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DeepBrain Chain was established in November 2017 with the goal of creating a blockchain-based, highly scalable, distributed computing network. The aim is to become a crucial infrastructure in the era of 5G+AI. As society enters the age of intelligence, artificial intelligence has become integrated into various aspects of daily life. The three pillars of artificial intelligence are deep models, big data (Internet, sensors, IOT), and high-performance computing (GPU, FPGA, special chips). The demand for computing power is increasing as individual deep models require significant resources. For instance, ImageNet image recognition uses 1-10 GPUs, AlphaFold/AlphaFold2 requires 100-200 GPUs, BERT language model uses 100-200 GPUs or 1026 TPUs to shorten training time to 76 minutes, GPT-3 language model by OpenAI requires 1,000 GPUs and consumes millions of dollars for training, and multimodal large-scale pre-training model by Beijing Academy of Artificial Intelligence (BAAI) utilizes 2,000 GPUs. The race in artificial intelligence is ultimately a race for computing power, and addressing the supply and demand of computing power while rewarding it is crucial. DeepBrain Chain aims to leverage blockchain technology to build a globally accessible, infinitely scalable, distributed high-performance computing network. This will lead to cost reduction and efficiency improvement in AI computing power worldwide, promote the democratization and popularization of AI computing power, and accelerate the arrival of the artificial intelligence era.
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What is the vision behind DeepBrain Chain?
DeepBrain Chain aims to build an infinitely scalable, distributed high-performance computing network based on blockchain technology. The goal is to become the most important infrastructure in the AI+Metaverse era. With the integration of artificial intelligence into various aspects of people's lives, the demand for computing power is increasing. Deep models, big data, and high-performance computing are the three pillars of artificial intelligence. Individual deep models, such as ImageNet image recognition, AlphaFold/AlphaFold2, BERT language model, and GPT-3, require significant computing power. The computing power race is crucial in the field of artificial intelligence. DeepBrain Chain aims to address the supply and demand problem of computing power, reduce costs, improve efficiency, and democratize AI computing power to accelerate the era of artificial intelligence.
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