66 4 Brief Survey of Cognitive Architectures

66 4 Brief Survey of Cognitive Architectures seem remotely capable of giving rise to such phenomena. It seems to us that the creation o a successful emergentist AGI will have to wait for either a detailed understanding of how the brain gives rise to abstract thought, or a much more thorough mathematical understanding o the dynamics of complex self-organizing systems. The concept of cognitive synergy is more relevant to emergentist than to symbolic archi- tectures. In a complex emergentist architecture with multiple specialized components, much o the emergence is expected to arise via synergy between different richly interacting components. Symbolic systems, at least in the forms currently seen in the literature, seem less likely to give rise to cognitive synergy as their dynamics tend to be simpler. And hybrid systems, as we shal see, are somewhat diverse in this regard: some rely heavily on cognitive synergies and others consist of more loosely coupled components. We now review the DeSTIN emergentist architecture in more detail, and then turn to the developmental robotics architectures. 4.3.1 DeSTIN: A Deep Reinforcement Learning Approach to AGI The DeSTIN architecture, created by Itamar Arel and his colleagues, addresses the problem of general intelligence using hierarchical spatiotemporal networks designed to enable scalable perception, state inference and reinforcement-learning-guided action in real-world environments. DeSTIN has been developed with the plan of gradually extending it into a complete system for humanoid robot control, founded on the same qualitative information-processing principles as the human brain (though without striving for detailed biological realism). However, the practical work with DeSTIN to date has focused on visual and auditory processing; and in the context of the present proposal, the intention is to utilize DeSTIN for perception and actuation oriented processing, hybridizing it with CogPrime which will handle abstract cognition and language. Here we will discuss DeSTIN primarily in the perception context, only briefly mentioning the application to actuation which is conceptually similar. In DeSTIN (see Figure 4.4), perception is carried out by a deep spatiotemporal inference network, which is connected to a similarly architected critic network that provides feedback on the inference network’s performance, and an action network that controls actuators based on the activity in the inference network (Figure 4.5 depicts a standard action hierarchy, of which the hierarchy in DeSTIN is an example). The nodes in these networks perform probabilistic pattern recognition according to algorithms to be described below; and the nodes in each of the networks may receive states of nodes in the other networks as inputs, providing rich interconnectivity and synergetic dynamics. 4.3.1.1 Deep versus Shallow Learning for Perceptual Data Processing The most critical feature of DeSTIN is its uniquely robust approach to modeling the world based on perceptual data. Mimicking the efficiency and robustness by which the human brain analyzes and represents information has been a core challenge in AI research for decades. For instance, humans are exposed to massive amounts of visual and auditory data every second of every day, and are somehow able to capture critical aspects of it in a way that allows for appropriate future recollection and action selection. For decades, it has been known that the HOUSE_OVERSIGHT_012982