AlphaGo uye AlphaZero Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Sei mutambo weGo wainzi wakaomera makombiyuta?
   • Iine nhamba huru yenyeredzi yezvinzvimbo zvinogoneka
   • Iine mashoma anogoneka mafambiro
   • Inoridzwa pabepa chete
   • Haina mitemo
2. Ndiani AlphaGo akakunda zvine mukurumbira 4-1 muna 2016?
   • Garry Kasparov
   • Lee Sedol
   • Magnus Carlsen
   • A random amateur
3. Which three games did AlphaZero’s paper evaluate?
   • Go, chess, and shogi
   • Poker, bridge, and backgammon
   • Atari, StarCraft, and Dota
   • Only Go and chess
4. Ndeipi nzira yekutsvaga iyo AlphaZero inobatanidza neneural network yayo?
   • Binary search yebhuku renhare
   • Linear regression
   • Bubble rudzi
   • Monte Carlo Tree Search
5. Ndezvipi zvinhu zviviri izvo AlphaZero's neural network inofanotaura kutungamira kutsvaga kwayo?
   • Gwaro (mafambiro akanaka) uye kukosha (ndiani ari kukunda)
   • Mamiriro ekunze uye nguva
   • Zita nezera reanopikisa
   • Mavara nemanzwi
6. What did MuZero add to the AlphaZero-style planning approach?
   • A requirement for human game records
   • A learned model of reward and environment dynamics used for planning without being given the rules
   • A replacement for all tree search
   • A fixed database of every possible board position
7. What information did AlphaZero need in its board-game experiments besides self-play?
   • A database of professional games
   • A complete endgame table for every position
   • The rules of the games
   • Human rankings for every possible move
8. How did the original AlphaGo system combine supervised and reinforcement learning?
   • It trained a policy from expert games and then improved through self-play
   • It used only random moves and never trained on human games
   • It learned the game rules from images without playing
   • It relied only on a fixed database of winning games