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NEW QUESTION # 20
What function is used in a Neural Network?
- A. Trigonometric.
- B. Activation.
- C. Linear.
- D. Statistical.
Answer: B
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 # 21
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Activation.
- B. Boosting.
- C. Over-fitting
- D. Iteration.
Answer: B
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 # 22
In an Al project the domain expert is the person...
- A. with technical and managerial oversight of the business plan
- B. with special knowledge or skills in the area of endeavour and defines what is fit for purpose'
- C. who manages the agile project and writes the technical terms of reference
- D. who measures the trustworthiness of the Al system
Answer: D
NEW QUESTION # 23
Ensemble learning methods do what with the hypothesis space?
- A. Use stochastic gradient descent to optimise a network.
- B. Test multiple hypotheses simultaneously.
- C. Select a combination of hypothesis to combine their predictions
- D. Extract ergodic solutions.
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 # 24
With a large dataset, limited computational resources or frequent new data to learn from, we can adopt what
type of machine learning?
- A. Big Data learning.
- B. Batch learning.
- C. Online learning.
- D. Patchwork learning.
Answer: A
NEW QUESTION # 25
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. Are only humans conscious and are machines always unconscious?
- D. What is the sub conscious and what is the conscious?
Answer: C
NEW QUESTION # 26
Which of the following is an advantage of a machine based system?
- A. Able to judge ambiguous and unknown situations.
- B. Capable of sympathising with humans.
- C. Can explain the output of an Al system
- D. Undertakes monotonous tasks reliably and accurately.
Answer: D
NEW QUESTION # 27
An Al agentrelies on its perceptual input.This is called the agent's what?
- A. Position
- B. Percept
- C. World
- D. Environment
Answer: B
Explanation:
Explanation
* 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.
Agent Terminology
https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_agents_and_environments.htm
NEW QUESTION # 28
What technique can be adopted when a weak learners hypothesis accuracy is only slightly better than 50%?
- A. Activation.
- B. Boosting.
- C. Over-fitting
- D. Iteration.
Answer: B
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 wellknown 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/ The best technique to adopt when a weak learner's hypothesis accuracy is only slightly better than 50% is boosting. Boosting is an ensemble learning technique that combines multiple weak learners (i.e., models with a low accuracy) to create a more powerful model. Boosting works by iteratively learning a series of weak learners, each of which is slightly better than random guessing. The output of each weak learner is then combined to form a more accurate model. Boosting is a powerful technique that has been proven to improve the accuracy of a wide range of machine learning tasks. For more information, please see the BCS Foundation Certificate In Artificial Intelligence Study Guide or the resources listed above.
NEW QUESTION # 29
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: D
NEW QUESTION # 30
In Machine learning what are a brain's axons called?
- A. Dendrites
- B. Nodes
- C. Edges
- D. Tetrahedra.
Answer: B
Explanation:
Explanation
In Machine Learning, the brain's axons are referred to as nodes. Nodes are the components of a neural network that are responsible for processing the input data and generating the output. A node is a mathematical function that takes input data, performs a computation on it, and produces an output. Each node is connected to other nodes in the network via edges, which represent the strength of the connection between the respective nodes. The strength of the connection between two nodes is determined by the weights assigned to each edge.
The weights are adjusted during the training process to generate the desired results.
For more information, please refer to the BCS Foundation Certificate In Artificial Intelligence Study Guide (https://www.bcs.org/upload/pdf/bcs-foundation-certificate-in-artificial-intelligence-study-guide.pdf) or the EXIN Artificial Intelligence Foundation Certification (https://www.exin.com/en/exams/artificial-intelligence-foundation).
NEW QUESTION # 31
Tensor flow is a typical open source what?
- A. Machine learning library.
- B. Cloud based AI application.
- C. Intelligent robot paradigm.
- D. Agent based modelling application
Answer: A
Explanation:
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
https://www.tensorflow.org/#:~:text=TensorFlow%20is%20an%20end%2Dto,and%20deploy%20ML%20powered%20applications.
NEW QUESTION # 32
If Al undertakes routine and monotonous tasks and takes these away fromhumans, what will humans do?
- A. Change jobs.
- B. Sabotage the Al.
- C. Higher value work.
- D. Leisure activities
Answer: C
Explanation:
Explanation
Al is designed to take on routine and monotonous tasks, freeing up humans to take on more complex, higher value work. This can include tasks such as research, problem-solving, and decision-making. This shift in work roles is expected to increase productivity and efficiency, allowing humans to focus on more creative and innovative tasks. For example, robots can be used to automate mundane manufacturing processes, freeing up human workers to take on jobs that require more creative thinking and problem-solving.
References:
[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-international
NEW QUESTION # 33
Which factor of a Waterfall' approach is most likely to result in the failed delivery of an Al project?
- A. Takes longer to complete the design phase of the project.
- B. Takes longer to deliver all functional requirements.
- C. Discourages revisiting and revising any prior phase once it is complete.
- D. Discourages collaboration and cross boundary communication.
Answer: C
Explanation:
Explanation
The Waterfall approach is a sequential design process in which each phase of development must be completed before the next phase can begin. This means that once a phase is complete, it is difficult to go back and make changes, as any changes made to the project could potentially affect all the other phases. As a result, the Waterfall approach can make it difficult to adapt to changing customer requirements or adjust to new technology. This can ultimately lead to the failed delivery of an AI project.
References: [1] BCS Foundation Certificate In Artificial Intelligence Study Guide, Page number 19 [2] APMG International, "What is a Waterfall Model?", https://apmg-international.com/en/blog/what-is-a-waterfall-model/ [3] EXIN, "What is the Waterfall Model?", https://www.exin.com/blog/what-is-the-waterfall-model/
NEW QUESTION # 34
What does TRL stand for?
- A. Technology Readiness Level.
- B. Transform Reinforced Learning
- C. Technical Robotic Level.
- D. Transport Ready Level.
Answer: A
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 TRL stands for Technology Readiness Level and is a measure of how close a technology is to being ready for use in a real-world environment. TRL is used to assess the progress of research and development of a technology, ranging from basic research (TRL 1) to fully operational (TRL 9). TRL is used to help determine the level of completion of a technology and its potential success in a real-world environment.
References:
[1] https://www.bcs.org/upload/pdf/foundation-certificate-ai-syllabus-v1.pdf [2] https://www.apmg-international
NEW QUESTION # 35
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