​Large Language Model or LLM inference has two phases, the prompt (or prefill) phase to output the first token and the extension (or decoding) phase to the generate subsequent tokens. In this work, we propose an efficient parallelization scheme, KV-Runahead to accelerate the prompt phase.… Read More »KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation Apple Machine Learning Research

​[[{“value”:”Federated Learning (FL) is an emerging ML training paradigm where clients own their data and collaborate to train a global model without revealing any data to the server and other participants. Researchers commonly perform experiments in a simulation environment to quickly iterate on ideas. However,… Read More »pfl-research: Simulation Framework for Accelerating Research in Private Federated Learning Apple Machine Learning Research

​[[{“value”:” In part 1 of this blog series, we discussed how a large language model (LLM) available on Amazon SageMaker JumpStart can be fine-tuned for the task of radiology report impression generation. Since then, Amazon Web Services (AWS) has introduced new services such as Amazon… Read More »Evaluation of generative AI techniques for clinical report summarization Ekta Walia Bhullar AWS Machine Learning Blog

​[[{“value”:” In the dynamic field of AI technology, a pressing challenge for the drug discovery (DD) community, especially in structural biology and computational chemistry, is the creation of innovative models finely tuned for drug design. The core challenge lies in accurately and efficiently predicting molecular… Read More »MISATO: A Machine Learning Dataset of Protein-Ligand Complexes for Structure-based Drug Discovery Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Anomaly detection has gained traction in various fields such as surveillance, medical analysis, and network security. Typically approached as a one-class classification problem, autoencoder (AE) models are commonly used. However, AEs tend to reconstruct anomalies too well, reducing discrimination between normal and abnormal data.… Read More »Enhancing Anomaly Detection with Adaptive Noise: A Pseudo Anomaly Approach Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Home Table of Contents Harnessing Power at the Edge: An Introduction to Local Large Language Models Introduction to Large Language Models (LLMs) What Are Large Language Models? Historical Context and Technological Evolution The Development of OpenAI’s Generative Pre-Trained Transformers Key Training Methodologies Broad Spectrum… Read More »Harnessing Power at the Edge: An Introduction to Local Large Language Models Aditya Sharma PyImageSearch

​[[{“value”:” The domain of large language model (LLM) quantization has garnered attention due to its potential to make powerful AI technologies more accessible, especially in environments where computational resources are scarce. By reducing the computational load required to run these models, quantization ensures that advanced… Read More »Intel Releases a Low-bit Quantized Open LLM Leaderboard for Evaluating Language Model Performance through 10 Key Benchmarks Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Vision Transformers (ViT) and Convolutional Neural Networks (CNN) have emerged as key players in image processing in the competitive landscape of machine learning technologies. Their development marks a significant epoch in the ongoing evolution of artificial intelligence. Let’s delve into the intricacies of both… Read More »Vision Transformers (ViTs) vs Convolutional Neural Networks (CNNs) in AI Image Processing Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Molecular representation learning is an essential field focusing on understanding and predicting molecular properties through advanced computational models. It plays a significant role in drug discovery and material science, providing insights by analyzing molecular structures. The fundamental challenge in molecular representation learning involves efficiently… Read More »This AI Research Introduces SubGDiff: Utilizing Diffusion Model to Improve Molecular Representation Learning Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Data centers need an upgraded dashboard to guide their journey to greater energy efficiency, one that shows progress running real-world applications. The formula for energy efficiency is simple: work done divided by energy used. Applying it to data centers calls for unpacking some details.… Read More »Dial It In: Data Centers Need New Metric for Energy Efficiency Jeremy Rodriguez – Archives Page 1 | NVIDIA Blog