AI deep fake misinformation hits the Bangladeshi election

Practical AI Solutions for the Medical Field Enhance LLM Performance with MedGraphRAG Large Language Models (LLMs) like ChatGPT and GPT-4 are transforming Natural Language Processing (NLP) and Generation (NLG). However, they face challenges in specialized fields…

Practical Solutions and Value of MaVEn Framework for MLLMs Challenges Addressed The existing Multimodal Large Language Models (MLLMs) face limitations in handling tasks involving multiple images, such as Knowledge-Based Visual Question Answering, Visual Relation Inference, and…

This text provides advice on selecting and reducing training time for neural networks. To learn more, visit the article on Towards Data Science.

Advancements in Natural Language Processing (NLP) rely on large language models (LLMs) for tasks like machine translation and content summarization. To address the computational demands of LLMs, a hybrid approach integrating LLMs and small language models…

Greptile, an innovative AI startup, addresses the challenges of complex codebases. It offers a unique approach: engineers can ask plain English questions to receive clear, detailed responses about code, saving time and aiding comprehension. Additionally, Greptile…

Research has introduced GPTSwarm, an open-source machine learning framework, proposing a revolutionary graph-based approach to language agents. By reimagining agent structure and introducing a dynamic graph framework, GPTSwarm enables interconnected, adaptable agents that collaborate more effectively,…

Practical Solutions for Mitigating Hallucinations in AI Systems Introduction Large language models (LLMs) sometimes produce incorrect, misleading, or nonsensical information, which can have serious consequences in high-stakes applications like medical diagnosis or legal advice. Minimizing these…

Researchers at NVIDIA and University of California, San Diego, have developed an innovative method for high-fidelity 3D geometry rendering in Generative Adversarial Networks (GANs). Based on SDF-based NeRF parametrization, the approach utilizes learning-based samplers to accelerate…

Introducing Ironwood: Google’s New TPU for AI Inference At the 2025 Google Cloud Next event, Google unveiled Ironwood, the latest generation of its Tensor Processing Units (TPUs). This new chip is specifically designed for large-scale AI…

Introduction to Web Agents Developing web agents is a complex area in AI research that has gained a lot of interest recently. As the web evolves, agents need to interact automatically with various online platforms. One…

Addressing Challenges in Trustworthiness Reasoning in Multiplayer Games Traditional Approaches Struggle in Dynamic Environments Assessing trust in multiplayer games with incomplete information is challenging. Current methods relying on pre-trained models lack real-time adaptability and struggle in…

Introduction to Differentiable Logic Cellular Automata For decades, researchers have been fascinated by how simple rules can lead to complex behaviors in cellular automata. Traditionally, this process involves defining local rules and observing the resulting patterns.…

Research in artificial intelligence is focused on integrating various types of data inputs to enhance video reasoning. The challenge lies in efficiently fusing diverse sensory data types, a problem addressed by UNC-Chapel Hill’s groundbreaking framework called…

Sailor, a suite of language models by Sea AI Lab and Singapore University of Technology and Design, caters to the intricate linguistic diversity of Southeast Asia. Its meticulous data handling equips it for accurate text generation…

GAIA, a benchmark by FAIR Meta and partners, tests AI assistants on real-world tasks that demand reasoning and multi-modal skills. It evaluates LLMs with practical, non-gameable questions reflecting actual use cases, aiming to bridge the gap…