Complementary Intelligence

A new conceptual framework titled Complementary Intelligence challenges the prevailing assumption that artificial intelligence should aim to replicate human cognition. The author argues that treating intelligence as a single-dimensional quantity is actively harmful, emphasizing instead the qualitative differences between biological and computational systems. This perspective shifts the research focus from imitation to finding types of intelligence that could be interesting to humans but do not currently exist. Historical analysis reveals that successful AI technologies often operate in fundamentally different ways than humans. Early successes in planning, such as chess-playing algorithms and the Robbins conjecture proof in 1996, demonstrated superior calculation but lacked human-like evaluation strategies. Expert systems from the 1970s, including the Stanford-developed MYCIN, failed because they could not capture the procedural knowledge humans struggle to articulate. Similarly, modern neural networks require massive training data and exhibit brittleness compared to human adaptability, as seen in DeepMind’s Atari experiments where machines required 38 simulated days to learn what a human grasps in minutes. Current large language models face specific limitations regarding memory and spatial reasoning, operating with a short-term context window akin to the protagonist in the movie Memento. While these models possess broad factual knowledge, they lack the capacity for novel insights derived from recombining existing information in creative ways. Furthermore, physical robots lag significantly behind non-embodied AI in performing trivial tasks like opening door handles, highlighting the gap between digital processing and physical interaction. The proposed solution involves leveraging AI for tasks where humans are physically incapable or unwilling to participate, such as high-frequency trading or generating video game environments. By recognizing that various AI technologies point in different intelligence vectors, researchers can avoid the moving goalpost of trying to approximate the human intelligence vector exactly. Instead, the goal becomes building systems that amplify human capabilities and explore new perceptual spaces, such as interpreting WiFi reflections or gravitational fields.