OpenAI patents analog circuit that learns by balancing electrical energy

1. A system for performing learning, comprising: a linear programmable network layer including a plurality of inputs, a plurality of outputs and a plurality of linear programmable network components interconnected between the plurality of inputs and the plurality of outputs; a nonlinear activation layer coupled with the plurality of outputs, wherein the nonlinear activation layer further includes: a nonlinear activation module; and a regeneration module coupled with the plurality of outputs and with the nonlinear activation module, the regeneration module being configured to scale a plurality of output signals from the plurality of outputs, wherein the regeneration module includes a bidirectional amplifier, wherein the regeneration module scales voltage in a forward direction towards output voltages or a next layer by a gain factor of G, wherein the regeneration module scales current in a reverse direction towards input voltages by a gain factor of 1/G, and wherein in the event that G is equal to 1, the regeneration module behaves as a short circuit and maintains a minimum of a content for the system such that in response to the input voltages, the output voltages have a stationary state at the minimum of the content for the linear programmable network layer and the nonlinear activation layer; and an additional linear programmable layer including an additional plurality of inputs, an additional plurality of outputs, and an additional plurality of linear programmable network components interconnected between the additional plurality of inputs and the additional plurality of outputs, the nonlinear activation layer coupled to the additional plurality of inputs, wherein the linear programmable network layer, the nonlinear activation layer, and the additional linear programmable layer are configured to have the stationary state at the minimum of the content for the system, the content for the system corresponding to a pseudo-power for electrical characteristics of the system, wherein a plurality of input signals provided to the plurality of inputs result in a plurality of output signals at the plurality of outputs, result in an additional plurality of output signals at the additional plurality of outputs, and correspond to the stationary state, and wherein the plurality of additional outputs are configured to receive a plurality of perturbations that results in a plurality of perturbation output signals at the plurality of outputs, the plurality of outputs including a plurality of nodes configured to measure the plurality of perturbation output signals, a gradient for the plurality of linear programmable network components of the linear programmable network layer being based on the plurality of perturbation output signals and the plurality of output signals, at least one of the plurality of linear programmable network components in the linear programmable network layer being configured to be reprogrammed based on the gradient.