研究储层计算机如何无意识生成错误信息
Confabulation dynamics in a reservoir computer: Filling in the gaps with untrained attractors
- 通过分析储层计算机的动态行为揭示错误生成机制
- 发现未训练吸引子在重建失败时会自动出现
- 适用于研究神经网络误判与系统内在缺陷的学者
近年来，人工智能因人工神经网络（ANNs）的设计与训练创新而显著进步。然而，我们仍不了解基础形式的ANN如何学习、未能学习以及无意识地生成虚假信息，这种现象称为‘错构’。本文分析储层计算机（RCs）中的错构现象：当RC被训练重建特定吸引子动力学时，有时会自发生成未训练过的‘未训练吸引子’（UA）。研究表明，当重建失败时，这些未训练吸引子起关键作用，并影响不同吸引子间转换的建模。结果表明，未训练吸引子是状态空间受限学习系统的内在特征，该类错构可能存在于储层计算机之外的其他系统中。
原文摘要 · Abstract (English)
Artificial Intelligence has advanced significantly in recent years thanks to innovations in the design and training of artificial neural networks (ANNs). Despite these advancements, we still understand relatively little about how elementary forms of ANNs learn, fail to learn, and generate false information without the intent to deceive, a phenomenon known as `confabulation'. To provide some foundational insight, in this paper we analyse how confabulation occurs in reservoir computers (RCs): a dynamical system in the form of an ANN. RCs are particularly useful to study as they are known to confabulate in a well-defined way: when RCs are trained to reconstruct the dynamics of a given attractor, they sometimes construct an attractor that they were not trained to construct, a so-called `untrained attractor' (UA). This paper sheds light on the role played by UAs when reconstruction fails and their influence when modelling transitions between reconstructed attractors. Based on our results, we conclude that UAs are an intrinsic feature of learning systems whose state spaces are bounded, and that this means of confabulation may be present in systems beyond RCs.
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