How

 COPILOT:

DeepSeek employed several innovative techniques in training its AI model, DeepSeek-R1, which have contributed to its success and efficiency. Here are some key techniques:

1. **Mixture of Experts (MoE) Architecture**: DeepSeek-R1 uses an MoE framework, which allows the model to dynamically allocate computational resources to different specialized components. This means that only a subset of experts is activated per query, optimizing efficiency.

2. **Parallelization Strategy**: The model employs distributed training techniques such as model parallelism, data parallelism, and pipeline parallelism. This helps in handling extensive computation and reducing latency.

3. **Reinforcement Learning (RL)**: Instead of relying solely on supervised learning, DeepSeek-R1 uses reinforcement learning for training. This approach allows the model to improve its logical consistency and adaptability without requiring large-scale human annotations.

4. **Cold-Start Data**: To address challenges like poor readability and language mixing, DeepSeek-R1 incorporates minimally labeled datasets (cold-start data) before applying RL. This helps the model get a general understanding of the task before fine-tuning.

5. **Open-Source Approach**: DeepSeek has made its models and training techniques open source, allowing the research community to study and build upon their work. This transparency has earned praise and contributed to the model's widespread adoption.

These techniques have made DeepSeek-R1 a significant breakthrough in the AI landscape, offering high reasoning capabilities with cost efficiency.

Would you like to know more about any specific technique or how it compares to other AI models?

How DeepSeek-R1 Was Built: Architecture and Training Explained

How DeepSeek-R1 Was Built; For dummies

GitHub - deepseek-ai/DeepSeek-R1

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The Chinese-language entry on DeepSeek on Wikipedia, as translated by META AI:

Here is the translation of the text:

DeepSeek

DeepSeek, officially known as Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., is a Chinese artificial intelligence and large language model company. Its headquarters is located in Hangzhou, Zhejiang Province, China.

History

DeepSeek was founded on July 17, 2023, by Liang Wenfeng, with investment from Fantasia Quantitative, a Chinese quantitative investment company.

Products

DeepSeek has developed several AI models, including DeepSeek-Coder, DeepSeek-LLM, DreamCraft3D, DeepSeek-MoE, DeepSeekMath, and DeepSeek-VL. Its latest model, DeepSeek-R1, was released on January 10, 2025.

Controversies

DeepSeek has been involved in several controversies, including allegations of violating US export controls, using knowledge distillation techniques to copy OpenAI's models, and collecting user data without proper consent.

Security and Privacy Concerns

DeepSeek's data collection practices have raised concerns among users and regulators. The company stores user data, including email addresses, phone numbers, chat logs, and device information, on servers located in China.

Censorship

DeepSeek's AI model has been found to censor sensitive topics, including the Tiananmen Square protests, Xinjiang human rights abuses, and Taiwan independence.

Trademark Dispute

DeepSeek is involved in a trademark dispute with Delson Group Inc., a US-based company that claims to have registered the "DeepSeek" trademark before DeepSeek's application.

Blockage

DeepSeek has been blocked in several countries, including Taiwan and South Korea, due to security and privacy concerns.