DeepSeek introduced Manifold-Constrained Hyper-Connections (mHC) to improve large-model training scalability and efficiency. The mHC method was tested on 3B, 9B, and 27B parameter models, showing ...

In January this year, an announcement from China rocked the world of artificial intelligence. The firm DeepSeek released its powerful but cheap R1 model out of the blue — instantly demonstrating that ...

Remember DeepSeek, the large language model (LLM) out of China that was released for free earlier this year and upended the AI industry? Without the funding and infrastructure of leaders in the space ...

What does it take to outshine giants in the fiercely competitive world of artificial intelligence? For years, proprietary systems like GPT-5 and Gemini Pro have dominated the landscape, setting ...

DeepSeek released its V3.2 model on Monday. It aims to keep accessible AI competitive for developers. V3.2 heats up the race between open and proprietary models. Chinese AI firm DeepSeek has made yet ...

Imad is a senior reporter covering Google and internet culture. Hailing from Texas, Imad started his journalism career in 2013 and has amassed bylines with The New York Times, The Washington Post, ...

China-based DeepSeek (DEEPSEEK) has launched a pair of new artificial intelligence models, DeepSeek-V3.2 and DeepSeek-V3.2-Speciale, which are open-sourced and topped or matched the results of ...

China’s DeepSeek unveiled two new versions of an experimental artificial-intelligence model it released weeks ago, adding fresh capabilities the startup said would help with combining reasoning and ...

Earlier this year, a Chinese AI chatbot called DeepSeek sent Silicon Valley into a tailspin when it released a new AI model that rivaled the likes of OpenAI’s ChatGPT, while relying on only a fraction ...

A team of AI researchers at Bloomberg have developed PExA, an agentic framework that achieved 70.2% execution accuracy, sharing one of the top positions on the Spider 2.0 (Snow) leaderboard, one of ...

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China's DeepSeek-R1 LLM generates up to 50% more insecure code when prompted with politically sensitive inputs such as "Falun Gong," "Uyghurs," or "Tibet," according to new research from CrowdStrike.