华人博彩 > 华人博彩评级 > 高中英语 > 高考 >  > 正文

奉贤区高三英语一模2018上海市奉贤区2018届高三一模英语试卷答案(8)

时间:2018-01-06 11:59  作者:David  来源:本站整理

(C)
With the coming of big data age, data science is supposed to be starved for, of which the adaption can point a profound change in corporate competitiveness. Companies, both born in the digital era and traditional world are showing off their skills in data science. Therefore, it seems to have been creating a great demand for the experts of this type.
Mr Carlos Guestrin, machine learning professor from University of Washington argues that all software applications will need in built intelligence within five years, making data scientists-people trained to analyze large bodies of information-key workers in this emerging “cognitive” technology economy. There are already critical applications that depend on machine learning, a subfield of data science, led by recommendation programs, fraud detection systems, forecasting tools and applications for predicting customer behavior.
Many companies that are born digital-particularly internet companies that have a great number of real-time customer interactions to handle-are all-in when it comes to data science. Pinterest, for instance, maintains more than 100 machine learning models that could be applied to different classes of problems, and it constantly fields requests from managers eager to use this resource to deal with their business problem.
The factors weighing on many traditional companies will be the high cost of mounting a serious machine-learning operation. Netflix is estimated to spend $ 150m a year on a single application and the total bill is probably four times that once all its uses of the technology are taken into account.
Another problem for many non-technology companies is talent. Of the computer science experts who use Kaggle, only about 1,000 have deep learning skills, compared to 100,000 who can apply other machine learning techniques, says Mr Goldbloom. He adds that even some big companies of this type are often reluctant to expand their pay scales to hire the top talent in this field.
The biggest barrier to adapting to the coming era of “smart” applications, however, is likely to be cultural. Some companies, such as General Electric, have been building their own
Silicon Valley presence to attract and develop the digital skills they will need.
Despite the obstacles, some may master this difficult transition. But companies that were built, from the beginning, with data science at their center, are likely to represent serious competition.
63. Which one is obstacle for many traditional companies to popularize learning operation?
A.Technological problem
B.Expert crisis
C.High cost
D.Customer interactions
64. What can not be interred from the passage about the machine learning?
A.Machine learning operations are costly in Netflix.
B.Machine learning plays an important role in existent applications.
C.Machine learning experts are not highly paid in some non-technology companies.
D.Machine learning models are not sufficient to solve business problems in Pinterest.
65. What’s the author’s main purpose in writing this article?
A.Data science: A forefront force in tech business
B.Corporate competition: An obstacle to the transition
C.Machine learning: A key to smart technology
D.Technique experts: A decisive factor of the coming era.

本文地址:http://www.lili1989.net/new/74536/index_8.html
文章摘要:奉贤区高三英语一模2018上海市奉贤区2018届高三一模英语试卷答案(8),入理切情预冷稍加,维文尿素东长安街。

华人博彩英语试卷 高三 上海市 一模 奉贤区