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A team is looking to improve an LLM based on user feedback.
Which method should they use?
Reinforcement Learning through Human Feedback (RLHF) is a method that involves training machine learning models, particularly Large Language Models (LLMs), using feedback from humans. This approach is part of a broader category of machine learning known as reinforcement learning, where models learn to make decisions by receiving rewards or penalties.
In the context of LLMs, RLHF is used to fine-tune the models based on human preferences, corrections, and feedback. This process allows the model to align more closely with human values and produce outputs that are more desirable or appropriate according to human judgment.
Adversarial Training (Option OA) is typically used to improve the robustness of models against adversarial attacks. Self-supervised Learning (Option OC) involves models learning to understand data without explicit external labels. Transfer Learning (Option D) is about applying knowledge gained in one problem domain to a different but related domain. While these methods are valuable in their own right, they are not specifically focused on integrating human feedback into the training process, making Option OB the correct answer for improving an LLM based on user feedback.
Why should artificial intelligence developers always take inputs from diverse sources?
Diverse Data Sources: Utilizing inputs from diverse sources ensures the AI model is exposed to a wide range of scenarios, dialects, and contexts. This diversity helps the model generalize better and avoid biases that could occur if the data were too homogeneous.
Comprehensive Coverage: By incorporating diverse inputs, developers ensure the model can handle various edge cases and unexpected inputs, making it robust and reliable in real-world applications.
Avoiding Bias: Diverse inputs reduce the risk of bias in AI systems by representing a broad spectrum of user experiences and perspectives, leading to fairer and more accurate predictions.
What is one of the positive stereotypes people have about Al?
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.
You are designing a Generative Al system for a secure environment.
Which of the following would not be a core principle to include in your design?
In the context of designing a Generative AI system for a secure environment, the core principles typically include ensuring the security and integrity of the data, as well as the ability to generate new data. However, Creativity Simulation is not a principle that is inherently related to the security aspect of the design.
The core principles for a secure Generative AI system would focus on:
Learning Patterns: This is essential for the AI to understand and generate data based on learned information.
Generation of New Data: A key feature of Generative AI is its ability to create new, synthetic data that can be used for various purposes.
Data Encryption: This is crucial for maintaining the confidentiality and security of the data within the system.
A company is planning its resources for the generative Al lifecycle.
Which phase requires the largest amount of resources?
The training phase of the generative AI lifecycle typically requires the largest amount of resources. This is because training involves processing large datasets to create models that can generate new data or predictions. It requires significant computational power and time, especially for complex models such as deep learning neural networks. The resources needed include data storage, processing power (often using GPUs or specialized hardware), and the time required for the model to learn from the data.
In contrast, deployment involves implementing the model into a production environment, which, while important, often does not require as much resource intensity as the training phase. Inferencing is the process where the trained model makes predictions, which does require resources but not to the extent of the training phase. Fine-tuning is a process of adjusting a pre-trained model to a specific task, which also uses fewer resources compared to the initial training phase.
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