Artificial intelligence (AI) is influencing our daily life and business operations. More companies are trying to use AI to replace certain human tasks and turn massive raw data into meaningful insights in the digital age. According to Gartner, Inc. 2019 CIO Survey, the number of enterprises implementing AI has increased by 270% in the past four years and tripled in the past year. It is estimated that 37% of enterprises have deployed AI in some form this year, much higher than 25% in 2018.
Artificial intelligence research was first established in 1956. Thanks to the advancement in hardware and significate improvement in computing speed, AI has grown significantly in these two decades. With the combination of Deep Neural Network (DNN), Graphics Processing Unit (GPU) and Big Data, the application of AI has been accelerated in various industries. Today, AI is seen as an important key to innovative operations. AI is particularly useful in finding trends, contradictions or things that should not exist. Some healthcare providers rely on computer-assisted diagnostics, such as using AI to spot abnormalities in X-rays. In the financial industry, AI can be used for financial risk assessment or fraud protection. Many companies are striving to use AI to increase their competitiveness.
For many years, there is an indivisible relationship between AI, data, algorithms, and computing power. The larger the amount of data and the more complex the training model, the higher the requirements for infrastructure specifications. Many companies focus on deep learning, which needs to process a large amount of data and requires large computing and storage resources. The computing power of CPU alone is insufficient to process the training model. That is where GPU comes into assistance. GPU has thousands of cores and capable of performing millions of mathematical operations in parallel, which can speed up the training speed significantly.
In many AI projects, numerous times of model training and corrections are required to get satisfactory results. When the infrastructure resources are insufficient, the model training is more time consuming. It may take several days to get one result. If the project has hundreds of model training, it may even take weeks or months to complete. To keep up the pace of the market, it is recommended to upgrade computing power, storage and network altogether. Enterprises should consider the infrastructure specifications based on the AI application and training model. For example, the model training of primitive data types is relatively simple. But image recognition and speech recognition often requires higher resources.
Both GPU-enabled servers and flash storage are important parts in AI projects, but it is not necessary to invest a large amount of money on hardware at the beginning. Companies can start from small AI projects and expand gradually. Since it is difficult to predict the development of AI in the future, it is advised to choose a computing and storage solutions that allow you to scale out when your AI project expands.
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