# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 7 Integrating RSA with Large Language Models (LLMs)
### Human-Centered AI: Future of Visual Prosthetics  
Based on the above discussion, both human agents and LLM-based AI have distinct impacts on visual prosthetics. We posit that human-centric AI represents the fundamental iteration of visual prosthetics in the future. With AI advancing in capability, the nature of RSA will undergo further evolution. Consequently, the dynamics between humans and AI within the realm of visual prosthetics will also undergo ongoing transformation. Figure 4: Example output of BeMyAI Bendel (2016). The German book title \(HEYMWERK\) was erroneously identified as \(HEIMWERK\) (image source: [PERSON] et al. (2018)). According to a previous study [165], PVI participants tend to opt for AI usage solely for uncomplicated tasks to reduce social burdens. In this research, PVI participants employed AI-powered applications for tasks they deemed trivial or unworthy of bothering human assistants. For example, they utilized AI to distinguish between sunglasses and prescription glasses, as they preferred not to inconvenience others with such inquiries. They perceived AI as a means to spare their social circle from the weight of requesting answers to seemingly minor questions. A previous study [165] also found that PVI opted for AI to access impartial information. PVI participants regarded AI as an impartial information outlet, especially in visual information disputes. For instance, they employed AI as a mediator to resolve disagreements, such as determining the accuracy of the view from an airplane window. They relied on AI to offer unbiased judgments in these scenarios. Our discourse on human-AI collaboration can draw inspiration from research on human-human collaboration. [PERSON] et al. [171] conducted a study on RSA involving paired volunteers. They discovered that paired volunteers were more effective in tackling open-ended tasks, particularly those demanding subjective opinions. In traditional RSA, volunteers typically offer objective descriptions. However, when it comes to subjective opinions, human-AI collaboration holds distinct advantages, as AI models like LLMs have assimilated broad perspectives and aesthetic sensibilities from vast online datasets. As depicted in Figure 5, in scenarios like choosing a tie to match a gray suit, BeMyAI can furnish subjective suggestions grounded in clothing matching expertise, accompanied by rationales [170]. Moreover, paired volunteers facilitate PVI engagement in more intriguing activities, such as appreciating art. As generative AI progresses, human-AI collaboration stands poised to further enrich the life experiences of PVI. For instance, leveraging AI tools for artistic endeavors presents a promising avenue. Presently, sighted individuals can utilize generative AI tools to generate images from text inputs (e.g., Midjourney [172], DALL-E [173]), or craft videos (e.g., Sora [174]). Through human-AI collaboration, PVI participants are poised to delight in the creative possibilities offered by AI tools. This nascent research direction warrants further exploration. Furthermore, a potential future trajectory involves the customization of personalized AI for individual users, diverging from the use of generalized LLM models. This personalization can be attained through ongoing user-AI interactions. For instance, a personalized AI would become acquainted with a PVI user's language preferences and lifestyle routines and even recognize the user's personal belongings. In contrast with randomly selected human agents in crowdsourcing scenarios, personalized AI holds greater promise in catering to the specific needs of PVI. To enable personalized AI, user profiling [175; 176] may enhance the training of these AI models effectively. For example, profiling users' height and walking stride length [177] could improve the customization of AI models and RSA services, providing better navigational support. Figure 5: BeMyAI can offer subjective tie and suit pairing suggestions, accompanied by explanations (image source: [170]).