In this paper, we present our research on programming human-level artificial intelligence (HLAI), including 1) a definition of HLAI, 2) an environment to develop and test HLAI, and 3) a cognitive architecture for HLAI. The term AI is used in a broad meaning, and HLAI is not clearly defined. I claim that the essence of Human-Level Intelligence to be the capability to learn from others' experiences via language. The key is that the event described by language has the same effect as if the agent experiences it firsthand for the update of the behavior policy. To develop and test models with such a capability, we are developing a simulated environment called SEDRo. There is a 3D Home, and a mother character takes care of the baby (the learning agent) and teaches languages. The environment provides comparable experiences to that of a human baby from birth to one year. Finally, I propose a cognitive architecture of HLAI called Modulated Heterarchical Prediction Memory (mHPM). In mHPM, there are three components: a universal module that learns to predict the next vector given the sequence of vector signals, a heterarchical network of those modules, and a reward-based modulation of learning. mHPM models the workings of the neocortex but the innate auxiliary units such hippocampus, reward system, instincts, and amygdala play critical roles, too.

翻译：在本文中,我们展示了我们关于制定人造人工智能(HLAI)的研究,包括1)HLAI的定义,2)HLAI的定义,2)开发和测试HLAI的环境,3)HLAI的认知架构。AI一词的含义广泛,而HLAI一词没有明确界定。我声称,人类层面情报的本质是通过语言学习他人经验的能力。关键在于语言描述的事件具有与代理人在更新行为政策时亲身经历相同的效果。为了开发并测试具有这种能力的模型,我们正在开发一个模拟环境,称为SEDRo。有一个3D Home,一个母性在照顾婴儿(学习代理人)和教授语言。环境提供了与人类婴儿从出生到一年的相似的经验。最后,我提议了HLAI的认知架构,称为Modelate Heter-Suranical Snal Medicial(MHPM) 。在 mHPMM中有三个组成部分:一个通用模块,根据矢量系统序列预测下一个矢量,但基于其本级模型的模型,一个 Heterchal-H 网络的模块,这些模型的学习。