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Robust decisioning
One potential solution involves adopting a robust decision approach. The AV sensors would gather real-time data to assess the appropriateness of various decisions — such as accelerating, changing lanes, braking — within a specific traffic scenario.
If critical factors raise doubts about the algorithmic rote response, the system then assesses the vulnerability of alternative decisions in the given context. ...
... As AI evolves, we may need to depart from the deep learning paradigm and emphasize the importance of decision context to advance towards AGI. Deep learning has been successful in many applications but has drawbacks for realizing AGI.
DMDU methods may provide the initial framework to pivot the contemporary AI paradigm towards robust, decision-driven AI methods that can handle uncertainties in the real world.
Swaptik Chowdhury is a Ph.D. student at the Pardee RAND Graduate School and an assistant policy researcher at nonprofit, nonpartisan RAND Corporation.
Steven Popper is an adjunct senior economist at the RAND Corporation and professor of decision sciences at Tecnológico de Monterrey.
See the full story here: https://venturebeat.com/ai/why-artificial-general-intelligence-lies-beyond-deep-learning/