Analysis of cloud computing behind artificial intelligence
Recently, you should often hear about the "small ice, peter" in the line of intelligent robot celebrities, all of them engaged in artificial intelligence research and development seem to have their own spokesperson, quiet machine creatures seem to come to us. Artificial intelligence key technology deep learning is making substantial progress in the context of the maturity of cloud computing and big data. Cloud computing provides a platform for deep learning. Big data provides ore for deep learning, so deep learning can be achieved in the cloud platform. , scouring gold in big data.
In fact, big data plays a decisive role in the evolutionary history of machine biology, whether it is voice, image recognition, semantic computing, all on the basis of having enough big data, the Internet, the big data brought by the mobile Internet is human
The study of industrial intelligence has taken a big step in the key reason, and the deep learning neural network has greatly improved the knowledge of speech and picture systems.
No rate, but at the same time put forward higher requirements for cloud computing supporting the big data platform. Many artificial intelligence experts have suggested that large
Data and artificial intelligence do not match the development of cloud computing.
Starting from Google's artificial intelligence engine TensorFlow open source, compared to its Facebook Torch, Microsoft's DMTK, BM's SystemML. Can not help but say that TensorFlow's support for heterogeneous end and powerful and flexible algorithms are unique to many artificial intelligence open source projects. This may also be the key to its success. Artificial intelligence research often requires a powerful computer cluster. Machine learning needs to train various algorithms. It needs to write deep learning software on tens of thousands of machines to build a deep learning network.
TensorFlow can be used to write a variety of algorithms, including deep neural network model training and interference algorithms, and it has been used in experimental research, and also deployed in the machine learning system of the product, has been applied to more than a dozen Computer science and other disciplines include speech recognition, machine vision , robotics , information retrieval, natural language processing geographic information extraction, and computer-aided drug design.
The author believes that TensorFlow can integrate various computing capabilities, and is an artificial intelligence platform that hopes to establish a common deep learning model. Its open source project brings opportunities to many developers. The research part needs to use GPU computing. When the research results are commercialized, it is more suitable for CPU computing. The fusion calculation between GPU and CPU is a new requirement for artificial intelligence and life science research. The usual understanding of the CPU is responsible for computing GPU responsible for graph processing, CPU versatility is better, but a single CPU performance becomes the bottleneck of the entire system computing power, CPU + GPU is a powerful combination, the CPU contains several for serial processing Optimized, the GPU is designed for parallel processing and consists of thousands of smaller, more energy-efficient cores.
The CPU runs the line part of the program, and the GPU runs the parallel part. A research project often uses more GPU operations in the R&D stage. After the commercial phase, the CPU operation is more. The GPU+CPU is not completely independent in the R&D process or the commercial stage, so the cloud service providers need to integrate now. Resources provide GPU+CPU computing services. Baode Technology has been at the forefront of high-performance computing. Zaoda and China completed the mapping of the first yellow human gene map and the giant panda gene map, and recently launched a full range of high-performance servers supporting multiple GPUs. Combined with Baode Yungongyun and private cloud, Baode Cloud Computing provides a full range of computing services for artificial intelligence, life science, and high-energy physics.
Facebook, Google, Microsoft, and IBM have opened up artificial intelligence projects that will bring more and more intelligent services, and high-performance computing and cloud computing will be involved. In the blend of computing, the blending of big data, the blending of business ecology will now open a new scene of harmonious symbiosis between bio-intelligence and intelligent biology.
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