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NEW QUESTION 39
Narrow or weak Al can be useful to robots.
Which of the following is an example of narrow Al?
- A. Conscious integration.
- B. Conscioussimul-ation.
- C. Artificial General Al.
- D. NLP – Natural Language Processing.
NEW QUESTION 40
An Al agent relies on its perceptual input. This is called the agent’s what?
- A. World
- B. Percept
- C. Environment
- D. Position
Performance Measure of Agent − It is the criteria, which determines how successful an agent is.
Behavior of Agent − It is the action that agent performs after any given sequence of percepts.
Percept − It is agent’s perceptual inputs at a given instance.
Percept Sequence − It is the history of all that an agent has perceived till date.
Agent Function − It is a map from the precept sequence to an action.
NEW QUESTION 41
How could machine learning make a robot autonomous?
- A. Use actuators to modify its environment
- B. Use NLP (Natural Language Processing) to listen
- C. Learn from sensor data and plan to carry out a task.
- D. Use OCR, optical character recognition, to read documents
NEW QUESTION 42
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Iteration.
- B. Activation.
- C. Over-fitting
- D. Boosting.
Weak Learner: Colloquially, a model that performs slightly better than a naive model.
More formally, the notion has been generalized to multi-class classification and has a different meaning beyond better than 50 percent accuracy.
For binary classification, it is well known that the exact requirement for weak learners is to be better than random guess. […] Notice that requiring base learners to be better than random guess is too weak for multi-class problems, yet requiring better than 50% accuracy is too stringent.
– Page 46, Ensemble Methods, 2012.
It is based on formal computational learning theory that proposes a class of learning methods that possess weakly learnability, meaning that they perform better than random guessing. Weak learnability is proposed as a simplification of the more desirable strong learnability, where a learnable achieved arbitrary good classification accuracy.
A weaker model of learnability, called weak learnability, drops the requirement that the learner be able to achieve arbitrarily high accuracy; a weak learning algorithm needs only output an hypothesis that performs slightly better (by an inverse polynomial) than random guessing.
– The Strength of Weak Learnability, 1990.
It is a useful concept as it is often used to describe the capabilities of contributing members of ensemble learning algorithms. For example, sometimes members of a bootstrap aggregation are referred to as weak learners as opposed to strong, at least in the colloquial meaning of the term.
More specifically, weak learners are the basis for the boosting class of ensemble learning algorithms.
The term boosting refers to a family of algorithms that are able to convert weak learners to strong learners.
NEW QUESTION 43
The Scrum Master is part of which team?
- A. Management team
- B. Agile project team.
- C. Data preparation team
- D. Software development team.
NEW QUESTION 44
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