Introduction to Artificial Intelligence Questions and Answers

Q: What does the Turing test propose as a criterion for intelligence?

Turing proposed an imitation game in which a judge converses with a human and a machine through a text channel and tries to tell which is which. It deliberately avoids any requirement about internal mechanism, replacing an unanswerable question about thinking with an operational one about behaviour. Speed, mechanism and learning capability are not part of the criterion. Substituting an answerable question for an unanswerable one is the move the paper is famous for.

Q: Why did Turing propose a behavioural test rather than a definition of thinking?

Turing argued that the original question is too meaningless to deserve discussion, and replaced it with a concrete test that can actually be conducted. That move set the pattern for much of the field, which prefers measurable performance to definitional argument. Measurement convenience, mechanism and funding were not his reasons. Replacing a philosophical question with an empirical one is a strategy that recurs whenever a discipline wants to make progress rather than argue.

Q: What is a common objection to the Turing test?

Critics observe that a system might pass by evasion, mimicry of human error and conversational trickery without any of the capabilities the test was meant to detect, and that the test is anthropocentric and unhelpful as a research goal. Judges are readily found, and arithmetic speed and memory are not what it measures. A test that can be gamed measures the gaming as much as the capability, which is a difficulty every benchmark in the field has faced since.

Q: What is the distinction between strong and weak artificial intelligence?

The strong position holds that a suitably programmed machine would genuinely have mental states, while the weak position claims only that machines can act as if they were intelligent. Almost all working research proceeds on the weak assumption, which is sufficient for engineering purposes. Generality, method and data scale are separate distinctions, and the general versus narrow contrast is often confused with this one. Keeping a philosophical claim separate from an engineering one avoids a great deal of fruitless argument.

Q: What was the Dartmouth workshop of 1956?

The summer project at Dartmouth brought together McCarthy, Minsky, Shannon and others and gave the field its name and its founding agenda. Expert systems came two decades later, the winters later still, and neural network research predated it. A field acquiring a name and a community at a single meeting is unusual and it shaped how the subject saw itself for decades afterwards.

Q: What characterised the early optimism of the field?

Founders predicted within a few years machines that would translate languages, prove theorems and match human intelligence generally, and those predictions were repeatedly unfulfilled. The gap between early successes on toy problems and the difficulty of real ones was underestimated. Statistical learning and formal verification came later and from other traditions. Early success on simplified cases misleading a field about the difficulty of the general case is a pattern worth recognising.

Q: What triggered the first artificial intelligence winter?

Reports such as Lighthill's in the United Kingdom concluded that the field had failed to deliver on its promises, particularly on the combinatorial explosion in real problems, and funding was withdrawn. The other options describe events that either did not occur or had no such effect. External assessment of a field against the claims it made for itself is a hazard of promising more than can be delivered, and the pattern repeated a decade later with expert systems.

Q: What were expert systems?

An expert system captured the if then rules of a human specialist in a narrow field such as medical diagnosis or mineral prospecting, together with an inference engine to apply them. Their commercial success in the nineteen eighties and their subsequent difficulties with maintenance and brittleness shaped the field's later direction. Automatic learning, generality and neural simulation describe other approaches. A technique that succeeds narrowly and fails to generalise is a recurring shape in this subject.

11.

What does the Turing test propose as a criterion for intelligence?

A.A machine passes if its internal processes resemble human reasoning
B.A machine passes if it can learn any task without being programmed
C.A machine passes if an interrogator cannot distinguish it from a person
D.A machine passes if it solves problems faster than any human being

Answer: C

Turing proposed an imitation game in which a judge converses with a human and a machine through a text channel and tries to tell which is which. It deliberately avoids any requirement about internal mechanism, replacing an unanswerable question about thinking with an operational one about behaviour. Speed, mechanism and learning capability are not part of the criterion. Substituting an answerable question for an unanswerable one is the move the paper is famous for.

Discuss in Forum

12.

Why did Turing propose a behavioural test rather than a definition of thinking?

A.Behaviour is easier to measure than the speed of a computation
B.He believed the internal mechanism of machines was already understood
C.A behavioural test was required by the funding body of his research
D.The question of whether machines can think is too ill defined to settle

Answer: D

Turing argued that the original question is too meaningless to deserve discussion, and replaced it with a concrete test that can actually be conducted. That move set the pattern for much of the field, which prefers measurable performance to definitional argument. Measurement convenience, mechanism and funding were not his reasons. Replacing a philosophical question with an empirical one is a strategy that recurs whenever a discipline wants to make progress rather than argue.

Discuss in Forum

13.

What is a common objection to the Turing test?

A.It rewards deception rather than genuine capability
B.It cannot be conducted because no suitable judges exist
C.It requires the machine to be faster than a human at arithmetic
D.It measures only the machine's memory capacity and nothing else

Answer: A

Critics observe that a system might pass by evasion, mimicry of human error and conversational trickery without any of the capabilities the test was meant to detect, and that the test is anthropocentric and unhelpful as a research goal. Judges are readily found, and arithmetic speed and memory are not what it measures. A test that can be gamed measures the gaming as much as the capability, which is a difficulty every benchmark in the field has faced since.

Discuss in Forum

14.

What does the Chinese room argument attempt to show?

A.That neural networks are superior to symbolic reasoning systems
B.That symbol manipulation alone does not constitute understanding
C.That a machine cannot manipulate symbols quickly enough to converse
D.That natural language processing is impossible in principle

Answer: B

Searle imagines a person following rules to produce Chinese responses without understanding Chinese, arguing that the system displays the behaviour without the semantics, so passing a behavioural test does not establish understanding. The systems reply and other responses contest it vigorously. It says nothing about speed, about the possibility of language processing or about the merits of neural approaches. A thought experiment attacking a criterion rather than a capability is best answered by examining what the criterion was for.

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15.

What is the distinction between strong and weak artificial intelligence?

A.Whether a system uses symbolic or statistical methods internally
B.Whether a system was trained on large or small quantities of data
C.Whether a machine genuinely has a mind or merely simulates one
D.Whether a system is general purpose or specialised to one task

Answer: C

The strong position holds that a suitably programmed machine would genuinely have mental states, while the weak position claims only that machines can act as if they were intelligent. Almost all working research proceeds on the weak assumption, which is sufficient for engineering purposes. Generality, method and data scale are separate distinctions, and the general versus narrow contrast is often confused with this one. Keeping a philosophical claim separate from an engineering one avoids a great deal of fruitless argument.

Discuss in Forum

16.

What was the Dartmouth workshop of 1956?

A.The conference at which the first expert system was demonstrated
B.The event that marked the start of the first artificial intelligence winter
C.The gathering that established the first neural network research group
D.The meeting at which the term artificial intelligence was adopted

Answer: D

The summer project at Dartmouth brought together McCarthy, Minsky, Shannon and others and gave the field its name and its founding agenda. Expert systems came two decades later, the winters later still, and neural network research predated it. A field acquiring a name and a community at a single meeting is unusual and it shaped how the subject saw itself for decades afterwards.

Discuss in Forum

17.

What characterised the early optimism of the field?

A.Confident predictions that general intelligence was a decade away
B.A refusal to make any predictions at all about future capability
C.A focus on statistical learning from very large data sets
D.An emphasis on formal verification of every program written

Answer: A

Founders predicted within a few years machines that would translate languages, prove theorems and match human intelligence generally, and those predictions were repeatedly unfulfilled. The gap between early successes on toy problems and the difficulty of real ones was underestimated. Statistical learning and formal verification came later and from other traditions. Early success on simplified cases misleading a field about the difficulty of the general case is a pattern worth recognising.

Discuss in Forum

18.

What is meant by an artificial intelligence winter?

A.A phase in which only neural network approaches received any support
B.A period of reduced funding and interest after unmet expectations
C.A season in which computing hardware was too cold to operate
D.A time when research was concentrated in a single country

Answer: B

Two such periods, in the mid seventies and the late eighties, followed influential critical reports and the collapse of commercial expectations, and funding and enrolment fell sharply. Each was preceded by overpromising. Geography, hardware and methodological narrowing are not what the term describes. A discipline whose funding follows a cycle of promise and disappointment has a strong incentive to describe its capabilities carefully, which is a lesson the field has had to relearn.

Discuss in Forum

19.

What triggered the first artificial intelligence winter?

A.A legal ruling that prevented commercial exploitation of the results
B.The retirement of the founders of the field from active research
C.Critical reports concluding that progress had not matched claims
D.The invention of the microprocessor, which made research too cheap

Answer: C

Reports such as Lighthill's in the United Kingdom concluded that the field had failed to deliver on its promises, particularly on the combinatorial explosion in real problems, and funding was withdrawn. The other options describe events that either did not occur or had no such effect. External assessment of a field against the claims it made for itself is a hazard of promising more than can be delivered, and the pattern repeated a decade later with expert systems.

Discuss in Forum

20.

What were expert systems?

A.Systems that learned their rules automatically from example data
B.General purpose reasoners applicable to any problem presented to them
C.Programs that simulated the neural structure of a human expert's brain
D.Rule based programs encoding a specialist's knowledge of a narrow domain

Answer: D

An expert system captured the if then rules of a human specialist in a narrow field such as medical diagnosis or mineral prospecting, together with an inference engine to apply them. Their commercial success in the nineteen eighties and their subsequent difficulties with maintenance and brittleness shaped the field's later direction. Automatic learning, generality and neural simulation describe other approaches. A technique that succeeds narrowly and fails to generalise is a recurring shape in this subject.

Discuss in Forum

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