The modern world relies on and is driven by software. Embedded systems, command-line interfaces and user interface (UI) software are present across systems all around the world. The ease of use coupled with their intuitive nature has allowed for UI systems to become a staple as a crucial tool in modern software and beyond. UI systems serve as a visually appealing packaging of function calls and event handlers, allowing for complex event pipelines and data flows to be abstracted by buttons, text fields, menus, etc. The evolutions made in large language models (LLMs) over the past year have exhibited true "cognitive" potential. This potent ability has unveiled innumerable new opportunities to revolutionize the way our contemporary software systems are expected to operate. In this paper, we explore our vision and progress toward developing a UI architectural paradigm which employs a multimodal engine powered by LLMs and state-of-the-art transformer models. This framework aims to abstract monotonous UI interactions with prompting mechanisms that serve as "cognitively aware", powering automated functional calling and data flow pipelines, which translate to full speech-based intelligence control over visual UI systems.

The promise of conversational AI is that, unlike virtually any other form of technology, all you have to do is talk. Natural language is the most natural and democratic form of communication. After all, humans are born capable of learning how to speak, but some never learn to read or use a graphical user interface. That's why AI researchers from Element AI, Stanford University, and CIFAR recommend academic researchers take steps to create more useful forms of AI that speak with people to get things done, including the elimination of existing benchmarks. "As many current [language user interface] benchmarks suffer from low ecological validity, we recommend researchers not to initiate incremental research projects on them. Benchmark-specific advances are less meaningful when it is unclear if they transfer to real LUI use cases. Instead, we suggest the community to focus on conceptual research ideas that can generalize well beyond the current datasets," the paper reads.