Methods, systems, and apparatus, including computer programs encoded on computer readable storage medium, for using transfer learning to train a predictive model. In one aspect, a system receives predictive model training data. The training data and one or more training methods are used to train multiple predictive models. Variables and a predictive model type are then selected from the trained predictive models. The selected variables are then transferred into the selected type of predictive model as long as they reduce an error measure. f) transfer the one or more stored input variables for one of the predictive model types into the preliminary predictive model and create an intermediate predictive model containing said input variables when said one or more input variables reduce an error measure when included as inputs to the preliminary predictive model and the preliminary predictive model is retrained using one of the one or more training methods;
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: a data processing apparatus; and a non-transitory computer readable storage medium in data communication with the data processing apparatus where said computer readable storage medium stores instructions executable by the data processing apparatus and upon such execution causes the data processing apparatus to perform operations comprising: a) receive a plurality of predictive modeling training data; b) partition the training data into a plurality of subsamples; c) train a plurality of different types of predictive models with one or more of the plurality of subsamples and one or more training methods; d) select a single type of trained predictive model as a current predictive model and a preliminary predictive model type using a model selection algorithm; e) select one or more input variables from each of the different types of trained predictive models that were not selected as the preliminary predictive model type with a variable selection algorithm and store said input variables by predictive model type in one or more data storage devices attached to the data processing apparatus; f) transfer the one or more stored input variables for one of the predictive model types that was not selected as the preliminary predictive model type into the current predictive model and create a new current predictive model containing said input variables when said one or more variables reduce an error measure when included as inputs to the current predictive model after the current predictive model is retrained using one of the one or more training methods and one or more of the plurality of subsamples; g) repeat step f) until the stored input variables for each of the different types of predictive models has been added to the current predictive model for at least one error measurement and then store the current predictive model as a final predictive model; and h) provide access to the final predictive model. 2. The system of claim 1, wherein the plurality of different types of predictive models are selected from the group consisting of ridge regression, projection pursuit regression; stepwise regression; power law, elastic net, classification and regression tree; generalized additive model (GAM), redundant regression network; linear regression; multivariate adaptive regression splines; neural network, primal graphical lasso, adaptive context tree Bayesian, randomized causation coefficient, Tetrad, information geometric inference, LaGrange, causal additive noise and path analysis. 3. The system of claim 3, wherein the one or more training methods are selected from the group consisting of: path-wise cyclical coordinated descent, flexible non-linear smoother, known causal relationship testing, F test series, minimize Kolmogorov-Smirnov statistic for cumulative distribution functions, cubic spline smoother, coordinate descent, greedy algorithm, scatterplot smoother, induction, ordinary least squares, automatic forward and backward pass, loglikelihood comparison, back propagation, forward propagation, block coordinate descent, genetic algorithm, discounted Krichevsky-Trofimov estimator, methods incorporated in an algorithm for the predictive model type, least absolute shrinkage and selection operator (LASSO), minimum message length, best first search, iterate to score every combination, greedy algorithm, and least angle regression and shrinkage (LARS). 4. They system of claim 1, wherein model selection algorithm comprises a k-fold cross validation algorithm, where the training data consists of data representing a physical object or substance and where the error measures comprises a root mean squared error measure. 5. They system of claim 1, wherein the data processing apparatus comprises a computer with at least one processor and wherein the access to the final predictive models comprises access using an Internet or other network. 6. They system of claim 1, wherein the variable selection algorithm comprises a stepwise regression algorithm. 7. An artificial intelligence system, comprising: computing hardware including at least one processor, one or more data storage devices, and a non-transitory data storage medium interfaced with the at least one processor, the non-transitory data storage medium containing instructions that, when executed cause the at least one processor to: prepare a plurality of data representative of an organization, a health plan and a plurality of organization employees covered by the health plan for processing with at least one computer processor associated with one or more data storage devices before storing said data in the one or more data storage devices, where said organization physically exists and comprises a plurality of segments of value and a plurality of elements of value that physically exist and where each of the elements of value consists of a plurality of items; develop one or more predictive models for a value of each of the segments of value of the organization with the at least one computer processor associated with the one or more data storage devices, where said predictive models each quantify a contribution by item of the plurality of elements of value and a contribution of one or more external factors to the value of the segment of value of the organization by learning from at least part of said stored data; develop a resilient context for one or more groups of employee in the plurality of organization employees covered by the health plan with the at least one computer processor associated with the one or more data storage devices by learning from at least part of said stored data; use said resilient context and the at least one computer processor associated with the one or more data storage devices to forecast a sustainable longevity for each of the one or more groups of employees in the plurality of employees and determine an annual health care expense for each of the one or more groups of employees based on said sustainable longevity; and output the contribution of the external factors and items to the organization value by segment of value and the annual health care expense for each of the one or more groups of employees where the segments of value comprise a current operation and a segment of value selected from the group consisting of derivatives, investments, real options and market sentiment. 8. The artificial intelligence system of claim 7, wherein developing each of the one or more predictive models for each of the segments of value that quantify the net contribution by item of the one or more elements of value and the net contribution of the one or more external factors to the value of the segment of value of the organization by learning from at least part of the stored data comprises: using a plurality of predictive model algorithms, the at least one computer processor associated with the one or more data storage devices and a plurality of causal models to analyze and select a portion of the data to input when modeling the net contribution of each of the plurality of elements of value by item; using the plurality of predictive model algorithms, the at least one computer processor associated with the one or more data storage devices and the plurality of causal models to analyze and select a portion of the data to input when modeling the net contribution of each of the one or more external factors; learning with the at least one computer processor associated with the one or more data storage devices which model from the plurality of causal models comprises a best fit for modeling the net contribution by item of the plurality of elements of value and the one or more external factors to the value of each of the segments of value of the portfolio when using the selected element of value and the selected external factor data; learning with the at least one computer processor associated with the one or more data storage devices which algorithm from the plurality of predictive model algorithms to include in the model for each of the segments of value to model the net contribution of each of the plurality of elements of value by item and each of the one or more external factors to a value of each of the segments of value of the portfolio when the input variables comprise the input variables to the best fit causal model; where the plurality of causal models are selected from the group consisting of Bayesian, randomized causation coefficient, Tetrad, information geometric inference, LaGrange, causal additive noise and path analysis, and where the plurality of predictive model algorithms are selected from the group consisting of ridge regression, projection pursuit regression; stepwise regression; power law, elastic net, classification and regression tree; generalized additive model (GAM), redundant regression network; linear regression; multivariate adaptive regression splines; neural network, primal graphical lasso, adaptive context tree and stepwise regression. 9. The artificial intelligence system of claim 8, wherein developing each of the one or more predictive models for each of the segments of value that quantify the net contribution by item of the one or more elements of value and the net contribution of the one or more external factors to the value of the segment of value of the organization by learning from at least part of said stored data further comprises: using the plurality of predictive models and the at least one computer processor associated with the one or more data storage devices to learn a relative contribution of each of the plurality of elements of value by item to the value of each of the segments of value, using the plurality of predictive models and the at least one computer processor associated with the one or more data storage devices to learn a relative contribution of each of the one or more external factors to the value of each of the segments of value, and using the plurality of predictive model algorithms and the at least one computer processor associated with the one or more data storage devices to learn a relative contribution of each of the one or more external factors to the organization value. 10. The artificial intelligence system of claim 8, wherein the plurality of elements of value are selected from the group consisting of: channels, customers, employees, information technology, intellectual property, processes, vendors and combinations thereof and wherein the plurality of risks are selected from the group consisting of event risks, element variability, factor variability and longevity where each risk consists of an expected reduction in value and where an event risk with a known expected reduction in value comprises a contingent liability that is measured using a real option algorithm instead of simulation. 11. The artificial intelligence system of claim 7, wherein the at least one processor further: identifies one or more scenarios for the organization, simulates with a simulation model and the at least one computer processor the organization value by segment of value and the total annual health care expense under each of the one or more scenarios to quantify a plurality of risks by item, employee group and external factor, and outputs said plurality of risks by item, employee group and external factor for each of the segments of value of the organization where the simulation model is iterated as required to ensure a convergence of a frequency distribution of one or more output variables and where the one or more scenarios are selected from the group consisting of normal, a negative scenario created by a genetic algorithm and extreme where the extreme scenario is developed by using a peak over threshold algorithm. 12. The artificial intelligence system of claim 7, wherein the at least one computer processor associated with the one or more data storage devices completes one or more additional tasks selected from the group consisting of: identifying, displaying and optionally implementing one or more changes by item that optimize value, risk or a combination thereof for the organization for one of the scenarios; identifying, displaying and optionally implementing one or more changes by employee group that optimize value, risk or a combination thereof for the organization health plan for one of the scenarios; identifying, displaying and optionally implementing an optimal set of risk transfer transactions for the health plan and the organization for one or more of the scenarios, identifying, displaying and optionally implementing one or more changes by item that place the organization on a resilient frontier for one of the scenarios; developing and outputting a custom risk transfer program for the organization, the health plan or a combination thereof for one or more of the scenarios; outputting one or more resilience index values; developing and offering for sale one or more securities where one or more of the terms of said securities comprise one or more of the resilience index values where said securities transfer one or more risks from the plurality of risks or transfer the plurality of risks for one of more of the employee groups of the health plan, the organization or a combination thereof for one or more of the scenarios. 13. The artificial intelligence system of claim 7, wherein the at least one computer processor associated with the one or more data storage devices completes one or more tasks selected from the group consisting of: prepare data regarding each of one or more existing investments and each of one or more existing liabilities for a financial service provider for processing; repeat the processing of claim 14 for a plurality of organizations and at least one health plans for each of the plurality of organization; develop and output a customized risk transfer program for each of the organizations and each of the health plans for each of the scenarios before completing one or more activities selected from the group consisting of: identify and output an optimal set of transactions for each of the plurality of organizations for each of the scenarios, develop and offer for sale one or more securities that transfer one or more risks from the plurality of risks of one or more of the plurality of health plans, one or more of the plurality of organizations or a combination thereof for one of the scenarios; developing and offering for sale one or more insurance policies that transfer one or more risks from the plurality of risks of one or more of the plurality of health plans, one or more of the plurality of organizations or a combination thereof for one or more of the scenarios; identify, display and optionally implement one or more changes by item that place one of the plurality of organizations on a resilient frontier for one of the scenarios; output one or more resilience index values; develop and offer for sale one or more securities where one or more of the terms of said securities comprise one or more of the resilience index values where said securities transfer one or more risks from the plurality of risks or transfer the plurality of risks for one of more of the employee groups of the plurality of health plans, the plurality of organizations or a combination thereof for one or more of the scenarios. 14. A non-transitory computer readable storage medium storing instructions executable by a data processing apparatus that upon such execution cause the data processing apparatus to perform operations comprising: receive a plurality of predictive modeling training data; partition the training data into a plurality of subsamples; train a plurality of different types of predictive models with the plurality of subsamples and one or more training methods before selecting one or more variables from each trained predictive model with a variable selection algorithm and storing said data in one or more data storage devices; train a plurality of different types of predictive causal models with the selected data and the one or more training methods; select a trained predictive causal model using a model selection algorithm; and provide access to the selected predictive causal model, where the model selection algorithm comprises a k-fold cross validation algorithm. 15. The non-transitory computer readable storage medium of claim 14, wherein the operations further comprise: select one or more variables from each trained causal predictive model with the variable selection algorithm and store said causal model variables in the one or more data storage devices; retrain the plurality of different types of predictive models with the selected causal model variables and the one or more training methods; select a retrained predictive model using the model selection algorithm; and provide access to the selected retrained predictive model. 16. The non-transitory computer readable storage medium of claim 15, wherein the plurality of different types of predictive models are selected from the group consisting of ridge regression, projection pursuit regression; stepwise regression; power law, elastic net, classification and regression tree; generalized additive model (GAM), redundant regression network; linear regression; multivariate adaptive regression splines; neural network, primal graphical lasso, and adaptive context tree. 17. The non-transitory computer readable storage medium of claim 16, wherein the one or more training methods are selected from the group consisting of path-wise cyclical coordinated descent, flexible non-linear smoother, known causal relationship testing, F test series, minimize Kolmogorov-Smirnov statistic for cumulative distribution functions, cubic spline smoother, coordinate descent, greedy algorithm, induction, scatterplot smoother, ordinary least squares, automatic forward and backward pass, loglikelihood comparison, back propagation, forward propagation, genetic algorithm, block coordinate descent, discounted Krichevsky-Trofimov estimator, methods incorporated in an algorithm for the predictive model type, least absolute shrinkage and selection operator (LASSO), minimum message length, best first search, iterate to score every combination, greedy algorithm, and least angle regression and shrinkage (LARS). 18. The non-transitory computer readable storage medium of claim 15, wherein the plurality of different types of causal predictive models are selected from the group consisting of Bayesian, randomized causation coefficient, Tetrad, information geometric inference, LaGrange, causal additive noise and path analysis. 19. They non-transitory computer readable storage medium of claim 15, wherein the data processing apparatus comprises a computer with at least one processor and wherein the access to the final predictive models comprises access using an Internet or other network. 20. The non-transitory computer readable storage medium of claim 15, wherein the k-fold cross validation algorithm comprises a 10 fold cross validation algorithm, wherein the variable selection algorithm comprises a stepwise regression algorithm and wherein the data processing apparatus comprises a computer with at least one processor and the one or more data storage devices.