exam

Question 1

alan turing - Question?

Answer 1

can machines think?

proposed imitation game to assess machine intelligence

Question 2

alan turing hypothesis

Answer 2

by year 2000, machines would be able to play the imitation game so well that an average interrogator would have only a 70% chance of distinguishing between a human and a machine after 5 min of questioning

Question 3

digital computers as universal machines?

Answer 3

meaning that a sufficiently powerful computer with proper programming can simulate any other machine, including a thinking mind.

Question 4

turing argument

Answer 4

digital computers, given enough storage and speed can simulate any form of computation, including intelligence.

Question 5

Types of Machines Considered:

Answer 5

Discrete-State Machines (Digital Computers)
Learning Machines (Machines that adapt and improve)

Question 6

turing proposal

Answer 6

Turing’s Proposal: Instead of building an adult mind, start with a child-like machine and allow it to learn over time through experience

Question 7

turing 4 predictions

Answer 7

1. Machines can convincingly mimic human responses
2. learning machines: AI will develop like a child and improve overtime

3.Intelligence is about behavior, not internal experience

4. Computers will be able to handle natural language convincingly

Question 8

Machines can convincingly mimic human responses

Answer 8

ChatGPT passes casual conversations and even professional discussions

Partially achieved (Fails in deep reasoning & complex problem-solving)

Question 9

Learning Machines: AI will develop like a child and improve over time

Answer 9

ChatGPT learns from pre-training and fine-tuning but does not adapt in real-time

Partially achieved (Pre-trained, but not dynamically learning)

Question 10

Intelligence is about behavior, not internal experience

Answer 10

ChatGPT demonstrates intelligent-like responses but lacks true self-awareness’

Achieved in a limited sense

Question 11

Computers will be able to handle natural language convincingly

Answer 11

ChatGPT can generate fluent, human-like text but struggles with long-term coherence

Mostly achieved

Question 12

turing objections

Answer 12

The Theological Argument – Only humans have souls

Mathematical Objection – Some tasks are beyond machine ability

Argument from Consciousness – Machines lack subjective experience

Learning Limitations – Machines can only do what they are programmed to do

Question 13

The Theological Argument – Only humans have souls

Answer 13

Intelligence should be measured by behavior, not metaphysical concepts

ChatGPT mimics intelligence but lacks consciousness and emotions

Question 14

Mathematical Objection – Some tasks are beyond machine ability

Answer 14

Humans also have cognitive limitations

ChatGPT cannot prove mathematical theorems independently

Question 15

Argument from Consciousness – Machines lack subjective experience

Answer 15

We cannot objectively prove anyone else’s consciousness

ChatGPT produces convincing text but lacks self-awareness

Question 16

Learning Limitations – Machines can only do what they are programmed to do

Answer 16

Machines can be programmed to learn and improve

ChatGPT uses Deep Learning but does not autonomously generate new knowledge

Question 17

What was Turing’s original purpose for proposing the Imitation Game?
A) To test emotional intelligence in AI
B) To avoid philosophical debates by focusing on observable behavior
C) To create a chatbot
D) To measure consciousness

Answer 17

B

Question 18

What is the Imitation Game now commonly known as?
A) The Reverse Turing Challenge
B) The Consciousness Test
C) The Intelligence Quotient Test
D) The Turing Test

Answer 18

d

Question 19

According to Turing, how should machine intelligence be evaluated?
A) Through the presence of emotions
B) By comparing machine and human brain scans
C) Through observable behavior and performance
D) By the machine’s processing speed

Answer 19

C

Question 20

What type of machine did Turing believe could simulate any other machine?
A) Analog computers
B) Mechanical calculators
C) Universal digital computers
D) Quantum machines

Answer 20

C

Question 21

Turing’s prediction was that by the year 2000, an average judge would fail to distinguish a machine from a human more than what percentage of the time?
A) 50%
B) 70%
C) 30%
D) 90%

Answer 21

B

Question 22

What is the main limitation of ChatGPT in relation to Turing’s idea of a “learning machine”?
A) It refuses to answer questions
B) It cannot mimic grammar
C) It does not learn in real-time after deployment
D) It is too slow to be effective

Answer 22

C

Question 23

How does ChatGPT reflect the Turing Test today?
A) It fully passes all advanced reasoning tests
B) It consistently fails to mimic human responses
C) It often fools users in casual conversation but fails deeper reasoning
D) It always identifies itself as AI and refuses imitatio

Answer 23

C

Question 24

Which of the following is not one of the objections to machine intelligence Turing responds to?
A) The Mathematical Objection
B) The Biological Rejection
C) The Argument from Consciousness
D) The Theological ObjectioN

Answer 24

B

Question 25

What does the Lady Lovelace Objection argue? A) Machines can love but not think B) Machines can learn from experience C) Machines can only do what they are programmed to do D) Machines are better than humans at reasoning

Answer 25

C

Question 26

In your own response, what was identified as a key limitation of LLMs like ChatGPT? A) They do not follow grammar rules B) They always repeat the same answers C) They lack deep reasoning and can produce biased or incorrect info D) They are emotionally manipulative

Answer 26

C

Question 27

Explain the purpose and structure of the Imitation Game and discuss how Large Language Models like ChatGPT both fulfill and fall short of Turing’s vision.

Answer 27

The Imitation Game, now known as the Turing Test, was proposed by Turing as a way to judge machine intelligence based on behavior. A human judge talks to both a human and a machine through text. If the judge can't reliably tell which is which, the machine is considered intelligent. ChatGPT partially fulfills this vision. It can produce human-like text that often fools users, especially in casual conversation. It follows grammar, understands prompts, and responds fluently. However, it falls short in deeper reasoning, consistency, and understanding — it mimics language but doesn't truly "think." It doesn’t learn during conversation and lacks real-world awareness. So, while ChatGPT shows Turing’s idea is possible in some ways, it's not fully there yet.

Question 28

Choose two objections Turing responded to and explain whether they still apply to today’s AI like ChatGPT.

Answer 28

Theological Objection – Turing said we shouldn’t judge intelligence by souls or spirituality, but by behavior. This still applies today. ChatGPT doesn’t have a soul or emotions, but it can act intelligently in conversations. So this objection isn’t very relevant to modern AI. Lady Lovelace Objection – She claimed machines can only do what they’re programmed to do. Turing responded that machines can be made to learn. Today, ChatGPT uses deep learning and can generate new text from training data. But it doesn’t truly “create” — it still relies on what it was trained on. So this objection is partly still valid.

Question 29

Turing predicted that by the year 2000, machines would be able to play the imitation game so well that an average interrogator would have only a 70% chance of correctly identifying a human over a machine after 5 minutes. Discuss how accurate this prediction is in relation to modern AI systems like ChatGPT

Answer 29

ChatGPT can produce very realistic and human-like responses. In short chats, it often fools users — just like Turing imagined. But it still struggles with deep understanding, logical reasoning, and staying consistent over long conversations. Also, unlike Turing’s idea of a “learning machine” that grows like a child, ChatGPT doesn’t learn during conversations. It’s trained in advance and doesn’t improve on its own. So Turing’s prediction was mostly accurate for surface-level imitation, but not fully realized in terms of learning and deep understanding.