​[[{“value”:” Graph neural networks (GNNs) are a powerful tool in materials science, particularly in predicting material properties. GNNs leverage the unique ability of graph representations to capture intricate atomic interactions within various materials. These models encode atoms as nodes and chemical bonds as edges, allowing… Read More »LESets Machine Learning Model: A Revolutionary Approach to Accurately Predicting High-Entropy Alloy Properties by Capturing Local Atomic Interactions in Disordered Materials Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Introduction to EXAONE 3.0: The Vision and Objectives EXAONE 3.0 represents a significant milestone in the evolution of language models developed by LG AI Research, particularly within Expert AI. The name “EXAONE” derives from “EXpert AI for EveryONE,” encapsulating LG AI Research‘s commitment to… Read More »LG AI Research Open-Sources EXAONE 3.0: A 7.8B Bilingual Language Model Excelling in English and Korean with Top Performance in Real-World Applications and Complex Reasoning Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have revolutionized natural language processing (NLP), particularly for English and other data-rich languages. However, this rapid advancement has created a significant development gap for underrepresented languages, with Cantonese being a prime example. Despite being spoken by over 85 million people… Read More »Advancing Cantonese NLP: Bridging Development Gaps in Large Language Models with New Benchmarks and Open-Source Innovations Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs), initially limited to text-based processing, faced significant challenges in comprehending visual data. This limitation led to the development of Visual Language Models (VLMs), which integrate visual understanding with language processing. Early models like VisualGLM, built on architectures such as BLIP-2… Read More »CogVLM2: Advancing Multimodal Visual Language Models for Enhanced Image, Video Understanding, and Temporal Grounding in Open-Source Applications Shoaib Nazir Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The competition to develop the most advanced Large Language Models (LLMs) has seen major advancements, with the four AI giants, OpenAI, Meta, Anthropic, and Google DeepMind, at the forefront. These LLMs are reshaping industries and significantly impacting the AI-powered applications we use daily, such… Read More »Top Large Language Models (LLMs): A Comprehensive Ranking of AI Giants Across 13 Metrics Including Multitask Reasoning, Coding, Math, Latency, Zero-Shot and Few-Shot Learning, and Many More Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Machine learning has made significant advancements, particularly through deep learning techniques. These advancements rely heavily on optimization algorithms to train large-scale models for various tasks, including language processing and image classification. At the core of this process lies the challenge of minimizing complex, non-convex… Read More »This AI Paper from Apple Introduces AdEMAMix: A Novel Optimization Approach Leveraging Dual Exponential Moving Averages to Enhance Gradient Efficiency and Improve Large-Scale Model Training Performance Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Together AI has introduced a groundbreaking technique known as TEAL (Training-Free Activation Sparsity in LLMs) that has the potential to advance the field of efficient machine learning model inference significantly. The company, a leader in open-source AI models, has been exploring innovative ways to… Read More »Together AI Present TEAL: A Groundbreaking Training-Free Activation Sparsity Method for Optimizing Large Language Models with Enhanced Efficiency and Minimal Degradation in Resource-Constrained Environments Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” LLMs like GPT-4, MedPaLM-2, and Med-Gemini perform well on medical benchmarks but need help to replicate physicians’ diagnostic abilities. Unlike doctors who gather patient information through structured questioning and examinations, LLMs often need more logical consistency and specialized knowledge, leading to inadequate diagnostic reasoning.… Read More »Enhancing Diagnostic Accuracy in LLMs with RuleAlign: A Case Study Using the UrologyRD Dataset Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” GNNs have excelled in analyzing structured data but face challenges with dynamic, temporal graphs. Traditional forecasting, often used in fields like economics and biology, relied on statistical models for time-series data. Deep learning, particularly GNNs, shifted focus to non-Euclidean data like social and biological… Read More »TempoKGAT: Enhancing Temporal Graph Analysis with Time-Decaying Weights and Selective Neighbor Aggregation Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Neural Architecture Search (NAS) has emerged as a powerful tool for automating the design of neural network architectures, providing a clear advantage over manual design methods. It significantly reduces the time and expert effort required in architecture development. However, traditional NAS faces significant challenges… Read More »TinyTNAS: A Groundbreaking Hardware-Aware NAS Tool for TinyML Time Series Classification Sajjad Ansari Artificial Intelligence Category – MarkTechPost