Fundamentally, artificial intelligence is a domain that integrates computer science with dependable datasets in order to facilitate the resolution of problems. Furthermore, it incorporates the sub-disciplines of deep learning and machine learning, which are often referenced in relation to artificial intelligence. These fields consist of artificial intelligence algorithms that aim to develop expert systems capable of generating predictions or classifications using input data. Despite the fact that artificial intelligence has experienced numerous cycles of exaggeration over the years, the introduction of OpenAI's ChatGPT appears to even sceptics as a turning point. The previous time generative AI loomed so large, advancements were concentrated in computer vision; however, natural language processing is now where the real progress is. In addition to language, generative models are capable of acquiring grammatical knowledge of molecules, natural images, software code, and an extensive range of other data formats.

The objective of this book is to facilitate constructive discourse and thoughtful consideration regarding artificial intelligence through the provision of an approachable examination of AI technology, its consequences, and available alternatives. The subsequent chapter will attempt to dispel the enigma surrounding AI by describing its fundamental methods, how they operate, as well as their capabilities and constraints. The following chapter analyses the primary prospects and obstacles that arise from the implementation of these technologies. In the penultimate chapter, a variety of regulatory, technological, and societal responses to these opportunities and challenges are outlined. The concluding chapter provides several essential messages.