Practice Examples and Dumps & Tips for 2022 Latest AIF Valid Tests Dumps
Latest [Aug 10, 2022] 100% Passing Guarantee - Brilliant AIF Exam Questions PDF
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NEW QUESTION 11
In Machine learning what are a brain's axons called?
- A. Tetrahedra.
- B. Edges
- C. Dendrites
- D. Nodes
Answer: C
NEW QUESTION 12
The EU and United Nations have made designing for all individuals a core principle. What is this type of design called?
- A. Utopic design.
- B. Universal design.
- C. Core design
- D. Biophilic design.
Answer: B
Explanation:
https://universaldesign.ie/What-is-Universal-Design/
NEW QUESTION 13
A human manipulates what using their intelligence?
- A. Mission
- B. Environment
- C. Space
- D. Objective
Answer: C
NEW QUESTION 14
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. An easy one and a hard one.
- C. What is the sub conscious and what is the conscious?
- D. Are only humans conscious and are machines always unconscious?
Answer: A
NEW QUESTION 15
Professor David Chalmers described consciousness as having two questions. What were these?
- A. Are only humans conscious and are machines always unconscious?
- B. Can we integrate our knowledge to form consciousness and can we simulate consciousness?
- C. An easy one and a hard one.
- D. What is the sub conscious and what is the conscious?
Answer: A
NEW QUESTION 16
Which of the following is an advantage of a machine based system?
- A. Able to judge ambiguous and unknown situations.
- B. Undertakes monotonous tasks reliably and accurately.
- C. Can explain the output of an Al system
- D. Capable of sympathising with humans.
Answer: B
NEW QUESTION 17
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Activation.
- B. Over-fitting
- C. Boosting.
- D. Iteration.
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 18
An agent based model is a simul-ation of autonomous agents (individual and collective). What can be used to learn from the data generated by the simul-ations?
- A. Paraview.
- B. Machine Learning.
- C. Python.
- D. A spreadsheet
Answer: D
Explanation:
https://www.pnas.org/doi/10.1073/pnas.082080899
NEW QUESTION 19
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. Patchwork learning.
- C. Big Data learning.
- D. Online learning.
Answer: A
Explanation:
NEW QUESTION 20
Splitting data into Training and Test data sets is part of what?
- A. Machine learning data preparation.
- B. Batch learning.
- C. High performance computing strategy.
- D. Machine learning post processing.
Answer: A
NEW QUESTION 21
What does TRL stand for?
- A. Technical Robotic Level.
- B. Technology Readiness Level.
- C. Transport Ready Level.
- D. Transform Reinforced Learning
Answer: B
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 22
Which of the following is an example of fitting a curve to a set of data?
- A. Bayesian network.
- B. Backward propagation.
- C. Python.
- D. Least squares regression.
Answer: D
NEW QUESTION 23
What is one of the MAIN contributions of Al to the rapid development of The Fourth Industrial Revolution?
- A. Automation
- B. Enhanced design.
- C. Big Data
- D. Al personal assistants.
Answer: C
Explanation:
https://research.com/careers/what-is-the-fourth-industrial-revolution
NEW QUESTION 24
Sustainability focuses on which three core areas?
- A. Social, Entrepreneurial and Environmental.
- B. Scientific, Environmental and Economic.
- C. Social, Economic and Entrepreneurial.
- D. Social, Economic and Environmental.
Answer: D
Explanation:
Explanation
The term sustainability is broadly used to indicate programs, initiatives and actions aimed at the preservation
of a particular resource. However, it actually refers to four distinct areas: human, social, economic and
environmental - known as the four pillars of sustainability.
https://www.futurelearn.com/info/courses/sustainable-business/0/steps/78337#:~:text=However%2C%20it%20ac
NEW QUESTION 25
Ensemble learning methods do what with the hypothesis space?
- A. Use stochastic gradient descent to optimise a network.
- B. Extract ergodic solutions.
- C. Test multiple hypotheses simultaneously.
- D. Select a combination of hypothesis to combine their predictions
Answer: D
Explanation:
https://link.springer.com/referenceworkentry/10.1007/978-0-387-73003-5_293#:~:text=Definition,and%20combine%20them%20to%20use.
NEW QUESTION 26
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