The application of Artificial Intelligence (AI) in healthcare has improved the diagnosis of diseases, forecasting outcomes and supported complex clinical decision making with intelligent systems. Yet the lack of transparency in a lot of AI models, especially those deep learning ones—which makes them hard to trust—presents an enormous safety and accountability problem, not least when lives are at stake.

By leveraging big datasets and neural architectures, foundation models have taken AI to new heights, allowing robust generalization and in-context learning. Going even further multi-modal foundation models (MMFMs) also integrate text through images and all sensory information such as audio, video. This paper offers a novel, more complete understanding of the MMFM and its principles of design as well as training methodology and architectural advancement.

As technology weaves its way even more into our daily lives, in the digital age where tech is no longer a separate part of society but deeply interwoven itself within society — the need for systems to not only understand what we say but how we feel has been exponentially increasing. No longer are our interactions with devices limited to utility — virtual assistants and customer service bots, intelligent healthcare monitors and tools for an interactive smart classroom all need to be capable of reacting functionally or even emotionally competent.

During the last several years natural language processing (NLP) has been revolutionized by a combination of advances in deep learning and the development of very large annotated corpora that are available to train on, particularly in high-resource languages like English, Chinese and Spanish. With the advent of such large-scale resources, researchers have trained language models which are capable of doing sentiment analysis, machine translation, question-answering and a lot more with nearly human level performance.

VHAAI means Very Highly Advanced Artificial Intelligence. ISVHAAI means International Society for VHAAI. This is ISVHAAI Artificial Intelligence Society Letter No. 11. In this Letter, a novel algorithm titled Student Mobile Phone Human Swarm Optimization (SMPHSO) has been designed.