Introduction to Artificial Intelligence Questions and Answers

Q: What is distribution shift?

A system trained on one population may perform badly on another that differs in ways the training data did not capture, and performance degrades silently rather than announcing itself. Monitoring deployed performance rather than trusting a test score is the standard response. Parameter drift, Bayesian updating and platform variation are separate concepts. A failure mode that produces confident wrong answers rather than obvious errors is particularly dangerous and deserves explicit monitoring.

Q: Consider the following statements about artificial intelligence. 1. The rational agent view defines intelligence by acting well given knowledge 2. The Turing test evaluates behaviour rather than internal mechanism 3. Deep learning encompasses all of artificial intelligence Which of the statements given above is or are correct?

The first two are correct. Deep learning is a subset of machine learning, which is itself a subset of artificial intelligence, and large areas of the field involve search, planning and reasoning with no learning at all. Getting the nesting the wrong way round is a common error and it leads to the assumption that every problem in the field is a data problem, which misdirects a great deal of effort.

Q: Consider the following pairings of environment property and description. Fully observable - sensors reveal the complete state Deterministic - the next state follows from the action and state Dynamic - the world changes while the agent deliberates Discrete - there is exactly one agent in the environment How many of the pairs given above are correctly matched?

The first three are correct. Discrete refers to whether states, time and actions take finitely many distinct values, while the number of agents is the separate single agent or multi-agent dimension. The taxonomy is worth learning precisely because each dimension selects a different technique: memory for partial observability, probability for stochasticity, real time constraints for dynamism and game theory for multiple agents.

Q: What is the primary purpose of a performance measure in agent design?

Everything else follows from the performance measure, so stating it carefully, in terms of what is actually wanted rather than what is easy to measure, is the most consequential decision in designing an agent. A measure that rewards the wrong thing produces a system that pursues it faithfully. Speed, resource use and human comparison are secondary evaluations. Specification gaming is the direct consequence of getting this step wrong, which is why it heads the PEAS checklist.

71.

What is distribution shift?

A.The change in a probability distribution after applying Bayes' theorem
B.The variation in performance between different hardware platforms
C.The deployed data differing from the data the system was trained on
D.The gradual drift of a model's parameters during training

Answer: C

A system trained on one population may perform badly on another that differs in ways the training data did not capture, and performance degrades silently rather than announcing itself. Monitoring deployed performance rather than trusting a test score is the standard response. Parameter drift, Bayesian updating and platform variation are separate concepts. A failure mode that produces confident wrong answers rather than obvious errors is particularly dangerous and deserves explicit monitoring.

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72.

Why is a test set score not a guarantee of deployed performance?

A.Test scores are always optimistic by a fixed and known margin
B.Deployment always uses different hardware from the test environment
C.Test sets are too small to give a statistically meaningful result
D.The test set may not represent the conditions the system will meet

Answer: D

A test set drawn from the same collection as the training data measures generalisation within that distribution and says nothing about performance on a population it does not represent. Repeated use of the same test set also inflates scores through selection. Hardware and sample size are lesser concerns. The distinction between generalising within a distribution and generalising to a new one is the most consequential subtlety in evaluating a learned system.

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73.

Consider the following statements about artificial intelligence.

1. The rational agent view defines intelligence by acting well given knowledge

2. The Turing test evaluates behaviour rather than internal mechanism

3. Deep learning encompasses all of artificial intelligence

Which of the statements given above is or are correct?

A.1 and 2 only
B.1 and 3 only
C.2 and 3 only
D.1, 2 and 3

Answer: A

The first two are correct. Deep learning is a subset of machine learning, which is itself a subset of artificial intelligence, and large areas of the field involve search, planning and reasoning with no learning at all. Getting the nesting the wrong way round is a common error and it leads to the assumption that every problem in the field is a data problem, which misdirects a great deal of effort.

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74.

Match each agent architecture with its distinguishing feature.

List I

A. Simple reflex

B. Model based reflex

C. Goal based

D. Utility based

List II

P. acts on the current percept alone

Q. maintains internal state

R. searches for actions achieving a goal

S. ranks outcomes by desirability

Select the correct match.

A.A-P, B-R, C-Q, D-S
B.A-P, B-Q, C-R, D-S
C.A-Q, B-P, C-S, D-R
D.A-R, B-S, C-P, D-Q

Answer: B

The four form a ladder, each adding a capability the previous one lacked: memory, then an explicit objective, then a graded preference over outcomes. Choosing the simplest architecture that the environment demands is the design principle, since each addition costs computation and design effort. Learning can be added to any of them, which is why it is treated as an orthogonal dimension rather than a fifth rung.

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75.

Consider the following pairings of environment property and description.

Fully observable - sensors reveal the complete state

Deterministic - the next state follows from the action and state

Dynamic - the world changes while the agent deliberates

Discrete - there is exactly one agent in the environment

How many of the pairs given above are correctly matched?

A.Only two
B.All four
C.Only three
D.Only one

Answer: C

The first three are correct. Discrete refers to whether states, time and actions take finitely many distinct values, while the number of agents is the separate single agent or multi-agent dimension. The taxonomy is worth learning precisely because each dimension selects a different technique: memory for partial observability, probability for stochasticity, real time constraints for dynamism and game theory for multiple agents.

Discuss in Forum

76.

Three of the following are branches of artificial intelligence. Which is the odd one out?

Numerical analysis

Natural language processing

Computer vision

Automated planning

A.Natural language processing
B.Computer vision
C.Automated planning
D.Numerical analysis

Answer: D

Language, vision and planning are all recognised subfields with their own conferences and literature. Numerical analysis is a branch of mathematics concerned with the accuracy and stability of computation, used throughout the field as a tool but not part of it. Distinguishing the disciplines a subject draws on from the subject itself keeps its boundaries clear, which matters when assessing whether a claimed result belongs to it at all.

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77.

Assertion (A): Modern artificial intelligence systems are described as narrow rather than general.

Reason (R): Each performs one class of task well and cannot transfer that competence elsewhere.

Select the correct answer from the options below.

A.Both A and R are true, and R is the correct explanation of A
B.Both A and R are true, but R is not the correct explanation of A
C.A is true but R is false
D.A is false but R is true

Answer: A

Both are true and the second is the reason for the first. A system that plays chess at superhuman level cannot play draughts, hold a conversation or fold a towel, and must be rebuilt or retrained for each new task. Whether generality will emerge from scaling current methods or requires something new is genuinely open. Being explicit about narrowness is the most useful discipline when reading claims about the field's progress.

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78.

Statement I: The Turing test evaluates a machine's internal reasoning process.

Statement II: The Turing test evaluates a machine's conversational behaviour.

In the light of the above statements, choose the correct answer.

A.Statement I is correct but Statement II is incorrect
B.Statement I is incorrect but Statement II is correct
C.Both Statement I and Statement II are correct
D.Both Statement I and Statement II are incorrect

Answer: B

The second is correct and the first is exactly what the test was designed to avoid. Turing deliberately replaced questions about mechanism with a purely behavioural criterion, because he regarded the mechanistic question as unanswerable. That choice is both the test's strength, since it can actually be conducted, and its weakness, since behaviour can be imitated without the capabilities the test was meant to detect. The pair captures the whole debate about it in two sentences.

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79.

A system trained on data from one hospital performs poorly when deployed at another.

What is the most likely explanation?

A.The second hospital's computers are far slower than the first's
B.The training process was stopped before it had converged
C.Distribution shift between the two populations and practices
D.The model has too few parameters to represent the problem

Answer: C

Different patient populations, equipment and recording practices mean the deployed data differs from the training distribution, and the model's confident predictions become unreliable. Validating on data from the deployment site, and monitoring performance continuously, are the standard responses. Capacity, hardware and convergence would have shown up in the original evaluation. A failure that appears only on deployment points at the data rather than at the model.

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80.

What is the primary purpose of a performance measure in agent design?

A.It records how quickly the agent executes each of its actions
B.It measures the computing resources the agent consumes in use
C.It compares the agent with a human performing the same task
D.It defines what counts as success and so what the agent optimises

Answer: D

Everything else follows from the performance measure, so stating it carefully, in terms of what is actually wanted rather than what is easy to measure, is the most consequential decision in designing an agent. A measure that rewards the wrong thing produces a system that pursues it faithfully. Speed, resource use and human comparison are secondary evaluations. Specification gaming is the direct consequence of getting this step wrong, which is why it heads the PEAS checklist.

Discuss in Forum

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