提出可验证的通用智能基础要求，通过信号预测测试系统评估智能水平。
Foundational Requirements for Artificial General Intelligence: A Falsifiable Framework Based on Signal Prediction
- 基于信号预测构建智能的底层需求框架
- 设计透明可复用的测试集，当前无非智能系统通过
- 适合研究通用智能与认知神经科学交叉的学者
基于高级智能可由低级信号处理生成的假设，我们提出人工通用智能所需的基本条件。这些条件描述了从初始无意义语义状态学习、实时感知等核心特性，源于认知神经科学中的基本原理。为实现可检验性与假说证伪，我们设计了一个由每项要求对应一个透明可复用测试组成的实证测试平台。迄今为止，尚未有非智能系统被报告成功通过该测试集。在出现反例前，该测试平台可作为通用智能的候选实证里程碑。测试平台参考实现已公开。
原文摘要 · Abstract (English)
Grounded in the premise that high-level intelligence can emerge from low-level signal processing, we advance a hypothesis regarding low-level requirements necessary for artificial general intelligence. The proposed requirements characterise core properties of systems that learn through prediction over spatially and temporally structured signals with initially unknown semantic content. They include a selection of basic principles observed in cognitive neuroscience, from learning from an uninformed state to real-time liveness. To enable empirical testing and hypothesis rejection, we introduce an operational testbed composed of transparent and reusable tests, one per requirement. To date, no non-intelligent system has been identified or reported as successfully passing the testbed. Pending such a counterexample, the testbed serves as a candidate empirical milestone toward general intelligence. The reference implementation of the testbed is publicly available.
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