When benchmark scores become targets rather than measurements, we risk mistaking good performance for actual competence; the real test lies in understanding how AI systems actually work, not just what they can do.

To truly understand language models, we must overcome anthropomorphic biases and embrace a nuanced, scientifically rigorous perspective.

The artistry in AI-generated images lies not in computer algorithms but in deliberate acts of human curation: designing datasets, crafting effective prompts, selecting meaningful outputs.

When artificial systems become expert mimics of human expression, how do we distinguish between imitation and intelligence?

Large language models like GPT-3 have industrialized bullshit – generating persuasive text at scale with no inherent concern for truth – and we're frighteningly unprepared for the consequences.