamic, and where the rules or patterns that govern the system are not known in advance. Adaptive AI systems typically use machine learning algorithms, such as reinforcement learning or online learning, to learn from data and improve their performance. They can also use feedback mechanisms, such as human input or sensor data, to adjust their behavior and make decisions based on changing circumstances. A Page 1
- 4. Big Data AI is a type of AI that is designed to process and analyze large and complex datasets, often referred to as "big data." It involves using machine learning algorithms and other advanced techniques to extract insights and make predictions from large volumes of data, often in real-time. Big data AI is particularly useful in fields such as healthcare, finance, and marketing, where there is a need to analyze large and complex datasets in order to make informed decisions. For example, in healthcare, big data AI can be used to analyze patient records and identify patterns that can help doctors make more accurate diagnoses and develop more effective treatment plans. In finance, big data AI can be used to analyze market trends and identify opportunities for investment. Big data AI typically involves using technologies such as distributed computing, cloud storage, and data visualization to manage and analyze large datasets. It also involves developing algorithms that can handle the complexities and varieties of big data, such as handling missing data, dealing with outliers, and dealing with noisy or incomplete data. Bayesian AI is a type of machine learning that uses Bayesian inference, a mathematical method for calculating probabilities based on prior knowledge and new data. It involves using probability distributions to represent the uncertainty and ambiguity in the data, and updating these distributions as new data becomes available. BAI is particularly well-suited for applications where there is a lot of uncertainty or complexity, such as in natural language processing, image recognition, and decision making under uncertainty. It can also be used to integrate multiple sources of information and make predictions based on the most likely outcomes. One of the key advantages of BAI is that it allows for the incorporation of prior knowledge and expertise into the machine learning process, which can improve the accuracy and reliability of the predictions. It is also highly flexible and can be adapted to different types of data and applications. B Page 2
- 5. Conversational AI, also known as chatbot technology, is a type of artificial intelligence that allows machines to communicate with humans in a natural and intuitive way, using natural language processing (NLP) and machine learning algorithms. It involves creating software systems that can understand and respond to user queries, commands, or requests in a human-like way, using text or voice input. Conversational AI is used in a wide range of applications, such as customer service, virtual assistants, and chatbots. It can be used to answer common questions, provide information, or help users complete tasks, such as booking a flight or ordering food. Conversational AI typically involves using natural language processing (NLP) and machine learning algorithms to analyze user input and generate appropriate responses. It also involves integrating with other systems and technologies, such as speech recognition, sentiment analysis, and machine vision, to provide a more comprehensive and personalized user experience. Conversational AI is a rapidly growing field, as it has the potential to revolutionize the way we interact with technology and provide more personalized and efficient customer service. Creative AI is a type of AI that is designed to generate new ideas, concepts, or outputs that are novel and innovative. It involves using machine learning algorithms and other advanced techniques to mimic human creativity and come up with original solutions to complex problems. Creative AI has many potential applications, such as in the fields of art, design, music, and writing. Creative AI typically involves training algorithms on large datasets of existing creative works, such as music, paintings, or literary texts. It also involves using techniques such as generative adversarial networks (GANs) or deep reinforcement learning to generate new and original outputs. Creative AI is a promising area of research and development, as it has the potential to create new and innovative outputs that can enhance human creativity and productivity in many fields. However, it also raises important questions about the ethics and ownership of creative works generated by AI, and the role of humans in the creative process. C Page 3
- 6. Discriminative AI is a type of machine learning algorithm that is designed to classify data into discrete categories or classes. It involves training a model to distinguish between different classes of data, based on a set of input features or variables. Discriminative AI algorithms are typically used in supervised learning tasks, where the goal is to predict a categorical output variable based on a set of input variables. For example, a discriminative AI algorithm might be trained to recognize handwritten digits, or to classify emails as spam or not spam. Discriminative AI algorithms typically involve using a cost function to measure the accuracy of the model's predictions, and adjusting the model's parameters to minimize the cost function. This involves iteratively training the model on a set of labeled training data, and testing its performance on a separate test set. Deep Learning is a type of machine learning that is based on artificial neural networks, which are modeled after the structure and function of the human brain. Deep learning involves training artificial neural networks with large amounts of data, and using them to make predictions or decisions based on that data. The networks can be designed with multiple layers, allowing them to learn increasingly complex representations of the data as they are trained. Deep learning has been particularly successful in tasks such as image recognition, speech recognition, and natural language processing, where it can learn to identify patterns and features in complex data. It has also been used in a wide range of other applications, such as autonomous vehicles and fraud detection. One of the key advantages of deep learning is its ability to learn from large amounts of data, even when that data is noisy or incomplete. It has also led to significant advances in computer vision and natural language processing, and has the potential to transform many other fields in the years to come. D Page 4
- 7. Evaluative AI is a type of machine learning algorithm that is designed to evaluate or score data or predictions based on predefined criteria or metrics. It involves assigning a numerical or qualitative score to each data point or prediction, based on a set of input features or variables. Evaluative AI algorithms are typically used in unsupervised learning tasks, where the goal is to identify patterns or relationships in the data, rather than making predictions or classifications. Evaluative AI algorithms typically involve using a scoring function to measure the distance or similarity between the input data and a set of reference data points or models. This involves iteratively comparing the input data to the reference data points or models, and adjusting the scoring function to minimize the difference or error. Overall, evaluative AI is a powerful tool for assessing and analyzing data, and has many practical applications in fields such as natural language processing, computer vision, and recommendation systems. Ethical AI refers to the development and use of AI systems that are designed to align with human values and promote ethical behavior. It involves ensuring that AI systems are transparent, fair, safe, and accountable, and that they do not perpetuate or amplify existing social, economic, or political inequalities. Ethical AI is important because AI systems can have significant impacts on society and individuals, ranging from automating decision-making processes to influencing the distribution of resources and opportunities. Therefore, it is essential to ensure that AI systems are designed and used in ways that align with human values and promote the common good. There are several key principles that underpin ethical AI, including transparency, accountability, fairness, safety, privacy, and inclusivity. Overall, ethical AI is an emerging field that is concerned with ensuring that AI systems are developed and used in ways that promote human well-being and social good. E Page 5
- 8. Fraud Detection AI refers to the use of AI algorithms and techniques to identify and prevent fraud in financial transactions, claims, and other types of transactions. It involves analyzing large amounts of data and identifying patterns or anomalies that may indicate fraudulent activity. Fraud detection AI typically involves using machine learning algorithms to analyze historical data and identify patterns or anomalies that may indicate fraud. These algorithms can be trained on a large dataset of known fraudulent transactions, as well as a large dataset of legitimate transactions, to learn the characteristics of legitimate transactions and identify those that deviate from these characteristics. One of the key challenges of fraud detection AI is that fraudulent activity can be difficult to detect, particularly in complex or dynamic systems. Additionally, fraudsters can use sophisticated techniques to evade detection, such as using fake identities or creating synthetic identities. Therefore, it is important to continuously update and improve fraud detection algorithms to stay ahead of emerging fraudulent tactics. Fuzzy Logic AI is a type of AI that uses fuzzy logic, which is a method of reasoning that allows for the use of partial truths and uncertain or incomplete information. In fuzzy logic, a decision is based on a degree of membership between different possibilities. Fuzzy logic AI involves using fuzzy logic algorithms and techniques to analyze and make decisions based on uncertain or incomplete data. These algorithms are designed to handle imprecise or vague data, and to provide a way of reasoning with incomplete or uncertain information. One of the key advantages of fuzzy logic AI is that it can handle situations where there is no clear-cut answer or decision, and where the information available is incomplete or uncertain. This makes it useful in a wide range of applications, including control systems, medical diagnosis, and image recognition. Overall, fuzzy logic AI is a powerful tool for handling uncertain or incomplete information, and for making decisions in situations where there is no clear-cut answer or decision. F Page 6
- 9. General AI (AgI) is a hypothetical form of artificial intelligence that is capable of understanding or learning any intellectual task that a human being can do. AGI is characterized by its ability to reason, learn, and solve problems in a wide range of domains, without the need for specific training or knowledge in a particular area. AGI is often contrasted with narrow or weak AI, which is designed to perform specific tasks or functions, such as image recognition or language translation. In contrast, AGI is designed to have a general intelligence that can apply to a wide range of tasks and domains, and to be able to learn and adapt in a flexible and intelligent way. While AGI is still a topic of active research and development in the field of artificial intelligence, there is currently no widely accepted definition of what it would take for a machine to achieve AGI, and there are many challenges and obstacles to overcome before it is possible. Some of the key challenges include developing algorithms and models that can handle the complexity and nuance of human reasoning, and creating machines that can learn and adapt in a flexible and intelligent way. Generative AI is a type of AI that is capable of generating new and original content, such as images, text, or music, based on a set of rules or algorithms. In generative AI, a machine is trained on a large dataset of examples, and is able to generate new content that is similar to the original examples. Generative AI has many practical applications, including in fields such as computer graphics, natural language processing, and music composition. For example, generative models can be used to generate realistic images or videos that mimic the style of a particular artist or photographer, or to generate new text or music that is similar to existing examples. One of the key challenges of generative AI is that it requires a large amount of data and computational power to train the models effectively. Additionally, generative models can be prone to producing output that is biased or inaccurate, if the training data is not representative or biased in some way. Therefore, it is important to carefully design and validate generative models to ensure that they produce accurate and useful output. Overall, generative AI is an exciting and rapidly evolving field of artificial intelligence, with many practical applications and potential uses for creativity and innovation. G Page 7
- 10. Human-in-the-Loop (HITL) refers to the use of human input and decision-making in the process of training and operating artificial intelligence systems. In HITL, humans are involved in the loop of the AI process, either by providing input or feedback to the system, or by making decisions based on the output of the system. HITL is used in a variety of applications, including in the design and operation of autonomous systems, such as self-driving cars or drones, and in the development of intelligent tutoring systems or virtual assistants. HITL is an important area of research and development in the field of artificial intelligence, as it seeks to balance the benefits of AI automation with the need for human oversight and control. By incorporating human input and decision-making into the AI process, HITL aims to create more intelligent, flexible, and safe systems that can work effectively with humans. Hybrid AI refers to a type of artificial intelligence that combines the strengths and capabilities of different AI approaches and techniques. In a hybrid AI system, different types of AI algorithms and models are integrated and combined in order to solve complex problems or make decisions in a more effective way. Hybrid AI systems are designed to take advantage of the strengths of different AI approaches, such as rule- based systems, machine learning algorithms, and human decision-making. For example, a hybrid AI system might use machine learning algorithms to learn and adapt over time, but also incorporate human input and decision-making at key points in the process. One of the key advantages of hybrid AI is that it allows for more flexibility and adaptability in the AI process, as different AI approaches can be used depending on the specific context or problem at hand. Hybrid AI can also help to overcome some of the limitations of individual AI approaches, such as the difficulty of handling uncertainty or the risk of bias in machine learning models. H Page 8
- 11. Industrial AI refers to the use of AI and machine learning techniques in industrial settings to optimize production processes, improve efficiency, and reduce costs. It involves the development and deployment of AI- powered systems that can automate tasks, make predictions, and provide insights to improve decision-making in various industries. In manufacturing, industrial AI can be used to optimize supply chains, predict demand, and improve quality control. In healthcare, it can be used to analyze medical data, predict disease outbreaks, and improve patient outcomes. In finance, it can be used for fraud detection, risk management, and investment analysis. Industrial AI has the potential to transform many industries, but it also raises concerns about job displacement, data privacy, and the ethical implications of using AI in decision-making. As such, it is important to develop and implement AI systems in a responsible and transparent manner, with appropriate safeguards in place to protect individuals and society. I Immersive AI refers to the use of AI in virtual and augmented reality environments, where the AI system is designed to interact with and respond to the user in a fully immersive and interactive way. Immersive AI systems are used in a variety of applications, including in gaming, entertainment, and training and education. In these applications, the AI system is designed to respond to the user's actions and movements in real-time, creating a more engaging and interactive experience. Immersive AI also has potential applications in fields such as healthcare, where it can be used to create virtual reality simulations for training and education, or to create personalized and immersive experiences for patients with chronic conditions. Immersive AI is an area of active research and development, as it requires the integration of AI with other technologies, such as virtual reality and augmented reality, and the development of new algorithms and models that can handle the complexity of interactive and immersive environments. Page 9
- 12. Junk AI refers to the use of AI in applications or systems that do not meet the criteria for high-quality or effective AI. Junk AI is often characterized by the use of simple algorithms, lack of transparency, and poor data quality. In some cases, junk AI may be used to generate simple predictions or recommendations that are not based on a thorough analysis of the data or the problem being addressed. In other cases, junk AI may be used to automate tasks that could be done more efficiently by humans. Junk AI can have negative consequences, including wasting resources, creating confusion, and undermining trust in AI. It is important to distinguish between junk AI and high-quality AI, and to invest in the development of AI systems that are transparent, accountable, and effective. Overall, junk AI highlights the need for caution and critical thinking when it comes to the use of AI, and the importance of ensuring that AI is developed and deployed in a responsible and ethical manner. J Journalism AI refers to the use of AI and machine learning techniques in the field of journalism to automate tasks, analyze data, and generate content. It involves the development of AI-powered systems that can help journalists to identify and verify sources, analyze large amounts of data, and generate news stories. AI can be used to automate tasks such as data entry, transcription, and fact-checking, freeing up journalists to focus on more complex tasks such as reporting and analysis. AI can also be used to identify patterns and trends in data that can help journalists to better understand social and economic issues. In addition, AI can be used to generate news stories, including sports scores, weather reports, and financial news. However, there are concerns about the impact of AI on journalism, including the potential for bias, the loss of jobs, and the need for journalists to have the skills and knowledge to work with AI systems effectively. Overall, AI has the potential to revolutionize journalism, but it is important to ensure that it is used in a responsible and ethical manner that respects journalistic standards and values. Page 10
- 13. Knowledge Graph AI is a type of artificial intelligence that uses graph-based models to represent and reason about knowledge in a structured manner. It involves the use of graph-based algorithms to analyze data and extract meaningful insights, and is often used in applications such as natural language processing, recommendation systems, and semantic search. A knowledge graph is a graphical representation of knowledge that includes nodes (representing entities) and edges (representing relationships between entities). KG-AI systems use this structure to represent and reason about knowledge in a way that is similar to how humans think and reason. KG-AI has the potential to improve the efficiency and accuracy of many applications, including search engines, recommendation systems, and fraud detection systems. It is also being used in the development of autonomous systems, where it can help to analyze complex data and make informed decisions. However, KG-AI also presents challenges, including the need for high-quality data, the complexity of graph- based algorithms, and the need for accurate and efficient algorithms for reasoning and inference. As such, KG- AI requires significant investment in research and development to ensure that it is used effectively and responsibly. K Page 11
- 14. Large Language Models are a type of artificial intelligence that use neural networks to process and understand human language. They are designed to learn from vast amounts of text data and can be used for a wide range of natural language processing (NLP) tasks such as language translation, text generation, and sentiment analysis. LLMs typically consist of multiple layers of artificial neural networks, each of which is designed to process and learn from different aspects of the input text. The neural networks are trained on large datasets of text, which allows them to learn complex relationships between words and phrases, and to generate predictions or decisions based on that learning. LLMs are particularly useful in NLP applications where the data is large and complex, and where the system needs to make decisions based on that data in real-time. They are also used in applications such as chatbots, virtual assistants, and voice recognition systems. Limited Memory AI is a type of artificial intelligence that uses a limited amount of memory to store and process information. It is a subfield of machine learning, and is often used in applications such as data analysis, pattern recognition, and decision-making. LM-AI systems use a form of artificial neural network that is designed to learn from data and make predictions or decisions based on that learning. Unlike other types of neural networks, LM-AI systems use a limited amount of memory to store information, which helps to make them more efficient and scalable. LM-AI systems are particularly useful in applications where the data is large and complex, and where the system needs to make decisions based on that data in real-time. They are also used in applications such as recommendation systems, fraud detection, and predictive maintenance. However, LM-AI systems have limitations, including the need for high-quality data and the risk of overfitting to the training data. As such, they require careful design and implementation to ensure that they are effective and reliable. L Page 12
- 15. Multimodal AI is a type of artificial intelligence that can process and understand information from multiple modalities, or types of data, such as images, audio, and text. It involves the development of algorithms and systems that can integrate information from multiple sources and use it to make predictions or decisions. Multimodal AI has a wide range of applications, including image and speech recognition, natural language processing, and recommendation systems. It has also been used in applications such as virtual assistants, chatbots, and self-driving cars, where it is important to be able to understand and interpret information from multiple sources. Multimodal AI can be challenging to develop because different modalities may have different characteristics, such as different scales, formats, and semantics. As such, it requires careful design and implementation to ensure that the different modalities are properly integrated and that the system is able to make accurate predictions or decisions. Multimodal AI is an active area of research, and there are many ongoing efforts to develop more advanced and effective systems that can process and understand information from multiple sources. Machine Learning is a type of artificial intelligence that allows computer systems to automatically improve their performance on a specific task without being explicitly programmed. It involves the use of algorithms and statistical models to learn patterns and relationships from data, and to use that learning to make predictions or decisions. Machine learning algorithms are typically trained on large datasets of labeled data, which allows them to learn from examples and improve their accuracy over time. Once trained, machine learning models can be used to make predictions or decisions on new data. There are many different types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Each type of machine learning is suited to different types of problems and data, and requires different design and implementation approaches. Machine learning has a wide range of applications, including image and speech recognition, natural language processing, and developing autonomous systems such as self-driving cars and drones. M Page 13
- 16. Nanorobotics is an active field of research that involves the development of small robots, or nanobots, that are typically on the scale of a few hundred nanometers or smaller. These nanorobots are designed to perform a wide range of tasks, including drug delivery, environmental monitoring, and microsurgery that can perform complex tasks in the human body. For example, researchers are exploring the use of nanorobots for drug delivery, where they could be used to target specific cells or tissues and deliver drugs in a precise and controlled manner. AI and nanorobotics can be combined to create "nanobot AI" systems that can perform complex tasks autonomously. For example, AI algorithms could be used to control the movement and behavior of nanorobots, allowing them to perform tasks such as targeting specific cells or tissues in the body. However, the development of nanorobots and nanobot AI systems is still in the early stages, and many technical and regulatory challenges must be overcome before they can be widely used in medicine and other fields. N Narrow AI (AnI) is a type of AI that is designed to perform a specific task or specific set of tasks, often with a high degree of accuracy and efficiency. It is characterized by its limited capabilities and lack of general intelligence, meaning that it is not able to perform a wide range of tasks or learn from new experiences in the same way that humans can. Narrow AI systems are typically based on machine learning algorithms that are trained on large datasets and can recognize patterns and relationships in the data. They are often used in applications such as image recognition, speech recognition, and natural language processing. One of the key advantages of narrow AI is its ability to perform tasks that would be difficult or impossible for humans to do quickly or accurately. For example, narrow AI systems can analyze large amounts of data in real-time and provide insights that would be difficult for humans to identify. Page 14
- 17. O Organic AI (OAI) is a hypothetical concept that refers to artificial intelligence systems that are modeled on the structure and function of biological systems. OAI systems would be designed to mimic the way that biological systems process and transmit information, and could potentially be used to create intelligent machines that are more similar to living organisms. The concept of OAI raises a number of interesting questions about the nature of intelligence and the relationship between humans and machines. Some researchers believe that OAI systems could potentially be more adaptable and flexible than traditional AI systems, and could be able to learn and evolve in response to changing environments. However, OAI is still largely a theoretical concept, and much more research is needed to fully understand the potential implications of such systems. There are also many technical challenges that must be overcome before OAI systems can be developed, including the need to create more advanced materials and technologies that can mimic the functions of biological systems. Optical Character Recognition (OCR) is a type of AI that is used to convert scanned, photographed, or digital images of text into machine-readable format. OCR algorithms are designed to identify and extract the text from an image, allowing it to be processed and analyzed by computer systems. OCR technology has come a long way in recent years, and is now able to accurately recognize and extract text from a wide range of images, including handwritten text, printed text, and mixed-language documents. OCR is used in a variety of applications, including document scanning, data entry, and language translation. OCR AI systems use a combination of computer vision, machine learning, and natural language processing techniques to recognize and extract text from images. These systems typically require large amounts of training data to learn the patterns and characteristics of different types of text, and may also use techniques such as segmentation and recognition to improve the accuracy of the text extraction. Page 15
- 18. P Personal AI is a hypothetical concept that refers to AI systems that are designed to interact with and assist individual humans. PAI systems would be tailored to the specific needs and preferences of each user, and could potentially be used to enhance a wide range of human activities, from personal productivity to healthcare. PAI systems could potentially be used to provide personalized recommendations and insights based on a user's preferences and behavior. They could also be used to assist with tasks such as scheduling, navigation, and communication, allowing users to save time and increase efficiency. However, the development of PAI systems is still in the early stages, and many technical and ethical challenges must be overcome before they can be widely used. One of the key challenges is creating AI systems that are able to understand and adapt to the complex and varied needs and preferences of individual users. Predictive Processing AI is a theoretical framework for understanding how the brain processes information and makes decisions. PP suggests that the brain is constantly making predictions about the sensory information it receives, and updating these predictions based on the actual sensory input it receives. In the context of AI, predictive processing can be used to develop machine learning algorithms that are more like the way the human brain processes information. PP-based AI systems would be designed to make predictions about the input they receive, and to update these predictions based on new data. This could potentially lead to more accurate and efficient AI systems that are better able to adapt to changing environments. One of the key advantages of predictive processing AI is its ability to handle complex, non-linear relationships between inputs and outputs. This makes it well-suited for applications such as natural language processing, where the relationship between words and their meanings is often difficult to predict. Page 16
- 19. Q Quantum AI is a hypothetical concept that refers to the use of quantum mechanics and quantum computing to develop artificial intelligence systems. QAI systems would potentially be able to take advantage of the unique properties of quantum systems, such as superposition and entanglement, to perform tasks that are difficult or impossible for classical computers. Quantum computing is still a relatively new and developing field, and much more research is needed to fully understand the potential of quantum mechanics for artificial intelligence. However, there are already a number of promising applications of quantum computing in areas such as cryptography and optimization, and it is possible that QAI could be a significant area of future development. It is important to note that the development of QAI is still in the early stages, and many technical and theoretical challenges must be overcome before it can be fully realized. One of the main challenges is developing a practical and scalable quantum computer, which is able to perform useful calculations and simulations. Quality Assurance Automation is a process of using technology to automate the testing and evaluation of software products. QA automation typically involves using software tools to perform automated tests on a software product, and to report on the results of these tests. Artificial intelligence (AI) can be used to enhance QA automation in several ways. For example, AI-powered tools can be used to automatically generate test cases based on the requirements and design of a software product. AI can also be used to analyze the results of automated tests and to identify potential issues or bugs. AI- powered QA automation can potentially lead to faster and more efficient testing, as well as higher quality software products. However, it is important to carefully evaluate the performance of AI-powered QA automation tools and to use them in conjunction with other testing methods to ensure the highest levels of quality. Overall, AI-powered QA automation represents an exciting area of development in the field of software development, and has the potential to transform the way we test and evaluate software products. Page 17
- 20. Reactive AI refers to a type of artificial intelligence that is designed to respond to events or inputs as they occur. Reactive AI systems are typically used in applications where it is important to be able to react quickly and efficiently to changing circumstances, such as in robotics, gaming, and automated systems. Reactive AI systems use machine learning algorithms to analyze data and make predictions about the future, based on patterns and trends in the data. These predictions are then used to guide the system's actions in response to new inputs or events. Reactive AI systems are generally designed to be passive and not take proactive action, but rather to react to events as they happen. However, some reactive AI systems may be designed to take a more proactive role, such as by learning from past experiences and adjusting their behavior accordingly. Reactive AI represents an important and widely used type of AI, and has the potential to transform a wide range of industries and applications. R Responsible AI refers to the ethical and socially responsible development and use of artificial intelligence (AI) technologies. The term "responsible AI" encompasses a broad range of issues related to the ethical, legal, and social implications of AI, and seeks to ensure that AI is developed and used in a way that is consistent with ethical principles and values. Responsible AI involves a range of activities, such as developing ethical guidelines and frameworks for AI development and use, ensuring that AI systems are transparent and explainable, and promoting public awareness and engagement with AI. It also involves addressing issues related to bias and discrimination in AI systems, and ensuring that AI is developed and used in a way that is consistent with human rights and social justice. Responsible AI is an important area of concern for organizations and governments around the world, as AI technologies become increasingly widespread and impactful. The development and use of responsible AI is seen as critical for ensuring that AI technologies are developed and used in a way that is beneficial for society as a whole, and that minimizes the risks and negative consequences of AI. Responsibility Page 18
- 21. Superintelligence AI also known as strong AI or hyperintelligence, is a hypothetical type of artificial intelligence that possesses significantly greater cognitive abilities and capabilities than human beings. Superintelligence AI is a topic of ongoing debate and discussion in the field of artificial intelligence, and it is currently not possible to create an AI system that is truly superintelligent. The concept of superintelligence AI is often discussed in terms of its potential implications for society and the economy. Some experts believe that the development of superintelligent AI could bring about significant benefits, such as the solution of complex problems and the improvement of human lives. However, others are concerned about the potential risks associated with superintelligent AI, such as the possibility of it becoming uncontrollable or causing harm to human beings. There are several challenges associated with creating superintelligence AI, including the lack of understanding of the nature of intelligence and consciousness, and the difficulty of creating AI systems that are able to learn and adapt at a rate that is similar to or faster than human beings. Self-Aware AI also known as Sentient AI, is a hypothetical type of artificial intelligence that has conscious awareness, self-awareness, and the ability to experience subjective feelings and emotions. Self-aware AI is a topic of ongoing debate and discussion in the field of artificial intelligence, and it is currently not possible to create an AI system that is truly self-aware. There are several challenges associated with creating self-aware AI, including the lack of understanding of the nature of consciousness and subjective experience, and the difficulty of creating AI systems that are able to understand and express themselves in a way that is similar to human beings. Despite these challenges, pragmatic researchers and scientists are exploring the possibility of creating self-aware AI, and are developing new technologies and methods for achieving this goal. However, many experts believe that it is unlikely that true self-aware AI will ever be developed, and that current AI systems are unlikely to achieve true consciousness or self-awareness. S Page 19
- 22. Theory of Mind AI also known as artificial theory of mind or AToM, is a hypothetical type of artificial intelligence that is able to understand and predict the mental states and behaviors of other individuals. Theory of mind AI is a topic of ongoing research and debate in the field of artificial intelligence, and it is currently not possible to create an AI system that truly has a theory of mind. The concept of theory of mind AI is based on the idea that human beings have the ability to understand and predict the mental states and behaviors of other individuals, such as by inferring their intentions, beliefs, and emotions. A theory of mind AI system would be able to do the same, by analyzing data and other inputs and making inferences about the mental states and behaviors of other individuals. There are several challenges associated with creating theory of mind AI, including the lack of understanding of the nature of human cognition and the difficulty of creating AI systems that are able to understand and express themselves in a way that is similar to human beings. T Transhumanist AI is a hypothetical type of artificial intelligence that is designed to enhance and improve human abilities and capabilities. Transhuman AI is a topic of ongoing research and debate in the field of artificial intelligence, and it is currently not possible to create an AI system that is truly transhuman. The concept of transhuman AI is based on the idea that human beings can be improved through the use of advanced technology, including AI. Transhuman AI systems would be designed to work seamlessly with human beings, and would be able to enhance and augment human abilities and capabilities, such as by improving cognitive function, physical abilities, or lifespan. There are several challenges associated with creating transhuman AI, including the ethical and philosophical implications of using AI to enhance human abilities and capabilities, and the technical challenges of creating AI systems that are able to work seamlessly with human beings. Page 20
- 23. Unsupervised AI is a type of machine learning in which an AI system is trained on a dataset without any labeled examples or instructions. In other words, unsupervised AI systems are able to learn and identify patterns and relationships in data on their own, without the need for human intervention or guidance. Unsupervised AI is used in a variety of applications and algorithms, such as data clustering, anomaly detection, association rule mining, and dimensionality reduction to identify patterns and relationships in data. One of the main challenges of unsupervised AI is that it can be difficult to know for sure whether the patterns that the AI system identifies are actually meaningful or useful. Because unsupervised AI systems do not have labeled examples or instructions, it can be difficult to verify the accuracy and effectiveness of their results. Despite these challenges, unsupervised AI is a powerful and widely used technique in the field of artificial intelligence, and it has many potential applications in areas on data analysis, machine learning, and natural language processing. U User Interface AI is a type of artificial intelligence that is designed to interact with users through natural language interfaces, such as chatbots, voice assistants, and virtual assistants. UI AI systems use techniques such as natural language processing (NLP), machine learning, and deep learning to understand and interpret user input, and to generate appropriate responses. They are designed to be able to understand and respond to a wide range of user requests and inquiries, and to provide accurate and helpful information and assistance. They can be used to provide information, answer questions, perform tasks, and help users complete tasks and achieve their goals. One of the main challenges of UI AI is that it can be difficult to ensure that the responses generated by the system are accurate, helpful, and relevant to the user's needs. Because UI AI systems rely on natural language processing and machine learning algorithms, they may not always understand or interpret user input correctly, and they may generate responses that are not useful or relevant to the user's needs. Despite these challenges, UI AI is a rapidly growing field, and it has many potential applications in front-end user interface such as customer service, healthcare, education, and entertainment. Page 21
- 24. Visual AI also known as computer vision or image recognition, is a type of artificial intelligence that is designed to interpret and understand visual data, such as images and videos. Visual AI systems use techniques such as convolutional neural networks (CNNs), deep learning, and machine learning to analyze and understand visual data. They are designed to be able to recognize and classify objects, identify patterns and relationships, and make predictions based on visual data. Visual AI has many potential applications, such as in healthcare, security, retail, and entertainment. In healthcare, for example, visual AI can be used to analyze medical images via emerging X-rays and CT scans to identify abnormalities and help diagnose diseases. In retail, visual AI can be used to analyze images of products to help improve inventory management and product recommendations. One of the main challenges of visual AI is the vast amount of data that is required to train the system effectively. Visual data is often complex and multi-layered, and it requires a large amount of annotated data to train the system. Additionally, visual AI systems may need to be adapted to work in different lighting conditions or with different types of visual data. V Virtual Agent AI is a type of artificial intelligence that is designed to interact with users through natural language interfaces, such as chatbots, voice assistants, and virtual assistants. Virtual agents use techniques such as natural language processing (NLP), machine learning, and deep learning to understand and interpret user input, and to generate appropriate responses. They are designed to be able to understand and respond to a wide range of user requests and inquiries, and to provide accurate and helpful information and assistance. Virtual agents are used in a variety of applications to provide information, answer questions, perform tasks, and help users complete tasks and achieve their goals. One of the main advantages of virtual agents is that they can be accessed remotely, allowing users to interact with them from anywhere and at any time. They can also be customized to meet the specific needs and preferences of different users, and they can learn and adapt over time based on user interactions and feedback. Page 22
- 25. Wearable AI refers to AI technology that is integrated into wearable devices, such as smartwatches, fitness trackers, and smart glasses. These devices are designed to be worn on the body and can collect data on various aspects of the wearer's health and activity, such as heart rate, sleep patterns, and movement. Wearable AI devices can use this data to provide personalized insights and recommendations to the wearer, such as suggesting ways to improve their health and fitness or providing reminders to take medication. They can also be used to monitor and track the wearer's health over time, and to alert them if there are any changes or concerns. Other than health, wearable AI devices can also be used for entertainment, education, and work. Smart glasses can be used for virtual meetings and sessions, and smartwatches can be used to control smart home devices and receive notifications. One of the main advantages of wearable AI is that it allows people to access AI technology in a more convenient and portable way. Because it is integrated into wearable devices, people can access AI-powered features and capabilities while on the go, without needing to use a computer or other device. However, wearable devices that are worn on the body may raise privacy and data security concerns, as the wearer's personal information may be accessible to other parties. Weak AI is a type of artificial intelligence that is designed to perform specific, well-defined tasks. It is designed to mimic human intelligence in a specific domain or task, and it is not capable of generalizing to other tasks or domains. Weak AI is typically trained on large amounts of data using machine learning algorithms, and it is able to learn from its experiences and improve its performance over time. Examples of weak AI include virtual personal assistants which are designed to perform specific tasks such as setting reminders or answering questions, and image recognition systems used in security or surveillance. One of the main advantages of weak AI is that it can be very effective at performing specific tasks, and it can be used to automate many routine and repetitive tasks. However, because it is not capable of generalizing to other tasks or domains, it may not be able to adapt to new or unexpected situations. Additionally, because it is limited to the specific tasks or functions that it has been trained on, it may not be able to solve more complex or multi-faceted problems. W Page 23
- 26. X-Ray AI also known as computer vision or image recognition, is a type of artificial intelligence that is designed to interpret and understand visual data, such as images and videos. Visual AI systems use techniques such as convolutional neural networks (CNNs), deep learning, and machine learning to analyze and understand visual data. They are designed to be able to recognize and classify objects, identify patterns and relationships, and make predictions based on visual data. Visual AI has many potential applications, such as in healthcare, security, retail, and entertainment. In healthcare, for example, visual AI can be used to analyze medical images such as X-rays and CT scans to identify abnormalities and help diagnose diseases. In retail, visual AI can be used to analyze images of products to help improve inventory management and product recommendations. Recent published research on X-Ray AI included studies and clinical practice on chest X-Ray AI for selected hospitals (Sun et al., 2021; Lee et al., 2021; Kvak et al., 2023; among others). XAI is a type of explanatory artificial intelligence that aims to provide clear and understandable explanations for the decisions and actions taken by AI systems. XAI is an important area of research and development in the field of AI, as it is increasingly recognized that people need to understand how AI systems work and how they make decisions. One of the main challenges associated with XAI is ensuring that explanations provided by AI systems are clear, accurate, and understandable to people. This requires a deep understanding of the decision-making process used by AI systems, as well as the ability to represent this process in a way that is understandable to people. XAI is used in a wide range of applications, such as in medical diagnosis, financial decision-making, and autonomous vehicles. Overall, XAI is focused on providing clear and understandable explanations for the decisions and actions taken by AI systems. This is an important step towards building trust and confidence in AI, and towards ensuring that AI systems are used in a way that is safe and beneficial for humanity. X Page 24
- 27. Young Artificial Intelligence refers to the generation of AI researchers and practitioners who are in the early stages of their careers and are actively engaged in the development and application of AI technologies. This generation of AI researchers and practitioners is often characterized by their enthusiasm, creativity, and innovative thinking, and is driving the rapid advancement of AI technologies in a variety of fields. Young Artificial Intelligence Researcher is someone who is actively engaged in the field of AI research and development and is typically in the early stages of their academic or developer career. AI researchers may also be pursuing a PhD or conducting research as part of a postdoctoral fellowship, and may be working in academia, industry, or government on the hype cycle of young artificial intelligence. Young AI researchers may also be using a range of techniques and tools to develop and test young AI systems, including deep learning algorithms, natural language processing tools, and computer vision algorithms. They may be working on projects that involve developing new algorithms, data sets, or applications for AI, and may be collaborating with other researchers and practitioners to advance the field of AI. Y You Only Look Once (YOLO) is a real-time object detection algorithm applied in computer vision. The algorithm is designed to quickly and efficiently detect objects in an image or video by processing the entire image and searching for objects at once, rather than scanning the image pixel by pixel. YOLO is a popular choice for applications such as self-driving cars, security cameras, and video games. The algorithm is typically trained on large datasets and can detect a wide range of objects, including humans, animals, and vehicles Its object detection algorithm uses a single convolutional neural network (CNN) to detect objects in images. YOLO is known for its speed and accuracy, and is often used in applications like self-driving cars and security systems. Page 25
- 28. Z Page 26 Zero-shot AI is a machine learning technique that allows a model to make predictions (zero-shot prediction) or classifications for new, unseen data (zero-shot learning) based on patterns and relationships learned from a small number of training examples. The key idea behind zero-shot learning is that a model can make accurate predictions or classifications without being explicitly trained on the specific data it is being tested on. Zero-shot AI is often used in applications where obtaining labeled data is difficult or time-consuming, or where the cost of labeling data is high. For example, zero-shot AI may be used in medical diagnosis to make predictions about new patients based on patterns learned from a small number of labeled examples, or in natural language processing to make predictions about new text data based on patterns learned from a small number of labeled examples. In other words, zero-shot learning allows a model to generalize from a small set of training examples to make predictions or classifications for new, unseen data. This can be useful in situations where it is difficult or time- consuming to obtain large amounts of labeled data, or when the data is too complex to be easily classified using traditional machine learning techniques. There are several different approaches to implementing zero-shot learning, including using generative models, such as generative adversarial networks (GANs), and using transfer learning, which involves leveraging knowledge learned from one task to improve performance on a related task.
- 29. A I T Y P E S A N D J A R G O N A ā¢ Activation function ā¢ Adaptive ā¢ Algorithm ā¢ Ambient ā¢ Artificial Neural Network (ANN) ā¢ Autoencoder ā¢ Autonomous B ā¢ Backpropagation ā¢ Base model ā¢ Bayesian ā¢ Beneficient ā¢ Bias ā¢ Big data C ā¢ Chatbot ā¢ Classification ā¢ Clustering ā¢ Cognitive ā¢ Compute ā¢ Conversational ā¢ Convolutional Neural Network (CNN) ā¢ Creative D ā¢ Data mining ā¢ Decision tree ā¢ Deep learning ā¢ Diffusion model ā¢ Dimensionality ā¢ Discriminative ā¢ Dropout E ā¢ Ethical ā¢ Emotion ā¢ Evaluative ā¢ Explainable ā¢ Expert systems F ā¢ Fake ā¢ Feedforward Neural Network (FNN) ā¢ Foundation model ā¢ Fraud detection ā¢ Friendly ā¢ Fuzzy logic G ā¢ General Artificial Intelligence (AGI) ā¢ Generative Adversarial Networks (GAN) ā¢ Generative ā¢ Genetic ā¢ Gradient descent H ā¢ Heuristic ā¢ Human-in-the-loop ā¢ Human Intelligence ā¢ Human-level ā¢ Hybrid ā¢ Hyperparameter I ā¢ Image recognition ā¢ Immersive ā¢ Independent component analysis (ICA) ā¢ Industrial ā¢ Input layer ā¢ Instructed bias J ā¢ Journalism ā¢ Junk K ā¢ Kernel method ā¢ Knowledge graph L ā¢ Labelling ā¢ Large Language Model (LLM) ā¢ Limited Memory ā¢ Linear regression ā¢ Ludic M ā¢ Machine Learning ā¢ Model ā¢ Multidimensionality ā¢ Multimodal N ā¢ Naive Bayes ā¢ Nanobot ā¢ Narrow AI (ANI) ā¢ Natural Language Processing (NLP) ā¢ Neural Network ā¢ Node O ā¢ Open-source ā¢ Optical character recognition (OCR) ā¢ Organic ā¢ Output layer ā¢ Overfitting p ā¢ Parameters ā¢ Pattern recognition ā¢ Personal ā¢ Pervasive ā¢ Predictive processing ā¢ Principal component analysis (PCA) Q ā¢ Q-Learning ā¢ Quality assurance automation ā¢ Quality control ā¢ Quality inspection ā¢ Quantum R ā¢ Random forest ā¢ Reactive ā¢ Real-time ā¢ Recurrent Neural Network (RNN) ā¢ Regression analysis ā¢ Reinforcement learning ā¢ Residual connections ā¢ Responsible ā¢ Robotic Process Automation (RPA) ā¢ Robotics Page 27
- 30. A I T Y P E S A N D J A R G O N S ā¢ Self-Aware ā¢ Semi-supervised ā¢ Sentient / Sentience ā¢ Sentiment ā¢ Sigmoid activation function ā¢ Speech recognition ā¢ Strong AI (ASI) ā¢ Superintelligence ā¢ Supervised learning ā¢ Support vector machine (SVM) ā¢ Sustainable T ā¢ Theory of Mind ā¢ Training set ā¢ Transhuman ā¢ Transfer learning ā¢ Transformer ā¢ Turing test U ā¢ Ubiquitous ā¢ Unstructured ā¢ Unsupervised ā¢ User-interface V ā¢ Vanishing gradient ā¢ Virtual agent ā¢ Visual ā¢ Visual perception ā¢ Voice recognition ā¢ Volatile activation function W ā¢ Weak ā¢ Wearable X ā¢ X-Ray ā¢ XAI (eXplainable AI) Y ā¢ You-Only-Look-Once (YOLO) ā¢ Young Artificial Intelligence ā¢ Young Artificial Intelligence researcher Z ā¢ Zero-shot ā¢ Zero-shot learning ā¢ Zero-shot prediction Page 28 The convergence of artificial intelligence (AI), web3.0, the metaverse, and cloud technologies is a rapidly growing field that is transforming the way we interact with technology and with each other. AI is being used to power new applications and experiences in the metaverse, including virtual reality (VR) and augmented reality (AR) environments. By leveraging the power of AI, the metaverse is becoming a more immersive and personalized experience for users. The convergence of AI, web3.0, the metaverse, and cloud technologies is creating a new era of innovation and growth in the tech industry, with exciting new possibilities for the future of technology and human experience.