AlphaGo is an artificial intelligence program developed by DeepMind, a subsidiary of Google, designed to master the ancient board game Go. Using advanced techniques like deep neural networks and reinforcement learning, AlphaGo achieved superhuman performance, defeating world champion Lee Sedol in a historic 2016 match. This breakthrough highlighted the potential of AI in complex strategic games, leading to further innovations such as AlphaGo Zero, which learned to play solely through self-play without human input. Overall, AlphaGo represents a major milestone in AI, showcasing Google’s contributions to machine learning and its applications beyond gaming.
Table of Contents
- Part 1: OnlineExamMaker – Generate and Share Google AlphaGo Quiz with AI Automatically
- Part 2: 20 Google AlphaGo Quiz Questions & Answers
- Part 3: Try OnlineExamMaker AI Question Generator to Create Quiz Questions
Part 1: OnlineExamMaker – Generate and Share Google AlphaGo Quiz with AI Automatically
The quickest way to assess the Google AlphaGo knowledge of candidates is using an AI assessment platform like OnlineExamMaker. With OnlineExamMaker AI Question Generator, you are able to input content—like text, documents, or topics—and then automatically generate questions in various formats (multiple-choice, true/false, short answer). Its AI Exam Grader can automatically grade the exam and generate insightful reports after your candidate submit the assessment.
What you will like:
● Create a question pool through the question bank and specify how many questions you want to be randomly selected among these questions.
● Allow the quiz taker to answer by uploading video or a Word document, adding an image, and recording an audio file.
● Display the feedback for correct or incorrect answers instantly after a question is answered.
● Create a lead generation form to collect an exam taker’s information, such as email, mobile phone, work title, company profile and so on.
Automatically generate questions using AI
Part 2: 20 Google AlphaGo Quiz Questions & Answers
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1. What is the primary game that AlphaGo was designed to play?
A) Chess
B) Go
C) Poker
D) Shogi
Answer: B
Explanation: AlphaGo was specifically developed by DeepMind to master the game of Go, which is known for its complexity and vast number of possible board positions.
2. Which company acquired DeepMind, the creators of AlphaGo?
A) Microsoft
B) Apple
C) Google
D) Facebook
Answer: C
Explanation: DeepMind was acquired by Google in 2014, allowing the development of AlphaGo under Google’s resources.
3. In what year did AlphaGo first defeat a professional Go player?
A) 2015
B) 2016
C) 2017
D) 2018
Answer: B
Explanation: AlphaGo defeated European champion Fan Hui in 2015 in an unofficial match, but its first major victory was against Lee Sedol in 2016.
4. What type of artificial intelligence technique does AlphaGo primarily use?
A) Rule-based systems
B) Deep neural networks
C) Genetic algorithms
D) Fuzzy logic
Answer: B
Explanation: AlphaGo relies on deep neural networks combined with Monte Carlo Tree Search to evaluate positions and make decisions in Go.
5. Who was the South Korean Go player that AlphaGo defeated in a five-game match in 2016?
A) Ke Jie
B) Lee Sedol
C) Shin Jinseo
D) Gu Li
Answer: B
Explanation: AlphaGo won 4-1 against Lee Sedol, a world champion, which was a landmark event in AI history.
6. What is the name of the version of AlphaGo that learned solely from self-play without human data?
A) AlphaGo Fan
B) AlphaGo Lee
C) AlphaGo Zero
D) AlphaGo Master
Answer: C
Explanation: AlphaGo Zero started from scratch and improved by playing against itself, demonstrating superior learning capabilities.
7. How many layers does the neural network in the original AlphaGo typically have?
A) 10
B) 13
C) 20
D) 50
Answer: B
Explanation: The original AlphaGo used a 13-layer neural network for policy and value estimations, which was a key factor in its success.
8. What was the score of AlphaGo’s match against Ke Jie in 2017?
A) 2-2 draw
B) 3-0 win
C) 3-0 loss
D) 2-1 win
Answer: B
Explanation: AlphaGo, in its Master version, defeated Ke Jie 3-0 in a best-of-three match, solidifying its dominance.
9. Which algorithm is combined with neural networks in AlphaGo to explore possible moves?
A) A* Search
B) Monte Carlo Tree Search
C) Breadth-First Search
D) Depth-First Search
Answer: B
Explanation: Monte Carlo Tree Search is integrated with neural networks in AlphaGo to simulate and evaluate game outcomes efficiently.
10. What is the main goal of AlphaGo’s policy network?
A) To evaluate the board’s value
B) To suggest probable moves
C) To optimize hardware
D) To detect opponents’ errors
Answer: B
Explanation: The policy network predicts the most promising moves, helping AlphaGo decide its next action based on the current board state.
11. How did AlphaGo initially learn to play Go?
A) From professional game databases
B) By random trial and error
C) Through human coaching sessions
D) By analyzing chess games
Answer: A
Explanation: The original AlphaGo was trained on a large database of professional Go games to build its initial knowledge base.
12. What significant achievement did AlphaGo accomplish in October 2015?
A) Defeating a computer in Go
B) Winning against a world champion
C) Beating Lee Sedol
D) Playing its first game
Answer: B
Explanation: In October 2015, AlphaGo defeated Fan Hui, the European Go champion, marking the first time an AI beat a professional human player.
13. Which of the following is NOT a variant of AlphaGo?
A) AlphaGo Zero
B) AlphaGo Lee
C) AlphaGo DeepBlue
D) AlphaGo Master
Answer: C
Explanation: AlphaGo DeepBlue does not exist; DeepBlue is a separate IBM chess program, while AlphaGo’s variants include Lee, Master, and Zero.
14. What does the “Zero” in AlphaGo Zero signify?
A) Zero human input
B) Zero losses
C) Zero games played
D) Zero updates
Answer: A
Explanation: AlphaGo Zero signifies that it started with zero prior knowledge of human games, learning entirely through self-play.
15. In AlphaGo’s architecture, what role does the value network play?
A) Predicting move sequences
B) Estimating the game’s outcome
C) Generating random boards
D) Optimizing speed
Answer: B
Explanation: The value network assesses the current board position to estimate the probability of winning, guiding long-term strategy.
16. Who is the CEO of DeepMind, the organization behind AlphaGo?
A) Sergey Brin
B) Demis Hassabis
C) Sundar Pichai
D) Jeff Dean
Answer: B
Explanation: Demis Hassabis is the co-founder and CEO of DeepMind, leading the development of AlphaGo.
17. What was the prize money for AlphaGo’s match against Lee Sedol?
A) $1 million
B) $100,000
C) No prize
D) $500,000
Answer: A
Explanation: The match against Lee Sedol had a $1 million prize, which was donated to charity by DeepMind after AlphaGo’s victory.
18. How many Go games did AlphaGo play against itself to train AlphaGo Zero?
A) Millions
B) Thousands
C) Billions
D) Hundreds
Answer: A
Explanation: AlphaGo Zero played millions of games against itself during training, allowing it to surpass human-level play rapidly.
19. What impact did AlphaGo have on the field of AI research?
A) It proved AI is superior in all games
B) It advanced reinforcement learning techniques
C) It ended the need for neural networks
D) It focused only on board games
Answer: B
Explanation: AlphaGo’s success highlighted the potential of reinforcement learning and deep neural networks, influencing broader AI applications.
20. Why was AlphaGo retired after its final match?
A) Due to hardware failures
B) To focus on new projects
C) Because it achieved perfection
D) Legal issues
Answer: B
Explanation: AlphaGo was retired in 2017 after its match with Ke Jie, as DeepMind shifted focus to more general AI advancements like AlphaZero.
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Part 3: Try OnlineExamMaker AI Question Generator to Create Quiz Questions
Automatically generate questions using AI