Autism spectrum disorder (ASD) affects 1 in every 36 children, including my brother Chad. Individuals with ASD as well as their loved ones and caregivers often struggle with communication, especially if the individual with ASD is non-verbal. Struggling to understand a loved one’s needs can take an enormous toll on a family as resources and support for children with ASD can be scarce. However, with the rapid improvement of artificially intelligent (AI) systems and the increasing amounts of available data through medical records, behavioral reports, pictures, videos, and the Internet of things, AI could provide a non-trivial amount of relief. For example, large language models (LLMs) supported with retrieval-augmented generation (RAG) could serve as a knowledgebase of personalized information for an individual with ASD. This knowledgebase can document years of behavioral patterns, preferences, and signs of distress that could take weeks, months, and even years for new caregivers to learn through experience. Thus, while there is little research on how LLMs can help individuals with ASD, the limited amount of it suggests that it can improve barriers with communication (Jang, Moharana, Carrington, & Begel, 2024) (Iannone & Giansanti, 2024).

ChadGPT stands for Caregiver helper for autism dynamics – a Generative Personalized Teammate. As the name suggests, it is designed to help caregivers understand their loved one or client with ASD better. As a prototype, it is a RAG application that is built using Llama3.1-8b, Pinecone, and Docker (inspired by the Cerebras example on the inference docs), and leverages Cerebras’s superior inference time compute, which could be vital for making real-time decisions in emergency situations. The system uses PyPDFLoader for processing documents, Cerebras's llama3.1-8b model to provide caregivers advice and instructions, and searches Pinecone to find the most relevant information chunks. A Pinecone and Cerebras API key are needed to run the system and are entered in a sidebar panel.

In its prototype phase, caregivers just need to import a document or set of documents that contain information regarding their client’s medical needs, behavioral patterns, hobbies, preferences, and even anecdotal experiences. While it is still in the prototype stage and relies on text retrieval and generation, the ultimate form of ChadGPT could potentially be multi-modal. By combining an up-to-date foundational knowledgebase with live video and audio, real-time behavioral, emotional, and physiological analysis is possible. While the realization of this kind of technology is still distant, the foundational RAG-augmented LLM will be a core component in the final design.

ChadGPT GitHub Repo

Bibliography

Iannone, A., & Giansanti, D. (2024). Breaking Barriers—The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review. PubMed. https://pmc.ncbi.nlm.nih.gov/articles/PMC10817661/

Jang, J., Moharana, S., Carrington, P., & Begel, A. (2024). "It's the only thing I can trust": Envisioning Large Language Model Use by Autistic Workers for Communication Assistance. arXiv. https://arxiv.org/html/2403.03297v1

Pictures of Me and Chad ~2005-2006 and in 2024