不用神经网络，仅靠物理形态实现数字图像分类。
Morphological Cognition: Classifying MNIST Digits Through Morphological Computation Alone
- 用固定行为的体素构建机器人，通过形态结构完成认知任务。
- 零图像触发向左移动，一图像触发向右移动，分类准确率接近100%。
- 为无神经系统的智能提供新范式，适合对具身智能感兴趣的读者。
随着现代深度学习的发展，神经网络已成为几乎所有人工智能系统的核心，使得难以想象其他智能模型。然而，自然界中存在多种未被充分研究的智能机制，尤其是具身化在智能行为中的作用。本文研究了由简单固定行为的模拟体素构成的物理身体如何产生可被外部观察者视为认知的涌现行为。具体而言，我们展示了一个机器人在面对MNIST数字零时向左移动，在面对数字一时向右移动。该机器人表现出我们称之为‘形态认知’的能力——即通过形态过程实现认知行为。据我们所知，这是首个在无任何神经电路的情况下实现高级认知功能（如图像分类）的演示。我们希望此项工作能作为概念验证，推动对不同智能模型的进一步研究。
原文摘要 · Abstract (English)
With the rise of modern deep learning, neural networks have become an essential part of virtually every artificial intelligence system, making it difficult even to imagine different models for intelligent behavior. In contrast, nature provides us with many different mechanisms for intelligent behavior, most of which we have yet to replicate. One of such underinvestigated aspects of intelligence is embodiment and the role it plays in intelligent behavior. In this work, we focus on how the simple and fixed behavior of constituent parts of a simulated physical body can result in an emergent behavior that can be classified as cognitive by an outside observer. Specifically, we show how simulated voxels with fixed behaviors can be combined to create a robot such that, when presented with an image of an MNIST digit zero, it moves towards the left; and when it is presented with an image of an MNIST digit one, it moves towards the right. Such robots possess what we refer to as ``morphological cognition'' -- the ability to perform cognitive behavior as a result of morphological processes. To the best of our knowledge, this is the first demonstration of a high-level mental faculty such as image classification performed by a robot without any neural circuitry. We hope that this work serves as a proof-of-concept and fosters further research into different models of intelligence.
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