RLHF
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human input to guide the training of AI models. Essential for improving the alignment and performance of AI systems in real-world applications.
16 topics found for:
“human feedback”
HITL
Human in the Loop (HITL) integrates human judgment into the decision-making process of AI systems. Crucial for ensuring AI reliability and alignment with human values.
Actor-Observer Bias
A cognitive bias that causes people to attribute their own actions to situational factors while attributing others' actions to their character. Essential for helping designers recognize their own situational influences on interpreting user behavior and feedback.
LLM
Large Language Model (LLM) is an advanced artificial intelligence system trained on vast amounts of text data to understand and generate human-like text. Essential for natural language processing tasks, content generation, and enhancing human-computer interactions across various applications in product design and development.
HCD
Human-Centered Design (HCD) is an approach to problem-solving that involves the human perspective in all steps of the process. It ensures designs are user-friendly and meet actual user needs.
Negativity Bias
The tendency to give more weight to negative experiences or information than positive ones. Crucial for understanding user behavior and designing systems that balance positive and negative feedback.
Difference Threshold
The minimum difference in stimulus intensity that a person can detect, also known as the just noticeable difference (JND). Crucial for designing user interfaces that are sensitive to changes in user input and feedback.
Wizard of Oz Testing
A usability testing method where users interact with a system they believe to be autonomous, but which is actually operated by a human. Essential for testing concepts and interactions before full development.
Dunning-Kruger Effect
A cognitive bias where individuals with low ability at a task overestimate their ability, while experts underestimate their competence. Crucial for designers to create educational content and user interfaces that accommodate varying levels of user expertise.
Error Prevention
Design strategies aimed at preventing user errors before they occur. Crucial for enhancing usability and ensuring a smooth user experience.
Continuous Deployment
A software development practice where code changes are automatically deployed to production without manual intervention. Important for maintaining a high level of productivity and quality in software development.
Prospect-Refuge
A theory in environmental psychology that suggests people prefer environments where they can see (prospect) without being seen (refuge). Useful for understanding environmental design and creating spaces that feel safe and inviting.
Egocentric Bias
A cognitive bias where people rely too heavily on their own perspective and experiences when making decisions. Important for designers to recognize and mitigate their own perspectives influencing design decisions.
ARA
Application Release Automation (ARA) is the process of automating the release of applications, ensuring consistency and reducing errors. Crucial for accelerating the delivery of software updates and maintaining high-quality digital products.
Subadditivity Effect
A cognitive bias where the total probability assigned to a set of events is less than the sum of the probabilities assigned to each event individually. Important for understanding how users estimate probabilities and make decisions under uncertainty.
Reciprocal Altruism
A behavior in which an individual provides a benefit to another with the expectation that the favor will be returned in the future, fostering mutual cooperation and long-term relationships. Important for building trust, cooperation, and mutually beneficial relationships in various social and professional contexts.