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Massive Combinatorial Search Space: The problem should involve navigating through an enormous array of possible solutions.
Clear Objective Function: A definitive metric to optimize or measure success is essential, such as winning a game or maximizing a score.
Data Availability: There must be substantial data available for training AI models, ideally supplemented by accurate simulation to generate additional synthetic data.
A prime example outlined by Hassabis is the game of Go, which contains a vast search space of potential board configurations—greater than the number of atoms in the universe. AI excels at navigating this complexity due to its ability to learn and adapt based on winning strategies and prior data.