I have been using tools like ChatGPT and Claude from AIGC to solve everyday work-related issues, which have become an indispensable part of my daily routine. I’ve also ventured into building open-source language models, and this journey has led me to ponder about the future direction of AIGC.
Over the past two years, the advancements in pretrained language models have propelled the rapid growth of the AI industry. General language models like GPT-3 are reshaping our understanding of artificial intelligence. However, this is just the first milestone on the path of AI development. The wave of AI that truly disrupts business models and transforms industries requires specialized, domain-specific deep learning capabilities as its foundation.
Whether it’s healthcare, law, education, or industrial manufacturing, practical business scenarios demand customized AI solutions that integrate domain expertise, rather than relying solely on a one-size-fits-all AI. This necessitates training AI models that understand specialized contexts and grasp industry knowledge. Compared to general models that require massive amounts of training data, specialized AI models can be fine-tuned with fewer samples, making them more practical for delivering value.
Simultaneously, emerging generative AI technologies will play a crucial role in the future. These technologies can auto-generate images, text, audio, and more, and can learn new tasks from a small number of samples, eliminating the need for extensive manual data annotation. These lightweight generative AIs will empower businesses to rapidly respond to evolving business needs.
For instance, a medical AI assistant could comprehend professional terminology, analyze medical records and test results, generate alerts for changes in patient conditions, and aid doctors in devising treatment plans. Legal AI could take in a small number of legal cases and statutes, learn to generate legal documents and agreement templates, thus reducing the repetitive workload for lawyers. In education, AI could automatically generate teaching materials and practice questions suitable for different stages based on subject knowledge graphs.
Another trend is the rise of lightweight generative AI models. With the proliferation of mobile devices and edge computing, running AI models in resource-constrained environments is crucial. Therefore, the future development direction leans toward creating more lightweight and efficient models to meet such demands. These lightweight models can be embedded in various terminals like smartphones and smart home devices, providing users with convenient and real-time AI support.
It’s foreseeable that as the cost of model training decreases, more AI applications specific to specialized domains and tailored to specific business scenarios will emerge. Accumulating years of experience, AIGC will play a pivotal role in bridging the gap between general AI and business reality, driving the evolution from general to specialized AI applications.