Creating Robots with Toddler-Level Intelligence Using the OpenCog AGI Architecture Research Proposal for the Epstein Foundation Ben Goertzel February, 2013 Introduction At its inception in the 1950s, the Al field aimed at producing human level general intelligence in computers and robots. Within a decade or so the difficulty of that goal became evident, and the Al field refocused on producing systems displaying intelligence within narrow domains. This focus on "narrow Al" has been strikingly successful in some regards, leading to practical Al applications such as Google's search and ad engines, Deep Blue and other game- playing Als, IBM's Watson Jeopardy-player, a host of profitable Al financial trading systems, and so forth. Over the past few years, however, there has been a resurgence of research interest in the original goals of AI, often using terminology such as Human-Level Al or Artificial General Intelligence (AGI) [1,2,3,4]. The core reason for this resurgence is a feeling that, due to advances in the Al field and in allied areas such as computer and robotic hardware, computer science, cognitive psychology and neuroscience, we are in a far better position to approach these goals today than were the founders of Al in the 1950s. One may ask why, given all the amazing recent developments in applied Al and allied areas, we have not yet seen Al software systems with humanlike general intelligence. We believe there is one key ingredient missing —the effective linkage of subsymbolic Al methods, dealing with raw perceptual and motoric data, with symbolic Al methods, dealing with abstract reasoning and language, and higher level cognition. The Al field now possesses able algorithms and architectures on both the symbolic and subsymbolic sides, but without both aspects working together, human level general intelligence is hard to come by. Some researchers aim to bridge the gap by making subsymbolic Al systems intelligent enough that they can learn