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 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 ...

Human-machine interaction is rapidly evolving. Today, 12% of Chinese users opt for voice input over typing. iFlytek, the country’s most popular voice input method, can translate English voice input ...

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 ...

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 ...

Most deep neural network training relies heavily on gradient descent, but choosing the optimal step size for an optimizer is challenging as it involves tedious and error-prone manual work. In the ...

Climate change and extreme weather events have made weather and climate modelling a challenging yet crucial real-world task. While current state-of-the-art approaches tend to employ numerical models ...

In less than two years since their introduction, vision transformers (ViT) have revolutionized the computer vision field, leveraging transformer architectures’ powerful self-attention mechanisms to ...

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 ...