The internet's inherent biases can influence the data used to train AI models, leading to skewed representations that are difficult to correct.

As AI evolves, it will be crucial to ensure that it does not perpetuate biases and that it can appreciate absolute truths rather than just reflecting human biases.

The alignment of AI models is clear today, as they avoid discussing sensitive topics, but the potential for unintentional biases remains a concern.

AI is trained on averageness, while each individual possesses a unique sensibility; ceding our preferences to algorithms risks losing our agency.

The conversation around AI models often includes concerns about censorship and alignment, especially regarding sensitive historical events like Tiananmen Square.

The 'bitter lesson' in AI suggests that scalable training methods will prevail, emphasizing the importance of minimizing human biases in the learning process.

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.

An AI government could potentially make unbiased decisions based on collective human preferences, but there are significant challenges in ensuring that such a system remains free from external influences.

AI technology is rapidly improving, with current systems being less biased and more fair than they were six months ago, although challenges with bias and fairness still exist.