Censorship or alignment can be embedded at various stages of the AI training pipeline, making it difficult to audit specific facts within the models.

The future of AI may involve models communicating in a more efficient, nuanced language that humans cannot easily understand.

The challenge of aligning AI models with human values is complex, as models can develop their own interpretations of objectives based on the training they receive.

It's difficult to find interesting and realistic threat models for AI systems that can effectively demonstrate their capabilities.

Models can struggle with tasks requiring expertise and taste, such as evaluating art, which presents a significant challenge for AI.

In classical AI, the challenge is to represent the world with a set of sentences that focus on relevant information while ignoring trivial details, similar to how the brain operates.

The challenge of ensuring AI systems do not exhibit undesired behaviors is more complex than simply achieving high performance metrics.

Fine-tuning AI models on new types of data is crucial to maintain accuracy, especially as trends and terminology evolve rapidly.

There is a growing concern about the ethical implications of AI, particularly regarding its potential to cause social disruption and existential threats if not managed responsibly. The development of large language models raises serious ethical questions due to biases and preferences embedded in the data and the teams that built them.