Liquid AI has released its first series of Liquid Foundation Models (LFMs), ushering in a new generation of generative AI models. These models are positioned as a new benchmark for performance and ...

Microsoft unveiled VoiceRAG, a voice-based retrieval-augmented generation (RAG) system that utilizes the new Azure OpenAI gpt-4o-realtime-preview model to combine audio input and output with powerful ...

Large language models (LLMs) have gained immense capabilities due to their training on vast internet-based datasets. However, this broad exposure has inadvertently incorporated harmful content, ...

Multimodal models aim to create systems that can seamlessly integrate and utilize multiple modalities to provide a comprehensive understanding of the given data. Such systems aim to replicate ...

With the increasing growth of artificial intelligence—introduction of large language models (LLMs) and generative AI—there has been a growing demand for more efficient graphics processing units (GPUs) ...

Large language models (LLMs) have gained widespread popularity, but their token generation process is computationally expensive due to the self-attention mechanism. This mechanism requires attending ...

ChatGPT is a versatile tool with immense potential for businesses across diverse industries. Its capability to comprehend and generate human-like text enables its use in numerous applications, making ...

CopilotKit has emerged as a leading open-source framework designed to streamline the integration of AI into modern applications. Widely appreciated within the open-source community, CopilotKit has ...

Large language models (LLMs) have garnered significant attention for their ability to understand and generate human-like text. These models possess the unique capability to encode factual knowledge ...

Climate and weather prediction has experienced rapid advancements through machine learning and deep learning models. Researchers have started to rely on artificial intelligence (AI) to enhance ...

Biomedical vision models are increasingly used in clinical settings, but a significant challenge is their inability to generalize effectively due to dataset shifts—discrepancies between training data ...

LLMs, characterized by their massive parameter sizes, often lead to inefficiencies in deployment due to high memory and computational demands. One practical solution is semi-structured pruning, ...