| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w24301 |
| 来源ID | Working Paper 24301 |
| The Technological Elements of Artificial Intelligence | |
| Matt Taddy | |
| 发表日期 | 2018-02-12 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | We have seen in the past decade a sharp increase in the extent that companies use data to optimize their businesses. Variously called the `Big Data' or `Data Science' revolution, this has been characterized by massive amounts of data, including unstructured and nontraditional data like text and images, and the use of fast and flexible Machine Learning (ML) algorithms in analysis. With recent improvements in Deep Neural Networks (DNNs) and related methods, application of high-performance ML algorithms has become more automatic and robust to different data scenarios. That has led to the rapid rise of an Artificial Intelligence (AI) that works by combining many ML algorithms together – each targeting a straightforward prediction task – to solve complex problems. |
| 主题 | Econometrics ; Estimation Methods ; Development and Growth ; Innovation and R& ; D |
| URL | https://www.nber.org/papers/w24301 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/581973 |
| 推荐引用方式 GB/T 7714 | Matt Taddy. The Technological Elements of Artificial Intelligence. 2018. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w24301.pdf（871KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| [Matt Taddy]的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| [Matt Taddy]的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| [Matt Taddy]的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
