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DeepMind AlphaZero

DeepMind AlphaZero is an advanced artificial intelligence program developed by DeepMind that uses reinforcement learning to master complex games like chess, shogi, and Go without prior knowledge of the rules, relying solely on self-play to achieve superhuman performance.

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About DeepMind AlphaZero

DeepMind AlphaZero was developed by DeepMind in 2017 to demonstrate the potential of reinforcement learning and self-play in mastering complex board games. It built upon the success of its predecessor, AlphaGo, and achieved superhuman performance in chess, shogi, and Go through self-training without human data.

Strengths of DeepMind AlphaZero included its ability to learn and master complex games through self-play, achieving superhuman performance without human input. Weaknesses involved the high computational resources required for training. Competitors included IBM's Deep Blue for chess and traditional game-specific AI like Stockfish for chess and other specialized engines.

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How to hire a DeepMind AlphaZero expert

A DeepMind AlphaZero expert must have strong skills in reinforcement learning, deep neural networks, and game theory. Proficiency in programming languages like Python and frameworks such as TensorFlow or PyTorch is essential. Experience with high-performance computing and distributed systems is also important for handling the computational demands of training the model.

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