摘要
以历史为线索,从设计思路和技术特征两个方面对“深蓝”和AlphaGo进行了梳理和概括。“深蓝”依赖人类在国际象棋领域的经验,借助强大的算力与算法实现了对人类的超越;20年后的AlphaGo,虽然最初的版本也是利用人类经验而获得成功的,但是它的不断进化却揭示了一个重要事实:人类经验具有局限性。放弃人类经验、完全采用机器自对弈经验的AlphaZero,不但具有最强的围棋对弈能力,而且同时具备国际象棋和日本将棋的最高棋力,3种最强技能集于一身。机器下棋的这一历史线索揭示了在棋类游戏中;囿于人类自身认知能力的局限,人类几千年积累下来的经验较之于机器在短期内所形成的“经验”已不占优势。在巨大的算力和不断完善的算法的支撑下,借助于机器自身“经验”,机器可以做得比人类更好。未来,“放弃人类经验,依靠自身经验”的机器将有可能在更为复杂的领域取得突破性进展。
Taking the historical evolution as the main line, this paper combs and summarizes "Deep Blue" and AlphaGo from the aspects of design philosophy and technical features. Relied on human experience of chess,"Deep Blue" achieved transcendence with humans by means of computational power and algorithms. Twenty years later, AlphaGo, although its original version was also successful by using human experience, and its evolution revealed an important fact that human experience has its limitation. AlphaZero, which gives up human experience and adopts machine seif-playing experience, convincingly defeated a world champion program in the games of chess and shogi (Japanese chess) as well as Go. It is clear that in chess games, limited by human cognition ability, experience accumulated by human beings for thousands of years is no longer superior to the "experience" formed by machines in a short term. Machines can do better than humans with the help of their own "experience", supported by enormous computing power and ever-improving algorithms. In the future, machines that "give up human experience and rely on their own experience" will likely make breakthroughs in more complex areas.
作者
薛永红
王洪鹏
XUE Yonghong;WANG Hongpeng(College of Science, North China University of Science and Technology, Beijing 101601, China;College of Philosophy, Beijing Normal University, Beijing 100875, China;China Science and Technology Museum, Beijing 100012, China)
出处
《科技导报》
CAS
CSCD
北大核心
2019年第19期87-96,共10页
Science & Technology Review
基金
2018年度国家社会科学基金重点项目(18AZX008)
中央高校基本科研业务费项目(3142018057)
作者简介
薛永红,副教授,研究方向为科学思想史与科学社会史,电子信箱:aristotle@ncist.edu.cn.
