Abstract
The best way to develop a Turing test passing AI is to follow the human model: an embodied agent that functions over a wide range of domains, is a human cognitive model, follows human neural functioning and learns. These properties will endow the agent with the deep semantics required to pass the test. An embodied agent functioning over a wide range of domains is needed to be exposed to and learn the semantics of those domains. Following human cognitive and neural functioning simplifies the search for sufficiently sophisticated mechanisms by reusing mechanisms that are already known to be sufficient. This is a difficult task, but initial steps have been taken, including the development of CABots, neural agents embodied in virtual environments. Several different CABots run in response to natural language commands, performing a cognitive mapping task. These initial agents are quite some distance from passing the test, and to develop an agent that passes will require broad collaboration. Several next steps are proposed, and these could be integrated using, for instance, the Platforms from the Human Brain Project as a foundation for this collaboration.
Highlights
A good, perhaps the best, way to get an AI that passes the Turing test (Turing, 1950) is to closely follow the human model. This does leave a wide range of options, but one path is to build systems that are situated in environments (Brooks, 1991), function over a wide range of domains, are sound cognitive models, and follow human neural functioning and learning
A CABot2 agent was developed for the Human Brain Project (HBP)’s Neurorobotics Platform (NRP) (Roehrbein et al, 2016). This Platform supports virtual environments and robots driven by simulated neurons; it can be accessed over the Internet and users can develop experiments with novel virtual robots, environments and brain models
The scientific community is quite some distance from understanding how cognition emerges from neural behavior
Summary
A good, perhaps the best, way to get an AI that passes the Turing test (Turing, 1950) is to closely follow the human model This does leave a wide range of options, but one path is to build systems that are situated in environments (Brooks, 1991), function over a wide range of domains, are sound cognitive models, and follow human neural functioning and learning. The HBP has a standard suite of modeling tools, and hardware resources, including high performance computers, neuromorphic hardware and virtual environments, that are accessible, interactively via the Internet, to the wider scientific community These provide practical platforms for developing agents in artificial neurons, and a place to develop a community of neural agent developers and researchers.
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