周剑铭 柳渝:智能哲学:AlphaGo Zero与围棋文化

  • 时间:
  • 浏览:1

   *法国儒勒·凡尔纳公立综合大学(Université de Picardie Jules Verne, France),计算机系

   摘要:继AlphaGo完胜人类棋手后AlphaGo Zero完胜AlphaGo,恰恰表明了作为人工智能的围棋机器的技术性本质。中国古围棋在日本的职业化也是围棋的技术化,这是今日围棋机器完胜人类的必然。中国围棋的文化本质含有于棋艺和棋道之中。围棋的棋理非要在科学与人文和益、西文化的交叉视域中要能得到真正的阐释。

   AlphaGo以学习人类经验棋谱而战胜了人类棋手,成为了人工智能的时代标志,而AlphaGo Zero则以“白板”(tabular rasa)学习而再次成为头号新闻,英国经验主义哲学家洛克(John Locke,1632-1704)著名的“白板”说(theory of tabula rasa)认为,人出生时心灵像白板一样空白,通过人的经验心灵中才有了观念和知识,洛克认为经验是观念、知识的惟一来源。AlphaGo Zero的“白板”是指与人类经验棋谱相对的空棋盘,即从0结束了了英语 的“学习”,但洛克的心灵“白板”是人从现实经验中认知或学习,两者的区别就在于AlphaGo Zero不须要人类的棋谱经验有随后我我本人与我本人在棋盘上对战的“经验”,这俩 区别的微妙之处就在于人类的经验与机器的“经验”有何本质的不同,这与AlphaGo对人类的伦理挑战不同,AlphaGo Zero的“白板”是对人类哲学现象的一另有一个 挑战,哪几个现象都深刻地与大家 对人工智能的本质的理解和定义有关,实际上将会成为了今天大家 对人的智能的基本认知理论的更新,其意义远超过AlphaGo Zero的成功。

   就AlphaGo Zero的情况汇报汇报来说,本文讨论1。AlphaGo Zero的“白板”与人类的心灵“白板”有何不同?2。AlphaGo Zero自我对弈的经验与人类的经验有何本质的不同?大家 都里能 在智能哲学的论域中研究哪几个现象的深刻意义。

   一、AlphaGo Zero的“白板”学习与人工智能的“先天”性赋予

   DeepMind团队在“自然”杂志上发表的论文,推出了人工智能围棋应用应用程序的最新版本的更强大的“学习”能力, AlphaGo Zero:Mastering the game of Go without human knowledge (不让人类知识的围棋大师),据称,AlphaGo Zero以1000 : 0的成绩击败李世乭版本的AlphaGo。(http://nature.com/articles/doi:10.1038/nature24270,中文介绍可见:http://mp.weixin.qq.com/s/68GTn-BaiRPmzi9F-0sCyw)最引人注意的地方是,“大家 介绍有五种单独基于强化学习土方法的算法,不让人类数据、人类的指导,或超越围棋规则的领域知识。AlphaGo成为了它我本人的老师,”(we introduce an algorithm based solely on reinforcement learning, without human data, guidance, or domain knowledge beyond game rules. AlphaGo becomes its own teacher)。

   这篇论文的第一作者、AlphaGo项目负责人DeepMind的David Silver在采访中从前解释说:

   ——AlphaGo Zero完整性从“乱打”(随机)结束了了英语 ,不须要任何人类数据从最初原理结束了了英语 而取得最高的综合棋艺水平。AlphaGo Zero最重要的理念有随后我它完整性从无知情况汇报结束了了英语 学习,也有随后我从白板(tabular rasa)上结束了了英语 ,从自我对弈中领悟,不须要任何人类知识或人类数据,不须要任何人类经验、结构或人类的干预。它去发现怎样从基本原理结束了了英语 下围棋。有随后白板学习对大家 DeepMind的目标和雄心非常重要,将会将会你能得到白板学习,你就得到了一另有一个 代理,它都里能 从围棋移植到任何其它领域。你就从你所在的专业领域解放了出来,你得到了一另有一个 算法,它具有普遍性都里能 应用到任何地方。对于大家 来说AlphaGo的意义没了于下棋战胜人类,有随后我去发现从事科学工作的意义,从应用应用程序的自我学习能力中了解知识是哪几个。大家 结束了了英语 发现,AlphaGo Zero不仅重新发现了人类下棋时的常用模式和开局,以及人类下在棋角上的定式,不仅是学习、发现哪几个有随后最终放弃它们而采用我本人的模式,其中许多甚至是人类别问我的或现在还没了用过的。有随后大家 都里能 说,事实上在短时间内AlphaGo Zero学到了人类上千年积累的围棋实战知识。AlphaGo Zero下棋中分析,靠我本人发现更多的知识。有随后它的选折 甚至超过哪几个,得到许多人类在这俩 随后尚未发现的东西,在不同的土方法上发展出具有创意的新的知识点。

   (AlphaGo Zero which has learned completely from scratch, from first principles without using any human data and has achieved the highest level of performance overall. The most important idea in AlphaGo Zero is that it learns completely tabular rasa. That means it starts completely from a blank slate and figures out for itself only from self-play, without any human knowledge, without any human date, without any human examples or features or intervention from humans. It discovers how to play the game of Go completely from fist principles. So tabular rasa learning is extremely important to our goals and ambitions at DeepMind. And the reason is that if you can achieve tabula rasa leaning, you really have an agent that can be transplanted from the game of Go to any other domain. You untie yourself from the specifics of the domain you’re in and you come up with an algorithm which is so general that it can be applied anywhere. For us the idea of AlphaGo is not to go out and defeat humans, but actually to discover what it means to do science, and for a program to be able to lean for itself what knowledge is. So, what we start to see was that AlphaGo Zero not only rediscovered the common patterns and openings that human tend to play, these joseki patterns that human play in the corners. It also leaned them, discovered them and ultimately discarded them in preference for its own variants which humans don’t even know about or play at the moment. And so we can say that really what’s happened is that in a short space of time, AlphaGo Zero has understood all of the Go knowledge that has been accumulated by humans over thousands of years of playing. And it’s analyzed it and started to look at it and discover much of this knowledge for itself. And sometimes it’s chosen to actually to beyond that and come up with something which the human hadn’t even discovered in this time period. And developed new pieces of knowledge which were creative and novel in many ways. )

DeepMind强调AlphaGo Zero从白板上结束了了英语 自我学习,这是指机器进入包括训练或实战情况汇报时不从学习巨量的人类数据结束了了英语 (People tend to assume that machine learning is all about big data massive amounts of computation),但这时的AlphaGo Zero有五种不言而喻白板(裸机),可是我我言而喻只含有了“操作系统”的纯净机器,有随后我具有了强大的机器学习能力的机器,David Silver说 “但实际上大家 从AlphaGo Zero中发现,算法比所谓计算或可用数据更重要,事实上大家 在AlphaGo Zero上使用的计算(量)比过去在AlphaGo上要少一另有一个 数量级,这是将会大家 使用了更多原理和算法。“(But actually what we saw in AlphaGo Zero is that algorithms matter much more than either compute or data availability. In fact in AlphaGo Zero, we use more than an order of magnitudes less computation than we used in previous versions of AlphaGo. And yet it was able to perform much higher level due to using much more principled algorithms than we had before.(点击此处阅读下一页)

本文责编:川先生 发信站:爱思想(http://www.aisixiang.com),栏目:天益学术 > 哲学 > 科学哲学 本文链接:http://www.aisixiang.com/data/106762.html 文章来源:爱思想首发,转载请注明出处(http://www.aisixiang.com)。