Scaling Problem
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 issue; […]
“attention”
Attention in Machine Learning (ML) refers to a mechanism that allows a model to focus selectively on certain parts of the input data, rather than treating all elements equally. This helps the model prioritize the most relevant information for the task. For example, in the sentence “Cat on a mat,” the attention mechanism might focus […]
algorithmic accountability
Algorithms have a document of accountability that outlines its decision-making process, evaluated against key criteria: Transparency, Auditability, Ethical Considerations, Risk Mitigation, Accountability Mechanisms, and Stakeholder Engagement. These criteria are crucial in high-stakes domains such as criminal justice, hiring and employment, healthcare, finance and credit, and government decision-making. However, holding algorithms accountable raises complex ethical questions. […]
Ethical protocols
Protocols are a set of guidelines designed to establish best practices for ensuring that artificial intelligence (AI) systems are developed, deployed, and governed according to ethical criteria. These criteria often include: While the primary goal may be social impact and responsibility, it’s important to recognize that the underlying motivations can vary. In some cases, ethical […]