Model-free model elimination: A new step in the model-free dynamic analysis of NMR relaxation data

2006 ◽  
Vol 35 (2) ◽  
pp. 117-135 ◽  
Author(s):  
Edward J. d’Auvergne ◽  
Paul R. Gooley
Keyword(s):  
Dynamic Analysis ◽  
Nmr Relaxation ◽  
Model Elimination ◽  
Relaxation Data ◽  
Model Free ◽  
Free Model ◽  
Free Dynamic

scholarly journals Model-free, Model-based, and General Intelligence

2018 ◽  
Author(s):  
Hector Geffner
Keyword(s):  
Intelligent Systems ◽  
Human Mind ◽  
Writing Programs ◽  
Scalable Algorithms ◽  
Model Based ◽  
Model Free ◽  
Considerable Success ◽  
Black Boxes ◽  
Free Model ◽  
Intelligent Behavior

During the 60s and 70s, AI researchers explored intuitions about intelligence by writing programs that displayed intelligent behavior. Many good ideas came out from this work but programs written by hand were not robust or general. After the 80s, research increasingly shifted to the development of learners capable of inferring behavior and functions from experience and data, and solvers capable of tackling well-defined but intractable models like SAT, classical planning, Bayesian networks, and POMDPs. The learning approach has achieved considerable success but results in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Model-based approaches, on the other hand, require models and scalable algorithms. Model-free learners and model-based solvers have indeed close parallels with Systems 1 and 2 in current theories of the human mind: the first, a fast, opaque, and inflexible intuitive mind; the second, a slow, transparent, and flexible analytical mind. In this paper, I review developments in AI and draw on these theories to discuss the gap between model-free learners and model-based solvers, a gap that needs to be bridged in order to have intelligent systems that are robust and general.

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