This is the fourth Synced year-end compilation of "Artificial Intelligence Failures." Our aim is not to shame nor downplay AI research, but to look at where and how it has gone awry with the hope that ...

Introduction Tree boosting has empirically proven to be efficient for predictive mining for both classification and regression. For many years, MART (multiple additive regression trees) has been the ...

Just hours after making waves and triggering a backlash on social media, Genderify — an AI-powered tool designed to identify a person’s gender by analyzing their name, username or email address — has ...

On June 28, Shenzhen promulgated the draft of Regulations on the Promotion of Artificial Intelligence Industry of Shenzhen Special Economic Zone, which seeks to establish an overarching framework for ...

Transformer architectures have come to dominate the natural language processing (NLP) field since their 2017 introduction. One of the only limitations to transformer application is the huge ...

In a new paper NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models, an NVIDIA research team introduces NV-Embed. This generalist embedding model significantly boosts the ...

Recent advancements in training large multimodal models have been driven by efforts to eliminate modeling constraints and unify architectures across domains. Despite these strides, many existing ...

For years, embedding models based on bidirectional language models have led the field, excelling in retrieval and general-purpose embedding tasks. However, past top-tier methods have relied on ...

This research addresses a well-known phenomenon regarding large batch sizes during training and the generalization gap.