LAION launched Re-LAION-5B, the world's first AI training dataset that fully removes links to CSAM, aimed at addressing the issue of Child Sexual Abuse Material (CSAM). This dataset has been significantly improved over LAION-5B and is mainly divided into two versions: Re-LAION-5B Research and Research-Safe. A total of 2,236 CSAM links have been removed, including 1,008 from child protection organizations' lists. The dataset contains 5.5 billion pairs of text and images, designed to help

The German research institution LAION has created and released a new dataset, Re-LAION-5B, claiming to have removed links to suspected Child Sexual Abuse Material (CSAM). This dataset is a revised version of the old LAION-5B dataset, incorporating recommendations from the Internet Observation Foundation, Human Rights Watch, the Canadian Centre for Child Protection, and Stanford Internet Observatory. Re-LAION-5B is divided into two versions: Research and Research-Safe, with the latter additionally removing NSFW content. The dataset aims to support

As AI technology rapidly advances, the challenges of data acquisition are becoming increasingly prominent. Research from institutions like MIT reveals that open-source datasets such as C4, RefineWeb, and Dolma are facing stricter licensing agreements from the websites they scrape, posing significant challenges for AI training and academic research. The study found that even major AI companies like OpenAI are subject to strict limitations due to r....

"MedTrinity-25M" is a large-scale multimodal medical dataset released by the UCSC-VLAA team, containing 25 million medical images along with detailed annotations. This dataset features multi-granularity annotations, supports the training of multimodal large medical models, and involves a complex construction process that includes data processing, metadata extraction, region of interest localization, and the collection of medical knowledge. Detailed descriptions are generated using large-scale language models to enhance data usability. The dataset was officially released on July 21, 2024, accompanied by a pre-trained model.

Recently, the 'Diting' seismic wave large model, jointly developed by the National Supercomputing Center in Chengdu, the Institute of Geophysics of the China Earthquake Administration, and Tsinghua University, was officially released in Chengdu, Sichuan. This model is the first seismic wave large model in the country to reach 100 million parameters, marking a significant breakthrough in the integration of seismology research and artificial intelligence technology in China.

Wuhan University, in collaboration with China Mobile's Jiutian AI team and Duke Kunshan University, has released the open-source audio-video speaker recognition dataset VoxBlink2, which is based on YouTube data and contains over 110,000 hours of audio-video recordings. The dataset includes 9,904,382 high-quality audio clips and their corresponding video segments, sourced from 111,284 users on YouTube, making it the largest publicly available audio-video speaker recognition dataset to date. The release of this dataset aims to enrich open-source speech corpora and support the training of voiceprint large models.

Recently, researchers at Microsoft have introduced a novel AI framework called Auto Evol-Instruct, which can automatically evolve instructional datasets without any human intervention.In the field of artificial intelligence, the development of large language models (LLMs) is crucial, especially in enhancing their ability to follow detailed instructions. Researchers have been exploring ways to improve the datasets used to train LLMs to enhance their performance and adaptability.Traditional evolut

Recently, an investigation revealed that several tech giants, including Apple, had utilized YouTube video subtitles to train AI models. These data encompassed over 170,000 videos, including content from well-known creators such as MKBHD and Mr. Beast. Apple used this data to train its open-source model, OpenELM, which was released in April of this year.In response, Apple recently clarified externally that OpenELM has not been applied to any of its AI or machine learning functions, including Appl

A French startup named Presti is revolutionizing product photography in the furniture industry by leveraging generative artificial intelligence (GenAI). Traditionally, furniture brands have incurred substantial costs for photo shoots when launching new collections, a process that is not only cumbersome but also expensive. Presti aims to address this issue by transforming single product images into realistic lifestyle photos.Image source: The image is AI-generated, provided by the image licensing

IBM opened the Granite13B LLM model for enterprise applications in May. Now, Armand Ruiz, the vice president of IBM's AI platform products, has publicly disclosed the full content of the comprehensive 6.48TB dataset used to train Granite13B.The dataset, after strict preprocessing, was reduced to 2.07TB, a reduction of 68%. Ruiz emphasizes that this step is crucial for ensuring a high-quality, unbiased, ethical, and legally compliant dataset to meet the needs of enterprise applications. The datas

Nvidia recently announced the launch of two groundbreaking AI technologies: HelpSteer2 and Llama3-70B-SteerLM-RM. These innovations promise to significantly enhance the capabilities of AI systems across a variety of applications, from autonomous driving to natural language processing.

OpenAI and Microsoft are being sued by news agencies for copyright infringement. Multimodal large models are becoming the mainstream trend in the field of large models. OpenAI is reportedly actively negotiating with publishers on AI policies while high-quality dataset copyright issues are receiving attention. CITIC Publishing collaborates with large model companies for language training, and Visual China holds a core advantage in the era of AIGC.

The American social media platform Reddit has reached an agreement with Google, valued at approximately $60 million annually. Springer Publishing Group collaborates with OpenAI, becoming the first publishing entity to deeply integrate journalism with artificial intelligence technology. OpenAI has signed a cooperation agreement with Axel Springer, indicating that future large model training may involve payments to data providers. Publishing industry companies possess a wealth of electronic graphic resources and are expected to become important datasets for large model training.

Is it possible to have a universal graph model that can predict toxicity based on molecular structure while also providing friend recommendations in social networks? Or one that can predict the citations of different authors to uncover human aging mechanisms in gene networks? It turns out, this model was jointly proposed by Professor Chen Yixin's team from Washington University in St. Louis, Zhang Muhan from Peking University, and researchers such as Tao Dacheng from JD.com Research Institute.

Shanghai AI Lab, together with Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, has released the medical multimodal foundational model group 'PuYi 2.0'. PuYi 2.0 adds multi-domain models and incremental language parameters, covering various data modalities such as medical images, medical texts, and bioinformatics. New open-source datasets include the medical image segmentation dataset SA-Med2D-20M and the pathology dataset SNOW. PuYi 2.0 incorporates an evaluation module to provide reference implementations for medical model capabilities, delivering a one-stop open-source solution for large medical models.

ZhiYuan Research Institute has released a code generation training dataset called TACO, aimed at providing more challenging training data and evaluation benchmarks for code generation models. TACO has advantages in terms of data scale, quality, and evaluation schemes, including a larger training and testing set, diverse problem-solving answers, and fine-grained labels. Experimental results show that current popular code generation models show significant differences compared to GPT-4 in TACO evaluations, indicating that there is still room for improvement in this field. TACO is not just a challenging...

OpenAI has launched the Data Partnerships initiative to collaborate with third parties in creating public and private datasets for AI model training. OpenAI seeks data across multiple domains, languages, and formats to enhance the understanding of AI models. The initiative aims to collect various forms of data, including images, audio, and video, to reflect human society and achieve the goal of safer, more beneficial AI for humanity. OpenAI's initiative has sparked discussions about the rights of data owners and transparency.

Researchers, in collaboration with MIT, Cohere for AI, and other institutions, have launched the Data Provenance Platform to tackle the issue of AI data transparency. The platform audits and tracks over 2,000 widely used fine-tuning datasets, emphasizing the importance of data transparency. Insufficient data provenance can lead to data leaks, exposure of personal information, bias, and legal risks. The Data Provenance Platform is expected to enhance AI data transparency and improve the quality and ethical compliance of datasets.

OBIA includes 937 patients and 1.93 million medical images from the 301 Hospital, covering 9 modalities and 30 anatomical sites. OBIA realizes the de-identification, management, and quality control of imaging data, providing users with browsing, retrieval, and download services. OBIA fills the gap in the open sharing of biomedical imaging data in the country and helps promote the development of medical imaging AI.