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The AI industry is sinking into a state of disarray, where terms have lost their meaning, and effective teaching is buried beneath endless disclaimers redefining concepts for technical correctness. Take AI agents, for example—there’s no genuine agency, autonomy, or decision-making involved. Instead, the process is simply sampling from a …
The AI industry is sinking into a state of disarray, where terms have lost their meaning, and effective teaching is buried beneath endless disclaimers redefining concepts for technical correctness. Take AI agents, for example—there’s no genuine agency, autonomy, or decision-making involved. Instead, the process is simply sampling from a stochastic distribution based on training data during pretraining, refined through fine-tuning for tasks like conversation or instruction-following. In practice, it’s just a for loop with if-then-else logic wrapped around LLM API calls, held together by poorly designed prompts. To make matters worse, AI development teams often lack critical interdisciplinary knowledge in areas such as philosophy, epistemology, linguistics, ethics, psychology, computer science, and engineering—not to mention a fundamental understanding of the transformer model itself.
Great post!