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.

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.

Neural networks have the potential to significantly impact healthcare by analyzing vast amounts of patient data to provide accurate diagnoses based on similar cases.

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

While there are significant potential benefits to large language models, we must tread carefully due to the ethical minefield they present.

The new technology of large language models is creating unprecedented opportunities for startups, enabling tasks that previously required extensive research teams to be accomplished more easily.

Our approach to AI in medicine involves using smaller, specialized models trained on peer-reviewed literature that outperform larger models on specific medical tasks, which was a novel insight at the time of development.

Large language models are crucial in the pursuit of artificial general intelligence, and the principles of scaling apply to other models as well, including image diffusion and robotics.

Customization of large language models is crucial for differentiation, as everyone has access to the same base models, but tailoring them to specific contexts enhances user experience.