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

Evaluating the performance of applications built with large language models is challenging because the use cases are often subjective, making traditional accuracy metrics insufficient.

Yann LeCun is recognized as a credible critic of large language models, arguing that AI needs a physical understanding of the world, which is difficult to achieve with current models.

I believe we are nearing the upper limits of large language models, as the future will likely involve many smaller, specialized models rather than a few large ones. We're at the upper end of large language models, and they're rapidly becoming a commodity as they mature.

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

Using large language models as judges, combined with human review, allows for scaling without overwhelming human subject matter experts.

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.

Developers are increasingly finding that a significant portion of their code is being generated by large language models, which raises questions about the future impact of these models on society.

The challenge with language models is that while they can learn from feedback, they often lack the ability to generalize effectively across different tasks without specific scaffolding.