TMCnet News
DeepBrain Chain's AI Miners Attract More Than $100m, Here Are Three Things You Need to KnowSAN FRANCISCO, June 1, 2018 /PRNewswire/ -- On May 18th 2018, DeepBrain Chain announced pre-order of AI Miners (the "DBC AIM") on its official website and WeChat HTML5 page. As of June 1st, more than 1,000 people have reserved DBC AIM, including over 200 128GPU large-scale AIM clusters, pledging in total more than $100m.
So why are DBC AIMs a big hit with investors? Solving the AI companies' Pain Points Since 2012, computing demand by AI companies has grown more than 300,000 times. Cost of hardware has become a hinderance to AI companies and in particular AI start-ups. Most of today's deep learning chips are either Google's TPUs or Nvidia GPU. While Google's TPU is mostly used in Google's cloud computing center, Nvidia GPU has the lion's share of the market. Different from gaming chips, deep-learning chips are extremely expensive, costing up to six figure. Even with cloud computing, computation is still beyond the reach of those without a deep pocket. DeepBrain Chain's TestNet will be live this June. Its tech team has finished the design of iteration 1 framework and testing has begun. They have also finished the design of the AI training status real-time monitor system and started the design of iteration 2. The launch of "DBC AIM" represents an important milestone in DeepBrain Chain's mission to build a globally decentralized AI cloud. As computing nodes in the DeepBrain Chain ecosystem, DBC AIM will provide cheaper and more convenient computing power for AI companies. Top Team Controlling DBC AIM Quality In April this year, Dr. Dongyan Wang, a top silicon valley AI expert joined DeepBrain Chain, serving as its Chief AI Officer and Executive VP of its Silicon Valley Research Center. Dr. Wang has close to 20 years of Silicon Valley experience in artificial intelligence and led top teams at Fortune 500 companies (Cisco, Netapp, Midea and Samsung). Within 1 year and 9 months, Dr. Wang established Midea's Silicon Valley research center from scratch, and built its heterogeneous distributed deep learning AI platform "Midea Brain". After he joined DeepBrain Chain, a lot of senior industry experts followed in his footstep. Jason Pai, who serves as DeepBrain Chain's Senior Director of Product Management, has over 15 years of experience with hardware development and product management at Supermicro, IBM, and Frd Motor. He introduced an industry-leading AI product line for Supermicro by improving bandwidth and latency performance for device-to-device communication. Also, in partnership with Nvidia, he brought first-to-market Nvlink technology which improves Deep Learning training by increasing over peer-to-peer bandwidth over five times the PCIe 3.0 counterparts. BrainXu, its chief data scientist, has proven skills and experiences in solving complex real-world problems. He has extensive experience in software with over 48 products (AI, ML, data analytics, etc.) and intelligent solutions as a tech lead since 1998. He has done 20 programs ($5M~$50M/Y) for big customers (Boeing, DARPA, etc.), and developed 25 commercial products (Intel, etc.) since 2005. He has had over 38 technical papers and USA patents, and 76 technical presentations. Jan Huang is an AI, Computer Vision, Machine Learning and Deep Learning expert. Prior to joining DeepBrain Chain, he worked at IBM as Web Replay Software Engineer, focusing on video/image processing and AI research & development, with publications and patents and was known as the open source go-to person for the team. He holds a Master's in Computer Vision from Yale University, as well as a Ph.D. in Image Processing from Washington University. Haisong Gu is DeepBrain Chain's Senior Director of AI Applications for Computer Vision & Robotics. Prior to joining DeepBrain Chain, Dr. Gu was the Division Manager and Senior Manager of Konica-Minolta and Midea, leading the AI teams in successfully creating DL PF for Image, Video and document analytics; launched an AI based cancer diagnosis system, which was recognized as one of the national 2020 strategic technologies in Japan; developed AI visual inspection technologies for electronic, auto, food and home appliance industries. The project has had several rounds of discussions about the AIM parameters to ensure its quality. It always follows industry standards and has never done any fraudulent publicity. Innovative and Good Investment Products In the blockchain industry, mining is very important. Compared to investing in the secondary market, mining is less risky and generates more steady income. Hence it is favored by most people. However, most of today's mining, such as mining of Bitcoin or Ethereum is still PoW, a mechanism much criticized for its waste of resources. Undoubtedly, blockchain mining provides miners income. However, in PoW mining, an enormous amount of computing power often gets wasted. By combining mining with AI training, DeepBrain Chain pools together globally scattered computing power to help AI companies to complete their training tasks. In other words, it turns wasteful blockchain mining into highly efficient AI deep learning, machine learning and computing while providing miners the same, if not higher, income. In addition, by plugging into the DeepBrain Chain MainNet, participants within the ecosystem have the ability to earn a large sum of DBC, while those with extra computing capacity can earn even more by putting their idle resources to full use. By aggregating decentralized AI computing capacity, the DeepBrain Chain ecosystem allows AI companies to purchase computing resources at a much lower cost. And as the number of mining nodes increases, it will form a virtuous cycle where every participant wins. For more information, please visit https://www.deepbrainchain.org/pc/en/miner.html About DeepBrain Chain DeepBrain Chain is the world's first AI computing platform powered by blockchain. It uses blockchain technology to help AI companies save up to 70% of computing power costs while protecting data privacy in AI training. DeepBrain Chain is committed to building an AI public chain to provide high-performance computing power and data privacy protection for AI companies and all clients with a need for AI. AI companies can deploy their AI products on DeepBrain Chain. Nodes in the DeepBrain Chain system have two sources of income: system reward and DBC paid by AI clients. Telegram, Twitter, Facebook, Reddit
SOURCE DeepBrain Chain 
|
View original content with multimedia: