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Welcome to the 【AI Daily】 column! This is your guide to exploring the world of artificial intelligence every day. Here we bring you hot content in the AI field, focusing on developers to help you gain insights into technical trends and understand innovative AI product applications. Fresh AI products click to learn more: https://top.aibase.com/1. The world's first Office intelligent agent APP! Kunlun Wonderworks TianGong Super Intelligent APP launched by Kunlun WonderWorks Group.

Recently, Google has once again stirred the artificial intelligence world by announcing the release of three new variants of the Gemma model: MedGemma, SignGemma, and DolphinGemma. These models are targeted at the healthcare, sign language translation, and dolphin communication fields respectively, showcasing the immense potential of AI technology across domains. Below, AIbase will detail the highlights and application prospects of these three models. MedGemma: Revolutionizing Medical AI, Enabling Precise Diagnosis and Treatment...

Google recently announced that the download volume of its AI model collection, Gemma, has exceeded 150 million times, marking significant progress for the project. Omar Sanseviero, a developer relations engineer at Google DeepMind, shared this news on social platform X and revealed that more than 70,000 different versions of the Gemma model have been created by developers on the AI development platform Hugging Face.

Google recently released a new version of its Gemma3 series, exciting many AI enthusiasts. Just a month after its initial launch, Google released a Quantization Aware Training (QAT) optimized version of Gemma3, aiming to significantly reduce memory requirements while maintaining model quality. Specifically, the QAT-optimized Gemma3 27B model reduces VRAM requirements from 54GB to 14.1GB, meaning users can now run it on a single NVIDIA RTX 3090.

Researchers at the Hebrew University of Jerusalem recently discovered that in Retrieval Augmented Generation (RAG) systems, the number of documents processed significantly impacts language model performance, even when the total text length remains constant. The research team conducted experiments using 2,417 questions from the MuSiQue validation dataset, each linked to 20 Wikipedia paragraphs. Two to four paragraphs contained relevant answer information, with the remaining paragraphs serving as distractors. To study the impact of the number of documents, the team created multiple data partitions, progressively reducing the number of documents from 20 to...

Reka AI, founded by a dozen former Google DeepMind scientists, has unveiled its first open-source model: Reka Flash 3. This 21-billion parameter inference model has garnered significant attention. Despite its relatively smaller parameter count, Reka Flash 3 is a general-purpose reasoning model trained from scratch. It underwent supervised fine-tuning on synthetic and public datasets and further refinement through model-based techniques.

Google CEO Sundar Pichai announced at a launch event that Google has open-sourced its latest multimodal large model, Gemma-3. This model is attracting significant attention for its low cost and high performance. Gemma-3 offers four different parameter scale options: 1 billion, 4 billion, 12 billion, and 27 billion parameters. Surprisingly, the largest 27 billion parameter model only requires a single H100 GPU for efficient inference, while similar models often require ten times the computing power.

IBM has officially launched its next-generation open-source large language model, Granite3.1, aiming to secure a leading position in the enterprise AI sector. This series of models features an extended context length of 128K, embedding models, built-in hallucination detection capabilities, and significant performance improvements. IBM claims that the Granite8B Instruct model outperforms its open-source competitors of similar scale, including Meta's Llama3.1, Qwen2.5, and Google's Gemma2.

Google recently launched its next-generation artificial intelligence model, PaliGemma2, which can analyze images and generate captions while answering questions about the emotions and actions of people in the photos. Built on Google's Gemma open model series, PaliGemma2 offers more profound image descriptions than traditional object recognition, enabling it to identify emotions and generate contextually relevant detailed descriptions. However, despite this technology appearing to be a groundbreaking innovation, experts have raised serious warnings about its potential ethical and societal implications. Emotion recognition is not...

On November 28, Fudan University officially released the "Guidelines for Using AI Tools in Undergraduate Theses (Draft)", which provides detailed regulations on the application of AI tools during the thesis writing process. This move marks the first instance of a Chinese university issuing dedicated regulatory management documents in this field, filling a policy gap. According to the guidelines, Fudan University allows students to use AI tools under specific conditions. Students may use these tools upon obtaining approval from their supervising teachers, provided that it does not hinder the assessment of their innovative capabilities.

At the recent Gemma Developer Day held in Tokyo, Google officially launched its new Japanese version of the Gemma AI model. This model's performance rivals that of GPT-3.5, but it has a parameter count of only 2 billion, making it very compact and suitable for operation on mobile devices. The newly released Gemma model excels in Japanese processing while also maintaining its capabilities in English. This is particularly important for small models, as they may face 'catastrophic forgetting' when fine-tuning for new languages.

Recently, Zyphra officially launched Zamba2-7B, a small language model with unprecedented performance, featuring 7B parameters. This model claims to surpass its current competitors in quality and speed, including Mistral-7B, Google's Gemma-7B, and Meta's Llama3-8B. The design goal of Zamba2-7B is to meet the needs of environments that require strong language processing capabilities but are constrained by hardware conditions, such as processing or using it on devices.