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

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.

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.

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

The last decade has shown that we found the right objective function for AI, which is simply to predict the next token, leading to a more beneficial form of intelligence.

AI currently lacks consciousness and is primarily focused on predicting the next token in language modeling, which is a simplistic objective.

Specific abilities of AI models are very hard to predict, even though statistical averages like loss and entropy can be predicted with high accuracy.

The surprising interpretability of thinking models allows us to understand how they reach their conclusions, which was not expected in earlier AI developments. The fact that AI models can articulate their reasoning provides a level of comfort regarding safety, even though it's not infinite comfort.

The challenge of filtering out specific facts from AI models is compounded by the complexity of language and the potential for encoded language to evade detection.