dbt Core, Snowflake, and GitHub Actions: pet project for Data Engineers

Challenges in Using LLMs for Mainframe Modernization: 1. Limited Training on Mainframe Languages: Existing large language models (LLMs) lack sufficient training on mainframe languages like COBOL, hindering their ability to understand and interact with legacy codebases.…

Practical Solutions for Long-Context LLMs Accelerating Processing with MInference The MInference method optimizes sparse calculations for GPUs, reducing latency without altering pre-training or needing fine-tuning. It achieves up to a 10x speedup, cutting the pre-filling stage…

Researchers at NTU Singapore have developed a new diffusion model, ResShift, which accelerates image restoration by cleverly leveraging the degraded image as a basis for restoring the original, high-quality version. The model efficiently balances performance and…

Understanding Geometry Representations in 3D Vision Geometry representations are essential for addressing complex 3D vision challenges. With advancements in deep learning, there’s a growing focus on creating data structures that work well with neural networks. Coordinate…

Practical Solutions and Value of MoE Architectures Sparse Activation for Efficient Model Scaling Mixture-of-experts (MoE) architectures use sparse activation to efficiently scale model sizes, preserving high training and inference efficiency. Challenges and Innovations in MoE Architectures…

Luke Farritor, a computer science student at the University of Nebraska–Lincoln, has used machine learning to decipher a carbonized scroll from ancient Herculaneum that was previously unreadable. His algorithm identified Greek letters on the papyrus, including…

Understanding Hierarchical Imitation Learning (HIL) Hierarchical Imitation Learning (HIL) helps in making long-term decisions by breaking tasks into smaller goals. However, it struggles with limited supervision and requires a lot of expert examples. Large Language Models…

Practical Solutions and Value of Vision State Space Models (VSSMs), Vision Transformers, and Convolutional Neural Networks (CNNs) Robustness of Deep Learning Models Deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers have shown success…

Microsoft AI researchers have developed ResLoRA, an enhanced framework for Low-Rank Adaptation (LoRA). It introduces residual paths during training and employs merging approaches for path removal during inference. Outperforming original LoRA and baseline methods, ResLoRA achieves…

Google AI has developed a groundbreaking technique called Audioplethysmography (APG) that enables active noise cancelling (ANC) headphones to monitor the user’s cardiac activities without additional sensors or complex hardware configurations. APG leverages low-intensity ultrasound signals transmitted…

Evaluating Large Language Models (LLMs) Challenges and Solutions Evaluating large language models (LLMs) has become increasingly challenging due to their complexity and versatility. Ensuring the reliability and quality of these models’ outputs is crucial for advancing…

QB-GPT is a model that can generate football plays based on provided elements. It aims to recreate plays from minimal information to understand how player setups and contextual elements affect team paths on the field. The…

Practical Solutions for Learning High-Dimensional Data Distributions Understanding Diffusion Models in AI A significant challenge in AI is understanding how diffusion models can effectively learn and generate high-dimensional data distributions. This is crucial for applications in…

Empowering Developers and Non-Coders Alike to Build Interactive Web Applications Effortlessly Taipy Designer: Seamless Integration from Python Code to Web Interface For those new to Python programming, navigating the abundance of available libraries can be overwhelming.…

Transforming Business Operations with AI In the digital age, the way we work is changing rapidly, but challenges remain. Traditional AI assistants and manual workflows often struggle with the complexity and volume of modern tasks. Businesses…

Large-scale training of generative models on video and image data is explored, utilizing text-conditional diffusion models. A transformer architecture operates on video and image latent codes to enable generation of high-fidelity video. Sora, the largest model,…

Ten global teams were funded to develop ideas and tools for collective AI governance. Their innovations were summarized, and learnings outlined, calling for researchers and engineers to join the ongoing effort.