How MIT is training AI language models in an era of quality data scarcity

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Improving the robustness of machine learning (ML) models for natural language tasks has become a major artificial intelligence (AI) topic in recent years. Large language models (LLMs) have always been one of the most trending areas in AI research, backed by the rise of generative AI and companies racing to release architectures that can create impressively readable content, even computer code. Language models have traditionally been trained using online texts from sources such as Wikipedia, news stories, scientific papers and novels. However, in recent years, the tendency has been to train these models on increasing amounts of data in order to improve their accuracy and versatility. But, according to a team of AI forecasters, there is a concern on the horizon: we may run out of data to train them on.