New Machine
Research on "new machines" broadly encompasses the development and application of machine learning across diverse fields, aiming to improve efficiency, accuracy, and decision-making. Current efforts focus on refining model architectures like convolutional neural networks, gradient boosting machines, and transformers for tasks ranging from image and signal processing to complex prediction and control problems. This research is significant because it drives advancements in various sectors, including healthcare, energy, manufacturing, and transportation, by enabling automated processes, improved diagnostics, and more efficient resource allocation.
Papers
Venire: A Machine Learning-Guided Panel Review System for Community Content Moderation
Vinay Koshy, Frederick Choi, Yi-Shyuan Chiang, Hari Sundaram, Eshwar Chandrasekharan, Karrie Karahalios
FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation
Yuechun Gu, Jiajie He, Keke Chen
RoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine Reading Comprehension
Abel Corrêa Dias, Viviane Pereira Moreira, João Luiz Dihl Comba
Can Machines Think Like Humans? A Behavioral Evaluation of LLM-Agents in Dictator Games
Ji Ma
Belief in the Machine: Investigating Epistemological Blind Spots of Language Models
Mirac Suzgun, Tayfun Gur, Federico Bianchi, Daniel E. Ho, Thomas Icard, Dan Jurafsky, James Zou
Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review
Catherine Ikae, Mascha Kurpicz-Briki
Visualizing attention zones in machine reading comprehension models
Yiming Cui, Wei-Nan Zhang, Ting Liu