What is Artificial Narrow Intelligence (ANI)?
Answer : 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.
What is the role of a decoder in a GPT model?
Answer : C
In the context of GPT (Generative Pre-trained Transformer) models, the decoder plays a crucial role. Here's a detailed explanation:
Decoder Function: The decoder in a GPT model is responsible for taking the input (often a sequence of text) and generating the appropriate output (such as a continuation of the text or an answer to a query).
Architecture: GPT models are based on the transformer architecture, where the decoder consists of multiple layers of self-attention and feed-forward neural networks.
Self-Attention Mechanism: This mechanism allows the model to weigh the importance of different words in the input sequence, enabling it to generate coherent and contextually relevant output.
Generation Process: During generation, the decoder processes the input through these layers to produce the next word in the sequence, iteratively constructing the complete output.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need. In Advances in Neural Information Processing Systems.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI Blog.
Why is diversity important in Al training data?
Answer : 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 purpose of the explainer loops in the context of Al models?
Answer : B
Explainer Loops: These are mechanisms or tools designed to interpret and explain the decisions made by AI models. They help users and developers understand the rationale behind a model's predictions.
Importance: Understanding the model's reasoning is vital for trust and transparency, especially in critical applications like healthcare, finance, and legal decisions. It helps stakeholders ensure the model's decisions are logical and justified.
Methods: Techniques like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are commonly used to create explainer loops that elucidate model behavior.
What is one of the positive stereotypes people have about Al?
Answer : D
24/7 Availability: AI systems can operate continuously without the need for breaks, which enhances productivity and efficiency. This is particularly beneficial for customer service, where AI chatbots can handle inquiries at any time.
Use Cases: Examples include automated customer support, monitoring and maintaining IT infrastructure, and processing transactions in financial services.
Business Benefits: The continuous operation of AI systems can lead to cost savings, improved customer satisfaction, and faster response times, which are critical competitive advantages.
What is the purpose of adversarial training in the lifecycle of a Large Language Model (LLM)?
Answer : A
Adversarial training is a technique used to improve the robustness of AI models, including Large Language Models (LLMs), against various types of attacks. Here's a detailed explanation:
Definition: Adversarial training involves exposing the model to adversarial examples---inputs specifically designed to deceive the model during training.
Purpose: The main goal is to make the model more resistant to attacks, such as prompt injections or other malicious inputs, by improving its ability to recognize and handle these inputs appropriately.
Process: During training, the model is repeatedly exposed to slightly modified input data that is designed to exploit its vulnerabilities, allowing it to learn how to maintain performance and accuracy despite these perturbations.
Benefits: This method helps in enhancing the security and reliability of AI models when they are deployed in production environments, ensuring they can handle unexpected or adversarial situations better.
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. arXiv preprint arXiv:1412.6572.
Kurakin, A., Goodfellow, I., & Bengio, S. (2017). Adversarial Machine Learning at Scale. arXiv preprint arXiv:1611.01236.
A tech startup is developing a chatbot that can generate human-like text to interact with its users.
What is the primary function of the Large Language Models (LLMs) they might use?
Answer : C
Large Language Models (LLMs), such as GPT-4, are designed to understand and generate human-like text. They are trained on vast amounts of text data, which enables them to produce responses that can mimic human writing styles and conversation patterns. The primary function of LLMs in the context of a chatbot is to interact with users by generating text that is coherent, contextually relevant, and engaging.
Storing data (Option OA), encrypting information (Option OB), and managing databases (Option OD) are not the primary functions of LLMs. While LLMs may be used in conjunction with systems that perform these tasks, their core capability lies in text generation, making Option OC the correct answer.