A survey by Elon University shows that 52% of US adults have used AI large language models like ChatGPT, Gemini, Claude, and Copilot. The January survey, conducted by the Imagining the Digital Future initiative at Elon University in North Carolina, polled 500 respondents. Of those who have used AI, 34% reported using large language models at least once a day. ChatGPT was the most popular, used by 72% of respondents; Google's G...

New York-based AI team Nous Research, known for its personalized, unrestricted language models, has launched a new model inference API. This marks a significant step for the unconventional AI group, making its advanced language models accessible to a wider range of developers and researchers through a programmatic interface. The unrestricted nature of these models has always set Nous Research apart, challenging the likes of OpenAI and Anthropic...

Frustrated by the slow processing speed of large language models on long documents? Researchers from Tsinghua University have unveiled a groundbreaking technology – the APB parallel inference framework – that dramatically accelerates processing. Benchmark tests show this technology achieves a 10x speed improvement over Flash Attention when handling ultra-long texts. With the rise of models like ChatGPT, AI's ability to process vast amounts of text (hundreds of thousands of words) has increased significantly. However, this often comes at the cost of processing speed...

The LLMs.txt Generator has received a major update with the release of version 2. This tool quickly converts any website content into text files usable by AI agents or Large Language Models (LLMs), greatly benefiting AI application developers and users. Developed by the @firecrawl_dev team and fully supported by their official llmstxt endpoint, the new version boasts an incredible 10x speed improvement over its predecessor. The LLMs.txt Generator v2...

2024 was a landmark year for the AI industry. According to TechCrunch, 49 startups secured over $100 million in funding, with seven raising over $1 billion and three securing multiple mega-funding rounds. This momentum continues into 2025. Despite it being early in the year, the number of US AI companies receiving over $100 million in funding is nearing double digits, with at least one round exceeding $1 billion. The following are the companies that have secured over $100 million in funding so far in 2025...

A revolutionary technology is quietly emerging in the field of artificial intelligence. Inception Labs recently announced the Mercury series of diffusion large language models (dLLMs), a new generation of language models designed for fast and efficient high-quality text generation. Compared to traditional autoregressive large language models, Mercury boasts up to 10x faster generation speeds, achieving over 1000 tokens per second on an NVIDIA H100 GPU. This speed is...

In the field of artificial intelligence (AI), while large language models (LLMs) excel at processing natural language, they often struggle with complex reasoning tasks. These tasks typically require multi-step reasoning, domain-specific knowledge, or effective integration of external tools. To overcome these limitations, researchers have been exploring how to enhance LLM capabilities through the use of external tools. Traditional enhancement methods usually necessitate fine-tuning or additional training of the models, which limits their adaptability and flexibility for tasks.

Recently, the successful launch of the VLM-R1 project has brought new hope to this field. This project represents the successful migration of the R1 Method from the DeepSeek team into visual language models, indicating that AI's understanding of visual content will enter a whole new phase. The inspiration for VLM-R1 comes from last year's open-source R1 Method by DeepSeek, which leverages GRPO (Generative Reward Processing Optimization) reinforcement.

Artificial intelligence is sweeping across the globe at an unprecedented speed, but a significant issue is becoming increasingly prominent: while general-purpose AI is proficient in many skills, it often struggles with specific regional cultures and languages. This is particularly true in culturally and linguistically diverse areas such as the Middle East and South Asia, where users urgently require AI models that understand the 'local language'. Although large general models can handle multiple languages, they often reveal a lack of cultural depth and in-depth regional knowledge when dealing with scenarios rich in regional context.

Recently, Microsoft launched OmniParser V2.0, a new parsing tool designed to convert user interface (UI) screenshots into structured formats. OmniParser enhances the performance of UI agents based on large language models (LLM), helping users better understand and interact with the information on their screens. The tool's training dataset includes an interactive icon detection dataset, meticulously curated and automatically annotated from popular websites to highlight clickable and actionable areas.

A recent study released by the University of Munich, the Munich Machine Learning Center, and Adobe Research shows that 12 top AI language models, including GPT-4o, Gemini1.5Pro, and Llama-3.3-70B, face significant performance degradation in long-text conceptual reasoning tasks. Despite these models supporting context processing of at least 128,000 tokens, their ability to establish deep logical connections still has fundamental limitations. The research team developed NOLIMA (No Text Matching).

Recently, Tencent Technology (Shenzhen) Co., Ltd. published a patent regarding a training method and related equipment for large language models on the Tianyancha app. The patent is titled 'Training Method, Device, Computer Equipment, and Storage Medium for Large Language Models' and aims to enhance the learning capacity and accuracy of large language models through innovative training methods. In the training process of large language models, traditional methods often rely on a single text summary, which may lead to model overfitting and negatively impact the accuracy and diversity of generated content. However, Tencent's new...

Realbotix, a leading manufacturer of humanoid robots and companion AI, is further expanding its technological capabilities by launching large language model (LLM) integration and advanced customization features. These new functionalities are set to debut in February 2025, allowing users to seamlessly connect with a variety of mainstream AI platforms including OpenAI's ChatGPT, Meta's Llama, Google's Gemini, and the newly launched DeepSeek R1.

In the current technological environment, artificial intelligence (AI) has sparked widespread discussion. Eerke Boiten, a professor of cybersecurity at De Montfort University, states that existing AI systems have fundamental flaws in management and reliability, and therefore should not be used in critical applications. Professor Boiten points out that most current AI systems rely heavily on large neural networks, particularly generative AI and large language models (like ChatGPT). The workings of these systems are relatively complex, even though each neural...

In the field of academic research, literature retrieval is a complex and crucial information acquisition task. Researchers need the capability to handle intricate, domain-specific search queries to meet detailed research requirements. However, existing academic search platforms, like Google Scholar, often struggle to address these complex research queries. For example, queries related to non-stationary reinforcement learning using the UCB method require stronger computational and analytical capabilities. Moreover, researchers typically need to spend a significant amount of time and effort manually browsing extensive academic literature when conducting literature reviews.