AlphaGo ati AlphaZero adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Kini idi ti ere ti Go ṣe pataki paapaa lile fun awọn kọnputa?
   • O ni nọmba nla ti astronomically ti awọn ipo ti o ṣeeṣe
   • O ni awọn gbigbe ti o ṣeeṣe pupọ diẹ
   • O ti wa ni dun nikan lori iwe
   • Ko ni awọn ofin
2. Tani AlphaGo ṣe ṣẹgun 4-1 olokiki ni ọdun 2016?
   • Garry Kasparov
   • Lee Sedol
   • Magnus Carlsen
   • A magbowo laileto
3. Awọn ere mẹta wo ni iwe AlphaZero ṣe ayẹwo?
   • Go, chess, and shogi
   • Poker, bridge, and backgammon
   • Atari, StarCraft, and Dota
   • Only Go and chess
4. Ọna wiwa wo ni AlphaZero ṣe so pọ pẹlu nẹtiwọọki nkankikan rẹ?
   • Iwadi alakomeji ti iwe foonu kan
   • Ipadasẹyin laini
   • Bubble too
   • Monte Carlo Tree Search
5. Awọn nkan meji wo ni Nẹtiwọọki Neural AlphaZero ṣe asọtẹlẹ lati ṣe itọsọna wiwa rẹ?
   • Ilana kan (awọn gbigbe to dara) ati iye kan (ẹniti o bori)
   • Oju ojo ati akoko
   • Orukọ alatako ati ọjọ ori
   • Awọn awọ ati awọn ohun
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