Formalizing objective function in AI?

Formalizing the objective function in AI involves defining a clear goal for the machine to optimize. In the context of machine learning, especially in reinforcement learning, this objective function can appear as a loss function or utility function. This is essential because, unlike biological evolution which may have shifting or ambiguous 'goals', AI systems require explicit, predefined objectives to guide their learning and decision-making processes.

Lex Fridman emphasizes the necessity of having such a function, explaining that "there's an equation that says what's good and... you're trying to optimize that equation," which underpins machine learning applications like chess or other problem-solving tasks. Moreover, he points out that while human purposes may evolve or remain undefined—the so-called "meaning of life"—AI must have a hard-coded, unambiguous statement of goals to operate effectively 1.

This characterization of AI's operational framework highlights the central role of clear, formal objective functions in artificial intelligence, distinguishing it from more adaptive or exploratory biological processes.