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 ...

Large Foundation Models (LFMs) such as ChatGPT and GPT-4 have demonstrated impressive zero-shot learning capabilities on a wide range of tasks. Their successes can be credited to model and dataset ...

Recent advancements in large language models (LLMs) have generated enthusiasm about their potential to accelerate scientific innovation. Many studies have proposed research agents that can ...

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 ...

In the new paper Generative Agents: Interactive Simulacra of Human Behavior, a team from Stanford University and Google Research presents agents that draw on generative models to simulate both ...

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

Facebook AI Chief Yann LeCun introduced his now-famous “cake analogy” at NIPS 2016: “If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised ...

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 ...