Toward Human-Level Artificial Intelligence
  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.
