Vladimir Dyachkov, Ph.D
Editor-in-Chief of itinai.com

Enhancing Instruction-Following AI Models with LIFT Artificial intelligence (AI) has made significant progress with the development of large language models (LLMs) that follow user instructions. These models aim to provide accurate and relevant responses to human…

Graph Neural Networks for Materials Science Graph neural networks (GNNs) are a powerful tool in predicting material properties by capturing intricate atomic interactions within various materials. They encode atoms as nodes and chemical bonds as edges,…

Hormesis Management in Agriculture: Leveraging AI for Crop Improvement Practical Solutions and Value Recent advancements in AI, particularly ML and DL, are crucial for analyzing complex datasets and accurately modeling plant stress responses. These AI tools…

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 the Challenges in AI Evaluation Recently, large language models (LLMs) and vision-language models (VLMs) have made great strides in artificial intelligence. However, these models still face difficulties with tasks that require deep reasoning, long-term planning,…

Practical Solutions for Large Language Models Challenges and Solutions Large language models like GPT-3 and Llama-2 face challenges due to their size and resource requirements. To address this, researchers have developed FLEXTRON, a flexible model architecture…

StreamVoice, a new streaming language model, offers real-time zero-shot voice conversion (VC) without the need for complete source speech. Developed by researchers from Northwestern Polytechnical University and ByteDance, the model employs a fully causal context-aware LM…

Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) by combining external data retrieval with generative AI, ensuring accurate, current information and greater transparency. It reduces computational costs and risk of misinformation, integrating databases into a…

Challenges in Video Simulation Creating high-quality, real-time video simulations is difficult, especially for longer videos without losing quality. Traditional video generation models face issues like high costs, short durations, and limited interactivity. Manual asset creation, common…

Recent advancements in deep learning have greatly improved image recognition, especially in Fine-Grained Image Recognition (FGIR). However, challenges persist due to the need to discern subtle visual disparities. To address this, researchers at Nanjing University introduce…

Google Quantum AI is conducting collaborative research to identify problems where quantum computers outperform classical ones and design practical quantum algorithms. Recent endeavors involve studying enzyme chemistry, exploring alternatives for lithium-ion batteries, and modeling materials for…

UC San Diego and New York University developed the V* algorithm, which outperforms GPT-4V in contextual understanding and precise targeting of specific visual elements in images. The algorithm employs a Visual Question Answering (VQA) LLM, SEAL,…

Challenges with Large Language Models Large language models have greatly improved our understanding of artificial intelligence, but efficiently scaling these models still poses challenges. Traditional Mixture-of-Experts (MoE) architectures activate only a few experts for each token…

Multimodal Large Language Models (MLLMs) have transformed AI by combining Large Language Models with visual encoders. InfiMM-HD is introduced to handle high-resolution images efficiently. It integrates a cross-attention module with visual windows, offering an innovative approach…