August 2022
Intermediate to advanced
394 pages
12h 32m
Chinese
Content preview from 金融人工智能：用Python实现AI量化交易
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40
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第
2
章
第一个主要原因是，从历史上看，人类在工程方面的成功往往是因为忽略了大自然和进化
中为解决某个问题而得到的类似方案。拿飞机的设计来说，其中利用了对物理学、空气动
力学、热力学等的现代理解，而不是试图模仿鸟类或昆虫的飞行方式。再以计算器为例，
当工程师建造第一台计算器时，并没有分析人脑是如何进行计算的，甚至也没有尝试复制
生物学方法，他们更依赖于在技术硬件上实现的数学算法。在这两种情况下，更重要的方
面是功能或能力本身（飞行、计算），其越有效越好，没有必要模仿大自然。
第二个主要原因是，人工智能的成功案例的数量似乎在不断增加。例如，将神经网络应用
于几年前人工智能似乎并不占优势的领域，已被证明是在许多领域实现
ANI
的有效途径
。
一个希望可以进一步推动人工智能泛化能力的例子是，
AlphaGo
正逐渐转变为
AlphaZero
，
在短时间内掌握了多款棋盘游戏。
第三个主要原因是，可能只有在许多
ANI
（甚至一些
AGI
）被观察到之后
，超级智能才会
出现（“奇点”）。由于人工智能在特定专业和领域的力量毋庸置疑，因此研究人员和企业
等将继续专注于改进人工智能算法和硬件。例如，大型对冲基金将努力通过人工智能方法
和智能体来产生
alpha
收益
（一种与市场基准相比，衡量基金业绩表现的指标），许多基金
拥有致力于此类工作的大型专门团队。跨不同行业的这些全球性努力可能会共同产生超级
智能所需的进步。
人工智能
在通往超级智能的所有可能途径中，人工智能似乎是最有前途的一种。在经
历了多个人工智能寒冬之后，我们迎来了另一个人工智能的春天，尤其以最 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.