In language understanding, we developed sequence learning models that can predict output sequences based on input sequences, useful for tasks like translation.

Further research confirmed that scaling laws apply to various models, including those for text-to-image and image-to-text tasks, while in early 2020, these laws for large language models (LLMs) were largely unknown outside of OpenAI, except for one researcher who focused on the scaling hypothesis, suggesting that increasing the size of data and compute would lead to the emergence of intelligence.

The ability to predict the next word in a sequence is deceptively complex and requires a model of reality, which is a significant achievement in AI.

Large language models are becoming significantly larger and smarter, with AI labs adopting a strategy of scaling more parameters, data, and compute to improve model performance.

The influential paper on scaling laws for neural language models published in January 2020 established that increasing model size, data, and compute leads to consistent improvements in performance, emphasizing the importance of scale over algorithm.

Large language models (LLMs) are primarily designed to regurgitate information based on statistical patterns in the text they've been trained on, which limits their ability to invent new concepts or solutions.

The core of generative AI relies on supervised learning to predict the next word in a sequence, enabling the creation of coherent text.

As we scale large language models (LLMs), we will eventually encounter a data wall, which limits the effectiveness of current scaling techniques.

I believe we are nearing the upper limits of large language models, as the future will likely involve many smaller, specialized models rather than a few large ones. We're at the upper end of large language models, and they're rapidly becoming a commodity as they mature.