Search AI Products and News

While most delivery automation systems are still stuck at the sidewalk, Rivr wants to bring robots directly to your doorstep. The robotics startup, headquartered in Zurich, is pilot testing a four-wheeled robot that can climb stairs with US logistics company Veho in Austin, Texas. This robot aims to solve the last tough part of end-to-end delivery: the final 100 yards. Designed to complete the most complex and physically demanding leg of the journey between Veho's van and the customer's door, this robot is being put through its paces by Rivr founder and CEO.

On April 24th, Kunlun Wanwei announced the official open-sourcing of its multimodal reasoning model, Skywork-R1V2.0 (hereinafter referred to as R1V2.0). This upgraded version demonstrates significant improvements in both visual and text reasoning capabilities, particularly excelling in deep reasoning for challenging science problems in the College Entrance Examination and general task scenarios. It is considered the currently most balanced open-source multimodal model, equally adept at visual and text reasoning.

OpenAI recently released its latest o3 and o4-mini AI models, which achieve state-of-the-art performance in many areas. However, these new models have not improved upon the issue of 'hallucinations,' exhibiting even more severe instances than previous OpenAI models. 'Hallucinations,' the generation of factually incorrect information by AI models, remain one of the most challenging problems in AI today. Previous generations of models showed improvements in reducing hallucinations; however, o3 and o4-mini have not.

Researchers at Westlake University have recently developed a new model capable of detecting text generated by artificial intelligence. Professor Zhang Yue, the team leader, stated in an interview that this model utilizes an unsupervised algorithm to effectively determine if an article is AI-generated, a crucial development in the rapidly advancing field of AI. Professor Zhang Yue mentioned that with continuous technological advancements, AI applications in creative fields are becoming increasingly prevalent. However, this rise brings challenges, such as AI hallucinations, a phenomenon where AI generates inaccurate or nonsensical information.

On March 6, 2025, a new language model called **Light-R1-32B** was officially launched. Built upon the **Qwen2.5-32B-Instruct** model, this mathematical problem-solving tool, specially trained for exceptional mathematical abilities, low training costs, and reproducibility, is a significant highlight in the field of artificial intelligence. The development team xAI stated that Light-R1-32B not only surpasses similar models in performance but also provides significant benefits for academic research and practical applications.

Zhou Hongyi discusses the challenges posed by AI hallucinations and their implications for cybersecurity. He emphasizes the need for a balanced approach, neither downplaying nor overstating the risks involved.

Recently, Vectara released a report titled 'Hallucination Ranking', comparing the performance of different large language models (LLMs) in generating hallucinations while summarizing short documents. This ranking utilized Vectara's Hughes Hallucination Evaluation Model (HHEM-2.1), which is regularly updated to assess the frequency of false information introduced by these models in summaries. According to the latest data, the report highlighted key metrics such as hallucination rates, factual consistency rates, response rates, and average summary lengths of several popular models.

Recently, an AI system developed by Google DeepMind—AlphaGeometry2—has successfully surpassed the average level of gold medalists at the International Mathematics Olympiad (IMO), demonstrating outstanding performance in solving geometry problems. AlphaGeometry2 is an upgraded version of the AlphaGeometry system released by DeepMind last year. According to the latest research from the research team, this system can solve 84% of the geometry problems from the IMO over the past 25 years.

As businesses increasingly adopt large language models (LLMs), improving the accuracy of model knowledge and reducing hallucination phenomena has become a significant challenge. Researchers at Meta AI have introduced a 'Scalable Memory Layer' in a recent paper, which may provide a solution to this issue. The core idea of the scalable memory layer is to add more parameters to the LLMs without increasing computational resources during inference, thereby enhancing their learning capabilities. This architecture is suitable for storing large amounts of factual knowledge while maintaining...

A characteristic widely regarded as a flaw of artificial intelligence - 'hallucination' - has surprisingly become key to scientific breakthroughs. According to Fortune magazine, Professor David Baker, one of the winners of the 2024 Nobel Prize in Chemistry, achieved significant research breakthroughs using the 'hallucination' feature of AI models. Professor Baker, from the University of Washington, is the only American scientist among this year's three Nobel Prize winners in Chemistry. He successfully invented a novel protein using computer software, an achievement that ultimately earned him this honor. Image source note: The image is AI-generated and authorized.

In the United States, despite the apparent benefits of recycling, the recycling rate has stagnated in recent years. To address this challenge, AMP Robotics has launched an AI system called AMP ONE to help waste processing plants sort garbage more efficiently. Recently, the company completed a $91 million Series D funding round to further advance the adoption of this technology. Image source note: Image generated by AI, image licensed from MidjourneyAMP Robotics.

The Alibaba International AI team has recently released a new reasoning model called Marco-o1, which focuses on solving open-ended questions, extending beyond traditional subjects with standard answers such as programming and mathematics. The research team is dedicated to exploring whether such models can be effectively adapted to fields that are difficult to quantify and lack clear rewards.