Why general artificial intelligence will not be realized
Why general artificial intelligence will not be realized
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DOI：
10.1057/s41599-020-0494-4
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发表时间：
2020-06-17
影响因子：
--
通讯作者：
Fjelland, Ragnar
中科院分区：
文献类型：
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作者：
Fjelland, Ragnar
创建类人人工智能（AI）的现代项目始于第二次世界大战之后，当时人们发现电子计算机不仅仅是数字运算机器，还可以操纵符号。我们可以在不假设机器智能与人类智能相同的情况下实现这一目标。这被称为弱AI。然而，许多人工智能研究人员追求的目标是开发原则上与人类智能相同的人工智能，称为强人工智能。弱人工智能没有强人工智能那么雄心勃勃，因此争议较少。然而，也存在与弱人工智能相关的重要争议。本文重点讨论了广义人工智能（AGI）和狭义人工智能（ANI）的区别。虽然AGI可以被归类为弱AI，但它接近强AI，因为人类智能的一个主要特征是它的通用性。虽然AGI没有强大的AI那么雄心勃勃，但几乎从一开始就有批评者。哲学家休伯特·德雷福斯（Hubert Dreyfus）是主要的批评者之一，他认为计算机没有身体，没有童年，没有文化实践，根本无法获得智能。德雷福斯的主要论点之一是，人类知识部分是隐性的，因此不能在计算机程序中表达和合并。然而，今天有人可能会说，人工智能研究的新方法已经使他的论点过时了。深度学习和大数据是最新的方法之一，支持者认为它们将能够实现AGI。仔细观察就会发现，尽管特定目的人工智能（ANI）的发展令人印象深刻，但我们还没有更接近开发人工通用智能（AGI）。这篇文章进一步认为，这在原则上是不可能的，它恢复了休伯特德雷福斯的论点，即计算机并不存在于世界上。
The modern project of creating human-like artificial intelligence (AI) started after World War II, when it was discovered that electronic computers are not just number-crunching machines, but can also manipulate symbols. It is possible to pursue this goal without assuming that machine intelligence is identical to human intelligence. This is known as weak AI. However, many AI researcher have pursued the aim of developing artificial intelligence that is in principle identical to human intelligence, called strong AI. Weak AI is less ambitious than strong AI, and therefore less controversial. However, there are important controversies related to weak AI as well. This paper focuses on the distinction between artificial general intelligence (AGI) and artificial narrow intelligence (ANI). Although AGI may be classified as weak AI, it is close to strong AI because one chief characteristics of human intelligence is its generality. Although AGI is less ambitious than strong AI, there were critics almost from the very beginning. One of the leading critics was the philosopher Hubert Dreyfus, who argued that computers, who have no body, no childhood and no cultural practice, could not acquire intelligence at all. One of Dreyfus' main arguments was that human knowledge is partly tacit, and therefore cannot be articulated and incorporated in a computer program. However, today one might argue that new approaches to artificial intelligence research have made his arguments obsolete. Deep learning and Big Data are among the latest approaches, and advocates argue that they will be able to realize AGI. A closer look reveals that although development of artificial intelligence for specific purposes (ANI) has been impressive, we have not come much closer to developing artificial general intelligence (AGI). The article further argues that this is in principle impossible, and it revives Hubert Dreyfus' argument that computers are not in the world.Y