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8. Name one component of Natural Language Processing (NLP) that focuses on understanding human
language.

Ans: Natural Language Understanding is a component of NLP that uses computer software to understand
human language.
9. What is the core of AI systems, and what problem may arise in AI systems that make decisions based on
biased data?
Ans: Data is the core of AI systems, and inclusion problems may arise when AI systems make decisions based
on biased data, such as inaccuracy in facial recognition for darker-skinned faces.

10. What is one adoption concern related to AI systems?
Ans: Job loss is a significant adoption concern, with various estimates suggesting potential displacement of
jobs due to automation and AI systems in different sectors.

F. Long answer type question.
1. Explain the relationship between Artificial Intelligence, Machine Learning, and Deep Learning. Provide
examples to illustrate each subfield.
Ans: Artificial Intelligence (AI) is a broad field encompassing various technologies. Machine Learning (ML) is
a subset of AI where systems learn and improve without explicit programming. Deep Learning (DL) is a
subset of ML, specifically utilizing deep neural networks for learning. For instance, Siri represents an AI
application, a product recommendation engine showcases ML, and driverless cars exemplify DL systems.

2. Compare data dependencies of AI, Machine Learning, and Deep Learning systems. How does the size of
the dataset impact their performance?
Ans: AI systems perform well on big datasets, while Machine Learning excels with small to medium datasets.
Deep Learning systems, on the other hand, exhibit excellent performance with large datasets. This
distinction arises from the complexity of tasks each handles. For instance, AI may need a vast array
of data for varied applications, while ML models can achieve good performance with more focused
datasets.
3. Discuss the ethical concerns related to biased data in AI systems. Provide an example from the chapter
and explain how biased data can impact AI outcomes.

Ans: Ethical concerns related to biased data involve potential for AI systems to perpetuate and amplify existing
prejudices. An example is Amazon’s AI hiring system, which favoured men over women due to biased
historical hiring data. Biased data can lead to discriminatory outcomes, reinforcing stereotypes and
inequities. This highlights the responsibility to ensure that AI systems are trained on unbiased datasets
to promote fairness and prevent the propagation of societal biases.
4. Explain the concept of intelligence in context of machines. How is it different from human intelligence?
Ans: Intelligence in machines refers to their intellectual functioning. Unlike human intelligence, machine
intelligence is designed for specific tasks and lacks the depth and breadth of human cognition.

5. Describe various types of intelligence mentioned in the chapter. Provide an example for each type.
Ans: The types of intelligence include Linguistic, Spatial Visual, Kinesthetic, Interpersonal, and Intrapersonal.
For example, linguistic intelligence is demonstrated when a person excels in verbal and written language
skills.

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