What is artificial intelligence?
Answer(s): B
Artificial intelligence (AI) is a broad field of computer science focused on creating systems capable of performing tasks that would normally require human intelligence. The correct answer is option B, which defines AI as "the study and design of intelligent agents." Here's a comprehensive breakdown:Definition of AI: AI involves the creation of algorithms and systems that can perceive their environment, reason about it, and take actions to achieve specific goals. Intelligent Agents: An intelligent agent is an entity that perceives its environment and takes actions to maximize its chances of success. This concept is central to AI and encompasses a wide range of systems, from simple rule-based programs to complex neural networks. Applications: AI is applied in various domains, including natural language processing, computer vision, robotics, and more.
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson. Poole, D., Mackworth, A., & Goebel, R. (1998). Computational Intelligence: A Logical Approach.Oxford University Press.
What is Artificial Narrow Intelligence (ANI)?
Answer(s): D
Artificial Narrow Intelligence (ANI) refers to AI systems that are designed to perform a specific task or a narrow set of tasks. The correct answer is option D. Here's a detailed explanation:Definition of ANI: ANI, also known as weak AI, is specialized in one area. It can perform a particular function very well, such as facial recognition, language translation, or playing a game like chess. Characteristics: Unlike general AI, ANI does not possess general cognitive abilities. It cannot perform tasks outside its specific domain without human intervention or retraining. Examples: Siri, Alexa, and Google's search algorithms are examples of ANI. These systems excel in their designated tasks but cannot transfer their learning to unrelated areas.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15- 25.
Why is diversity important in Al training data?
Answer(s): C
Diversity in AI training data is crucial for developing robust and fair AI models. The correct answer is option C. Here's why:Generalization: A diverse training dataset ensures that the AI model can generalize well across different scenarios and perform accurately in real-world applications. Bias Reduction: Diverse data helps in mitigating biases that can arise from over-representation or under-representation of certain groups or scenarios.Fairness and Inclusivity: Ensuring diversity in data helps in creating AI systems that are fair and inclusive, which is essential for ethical AI development.
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys (CSUR), 54(6), 1-35.
What is the first step an organization must take towards developing an Al-based application?
The first step an organization must take towards developing an AI-based application is to develop a data strategy. The correct answer is option D. Here's an in-depth explanation:Importance of Data: Data is the foundation of any AI system. Without a well-defined data strategy, AI initiatives are likely to fail because the model's performance heavily depends on the quality and quantity of data.Components of a Data Strategy: A comprehensive data strategy includes data collection, storage, management, and ensuring data quality. It also involves establishing data governance policies to maintain data integrity and security.Alignment with Business Goals: The data strategy should align with the organization's business goals to ensure that the AI applications developed are relevant and add value.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.Marr, B. (2017). Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things. Kogan Page Publishers.
What is the primary purpose of fine-tuning in the lifecycle of a Large Language Model (LLM)?
Definition of Fine-Tuning: Fine-tuning is a process in which a pretrained model is further trained on a smaller, task-specific dataset. This helps the model adapt to particular tasks or domains, improving its performance in those areas. "Fine-tuning adjusts a pretrained model to perform specific tasks by training it on specialized data." (Stanford University, 2020)Purpose: The primary purpose is to refine the model's parameters so that it performs optimally on the specific content it will encounter in real-world applications. This makes the model more accurate and efficient for the given task. "Fine-tuning makes a general model more applicable to specific problems by further training on relevant data." (OpenAI, 2021)Example: For instance, a general language model can be fine-tuned on legal documents to create a specialized model for legal text analysis, improving its ability to understand and generate text in that specific context. "Fine-tuning enables a general language model to excel in specific domains like legal or medical texts." (Nature, 2019)
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