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.

The core idea behind GPT-1 was that using a transformer to predict the next word would provide enough signal to generate coherent text, a concept that seemed obvious in hindsight but was initially doubted.

The distinction between supervised learning and generative models is crucial; generative models focus on creating new content rather than merely identifying or predicting existing data.

This decade is characterized by the addition of generative AI tools, which build on the foundations of supervised learning.

The autoregressive process in language generation means that the brain is not just producing outputs but also generating the next input for itself.

The sequence-to-sequence model allows for translating sentences by consuming words one by one and generating translations word by word, which was a significant advancement in AI.

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

Language models are based on statistical predictions of word sequences, and as they scale in parameters and training data, they improve in their ability to predict the next word in a sequence.

The recent advancements in AI, particularly in video generation, demonstrate the potential for creating highly realistic content based on text prompts, which could revolutionize media production.