Is your AI appropriately self-aware? How analogues of attitudinal theory from cognitive neuroscience improve enterprise deep learning applications.

Advances in deep learning algorithms, modeled after the parallel processing of cognitive neuroscience, have led to an explosion of applications embedded directly into the core processes of most large global corporations and organizations. As deep learning and other neural correlates become the norm for artificial intelligence and machine learning, many underlying theories of cognitive neuroscience have been shown to have direct relevance on achieving positive outcomes from deployed algorithms. The speaker will draw from his experience in computational cognitive neuroscience as well as in managing dispersed enterprise data science teams and provide insight into the growing acceptance of neural correlates such as 'attitude' as highly relevant to the enterprise big data workflow.