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BCS AIF Exam Syllabus Topics:
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NEW QUESTION 18
What function is used in a Neural Network?
- A. Activation.
- B. Statistical.
- C. Trigonometric.
- D. Linear.
Answer: A
Explanation:
Activation Functions
An activation function in a neural network defines how the weighted sum of the input is transformed into an output from a node or nodes in a layer of the network.
https://machinelearningmastery.com/choose-an-activation-function-for-deep-learning/#:~:text=An%20activation%20function%20in%20a,a%20layer%20of%20the%20network.
NEW QUESTION 19
Human-centric trustworthy Al must be...
- A. tested by humans.
- B. financially sustainable.
- C. continually assessed and monitored.
- D. quality assurance certified.
Answer: D
NEW QUESTION 20
Ensemble learning methods do what with the hypothesis space?
- A. Extract ergodic solutions.
- B. Test multiple hypotheses simultaneously.
- C. Select a combination of hypothesis to combine their predictions
- D. Use stochastic gradient descent to optimise a network.
Answer: C
Explanation:
https://link.springer.com/referenceworkentry/10.1007/978-0-387-73003-5_293#:~:text=Definition,and%20combine%20them%20to%20use.
NEW QUESTION 21
With a large dataset, limited computational resources or frequent new data to learn from, we can adopt what
type of machine learning?
- A. Batch learning.
- B. Online learning.
- C. Patchwork learning.
- D. Big Data learning.
Answer: D
NEW QUESTION 22
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Over-fitting
- B. Activation.
- C. Iteration.
- D. Boosting.
Answer: D
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 23
What is an intelligent robot?
- A. A robot that takes the place of a human.
- B. A robot that acts like a human.
- C. A robot that uses Al techniques.
- D. A robot that has consciousness
Answer: C
NEW QUESTION 24
Which of the following is an advantage of a machine based system?
- A. Undertakes monotonous tasks reliably and accurately.
- B. Can explain the output of an Al system
- C. Capable of sympathising with humans.
- D. Able to judge ambiguous and unknown situations.
Answer: A
NEW QUESTION 25
If Al undertakes routine and monotonous tasks and takes these away from humans, what will humans do?
- A. Sabotage the Al.
- B. Change jobs.
- C. Higher value work.
- D. Leisure activities
Answer: B
NEW QUESTION 26
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:
https://research.com/careers/what-is-the-fourth-industrial-revolution
NEW QUESTION 27
Ensemble learning methods do what with the hypothesis space?
- A. Extract ergodic solutions.
- B. Test multiple hypotheses simultaneously.
- C. Select a combination of hypothesis to combine their predictions
- D. Use stochastic gradient descent to optimise a network.
Answer: C
Explanation:
Explanation
https://link.springer.com/referenceworkentry/10.1007/978-0-387-73003-5_293#:~:text=Definition,and%20comb
NEW QUESTION 28
What is defined as a philosophy, or set of assumptions and/or techniques, which characterise an approach to a class of problems?
- A. An approach.
- B. A paradigm.
- C. A set
- D. An algorithm.
Answer: B
NEW QUESTION 29
What does TRL stand for?
- A. Transport Ready Level.
- B. Technical Robotic Level.
- C. Transform Reinforced Learning
- D. Technology Readiness Level.
Answer: D
Explanation:
Explanation
Technology Readiness Level (TRL) Technology Readiness Levels (TRL) are a method of estimating the
technology maturity of Critical Technology Elements (CTE) of a program during the acquisition process.
https://acqnotes.com/acqnote/tasks/technology-readiness-level#:~:text=Technology%20Development-,Technolog
NEW QUESTION 30
Splitting data into Training and Test data sets is part of what?
- A. Batch learning.
- B. High performance computing strategy.
- C. Machine learning post processing.
- D. Machine learning data preparation.
Answer: D
NEW QUESTION 31
If Al undertakes routine and monotonous tasks and takes these away from humans, what will humans do?
- A. Higher value work.
- B. Sabotage the Al.
- C. Change jobs.
- D. Leisure activities
Answer: A
NEW QUESTION 32
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Over-fitting
- B. Activation.
- C. Iteration.
- D. Boosting.
Answer: D
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 33
Professor David Chalmers described consciousness as having two questions. What were these?
- A. Can we integrate our knowledge to form consciousness and can we simulate consciousness?
- B. Are only humans conscious and are machines always unconscious?
- C. What is the sub conscious and what is the conscious?
- D. An easy one and a hard one.
Answer: B
NEW QUESTION 34
What is defined as a philosophy, or set of assumptions and/or techniques, which characterise an approach to a
class of problems?
- A. An approach.
- B. A paradigm.
- C. A set
- D. An algorithm.
Answer: B
NEW QUESTION 35
Para View allows large data sets to be visualised on a parallel computer.
Which of the following is one of the techniques used?
- A. Eigen function analysis.
- B. Contour plot
- C. Dashboard.
- D. Norm calculation.
Answer: B
NEW QUESTION 36
Which factor of a Waterfall' approach is most likely to result in the failed delivery of an Al project?
- A. Takes longer to deliver all functional requirements.
- B. Takes longer to complete the design phase of the project.
- C. Discourages collaboration and cross boundary communication.
- D. Discourages revisiting and revising any prior phase once it is complete.
Answer: B
NEW QUESTION 37
What does Prof David Chalmers describe the hard consciousness problem to be as comples as?
- A. Psychology.
- B. Turbulence.
- C. Quantum mechanics.
- D. The universe.
Answer: A
NEW QUESTION 38
What are monotonous and repetitive tasks, that require accuracy BEST suited to?
- A. Artificial General Intelligence.
- B. Human plus machine.
- C. Human.
- D. Machine.
Answer: A
NEW QUESTION 39
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