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Recently, Volc Engine officially announced the launch of the enterprise AI mid-platform HiAgent 2.0, aiming to address real-world business needs and pain points in the development of Agent applications, helping enterprises achieve efficient delivery of intelligent bodies from development to operations. HiAgent 2.0 is positioned as an enterprise AI mid-platform, and compared to its predecessor HiAgent 1.0, it has expanded in four dimensions of functionality. Upwardly, HiAgent 2.0 provides more industry scenario templates and a rich plugin market, significantly lowering the barrier to building intelligent bodies; towards

Based on the Harmony Intelligent Body, the interaction mode between consumers and the Harmony system and applications will undergo a fundamental transformation. The Harmony Intelligent Body features system-level security, trustworthiness, and personalized controllability. It can achieve efficient collaboration among multiple intelligent bodies and natural transitions between multiple devices, thereby truly transforming the interaction mode from the traditional instruction-centered model to an intention-centered model.

A comprehensive review of MyBodyType.net's AI Body Type Calculator, comparing seven body type classification systems and differences between free and paid versions, revealing the accuracy and practical value of AI-based body analysis to help users decide if this body assessment service is worth investing in.

Recently, at a highly anticipated press conference, 360 Group announced that its 'Nano AI Search' will be upgraded to the new 'Nano AI Super Search Intelligence Body'. This upgrade not only marks further breakthroughs by 360 in the field of intelligent search, but also raises expectations for the application of artificial intelligence in information queries. It is reported that the 'Nano AI Super Search Intelligence Body' is equipped with more than 80 large models, enabling it to accurately break down users' search intentions. It can autonomously call various tools and content platforms for information inquiries, and can also...

Recently, Stanford University released a comprehensive evaluation of clinical medical AI models. DeepSeek R1 stood out as the champion among nine leading large models, achieving a 66% win rate and a macro average score of 0.75. The highlight of this evaluation is that it not only focuses on traditional medical license exam questions but also delves into the daily work scenarios of clinical doctors, providing more practical assessments. The evaluation team developed an integrated assessment framework called MedHELM, which includes 35 benchmarks covering 22 subtasks in medicine.

Recently, Google officially released the open source framework LMEval, aimed at providing standardized evaluation tools for large language models (LLMs) and multimodal models. The launch of this framework not only simplifies cross-platform model performance comparisons, but also supports assessments in areas such as text, images, and code, showcasing Google's latest breakthroughs in the field of AI evaluations. AIbase has compiled the latest developments of LMEval and its impact on the AI industry. Standardized Evaluations: Simplified Cross-Platform Model Comparisons

Recently, a new evaluation benchmark - RBench-V, specifically designed to test the visual reasoning capabilities of multi-modal large models, was released by research teams from Tsinghua University, Tencent HUNYUAN, Stanford University, and Carnegie Mellon University. The introduction of this benchmark aims to fill the gap in the current evaluation system regarding the model's visual output capabilities, allowing for a more comprehensive understanding of existing model performance. The RBench-V benchmark consists of 803 questions covering multiple fields, including geometry and graph theory, mechanics and electromagnetism, multi-target recognition, and path planning.

OpenAI has officially released a large dataset designed to evaluate the ability of large language models to answer questions in the healthcare field. This project is named HealthBench, and experts have highly praised this open-source data and detailed evaluation criteria, calling it "unprecedented" in scale and breadth. Image source note: The image was generated by AI, and the image authorization service provider is Midjourney. The HealthBench project marks OpenAI's first attempt in the healthcare sector.

The Chartered Institute of Arbitrators (CIArb), a leading international arbitration institution, recently released guidelines on the use of artificial intelligence (AI) in arbitration. This initiative aims to provide legal professionals and arbitrators with practical advice on the ethical use of this emerging technology in arbitration proceedings. With rapid technological advancements, AI is increasingly being integrated into various industries, including law and arbitration. AI can play a significant role in document review, evidence analysis, and decision-making support, but its application also raises a number of ethical considerations.

Tech giant OpenAI recently announced the acquisition of the startup Context.ai team to bolster its AI model evaluation and analysis capabilities. Founded in 2023 by former Google employees Henry Scott-Green and Alex Gamble, Context.ai provides developers with in-depth analysis and visualization tools for AI model performance. This acquisition underscores OpenAI's commitment to advancing AI technology.

OpenAI has announced the launch of its 'OpenAI Pioneers Program', aimed at improving the current scoring system for AI models to create evaluation standards more aligned with real-world applications. With the rapid advancement of AI across various industries, understanding and enhancing AI's performance in real-world scenarios is crucial. OpenAI states that focusing on domain-specific evaluation metrics will more effectively reflect real-world performance and help teams assess model performance in high-stakes environments.

OpenAI, a leading artificial intelligence company, recently announced the launch of its Evals API. This new tool has generated significant excitement among developers and the tech community. The Evals API allows users to programmatically define tests, automate evaluation workflows, and rapidly iterate on prompts. This launch marks a shift from manual to highly automated model evaluation, providing developers with more flexible and efficient tools to accelerate AI application development.

Meta released its new flagship AI model, Maverick, on Saturday. The model ranked second in the LM Arena benchmark. LM Arena is a testing platform that relies on human raters to compare different model outputs and select their preferences. However, several AI researchers quickly discovered that the version of Maverick deployed to LM Arena appears significantly different from the version widely used by developers. Meta acknowledged in its announcement that the Maverick on LM Arena is an experimental version.