更改

机械化或者说“形式化”推理的研究始于古代的哲学家和数学家。这些数理逻辑的研究直接催生了图灵的计算理论，即机器可以通过移动如“0”和“1”的简单的符号，就能模拟任何通过数学推演可以想到的过程，这一观点被称为'''邱奇-图灵论题 Church–Turing Thesis'''<ref name="Formal reasoning">Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682.</ref>。图灵提出“如果人类无法区分机器和人类的回应，那么机器可以被认为是“智能的”。</ref>{{Citation | last = Turing | first = Alan | authorlink=Alan Turing | year=1948 | chapter=Machine Intelligence | title = The Essential Turing: The ideas that gave birth to the computer age | editor=Copeland, B. Jack | isbn = 978-0-19-825080-7 | publisher = Oxford University Press | location = Oxford | page = 412 }}</ref>目前人们公认的最早的AI工作是由McCullouch和Pitts 在1943年正式设计的图灵完备“人工神经元”。<ref>Russell, Stuart J.; Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, New Jersey: Prentice Hall. ISBN 978-0-13-604259-4..</ref>

机械化或者说“形式化”推理的研究始于古代的哲学家和数学家。这些数理逻辑的研究直接催生了图灵的计算理论，即机器可以通过移动如“0”和“1”的简单的符号，就能模拟任何通过数学推演可以想到的过程，这一观点被称为'''邱奇-图灵论题 Church–Turing Thesis'''<ref name="Formal reasoning">Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682.</ref>。图灵提出“如果人类无法区分机器和人类的回应，那么机器可以被认为是“智能的”。<ref>{{Citation | last = Turing | first = Alan | authorlink=Alan Turing | year=1948 | chapter=Machine Intelligence | title = The Essential Turing: The ideas that gave birth to the computer age | editor=Copeland, B. Jack | isbn = 978-0-19-825080-7 | publisher = Oxford University Press | location = Oxford | page = 412 }}</ref>目前人们公认的最早的AI工作是由McCullouch和Pitts 在1943年正式设计的图灵完备“人工神经元”。<ref>Russell, Stuart J.; Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, New Jersey: Prentice Hall. ISBN 978-0-13-604259-4.</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>

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>

== 目标 ==

== 目标 ==