Challenges and opportunities in modern artificial intelligence systems: A focus on natural language processing

Recent artificial intelligence (AI) breakthroughs have led to systems capable of performing myriad tasks in industrial applications. Natural language processing (NLP) is a field of AI responsible for analysing and understanding natural language text and speech [1-2]. Several fundamental NLP problems exist, including text classification and sentiment analysis. These problems benefit from the latest advances in AI and deep learning, such as the revolutionary large language models trained on vast datasets [3-5]. The field of AI at the edge has also emerged, bringing new opportunities and challenges. NLP techniques have been applied in several industry use cases, such as healthcare, finance, and customer services. These advancements have made AI models particularly attractive for deployment on constrained devices at the edge. Nonetheless, a wide range of critical challenges must be addressed before these technologies can be widely adopted on a large scale.