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DeepSeek is a Chinese AI research lab that just launched its first open-source model, DeepSeek-R1. The highly innovative model that can compete with big industry players, such as OpenAI, in various fields, for example, in mathematical reasoning, code generation, and cost efficiency. This marks the beginning of an entirely new level in the AI race globally.
Founded by Liang Wenfeng in 2023, DeepSeek is a company that originated from the deep-learning division of Fire-Flyer, a branch of the High-Flyer hedge fund. In contrast to most Chinese companies, DeepSeek is not affiliated with big tech companies like Baidu or Alibaba. According to Liang, his goal for DeepSeek is scientific curiosity: he wants to create the most advanced AI solutions beyond financial gain.
High-Flyer was founded in 2015. Initially, it focused on the analysis of financial data using high-performance computing. Liang redirected the company toward AI research; he prioritized innovation over any immediate financial returns or bottom line.
DeepSeek-R1 uses large-scale reinforcement learning and multi-stage training to perform complex tasks. The model is designed to rival OpenAI’s solutions, especially in reasoning and code generation. The lab has open-sourced DeepSeek-R1 and six smaller variants under an MIT license, allowing researchers worldwide to build upon it.
DeepSeek-R1 stands out in efficiency and innovation. DeepSeek-R1-Zero alone reached an advanced reasoning ability by the only technique of reinforcement learning. DeepSeek-R1 was presented to increase usability and be competitive with OpenAI models for tasks of reasoning but using orders of magnitude less computation.
The lab also open-sourced six smaller versions of its flagship model, which ranged in size from 1.5 billion to 70 billion parameters. Such models are published under an MIT license, so researchers and developers have the freedom to fine-tune and commercialize them just as they would like to, thus providing opportunities for open collaboration and innovation in the AI community.
While OpenAI mainly relies on supervised fine-tuning for its models, the DeepSeek has pioneered new approaches. For example, DeepSeek-R1-Zero depends exclusively on reinforcement learning to be excellent at tasks in reasoning. More so, DeepSeek models exploit technical advancements such as multi-head latent attention and mixtures of experts that make them cheaper and more cost-effective than rivals put forward by firms like Meta.
Its highly scalable models, deep learning techniques (multi-head latent attention, Mixture of Experts), and reduced computing power render DeepSeek more than just cost-efficient. According to reports, its model, the DeepSeek-R1, works on just a tenth of Meta's Llama 3.1 required computing power to be an efficiency resource in AI.
Despite facing export restrictions from the US on advanced chips, DeepSeek has managed to thrive in optimizing the resource usage of its model. Being long-term innovation-oriented, the lab has introduced strategies such as custom communication schemes and optimization in memory usage, where its AI models not only are powerful but also resource-efficient.
DeepSeek's team consists of recent graduates from Peking and Tsinghua Universities. These young researchers bring in academic expertise as well as a collaborative mindset to deal with the competitive AI landscape.
DeepSeek is democratizing access to high-end AI tools by open-sourcing its models, which puts the firm at the forefront of AI research and challenges Western firms such as OpenAI and Meta.
The firm's emphasis on efficiency and cooperation marks a new era in the global AI industry as it promises something different from the conventional models.
It has marked waves globally in AI research community by open-sourcing its models. This means it challenges the dominance of Western AI firms, such as OpenAI, which are now being democratized by access to higher performance in AI tools. DeepSeek thus happens to be preparing for more inclusive AI development across the world.
