Question 1

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?

Accepted Answer

B

Explanation: An agent based model is a simulation of autonomous agents (individual and collective). Machine learning can be used to learn from the data generated by the simulations. Machine learning algorithms can analyze the data generated by simulations and identify patterns, which can then be used to help the agent make decisions and take actions.

Question 2

Which factor of a Waterfall' approach is most likely to result in the failed delivery of an Al project?

Accepted Answer

D

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.

Question 3

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

Accepted Answer

B

Explanation: https://research.com/careers/what-is-the-fourth-industrial-revolution Artificial Intelligence (AI) is playing a major role in the rapid development of the Fourth Industrial Revolution. AI technologies are enabling the automation of many processes that were previously carried out by humans or machines, which has greatly increased the speed, efficiency, and accuracy of these processes. Automation is one of the main contributions of AI to the Fourth Industrial Revolution, as it has greatly increased the productivity of businesses and industries, while reducing the cost of production and improving the quality of products.

Question 4

A human manipulates what using their intelligence?

Accepted Answer

A

Explanation: Humans use their intelligence to manipulate their environment in order to achieve their objectives and complete their mission. This can involve a wide range of activities, such as building tools, constructing shelters, and creating strategies to solve problems.

Question 5

Collaboration, learning and iterative are terms used to describe what?

Accepted Answer

D

Explanation: Collaboration, learning, and iterative are terms used to describe agile projects. Agile projects are designed to be adaptive and flexible, allowing teams to incorporate feedback and learn from their mistakes. This process encourages collaboration between team members, and emphasizes the importance of iterative development and continual improvement. Agile projects focus on delivering value quickly and efficiently, allowing teams to make changes and adapt to changing customer needs.

Question 6

Professor David Chalmers described consciousness as having two questions. What were these?

Accepted Answer

B

Explanation: Professor David Chalmers described consciousness as having two questions: "What is it like to be conscious?" and "Can machines be conscious?". The first question, "What is it like to be conscious?", is an attempt to understand what it is like to experience the subjective aspects of consciousness, such as feeling, emotion, and perception. The second question, "Can machines be conscious?", is an attempt to understand whether or not machines can have the same kinds of subjective experiences as humans. For more information, please see the BCS Foundation Certificate In Artificial Intelligence Study Guide or the resources listed above.

Question 7

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

Accepted 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/ 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.

Question 8

An Al agent relies on its perceptual input. This is called the agent's what?

Accepted Answer

D

Explanation: Agent Terminology 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. https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_agents_and_environme nts.htm An AI agent relies on its perceptual input, which is referred to as the agent's percept. This is the data that the agent collects through its sensors about its environment. The percept allows the agent to make decisions and take actions based on its environment. The agent's percept is important for Artificial Intelligence systems to be able to operate effectively.

Question 9

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

Accepted Answer

D

Explanation: NLP - Natural Language Processing is an example of narrow AI. It is a type of AI system that is able to understand, interpret, and generate natural language. NLP has become increasingly popular over the past few years, as it has been used to create applications such as chatbots, virtual assistants, and search engines. NLP systems are able to learn language and the context in which it is used, and they are able to understand the nuances of language and its different meanings.

Question 10

Splitting data into Training and Test data sets is part of what?

Accepted Answer

A

Explanation: Splitting data into training and test data sets is an important step in the machine learning data preparation process. This process involves splitting the data into subsets, usually in a 70:30 ratio, to create a training set and a test set. The training set is used to train the machine learning model, while the test set is used to evaluate the model’s performance. This process allows for the model to be tested and evaluated on data that it has not seen before, in order to ensure that it is accurate and able to generalize to new data.

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