Final answer:
Deep Blue and AlphaGo are both examples of artificial intelligence programs designed for game playing. They both use complex algorithms and machine learning techniques, but differ in their approach to move selection.
Step-by-step explanation:
Deep Blue and AlphaGo are both examples of artificial intelligence programs that have made significant advancements in the field of game playing. They both use complex algorithms and machine learning techniques to analyze game positions and make strategic decisions.
One similarity between Deep Blue and AlphaGo is that they were both designed to play and excel at specific games. Deep Blue was developed to play chess, while AlphaGo was designed to play the ancient board game Go.
One difference between Deep Blue and AlphaGo is the way they handle game playing. Deep Blue relied on a brute-force search algorithm and evaluated millions of positions per second to find the best move. On the other hand, AlphaGo used a combination of Monte Carlo Tree Search and deep neural networks to assess the board state and make decisions.