更改

更快的计算机、算法改进和对大量数据的访问使机器学习和感知取得进步；数据饥渴的深度学习方法在 2012 年左右开始主导准确性基准。<ref>{{cite web|title=Ask the AI experts: What's driving today's progress in AI?|url=https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai|website=McKinsey & Company|access-date=13 April 2018|archive-date=13 April 2018 |archive-url=https://web.archive.org/web/20180413190018/https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai|url-status=live}}</ref>据彭博社的Jack Clark称，2015 年是人工智能具有里程碑意义的一年，谷歌内部使用人工智能的软件项目数量从 2012 年的“零星使用”增加到 2700 多个项目。克拉克还提供了事实数据，表明自 2012 年以来 AI 的改进受到图像处理任务中较低错误率的支持。<ref name="AI 2015" />他将此归因于可负担得起的神经网络的增加，这是由于云计算基础设施的增加以及研究工具和数据集的增加。<ref name="AI in 2000s" />在 2017 年的一项调查中，五分之一的公司表示他们“在某些产品或流程中加入了人工智能”。<ref>{{cite web|title=Reshaping Business With Artificial Intelligence|url=https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/|website=MIT Sloan Management Review |access-date=2 May 2018|archive-date=19 May 2018|archive-url=https://web.archive.org/web/20180519171905/https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/|url-status=live}}</ref><ref>{{cite web |last1=Lorica|first1=Ben|title=The state of AI adoption|url=https://www.oreilly.com/ideas/the-state-of-ai-adoption|website=O'Reilly Media|access-date=2 May 2018|date=18 December 2017|archive-date=2 May 2018|archive-url=https://web.archive.org/web/20180502140700/https://www.oreilly.com/ideas/the-state-of-ai-adoption|url-status=live}}</ref>

更快的计算机、算法改进和对大量数据的访问使机器学习和感知取得进步；数据饥渴的深度学习方法在 2012 年左右开始主导准确性基准。<ref>{{cite web|title=Ask the AI experts: What's driving today's progress in AI?|url=https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai|website=McKinsey & Company|access-date=13 April 2018|archive-date=13 April 2018 |archive-url=https://web.archive.org/web/20180413190018/https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai|url-status=live}}</ref>据彭博社的Jack Clark称，2015 年是人工智能具有里程碑意义的一年，谷歌内部使用人工智能的软件项目数量从 2012 年的“零星使用”增加到 2700 多个项目。克拉克还提供了事实数据，表明自 2012 年以来 AI 的改进受到图像处理任务中较低错误率的支持。<ref name="AI 2015">Clark, Jack (8 December 2015b). "Why 2015 Was a Breakthrough Year in Artificial Intelligence". Bloomberg.com. Archived from the original on 23 November 2016.</ref>他将此归因于可负担得起的神经网络的增加，这是由于云计算基础设施的增加以及研究工具和数据集的增加。<ref name="AI in 2000s" />在 2017 年的一项调查中，五分之一的公司表示他们“在某些产品或流程中加入了人工智能”。<ref>{{cite web|title=Reshaping Business With Artificial Intelligence|url=https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/|website=MIT Sloan Management Review |access-date=2 May 2018|archive-date=19 May 2018|archive-url=https://web.archive.org/web/20180519171905/https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/|url-status=live}}</ref><ref>{{cite web |last1=Lorica|first1=Ben|title=The state of AI adoption|url=https://www.oreilly.com/ideas/the-state-of-ai-adoption|website=O'Reilly Media|access-date=2 May 2018|date=18 December 2017|archive-date=2 May 2018|archive-url=https://web.archive.org/web/20180502140700/https://www.oreilly.com/ideas/the-state-of-ai-adoption|url-status=live}}</ref>

 

== 挑战 Challenges ==

== 挑战 Challenges ==