Are We Thinking Correctly about AI Intelligence?

Artificial intelligence systems exhibit an alien form of intelligence that requires rigorous scientific evaluation rather than reliance on simple performance benchmarks. Cognitive scientist Melanie Mitchell argues that current models often achieve results through spurious associations rather than genuine understanding, mirroring the "Clever Hans" effect where a horse appeared to perform arithmetic by reading human social cues. The "tyranny of tasks" in AI research obscures the distinction between specific problem-solving and true competence, necessitating a shift toward mechanistic interpretability to probe the internal logic of these systems. While AI may soon surpass human capabilities in domains like mathematics or research, the fundamental human drive to understand the world persists. This inherent curiosity ensures that human inquiry remains meaningful, even as AI tools fundamentally transform the landscape of scientific discovery and professional labor.