Are We Underestimating Human Potential? AI Model Hints Humans Can Do More

A recent paper from DeepMind has intriguing implications for understanding exceptional human talents. In the paper "Grandmaster-Level Chess Without Search," the authors showed they could play high-level chess without thinking ahead multiple moves.

The model only considered the immediate next move, not planning out an extended strategy. Despite this limitation, it rivaled top human chess players, though it was weaker compared to chess computers that do employ extensive search.

This success likely stems from the intuitive nature of the model's play. The particular model architecture, GPT, can capture logical reasoning capabilities, even in small versions like that used here. In this case, the model seems to have developed an innate "feel" for strong chess moves, rather than relying on analysis.

These results may help explain child prodigies in various domains. Such individuals exhibit an intuitive grasp of their skill, finding it easy or "obvious" compared to others. This intuition develops through exposure and practice, allowing them to excel from a young age.

Moreover, the paper indicates there may be no ceiling for innate human potential. With the right training, there could be new prodigies whose natural abilities far exceed even today's top performers. Access to tools like chess engines provides endless opportunities to develop pattern recognition and intuition through practice.

By better understanding how models like GPT mimic human cognition, we can gain insight into the origins of talent. Some minds are wired to fluently speak the "language" of a domain. This research suggests strong intuition can be trained, pointing to the possibilities of human ability when innate talent combines with deep practice.