It is the subfield of computer science that focuses on creating systems capable of intelligent behavior, including problem solving, learning, adaptation, perception, and language understanding.
The article presents a new framework for Interactive Machine Learning (IML), aiming to incorporate human expertise into machine learning algorithms. The framework is grounded in Bayesian Optimization (BO), thus called…
Continue readingThe paper introduces Meta-Prompting, a scaffolding technique designed to enhance the functionality of language models. This approach transforms a single Language Model into a multi-faceted conductor, capable of managing and…
Continue readingThe article presents a new method called crowdsourced adaptive survey (CSAS), which combines natural language processing and adaptive algorithms. The method transforms open-ended text from participants into potential survey questions,…
Continue readingThe paper presents a new approach to make Explainable Artificial Intelligence (XAI) more accessible to a wider audience, including non-experts. The researchers developed a Large Language Model (LLM) using ChatGPT…
Continue readingThe paper ‘Causal Perception’ by Jose M. Alvarez and Salvatore Ruggieri, published in January 2024, explores the concept of perception in the context of automated decision-making (ADM) systems. The authors…
Continue readingThe article presents a study investigating the impact of AI-generated ideas on human creativity and idea diversity. Over 800 participants from more than 40 countries were exposed to ideas generated…
Continue readingThe article explores the gap between the information provided by AI systems and the information needs of people affected by algorithmic decision-making. It introduces the ‘XAI Novice Question Bank’, a…
Continue readingThe authors respond to Makelov et al.’s recent paper that reviews subspace interchange intervention methods like Distributed Alignment Search (DAS) and claims these could cause interpretability illusions. They argue that…
Continue readingThe article introduces a novel approach to Kriging, a method of estimating attributes of unseen locations based on neighboring data. The proposed method, called Contrastive- Prototypical self-supervised learning for Kriging…
Continue readingThe study tackles the issues in Automated Model Evaluation (AutoEval), such as overconfidence and high computational cost. It introduces a novel measure, Meta-Distribution Energy (MDE), to enhance the AutoEval framework’s…
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