Artificial intelligence foundation and pre-trained models: Fundamentals, applications, opportunities, and social impacts

With the emergence of foundation models (FMs) that are trained on large amounts of data at scale and adaptable to a wide range of downstream applications, AI is experiencing a paradigm revolution. BERT, T5, ChatGPT, GPT-3, Codex, DALL-E, Whisper, and CLIP are now the foundation for new applications ranging from computer vision to protein sequence study and from speech recognition to coding. Earlier models had a reputation of starting from scratch with each new challenge. The capacity to experiment with, examine, and comprehend the capabilities and potentials of next-generation FMs is critical to undertaking this research and guiding its path. Nevertheless, these models are currently inaccessible as the resources required to train these models are highly concentrated in industry, and even the assets (data, code) required to replicate their training are frequently not released due to their demand in the real-time industry. At the moment, only large tech companies such as OpenAI, Google, Facebook, and Baidu can afford to construct FMs. We attempt to analyze and examine the main capabilities, key implementations, technological fundamentals, and socially constructed possible consequences of these models inside this research. Despite the expected widely publicized use of FMs, we still lack a comprehensive knowledge of how they operate, why they underperform, and what they are even capable of because of their emerging global qualities. To deal with these problems, we believe that much critical research on FMs would necessitate extensive multidisciplinary collaboration, given their essentially social and technical structure. Throughout the investigation, we will also have to deal with the problem of misrepresentation created by these systems. If FMs live up to their promise, AI might see far wider commercial use. As researchers studying the ramifications on society, we believe FMs will lead the way in massive changes. They are closely managed for the time being, so we should have time to comprehend their implications before they become a major concern.