Large language models (LLMs), exemplified by Generative Pre-trained Transformer 4 (GPT-4) from OpenAI, launched the year 2023, ushering in a new revolution in AI and machine learning. While traditional machine learning models often rely on single-source data sets, LLMs, built on transformer neural network architectures, undergo training on an unprecedented scale of compute and data. This results in impressive capabilities, encompassing tasks that were once exclusive to humans, such as reasoning, abstraction, and projection.
As we explore the potential of these models, it's essential to contemplate the future. Here are five predictions.
LLMs WILL BECOME (EVEN) BETTER SKILLED
In less than a year, LLMs have delivered an impressive evolution, expanding from text-based generative AI to incorporating voice and vision in models like GPT4.0. Looking back, the original GPT could not generally produce coherent text by 2018, while a few months later, GPT-2 could only follow simple instructions. GPT-3 and now GPT-4 now can perform a wide range of language tasks on a par with humans.
Other models, such as Google's Gemini, Nvidia's Falcon, and Claude, have also been enhancing their performance to compete with OpenAI products.
Over the recent five years, LLMs have, on average, improved their accuracy on the multitask understanding (MMLU) scale, reaching human expert-level accuracy. This performance has been closely scaling with computational resources. Larger models, such as GPT-3, showcase significantly enhanced capabilities compared to their
