4.3 Emergentist Cognitive Architectures 67

4.3 Emergentist Cognitive Architectures 67 Inferred state Action/ Correction | J -—— Critic “< : ~ h A Deep y Learning System Actor Pa Ee. (state inference) — A Actions Observations Rewards Environment ] Fig. 4.4: High-level architecture of DeSTIN brain is a massively parallel fabric, in which computation processes and memory storage are highly distributed. But massive parallelism is not in itself a solution — one also needs the right architecture; which DeSTIN provides, building on prior work in the area of deep learning. Humanlike intelligence is heavily adapted to the physical environments in which humans evolved; and one key aspect of sensory data coming from our physical environments is its hierarchical structure. However, most machine learning and pattern recognition systems are “shallow” in structure, not explicitly incorporating the hierarchical structure of the world in their architecture. In the context of perceptual data processing, the practical result of this is the need to couple each shallow learner with a pre-processing stage, wherein high-dimensional sensory signals are reduced to a lower-dimension feature space that can be understood by the shallow learner. The hierarchical structure of the world is thus crudely captured in the hierarchy of “preprocessor plus shallow learner.” In this sort of approach, much of the intelligence of the system shifts to the feature extraction process, which is often imperfect and always application- domain specific. Deep machine learning has emerged as a more promising framework for dealing with complex, high-dimensional real-world data. Deep learning systems possess a hierarchical structure that intrinsically biases them to recognize the hierarchical patterns present in real-world data. Thus, they hierarchically form a feature space that is driven by regularities in the observations, rather than by hand-crafted techniques. They also offer robustness to many of the distortions and transformations that characterize real-world signals, such as noise, displacement, scaling, etc. Deep belief networks [HOTO06] and Convolutional Neural Networks [LBDE90] have been demonstrated to successfully address pattern inference in high dimensional data (e.g. images). They owe their success to their underlying paradigm of partitioning large data structures into smaller, more manageable units, and discovering the dependencies that may or may not exist HOUSE_OVERSIGHT_012983