Concept
UX Research
AI ethics
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Socio-technical Systems
Back Technology and society are inextricably linked in the same system of networked interconnected components. These networks often include not only visible elements like hardware, software, and infrastructure, but also invisible factors like impacts on society, culture, and human rights. A Socio-Technical System (STS) refers to this integrated approach, considering both the technical and social […]
AI Model
Back AI models are designed to detect patterns in large datasets using algorithms, with their output depending on the quality and type of data they are trained on. For example, Large Language Models (LLMs) take text as input and generate text-based responses based on learned patterns. Training an AI involves feeding vast amounts of labeled […]
RLHF (Reinforcement Learning Through Human Feedback)
Back To align the output of Large Language Models (LLMs) with users’ values and expectations, a human-in-the-loop feedback process is essential. This process involves a team of evaluators who review the model’s output across various tasks and use cases, providing rankings based on criteria like helpfulness, fairness, and clarity. These rankings can range from “best” […]
Model Card
Back AI Model documentation, also known as a “Model Card,” provides essential information about a model’s characteristics, performance, and limitations. The purpose of a Model Card is to ensure the AI model is transparent, accessible, and understandable. A well-crafted Model Card is written in clear language and typically covers the following key areas: As Google […]
ELIZA Effect
Back The Eliza Effect is often associated with the first symbolic AI Chatbot, developed in 1966 by Joseph Weizenbaum. The program was designed to simulate a psychotherapist, with the user typing their thoughts and feelings into a computer. The system would then respond with therapist-style questions that are based on changing the end or the […]
Scaling Problem
Back AI models are increasingly growing in size, with the belief that bigger is better. However, research shows that larger models are not always more effective. For example, a small model might perform poorly due to noise (uncategorized data), causing it to hallucinate or make incorrect predictions. Simply increasing the model’s size doesn’t resolve this […]