In this case, the agent takes on puzzles in the form of a 3D tensor or a grid of numbers, which it must complete in the fewest moves. Each step represents a move in solving the matrix-based puzzle, which might contain trillions of possible moves.
Fawzi told a press briefing this week that mapping out the space of algorithmic discovery was tough work, although navigating it was even more difficult. Nonetheless, the resulting research developed new algorithms for problems which have not been improved on in more than 50 years of human research, he said.
The researchers claim the technique could benefit computational tasks that use multiplication algorithms as well as demonstrate how reinforcement learning can be used to find new and unexpected solutions to known problems, while also noting some limitations. For example, predefined components are necessary to avoid the system missing a subset of efficient algorithms.
Skeptics may point to the application of AlphaFold, which promised breakthroughs in drug discovery via AI-supported protein research. Although the model has predicted nearly all known protein structures discovered, its
Source: The AI Report (theaireport.net)
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