HOUSE_OVERSIGHT_012982.jpg

66 4 Brief Survey of Cognitive Architectures

seem remotely capable of giving rise to such phenomena. It seems to us that the creation of
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 of
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 of
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 shall
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