Alibaba's cloud computing division has released the new AI model Qwen2-VL, which possesses strong capabilities to understand visual content. It can analyze videos lasting up to 20 minutes in real-time, summarize the video content, answer related questions, and provide online chat support. In third-party benchmark tests, Qwen2-VL surpassed several metrics including Meta's Llama 3.1, OpenAI's GPT-4o, Anthropic's Claude 3 Haiku, and Google's Gemini 1.5 Flash.

Microsoft has released Phi-3.5-vision, a lightweight, multimodal open source AI model designed for processing textual and visual inputs, supporting a context length of 128K. This model is suitable for resource-constrained environments and features capabilities such as image understanding, OCR, chart parsing, and multi-image summarization, showcasing excellent performance and low latency. Comprised of 4.2 billion parameters, it is trained with high-quality data to ensure performance and privacy. It includes three models: lightweight AI, expert mix, and multimodal model, all demonstrating outstanding performance in image and video processing benchmarks.

LG's AI research department has launched Exaone 3.0, South Korea's first open-source AI model, which features 7.8 billion parameters. The model aims to accelerate AI research and build a robust AI ecosystem. It performs exceptionally well in Korean and English tasks. Through its open-source strategy, LG demonstrates its technological prowess and may create new revenue streams in cloud computing and AI services. To compete in the global AI landscape, LG is similar to companies like Alibaba, driving cloud business growth through open-source AI models.

AI organization LAION and leading AI developers suggest that the EU encourage the use of open source AI models. Open source AI models offer greater safety, accountability, replicability, and robustness. LAION has launched the PyTorch codebase OpenLM for training medium-sized language models. The OpenLM model outperforms similar open source models. LAION urges EU regulatory efforts to promote the application of open source AI models.

Arthur has launched the open source tool ArthurBench for evaluating and comparing the performance of large language models. ArthurBench helps companies test the performance of different language models on specific use cases and provides metrics such as accuracy, readability, and risk mitigation for comparison. Financial services firms, automotive manufacturers, and media platforms have already begun using ArthurBench, accelerating analysis and providing more accurate answers.