Character Detection Matching (CDM): A Novel Evaluation Metric for Formula Recognition

MIT researchers have developed a new approach, called StableRep, for training self-supervised methods using synthetic images generated by text-to-image models. By treating multiple images from the same text prompt as positive examples for each other, StableRep…

Advancements in Large Language Models (LLMs) enabled by Natural Language Processing and Generation have broad applications. However, their biased representations of human viewpoints stemming from pretraining data composition have prompted researchers to focus on data curation.…

Artificial Intelligence in Healthcare Artificial intelligence (AI) is revolutionizing healthcare by leveraging advanced computational techniques for diagnostics and treatment planning. Large language models (LLMs) are emerging as powerful tools for parsing complex medical data, promising to…

Introducing the Predibase Inference Engine Predibase has launched the Predibase Inference Engine, a powerful platform designed for deploying fine-tuned small language models (SLMs). This engine enhances SLM performance by making deployments faster, scalable, and cost-effective for…

OpenAI has unveiled a safety framework for its advanced AI models, allowing the board to override executive decisions on safety matters. This move, reflecting the company’s commitment to responsible deployment of technology, aims to address growing…

Large language models (LLMs) trained on extensive text data exhibit impressive abilities across various tasks, challenging the traditional benchmarks. Studies by MIT and others show that when LLMs utilize collective intelligence, they can compete with human…

Practical Solutions and Value of RanDumb in Continual Learning Overview: Continual learning involves adapting models to new data streams while retaining past knowledge, crucial for real-world applications. Challenges: Catastrophic forgetting is a major issue where models…

Researchers from MIT, the MIT spinout Inkbit, and ETH Zurich have developed a new 3D inkjet printing system that uses computer vision to adjust the amount of resin each nozzle deposits in real-time. This contactless system…

The study introduces ‘ImMesh,’ a SLAM framework by The University of Hong Kong and the Southern University of Science and Technology for real-time, large-scale mesh reconstruction using a CPU. It efficiently combines localization and meshing using…

Researchers from TH Nürnberg and Apple propose a multimodal approach to improve virtual assistant interactions. By combining audio and linguistic information, their model differentiates user-directed and non-directed audio without requiring trigger phrases, creating a more natural…

Reinforcement Learning in Language Model Training Reinforcement learning (RL) is essential for training large language models (LLMs) to enhance their reasoning capabilities, especially in mathematical problem-solving. However, the training process often suffers from inefficiencies, such as…

Understanding Reward Functions in Reinforcement Learning Reward functions are essential in reinforcement learning (RL) systems. They help define tasks but can be challenging to design effectively. A common method uses binary rewards, which are simple but…

Google’s recent demo video showcasing the Gemini AI model’s capabilities has been revealed to be edited, raising concerns about transparency in AI demonstrations. Initially perceived as real-time interactions, the video was actually a carefully crafted portrayal…

OpenAI’s GPT-4 Turbo has received mixed reactions since its launch. While OpenAI claims it is an improvement over its predecessor, user experiences suggest otherwise. An independent benchmark test showed a drop in performance from GPT-4 to…