标题： 千脑系统：用于快速、稳健学习和推理的感知运动智能 显示英文标题

标题： Thousand-Brains Systems: Sensorimotor Intelligence for Rapid, Robust Learning and Inference

摘要： 当前的人工智能系统在许多任务上取得了令人印象深刻的表现，但它们缺乏生物智能的核心属性，包括快速的、持续的学习能力，基于感觉运动交互的表示，以及能够实现高效泛化的结构化知识。 神经科学理论表明，哺乳动物通过复制一个半独立的感觉运动模块，进化出了灵活的智能，这个功能单元被称为皮层柱。 为了解决生物智能与人工智能之间的差异，提出了千脑系统，作为一种模仿皮层柱及其相互作用的架构的方法。 在当前的研究中，我们评估了蒙蒂（Monty），即第一个千脑系统的实现的独特特性。 我们关注三维物体感知，特别是物体识别和姿态估计的综合任务。 利用家用物体的YCB数据集，我们首先评估蒙蒂使用感觉运动学习来构建结构化表示的效果，发现这些表示能够实现稳健的泛化。 这些表示包括对物体整体形状进行分类的重点，以及自然地检测物体对称性的能力。 然后，我们探讨蒙蒂如何使用无模型和有模型的策略，通过支持合理的运动来实现快速推理。 我们发现，这些策略补充了蒙蒂的模块化架构，这种设计可以容纳模块之间的通信，从而通过一种新颖的“投票”算法进一步加速推理速度。 最后，我们研究了蒙蒂使用关联的、类似赫布的绑定来实现快速、持续且计算效率高的学习，这些特性相比当前的深度学习架构具有明显优势。 虽然蒙蒂仍处于发展的初级阶段，但这些发现支持千脑系统作为人工智能的一种强大且有前景的新方法。 显示英文摘要

摘要： Current AI systems achieve impressive performance on many tasks, yet they lack core attributes of biological intelligence, including rapid, continual learning, representations grounded in sensorimotor interactions, and structured knowledge that enables efficient generalization. Neuroscience theory suggests that mammals evolved flexible intelligence through the replication of a semi-independent, sensorimotor module, a functional unit known as a cortical column. To address the disparity between biological and artificial intelligence, thousand-brains systems were proposed as a means of mirroring the architecture of cortical columns and their interactions. In the current work, we evaluate the unique properties of Monty, the first implementation of a thousand-brains system. We focus on 3D object perception, and in particular, the combined task of object recognition and pose estimation. Utilizing the YCB dataset of household objects, we first assess Monty's use of sensorimotor learning to build structured representations, finding that these enable robust generalization. These representations include an emphasis on classifying objects by their global shape, as well as a natural ability to detect object symmetries. We then explore Monty's use of model-free and model-based policies to enable rapid inference by supporting principled movements. We find that such policies complement Monty's modular architecture, a design that can accommodate communication between modules to further accelerate inference speed via a novel `voting' algorithm. Finally, we examine Monty's use of associative, Hebbian-like binding to enable rapid, continual, and computationally efficient learning, properties that compare favorably to current deep learning architectures. While Monty is still in a nascent stage of development, these findings support thousand-brains systems as a powerful and promising new approach to AI.