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Download BCS Foundation Certificate In Artificial Intelligence Exam Dumps

NEW QUESTION 38
What is one of the MAIN contributions of Al to the rapid development of The Fourth Industrial Revolution?

  • A. Automation
  • B. Al personal assistants.
  • C. Enhanced design.
  • D. Big Data

Answer: D

Explanation:
Explanation
https://research.com/careers/what-is-the-fourth-industrial-revolution

 

NEW QUESTION 39
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?

  • A. Over-fitting
  • B. Iteration.
  • C. Boosting.
  • D. Activation.

Answer: C

Explanation:
Explanation
* 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.
https://machinelearningmastery.com/strong-learners-vs-weak-learners-for-ensemble-learning/

 

NEW QUESTION 40
The EU and United Nations have made designing for all individuals a core principle. What is this type of design called?

  • A. Universal design.
  • B. Utopic design.
  • C. Biophilic design.
  • D. Core design

Answer: A

Explanation:
https://universaldesign.ie/What-is-Universal-Design/

 

NEW QUESTION 41
Narrow or weak Al can be useful to robots.
Which of the following is an example of narrow Al?

  • A. NLP – Natural Language Processing.
  • B. Artificial General Al.
  • C. Conscious integration.
  • D. Conscioussimul-ation.

Answer: A

 

NEW QUESTION 42
Which of the following is an example of fitting a curve to a set of data?

  • A. Python.
  • B. Least squares regression.
  • C. Bayesian network.
  • D. Backward propagation.

Answer: B

 

NEW QUESTION 43
……

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