Artificial intelligence has begun to infiltrate into people's lives. Intelligent robots, driverless cars, and smart phone services for mobile phones have become part of people's lives and may become an important part in the future. Artificial intelligence not only makes people's lives better, but also plays an increasingly important role in enterprise-level applications, such as the operation and maintenance of data centers, big data analysis, enterprise-level software development and applications, etc. field.
In enterprise IT applications, people pay more and more attention to "smart" and "smart". Bringing more intelligent features to the hardware has become an important standard for the development of enterprise IT solutions. IT vendors have made hardware use and management more automated, smarter, and faster through continuous innovation in software.
In the field of data center infrastructure, users are not only trying their best to make the design and deployment of infrastructures such as wind, fire, water, and electricity more efficient, but also have a great deal of automation and intelligence in data center infrastructure management. effort.
For example, data center infrastructure management (DCIM) has become a hot spot in the data center market for the past two years. The mainstream data center solution providers have all started to develop their own DCIM software. The main purpose is to continuously improve the automation and intelligence of data center infrastructure. . Artificial intelligence technology has been integrated into the field of data center infrastructure management and has had a subtle impact on its development.
Google can be said to be the first to use artificial intelligence technology in the management of data centers. Google is trying to use artificial neural networks to analyze the operation of large data centers to further increase its efficiency.
Google's artificial neural network is essentially a computer algorithm that can identify patterns and make timely decisions and decisions based on those patterns. In addition, these models can also achieve self-learning by repeatedly mining data.
Does Google apply these computer algorithms to the operation and management of the data center, can it really have the effect of anticipating it? It turns out that this is all true.
According to foreign media reports, Google automatically collects data related to the operation of its data center every few seconds, such as the power consumption of the data center infrastructure, and how much water is used to achieve certain cooling effects. These data will be automatically collected and summarized, and then the efficiency of the data center will be analyzed and evaluated through Google's artificial intelligence calculation model. Finally, suggestions for improving the efficiency of data center operations will be proposed.
Google called its artificial intelligence computing model for data center design an "engine inspection light." It can detect whether the actual operating efficiency of the data center matches the prediction of the model. If there is a problem, Google will know what to use. This kind of method can solve these problems more effectively.
For example, through the results of the “engine check lightâ€, Google can determine the best time for the data center to make adjustments, such as when it is time to clean heat exchangers for cooling.
The use of artificial intelligence technology for data center management and operation is a positive and beneficial exploration. Some Internet companies with very large data centers are also making similar attempts with Google. However, from the current situation, only Google has announced some exciting progress in data center artificial intelligence.
A few months ago, for business reasons, Google had to go offline for a part of the servers in its data center. In general, if the data center makes such a large adjustment, the data center's energy efficiency will change, and may even result in a significant decline. However, before the offline server, Google has learned about the possible impact of server tuning through its artificial intelligence computing model, and adjusted the data center cooling device in time so that the energy efficiency of the data center is still maintained at a relatively high level. on. The artificial intelligence computing model can discover potential problems in data centers that could not be discovered by traditional tools. This is an unexpected surprise for artificial intelligence.
However, according to a data released by Google, the current data center artificial intelligence computing model used by Google does not include deep learning. Instead, it uses an old-style neural network framework that has long been used in retail products. recommend.
With the continuous development of artificial intelligence technology and the increasing importance of data center manufacturers for intelligent management, it is a general trend to use artificial intelligence, deep learning and other technologies for the operation, maintenance, and management of the entire data center. Google’s attempts in its data center set an example for other users.
At present, China's data center users are focusing more on the construction of data center infrastructure. Next, how to improve overall data center operating efficiency and energy efficiency through software, big data analysis, etc. will be the focus of attention of users. . From this perspective, the application of new technologies such as artificial intelligence in the field of data centers has broad prospects.
Through the analysis of traffic surveillance video, people can pre-judge which road sections are congested; through the analysis of Internet data, it can even predict when the epidemic will explode. Finding some regular results from a large number of seemingly disorganized data and guiding people's production practices is the value of big data analysis.
Now, more and more people believe that the further integration of big data and artificial intelligence technology will enable Big Data to release more energy.
Recently, at the 2014 China Big Data Technology Conference (BDTC 2014), the "China Big Data Technology and Industry Development White Paper (2014)" and "2015 Big Data Ten Development Trends Forecast" were officially released. The CCF Big Data Expert Committee voted for 2015 Big Data from 6 different aspects such as Big Data Science, Big Data Technology, Big Data Systems and Engineering, Big Data Applications, Data Resources, and Industrial Eco-environment, and a total of 54 candidates. The trend of development, the integration of big data and artificial intelligence is among them.
Some big data vendors have actually combined big data and artificial intelligence to conduct research. Nowadays, many people may only look at the conclusion obtained by major data analysis. In the future, having a “brain†that can calculate and think may be something that every company and even individuals are longing for. Artificial intelligence is one of the indispensable technologies for achieving this goal.
For example, artificial intelligence based on big data has begun to affect the field of securities investment.
In the past, due to the limitation of computing power and data volume, most of the intellectual investment agents that were developed by people simply copied some known investment strategies. Nowadays, many fields in the financial sector are high-frequency trading and quantitative trading. These new trading methods rely on powerful computing power and big data.
The emergence of artificial intelligence based on big data will overturn the traditional investment strategy production model, and most analysts' work can be replaced by intelligent agents. In the field of securities, manual orders will gradually become history. The new, more efficient, intelligent agent trading program can easily track hundreds of different securities at the same time. It can also observe the status of declarations and high-frequency trading data in real time.
Big data is a research object, and artificial intelligence is a goal. People can use artificial intelligence to better understand data. The methodology of artificial intelligence is machine learning or intelligent computing. Deep learning is only one of 10 fingers of machine learning. Deep learning focuses on building a "neural network" that simulates the human brain for analytical learning.
Big data and artificial intelligence are not mutually inclusive and cannot be equated. The future of big data may become an independent discipline, but it is still only a phenomenon. Nowadays, people often say that intelligent computing refers to how to embody intelligence in big data computing, or solve the problem of intelligence. This is the most concern in the academic and industrial fields in the future.
Artificial intelligence is not just about big data. Intelligence is the main direction of research for a long time to come. Looking back at history, we first digitized, then networked, automated, and now it is time to achieve intelligence. People have higher and higher expectations for intelligence and hope that it can make greater contributions to the development of the IT industry. The development of intelligent technology is endless.
Previously, artificial intelligence research was once limited by the computer's computing power. But now, with the dramatic increase in the performance of computer hardware, the rapid development of cloud computing technology, and the emergence of big data analysis technology, the research on workers' intelligence will have a new upsurge. The research and progress of artificial intelligence technology will profoundly influence the future development of technologies such as computers, networks, storage, and internet of things.
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