SILO AI Releases New Viking Model Family (Pre-Release): An Open-Source LLM for all Nordic languages, English and Programming Languages

Understanding Hallucination Rates in Language Models: Insights from Training on Knowledge Graphs and Their Detectability Challenges Practical Solutions and Value Highlights Language models (LMs) perform better with larger size and training data, but face challenges with…

Revolutionizing Protein Design with AI Solutions Transformative Tools in Protein Engineering Autoregressive protein language models (pLMs) are changing how we design functional proteins. They can create diverse enzyme families, such as lysozymes and carbonic anhydrases, by…

Introduction to MAPS: A New Era in Test Case Generation With the rise of Artificial Intelligence (AI), the software industry is now utilizing Large Language Models (LLMs) for tasks like code completion and debugging. However, traditional…

Understanding the Brain with NeuroFly Advancements in Neuroscience Neuroscience has made great strides in mapping brain neurons. Neurons have branch-like structures called dendrites and axons that connect them. Understanding these connections helps us learn how the…

“`html Challenges of Long-Context Alignment in LLMs Large Language Models (LLMs) have demonstrated exceptional capabilities; however, they struggle with long-context tasks due to a lack of high-quality annotated data. Human annotation isn’t feasible for long contexts,…

PERF (Panoramic Neural Radiance Fields) is a new framework that allows the transformation of single panorama images into 3D scenes that can be explored. It uses a collaborative RGBD inpainting method and a monocular depth estimator…

Researchers from the University of Washington and the University of Hong Kong have proposed a human-like generative agent system that mimics human behavior. The system uses a two-system mechanism, inspired by human psychology, to guide generative…

Introduction to Unstructured Serverless API The Unstructured Serverless API simplifies, accelerates, and reduces costs for enterprise data AI-readiness. The Unstructured Serverless API is designed to render enterprise data ready for AI applications seamlessly and cost-effectively. It…

Practical AI Solutions for Large Language Models Energy and Cost Optimization with AI Many applications utilize large language models (LLMs), but deploying them on GPU servers can result in significant energy and financial expenditures. Some acceleration…

The Challenge of Linearizing Large Language Models (LLMs) Efficiently linearizing large language models (LLMs) is complex. Traditional LLMs use a quadratic attention mechanism, which is powerful but requires a lot of computational resources and memory. Current…

AMD Launches MI325x AI Chip to Compete with Nvidia Introduction Advanced Micro Devices (AMD) has introduced the MI325x AI chip, a powerful new accelerator designed to challenge Nvidia’s Blackwell series. This launch, announced on October 10,…

Google set a new world record for calculating the most digits of Pi using the y-cruncher program running on Google Cloud. While math.pi has a precision of 15 digits, the article explores using Ramanujan’s formula and…

Practical Solutions and Value of Aquila2: Advanced Bilingual Language Models Efficient Training Methodologies Large Language Models (LLMs) like Aquila2 face challenges in training due to static datasets and long training periods. The Aquila2 series offers more…

The text discusses using Python, MIDI, and Matplotlib to analyze music and help beginners find the right instrument to learn piano. It explores extracting musical notes from MIDI files, visualizing note distribution using Matplotlib, and understanding…

Advancements in Reinforcement Learning from Human Feedback and instruction fine-tuning are enhancing Language Model’s (LLM) capabilities, aligning them more closely with human preferences and making complex behaviors more accessible. Expert Iteration is found to outperform other…

Practical Solutions for Multilingual AI Efficiency Challenges in Multilingual AI Deployment Natural language processing (NLP) faces challenges in deploying large language models (LLMs) across multiple languages due to high computational demands. Improving Multilingual Inference Efficiency Researchers…