ChatGPT reshaped several aspects of AI technology and its usages in real life including searching, learning, reading, writing, and communicating using AI technology. It is powered with generative AI, and generative AI is an AI technology that is designed to process and generate different types of contents including text, image, voice, video, code, virtual world, dataset, and web data. Therefore, generative AI powered ChatGPT is able to process and generate different types of contents, and has the capability to assist humans in almost all areas of real life, and it is still expanding its capabilities regularly. This continuous expansion of the capabilities of ChatGPT requires a better understanding regarding its strengths and corresponding usages. Therefore, this paper will assess twenty capabilities of ChatGPT and provide an in-depth assessment, where it will highlight the strengths and limitations of each capability. This assessment of various capabilities will be based on the different types of contents it can generate and analyse: text, image, voice, code, virtual world, dataset, and web data. This indepth assessment of the capabilities of ChatGPT is focused on providing a better understanding of the strengths of ChatGPT and its corresponding usages based on these capabilities.

Innovation and creativity are the most essential requirements for making education effective. Several innovative and creative tools and techniques have been developed and used for making education effective. Progressively, AI chatbots are another means by which educational institutions are able to provide an innovative, creative and interactive learning environment. AI chatbots are not just providing an innovative learning environment to students, but they are also providing help in almost all educational activities. In education, AI chatbots provide several benefits to individuals and educational institutions, however, there are several barriers in their use as well, and it is important to deliberate all these barriers when using these AI chatbots in education. Therefore, this paper will investigate several benefits and barriers of AI chatbots in education. Firstly, it will discuss the evolution of chatbots, and the use of some modern AI chatbots in education. Subsequently, it will investigate several benefits of AI chatbots in education. Finally, it will investigate several barriers of AI chatbots in education. This investigation will provide a detailed insight into both positive and negative aspects of AI chatbots alongside its use in education for better understanding.

Novel cybersecurity threats are constantly emerging and posing significant security challenges to organisations; therefore, it is important for organisations to proactively analyse existing and emerging cybersecurity threats against their systems. Threat modelling methods are very effective in proactively analysing cybersecurity threats and enhancing organisational security policies and defence mechanisms against these cybersecurity threats. Several threat modelling methods have been proposed, and it is important for security experts to select the appropriate threat modelling method for an organisation according to their specific security challenges and cybersecurity threats. This paper will present a comparative analysis of six threat modelling methods: STRIDE, DREAD, VAST, PASTA, OCTAVE, and LINDDUN. It will provide a concise description of all the aforementioned threat modelling methods, and subsequently, a comparative analysis of these six threat modelling methods for highlighting their relative strengths and limitations.

The rise of creative machines is attributed to generative AI which enabled machines to create new contents. Wherein the introduction of the advanced neural network architecture known as a transformer revolutionized the landscape of generative AI. A transformer transforms one sequence into another sequence, and is primarily used in natural language processing and computer vision tasks. Which determines the relationship between tokens or words in a sequence to understand the context, while processing these tokens or words simultaneously. Transformers were built to resolve various issues of its previous neural networks, such as a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM); and are now the brains behind majority of generative AI models, for example, most Large Language Models (LLMs) and Large Multimodal Models (LMMs). This paper will explain about the transformer and its architectural components and working. Subsequently, it will illustrate the decoder-only transformer architecture and its components and working, including the reason why this type of transformer architecture is used in most generative AI models such as majority of LLMs and LMMs.

AI chatbots are progressively becoming more human-like in their creativity and intelligence. Predominantly, AI chatbots based on generative AI models such as Large Language Models (LLMs) and Large Multimodal Model (LMMs) are a significant breakthrough in AI, as they have become more accurate, creative, context-aware, and can better mimic human-like interactions. Generative AI is an umbrella term for all AI technologies used to generate contents, where LLMs can process and generate textual data, and LMMs can process and generate multiple types of data modalities which are also known as multimodal AI models. AI chatbots and multimodal AI models offer several benefits, such as speed, accuracy, creativity, efficiency, and versatility; therefore, they can be used in nearly every sector such as a content creator, proactive assistant, reliable companion, customer assistant, and business operations facilitator. Nonetheless, these AI chatbots and multimodal AI models have some limitations, specially, inaccuracy and hallucination; that can affect and limit their usage for any critical task in any application area. Therefore, human verification and supervision is required for its reliable, safe and guided use. Moreover, their use in any application area also requires appropriate ethical consideration and regulatory compliance, alongside the human verification of generated responses. This paper will present twenty-two applications of AI chatbots and multimodal AI models, which are organised into two categories: personal applications and organisational applications. Subsequently, it will also describe the crucial ethical consideration and regulatory compliance for each application area to emphasise its related ethics, regulations and other risks for humans.

Generative AI has transformed the landscape of AI chatbots in a way that they are increasingly becoming human-like in their understanding and working. Generative AI is used for creating new contents, and most modern AI chatbots are designed using two types of generative AI models: Large Language Models (LLMs) and Large Multimodal Model (LMMs). LLMs deal with the text, whereas LMMs deal with more modalities including text, image, audio and video. This continuous enhancement of generative AI models is bringing AI chatbots closer to the human working. Nonetheless, these AI chatbots are not perfect, as they have several limitations, and their users should comprehend before fully relying on these imperfectly perfect AI chatbots. Depending on the type of use of AI chatbots, these limitations may or may not impact them significantly. This paper will organise the limitations of AI chatbots into six main categories: intelligence and understanding related limitations; accuracy and credibility related limitations; ethics and regulations related limitations; accountability, transparency and consistency related limitations; design, coding and training related limitations; and human, machine and vender related limitations. The aim of this paper is to emphasise, organise and analyse numerous limitations of AI chatbots into the proposed categories; accordingly, users should become more cognisant about all these limitations in deciding their suitability or unsuitability for the specific use cases.

ChatGPT is currently one of the most prevalent AI language models, which is constantly astounding its users with each update. It is powered with generative AI and built using a Large Language Model (LLM). ChatGPT demonstrated an exceptional ability to understand natural language whilst generating credible and coherent responses similar to humans. However, as an AI language model, ChatGPT analyses huge amounts of text and generates new text by finding relationships and patterns in the text, without actually understanding the meaning or context of that text, thus lacking human intelligence. Additionally, it has several other limitations related to its development, training, AI model, intelligence, understanding, accuracy, credibility, accountability, transparency, consistency, privacy, security, ethics, and regulations. Each limitation of ChatGPT may not necessarily affect every user; however, it has some consequences for its users depending on the nature of its use and application area. This paper will critically analyse twenty-one limitations of ChatGPT in order to provide a detailed insight into each of its limitation and clarify any hype about it for its balanced study, therefore, users can determine the appropriateness or inappropriateness for its use.

AI chatbots have been reincarnated with the soul of generative AI, and now they are able to respond with intelligence and consciousness similar to that of humans. These AI chatbots that are based on generative AI are continuously evolving and enhancing their capabilities to act increasingly similar to humans. Generative AI represents all those AI technologies that can generate variety of contents including text, image, audio, video, code, virtual world, dataset, and web data. Different types of generative AI models have been developed to generate such contents, and Large Language Models (LLMs) and Large Multimodal Models (LMMs) are currently the most popular generative AI models used in most modern AI chatbots; where LLMs mostly handle and generate text data, whilst LMMs are able to handle and generate multiple data types or modalities such as text, image, audio, and video. The growing importance of AI chatbots based on generative AI necessitates an exploration of their capabilities in a detailed manner to obtain better understanding about their strengths and utilisations. Exploring the capabilities of AI chatbots that are based on generative AI, this paper will provide a systematic classification and thorough explanation of the capabilities of AI chatbots. It will classify the capabilities of AI chatbots into eight major categories: text generation and analysis, image generation and analysis, audio generation and analysis, video generation and analysis, code generation and analysis, virtual world generation and analysis, data (dataset) generation and analysis, and web data generation and analysis. This systematic classification and thorough exploration of the capabilities of AI chatbots will enable users to analyse these capabilities and the effective utilisation of AI chatbots in real life.

The emergence of Large Language Models (LLMs) transformed the domain of Natural Language Processing (NLP) by enhancing the capability of machines to effectively comprehend and generate natural language. These LLMs are a type of generative AI and the underlying AI model that work behind the scenes of most modern AI chatbots. Generative AI is an umbrella term for all AI technologies which can generate original contents. These LLMs are pre-trained on a large amount of textual data and billions of parameters. This pre-training is normally unsupervised learning, meaning that it processes the unlabelled data for a comprehensive understanding of context, semantics, and grammar of natural language to generate coherent, context-relevant and credible text. In view of the significance of LLMs, this paper aims to perform a comprehensive study of LLMs, which will elucidate the historical journey of language processing and modelling, evolution and types of language models, and working, benefits and limitations of LLMs.

ChatGPT has now become a global phenomenon that has revolutionized the manner in which machines interact with humans. It is a noteworthy enhancement in the field of generative AI, where generative AI is designed to create new contents similar to human-generated contents. ChatGPT has been developed to utilise a generative AI model as its underlying model. It is self-adaptive as it learns from its multimodal interactions with users and improves its responses based on the feedback received from users; thus, providing more accurate and personalised responses to users. ChatGPT is constantly evolving to incorporate new technologies and capabilities within it; therefore, a comprehensive study is required to understand its various aspects. This paper will present a comprehensive study of ChatGPT exploring its architecture, underlying AI models and techniques, training procedures, capabilities, limitations and applications. This comprehensive study will provide users a stepby-step guide with detailed information by analysing the various aspects of ChatGPT systematically.

ChatGPT is powered with generative AI and has unlocked the realm of creativity and intelligence to offer virtually all services required by its users. ChatGPT is an AI language model that can understand human language and generate its proficient and coherent responses in human language for the given prompts. ChatGPT has numerous applications such as in education and training, healthcare and medicine, banking and finance, travel and tourism, journalism and media, research and development, customer service and support, and sales and marketing. Despite the fact that ChatGPT is an impressive AI tool and used in almost all application areas, it is not without its flaws and may not always provide accurate or reliable information. Therefore, its use by individuals and businesses requires a quality control over its generated contents to ensure its accuracy and consistency. Moreover, its use in any application area also requires appropriate ethical and regulatory considerations, in addition to the verification of its responses by humans. This paper will explore twenty real-world applications of ChatGPT and its corresponding operations in these application areas. Furthermore, it will also discuss the necessary ethical and regulatory considerations for each application area to highlight its associated ethics, regulations and other risks for users.

ChatGPT has transformed the way how education is accessed and made education more interactive and personalised; however, depending on the use, it can be effective or disruptive in education. The use of ChatGPT in education has brought both opportunities and challenges to users and academic institutes. It can make virtually all educational services personalised, automated, efficient and cost-effective; however, users and academic institutes should also be concerned about it using in education due to a number of issues with it, such as misinformation, inaccuracy, bias, cheating, privacy and security; therefore, a careful consideration should be given by them before using it in education. This paper will provide both effective and disruptive aspects of ChatGPT, and elucidate how it can be effective and how it can be disruptive in education. Initially, it will discuss the historical development of chatbots from ELIZA to ChatGPT, and applications of various AI chatbots in education. Afterwards, this paper will elucidate various advantages of ChatGPT in education. Lastly, it will elucidate various challenges of ChatGPT in education. This elucidation will offer a comprehensive and simplified analysis of educational use of ChatGPT.