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人工智能算法的复杂性特质及伦理挑战

2017-09-04 05:15:49   中国青年网

当下人工智能的伦理思考多侧重于实践:如人工智能是否最终会成为人类事务的主导,该以什么伦理原则来引导、规范人工智能的应用等。可更大的挑战却是理论上的困惑:算法是人工智能的内核,不同算法实则暗含有不一样的价值选择,何谓善的算法?善的算法必会导向行为的善?人工智能算法可以自我学习、自我进化,伦理上该如何看待这种智能主体?鉴于人工智能是一种典型的复杂行为,从复杂性的角度我们能更深入地揭示这些困惑和挑战。

人工智能算法的传统通常有两个:一是符号认知主义,主张智能行为依赖于符号操作,通过基于符号表征的计算可实现学习、记忆、推理、决策,甚至情感活动,如早期的专家系统;二是联结主义,受人和动物大脑神经结构启发,认为通过大量底层简单的“激活单元”相互交织可在高层自发涌现出复杂的智能行为,这一传统以人工神经网络为代表。阿尔法围棋的成功主要得益于后一种传统,基于神经网络的深度学习实现算法的突破。

以复杂性视角观之,基于神经网络的人工智能算法有一个突出特质——涌现性,即智能是一种由算法底层的简单规则生成的复杂行为,智能并不由预定的算法前提所决定。“涌现”被遗传算法的创立者、美国科学家霍兰称为复杂世界的“隐秩序”,生命诞生、交通堵塞等都是涌现现象。以棋类游戏智能算法为例,棋子数是有限的,游戏规则是简单的,但棋局变化的可能性却无法穷尽。棋局的最终输赢是一种涌现,决定棋局走向的不是底层的简单规则,而是由它们生成的更高层的组织过程。

人工智能算法的涌现性具有这样一些特点:1.智能行为不是一种底层简单规则的加和,而是从底层到高层的层次跨越,高层具有底层个体所不具备的复杂特性。2.无法通过底层算法来准确预测高层的涌现,智能是算法前提无法决定的“新奇性”。3.涌现不是单一行为而是由众多简单个体行为到复杂集体行为的演化。智能行为是一个过程,棋局的最终取胜不是依靠单次行为的累加,而是算法演化系列的整体取胜。阿尔法围棋在与人类棋手对弈时有一些令顶尖职业棋手难以理解的“昏招”,可这些昏招到了棋局结束时竟成为取胜的“神之一手”。这并不是证明了所谓关键招的重要性,而是表明“招数系列”比“某一招”更有意义。在伦理上,涌现性特质揭示出人工智能算法具有不同于传统的行为特征:人工智能算法行为不是边界清晰的单个行为而是集体行为的演化,其行为效果既不由“某一”行为所决定,亦不由其前提完全决定。

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