QCM : AI in Economics and Data Science — 11 questions

Questions et réponses du QCM

1. What best describes predictive economic modeling in AI?

Measuring model performance by counting correct predictions
Grouping unlabeled data into clusters based on distance alone
Using AI methods to forecast market trends, consumer behavior, and economic impacts
Using fixed rules to classify legal contracts into categories

Using AI methods to forecast market trends, consumer behavior, and economic impacts

Explication

Predictive economic modeling applies AI to anticipate market trends, consumer behavior, and economic impacts. The other options describe different AI tasks such as legal classification, clustering, or evaluation.

2. What is the primary purpose of AI in economic decision-making?

To replace human economists in policy formulation
To automate all economic transactions without human intervention
To generate random economic forecasts for entertainment
To improve outcomes in markets, businesses, or public administration through data-driven actions

To improve outcomes in markets, businesses, or public administration through data-driven actions

Explication

AI in economic decision-making aims to enhance outcomes by analyzing data to support better decisions in markets, businesses, or public administration, not to automate all transactions or replace human judgment entirely.

3. How does machine learning support economic decision-making?

It depends on random guesses when market data are incomplete
It replaces the need for any economic data collection
It learns from large datasets to make predictions or decisions instead of following fixed instructions
It works only with hand-written rules that experts define in advance

It learns from large datasets to make predictions or decisions instead of following fixed instructions

Explication

Machine learning uses data to learn patterns and produce predictions or decisions, which makes it useful for economic analysis. It does not rely on fixed instructions as traditional programming does.

4. What is the main goal of applying AI in economic decision-making?

To replace human economists entirely
To automate all market transactions
To improve outcomes in markets, businesses, or public administration through data-driven actions
To eliminate market risks completely

To improve outcomes in markets, businesses, or public administration through data-driven actions

Explication

AI in economic decision-making aims to enhance outcomes by analyzing data to support better decisions in markets, businesses, and public administration, not to automate all transactions or eliminate risks entirely.

5. Which AI system was created in 1956 to prove mathematical theorems?

The Turing Test
Logic Theorist
ELIZA
Sad Sam

Logic Theorist

Explication

Logic Theorist was an early 1956 AI program focused on proving mathematical theorems. ELIZA and Sad Sam came later, and the Turing Test is a proposal rather than a program.

6. What is the primary purpose of applying AI in economic decision-making?

To automate routine tasks without human intervention
To improve decision quality by analyzing large datasets and forecasting trends
To increase the speed of transactions without regard to accuracy
To replace human economists entirely in market analysis

To improve decision quality by analyzing large datasets and forecasting trends

Explication

AI in economic decision-making aims to enhance the quality of decisions by analyzing large datasets, forecasting market trends, and supporting strategic planning, rather than just automating tasks or replacing humans.

7. What was ELIZA designed to do?

Generate conclusions in English from simple sentences
Measure whether a machine shows intelligence through conversation
Simulate therapist-patient style interactions
Separate classes using a decision boundary

Simulate therapist-patient style interactions

Explication

ELIZA was developed in 1967 by Joseph Weizenbaum to simulate therapeutic conversations. The Turing Test checks intelligence, while Sad Sam generated English conclusions from simple sentences.

8. When was the term 'artificial intelligence' first coined, marking the beginning of AI as an academic discipline?

1988
1950
1956
1967

1956

Explication

The term 'artificial intelligence' was first coined by John McCarthy in 1956, establishing AI as an academic discipline. The other dates relate to different milestones: ELIZA was developed in 1967, the 1950s marked the start of AI research, and 1988 was when Hunt defined AI.

9. How does supervised learning differ from unsupervised learning in the way they handle data?

Supervised learning finds patterns without labels, whereas unsupervised learning relies on labeled data to classify data points.
Supervised learning and unsupervised learning both require labeled data but differ in the algorithms used.
Supervised learning is used only for classification tasks, while unsupervised learning is used only for clustering.
Supervised learning uses labeled data to learn a mapping to outputs, while unsupervised learning finds patterns without labels.

Supervised learning uses labeled data to learn a mapping to outputs, while unsupervised learning finds patterns without labels.

Explication

Supervised learning relies on labeled data to train models to predict or classify, whereas unsupervised learning finds patterns or groupings in unlabeled data.

10. Who is credited with coining the term 'artificial intelligence' at a conference in 1956?

Joseph Weizenbaum
Alan Turing
Hugo de Garis
John McCarthy

John McCarthy

Explication

John McCarthy is credited with coining the term 'artificial intelligence' during a conference he organized in 1956, marking the formal beginning of AI as an academic discipline.

11. What is a key consequence of using hybrid recommendation systems in analyzing purchasing behavior?

They enable more personalized marketing by combining multiple data sources.
They reduce the amount of data required for predictions.
They eliminate the need for customer categorization.
They simplify the decision-making process by focusing on a single data type.

They enable more personalized marketing by combining multiple data sources.

Explication

Hybrid recommendation systems combine different data signals, such as purchasing behavior and customer categories, to produce more accurate and personalized recommendations, which enhances marketing strategies.

Révisez avec les flashcards

Mémorisez les réponses avec 9 flashcards sur AI in Economics and Data Science.

AI for economic decisions

Supports forecasts, scenarios, dashboards.

AI decision-making role

Supports market, business, public choices

AI history start

1950s as an academic discipline.

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