arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.09318cs.AIcs.LGcs.NE
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Deutsch的好解释概念在人工智能和神经科学中的应用--初步探索

Applying Deutsch's concept of good explanations to artificial intelligence and neuroscience -- an initial exploration

National Institutes of Health Clinical Center(美国国立卫生研究院临床中心)

Daniel C. Elton

摘要:

自深度学习革命以来,人工智能已经取得了长足的进步,但人工智能系统仍然难以在其训练数据之外进行推断并适应新的情况。为了获得灵感,我们将目光投向科学领域,科学家们已经能够发展出一些理论,这些理论显示出非凡的外推能力,有时还能预测以前从未观察到的现象的存在。根据David Deutsch的说法,这种类型的外推,他称之为“范围”,是由于科学理论很难改变。在这项工作中,我们研究了Deutsch的难以改变的原则,以及它如何与深度学习中更形式化的原则,如偏差-方差权衡和奥卡姆剃刀。我们区分了内部可变性,即模型/理论可以在内部变化多少,同时仍然产生相同的预测,以及外部可变性,即模型必须变化多少才能准确预测新的,分布外的数据。我们讨论了如何使用罗生门集的大小来度量内部变异性,以及如何使用柯尔莫哥洛夫复杂度来度量外部变异性。我们通过观察人脑来探索难以改变的解释在智力中扮演的角色,并区分大脑中的两个学习系统。第一个系统的运作类似于深度学习,可能是大部分感知和运动控制的基础,而第二个系统是一个更具创造性的系统,能够对世界产生难以改变的解释。我们认为,弄清楚如何复制这个能够产生难以改变的解释的第二个系统,是实现人工通用智能需要解决的一个关键挑战。我们接触的框架Popperian认识论拒绝归纳,并断言,知识的产生是一个进化的过程,通过猜测和反驳。

英文摘要:

Artificial intelligence has made great strides since the deep learning revolution, but AI systems still struggle to extrapolate outside of their training data and adapt to new situations. For inspiration we look to the domain of science, where scientists have been able to develop theories which show remarkable ability to extrapolate and sometimes predict the existence of phenomena which have never been observed before. According to David Deutsch, this type of extrapolation, which he calls "reach", is due to scientific theories being hard to vary. In this work we investigate Deutsch's hard-to-vary principle and how it relates to more formalized principles in deep learning such as the bias-variance trade-off and Occam's razor. We distinguish internal variability, how much a model/theory can be varied internally while still yielding the same predictions, with external variability, which is how much a model must be varied to accurately predict new, out-of-distribution data. We discuss how to measure internal variability using the size of the Rashomon set and how to measure external variability using Kolmogorov complexity. We explore what role hard-to-vary explanations play in intelligence by looking at the human brain and distinguish two learning systems in the brain. The first system operates similar to deep learning and likely underlies most of perception and motor control while the second is a more creative system capable of generating hard-to-vary explanations of the world. We argue that figuring out how replicate this second system, which is capable of generating hard-to-vary explanations, is a key challenge which needs to be solved in order to realize artificial general intelligence. We make contact with the framework of Popperian epistemology which rejects induction and asserts that knowledge generation is an evolutionary process which proceeds through conjecture and refutation.

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