marktechpost4d
FuXi-2.0: Advancement in Machine Learning ML-based Weather Forecasting for Practical Applications
ML models are increasingly used in weather forecasting, offering accurate predictions and reduced computational costs compared to traditional numerical weather prediction (NWP) models. However, ...
marktechpost4d
Understanding the Inevitable Nature of Hallucinations in Large Language Models: A Call for Realistic Expectations and Management Strategies
Prior research on Large Language Models (LLMs) demonstrated significant advancements in fluency and accuracy across various tasks, influencing sectors like healthcare and education. This progress ...
marktechpost4d
Deep Learning Approach for Lithium-Ion Battery Life Prediction via Dual-Stream Vision Transformer
Predicting battery lifespan is difficult due to the nonlinear nature of capacity degradation and the uncertainty of operating conditions. As battery lifespan prediction is vital for the reliability ...
marktechpost4d
Allen Institute for AI Researchers Propose SUPER: A Benchmark for Evaluating the Ability of LLMs to Set Up and Execute Research Experiments
Artificial Intelligence (AI) and Machine Learning (ML) have been transformative in numerous fields, but a significant challenge remains in the reproducibility of experiments. Researchers frequently ...
marktechpost6d
HNSW, Flat, or Inverted Index: Which Should You Choose for Your Search? This AI Paper Offers Operational Advice for Dense and Sparse Retrievers
A significant challenge in information retrieval today is determining the most efficient method for nearest-neighbor vector search, especially with the growing complexity of dense and sparse retrieval ...
marktechpost6d
Windows Agent Arena (WAA): A Scalable Open-Sourced Windows AI Agent Platform for Testing and Benchmarking Multi-modal, Desktop AI Agent
Artificial intelligence (AI) has been advancing in developing agents capable of executing complex tasks across digital platforms. These agents, often powered by large language models (LLMs), have the ...
marktechpost4d
Learning and Knowledge Retrieval: A Comprehensive Framework for In-Context Learning in Large Language Models (LLMs)
Generative Large Language Models (LLMs) are capable of in-context learning (ICL), which is the process of learning from examples given within a prompt. However, research on the precise principles ...
marktechpost4d
A Systematic Literature Review: Optimization and Acceleration Techniques for LLMs
Large language models (LLMs) have seen remarkable success in natural language processing (NLP). Large-scale deep learning models, especially transformer-based architectures, have grown exponentially ...
marktechpost4d
Collaborative Small Language Models for Finance: Meet The Mixture of Agents MoA Framework from Vanguard IMFS
Language model research has rapidly advanced, focusing on improving how models understand and process language, particularly in specialized fields like finance. Large Language Models (LLMs) have moved ...
marktechpost4d
NiNo: A Novel Machine Learning Approach to Accelerate Neural Network Training through Neuron Interaction and Nowcasting
In deep learning, neural network optimization has long been a crucial area of focus. Training large models like transformers and convolutional networks requires significant computational resources and ...
marktechpost4d
Agent Zero: A Dynamic Agentic Framework Leveraging the Operating System as a Tool for Task Completion
AI assistants have the drawback of being rigid, pre-programmed for specific tasks, and in need of more flexibility. The limited utility of these systems stems from their inability to learn and adapt ...
marktechpost4d
What’s Slowing Down Text-to-Speech Systems—And How Can We Fix It? This AI Paper Present Super Monotonic Alignment Search
A significant challenge in text-to-speech (TTS) systems is the computational inefficiency of the Monotonic Alignment Search (MAS) algorithm, which is responsible for estimating alignments between text ...