大数据时代,人工智能数据挖掘取代人类.docx

大数据时代，人工智能数据挖掘取代人类【大数据时代，人工智能数据挖掘取代人类】MIT的研究人员设计了一款数据科学机器，可以更好地在数量庞杂的数据背后发现隐藏的模式。在测试中，这款原型系统仅花费了2小时12分钟便分别完成了94%和96%的数据预测，而人们花费的时间远超系统。人们可能在大数据分析方面很快被取代。MITs Data Science Machine Could Soon Replace Human IntuitionResearchers at Massachusetts Institute of Technology (MIT) and Artificial Intelligence Laboratory (CSAIL) have developed a new system which can outperform even a few of the smartest people in the world. Even though big data scientists would have to code the analysis portions for the engines manually to let the computerized brains loose, it would still be a massive improvement in the existing field of big data analysis. But selecting which features of the data to examine normally requires a few human intuition.The MIT team said that their machine participated in three competitions against humans and it finished ahead of 615 human teams. According to the developers of the machine, it aims to take the human element out of big-data analysis, and is able to search patterns as well as design the feature set.Manufacturing the feature, he says, is critical. The Data Science Machine tracks these correlations, using them as a cue to feature construction. In this case, though, the researchers tested their first prototype system that analysis big-data against human teams in three data science competitions.The Data Science Machine successfully made predictions of 94 percent and 96 percent in the first two tests while ended up making a modest prediction of 87 percent on the third test. But the best part was the fact that it only took the Data Science Machine approximately 2 hours to 12 hours to make the decision, compared to longer time periods taken by humans.Max Kanter, an MIT student whose thesis served as the foundation for the Machine, says the device could be a natural complement to human intelligence and expedite the process of analyzing data. And the human counterparts - yes, you guessed it right - they labored tirelessly for months to come to the same outcome. The Data Science Machine would begin by importing costs from the first table into the second.Human teams worked on their algorithms for months. a few scientists and mathematicians theorized that robot with artificial intelligence (A.I) will one day outpace the computing powers of humans. How to predict the products a person is apt to purchase, or to determine the likelihood a particular student or any student will end up dropping out of a class are two examples of analyses that can be done with larger data sets. The Data Science Machine is the main subject of Kanters MIT masters thesis in computer science.And right now its just sitting there not doing anything, he said. So maybe we can come up with a solution that will at least get us started on it, at least get us moving. Kalyan Veeramachaneni, research scientist at the Computer Science and Artificial Intelligence Laboratory (CSAIL), is the co-leader of CSAILs Anyscale Learning for All group.The system exploits structural relationships inherent in database design; databases typically store different types of data in different tables, indicating the correlations between them using numerical identifiers. As numerical identifiers proliferate across tables, the Data Science Machine layers operations on top of each other, finding minima of averages, averages of sums, and so on.It also looks for so-called categorical data, which appear to be restricted to a limited range of values, such as days of the week or brand names. And, perhaps more importantly, it accomplished this in a fraction of the time taken by humans working on the same task.The Data Science Machine already predicted with great accuracy what the chances for a student