Exploring Uncertainty in AI

What is a Bayesian Network and how is it used in AI?

• A Bayesian Network is a decision tree used for classification tasks.

• A Bayesian Network is a graphical model used in AI to represent and reason about uncertain knowledge through probabilistic dependencies.

• A Bayesian Network is a linear regression model for predicting outcomes.

• A Bayesian Network is a type of neural network used for deep learning.

Explain the concept of conditional independence in Bayesian Networks.

• Conditional independence means A and B are always independent.

• In Bayesian Networks, two variables A and B are conditionally independent given a third variable C if P(A, B | C) = P(A | C) * P(B | C).

• In Bayesian Networks, A and B are dependent if C is known.

• A and B are independent if P(A | C) = P(B | C).

Describe the process of statistical inference.

• Statistical inference involves only qualitative data analysis.

• Statistical inference is the method of predicting future events without data.

• Statistical inference is the process of collecting data from a population.

• Statistical inference is the process of using sample data to make generalizations about a population.

What are the differences between parametric and non-parametric statistical inference techniques?

• Parametric techniques are only used for categorical data.

• Non-parametric techniques require a known distribution to function properly.

• Parametric techniques are always more accurate than non-parametric techniques.

• Parametric techniques assume a known distribution and use parameters, while non-parametric techniques do not assume any distribution and are more flexible.

How can the ABC Mystery be resolved using Bayesian reasoning?

• The ABC Mystery is best resolved through random guessing.

• The ABC Mystery can be resolved by applying Bayes' theorem to update the probabilities of hypotheses based on new evidence.

• The ABC Mystery can be solved by ignoring prior probabilities.

• The ABC Mystery requires only qualitative analysis without data.

What is a closed world assumption in artificial intelligence?

• The closed world assumption assumes that if something is not known to be true, it is false.

• The closed world assumption allows for unknowns to be considered true.

• It assumes that if something is false, it must be known to be false.

• The closed world assumption states that all knowledge is known to be true.

Define a justification-based truth maintenance system.

• A justification-based truth maintenance system is a reasoning system that tracks justifications for beliefs and maintains consistency by allowing retraction of beliefs when justifications are invalidated.

• A reasoning system that focuses solely on factual accuracy without justifications.

• A truth maintenance system that does not allow belief retraction.

• A system that only tracks beliefs without justifications.

What role does uncertainty play in the development of AI models?

• Uncertainty is irrelevant to the accuracy of AI predictions.

• Uncertainty has no impact on AI model performance.

• Uncertainty plays a critical role in shaping the robustness and reliability of AI models.

• Uncertainty only complicates the development process of AI models.