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.

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.

Scaling laws are prevalent in AI progress, suggesting that larger models and datasets lead to better performance across various domains, including robotics. However, scaling AI models is a complex challenge that involves addressing data, systems, and algorithmic issues, and once a model is operational, scaling laws can be applied to enhance its performance.

The scaling laws became a foundational principle for AI development after being popularized by researchers, including those at OpenAI and Google DeepMind, who emphasized that it's not just about making models larger, but also about training them on sufficient data.

The scaling laws observed in AI suggest that larger models trained longer lead to more compression and intelligence, which is supported by the philosophical grounding of these concepts.

Large language models should be referred to as large event models because they are not only effective in language but also applicable in pharmaceuticals and various other fields.

By allowing models to think longer, large language models can leverage more compute on the fly, enhancing their intelligence for complex problems.

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.

Achieving human-level AI will not be possible by merely scaling up existing large language models; a fundamental shift in approach is necessary to reach that goal.