Why Your 'Accurate' AI Model Might Still Be Dangerously Wrong: The Hidden Importance Of Model Calibration

Trustworthy AI isn’t just about predicting the right outcome; it’s about knowing how confident we should actually be.
InfoWorld

Researchers propose a self-distillation fix for ‘catastrophic forgetting’ in LLMs

LLMs tend to lose prior skills when fine-tuned for new tasks. A new self-distillation approach aims to reduce regression and ...
Nature

Uncertain Regression Models and Imprecise Observations

Uncertain regression models represent an evolving paradigm in statistical analysis, designed to address the challenges posed by imprecise observations. Traditional regression techniques typically ...
Forbes

The Inference Economy: How Sparse Computing And Model Optimization Are Reshaping Enterprise AI Deployment

The AI industry stands at an inflection point. While the previous era pursued larger models—GPT-3's 175 billion parameters to PaLM's 540 billion—focus has shifted toward efficiency and economic ...
Semiconductor Engineering

Small Language Models: A Solution To Language Model Deployment At The Edge?

While Large Language Models (LLMs) like GPT-3 and GPT-4 have quickly become synonymous with AI, LLM mass deployments in both training and inference applications have, to date, been predominately cloud ...
techtimes

The Role of Assumptions in Statistical Model Selection

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...