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A new approach for general artificial intelligence (GAI), building on neuralnetwork deep learning architectures, can make use of one or more hidden layersthat have the ability to continuously reach a free energy minimum even afterinput stimulus is removed, allowing for a variety of possible behaviors. Onereason that this approach has not been developed until now has been the lack ofa suitable free energy equation. The Cluster Variation Method (CVM) offers ameans for characterizing 2-D local pattern distributions, or configurationvariables, and provides a free energy formalism in terms of these configurationvariables. The equilibrium distribution of these configuration variables isdefined in terms of a single interaction enthalpy parameter, h, for the case ofequiprobable distribution of bistate units. For non-equiprobable distributions,the equilibrium distribution can be characterized by providing a fixed valuefor the fraction of units in the active state (x1), corresponding to theinfluence of a per-unit activation enthalpy, together with the pairwiseinteraction enthalpy parameter h. This paper provides verification andvalidation (V&V) for code that computes the configuration variable andthermodynamic values for 2-D CVM grids characterized by different interactionenthalpy parameters, or h-values. This work provides a foundation forexperimenting with a 2-D CVM-based hidden layer that can, as an alternative toresponding strictly to inputs, also now independently come to its own freeenergy minimum and also return to a free energy-minimized state afterperturbations, which will enable a range of input-independent behaviors. Afurther use of this 2-D CVM grid is that by characterizing local patterns interms of their corresponding h-values (together with their x1 values), we havea means for quantitatively characterizing different kinds of neuraltopographies.

Free Energy Minimization Using the 2-D Cluster Variation Method: Initial Code Verification and Validation