System and methods for machine learning are described. A first input value is obtained. A second input value is also obtained. A decision to use for generating a cycle output is selected based on a randomness factor. The decision is at least one of a random decision or a best decision from a previous cycle. A cycle output for the first and second inputs is generated using the selected decision. The selected decision and the resulting cycle output are stored.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method by a decision making neuron for machine learning, comprising: obtaining a first input; obtaining a second input; selecting a decision to use for generating a cycle output based on a randomness factor, wherein the decision is at least one of a random decision and a best decision from a previous cycle; generating a cycle output for the first and second inputs using the selected decision; and storing the selected decision and the resulting cycle output. 2. The method of claim 1, wherein the randomness factor at least one of increases and decreases from a first cycle to a second cycle based on at least one of a predetermined number of cycles and a threshold. 3. The method of claim 1, further comprising: determining a best decision based on a plurality of selected decisions and their resulting cycle outputs. 4. The method of claim 3, wherein determining the best decision based on the plurality of selected decisions and their resulting cycle outputs comprises: determining that the first input has a linear relationship with a third input, wherein the third input is associated with a second decision making neuron; obtaining a plurality of selected decisions and their resulting cycle outputs from the second decision making neuron; comparing the obtained selected decisions and their resulting cycle outputs with a plurality of stored selected decisions and their resulting cycle outputs; and identifying a best decision based on the comparison. 5. The method of claim 3, wherein determining the best decision based on the plurality of selected decisions and their resulting cycle outputs comprises: identifying a critical data point based on the plurality of selected decisions and their resulting cycle outputs; and determining the best decision based on the identified critical data point. 6. The method of claim 3, wherein determining the best decision based on the plurality of selected decisions and their resulting cycle outputs comprises: obtaining a plurality of global outputs, wherein a global output is based on at least one cycle output from at least one decision making neuron, and wherein the at least one cycle output includes the generated cycle output; identifying a trend between the stored cycle outputs and the plurality of global outputs, wherein the trend relates stored cycle outputs with desirable global outputs; and determine the best decision based on the identified trend. 7. The method of claim 6, wherein determining the best decision based on the identified trend comprises: identifying a stored decision where the resulting cycle output leads the trend, wherein the identified decision is the best decision. 8. The method of claim 6, further comprising: associating each global output of the plurality of global outputs with its corresponding stored cycle output. 9. The method of claim 1, wherein the random decision is based at least in part on the best decision. 10. The method of claim 1, further comprising: identifying a first data type for the first input; identifying a second data type for the second input; and defining a third data type for the cycle output based on the identified first data type and the identified second data type. 11. The method of claim 10, wherein the first data type, second data type, and third data type are each selected from the group consisting of: Boolean, integer, integer, double, picture, geolocation, and user-defined data type; and wherein the selected data type is within a defined range. 12. An apparatus for machine learning, comprising: a processor; memory in electronic communication with the processor; and instructions stored in the memory, the instructions being executable by the processor to: obtain a first input; obtain a second input; select a decision to use for generating a cycle output based on a randomness factor, wherein the decision is at least one of a random decision and a best decision from a previous cycle; generate a cycle output for the first and second inputs using the selected decision; and store the selected decision and the resulting cycle output. 13. The apparatus of claim 12, wherein the randomness factor at least one of increases and decreases from a first cycle to a second cycle based on a predetermined number of cycles and a threshold. 14. The apparatus of claim 12, wherein the instructions are further executable by the processor to: determine a best decision based on a plurality of selected decisions and their resulting cycle outputs. 15. The apparatus of claim 14, wherein the instructions to determine the best decision based on the plurality of selected decisions and their resulting cycle outputs comprise instructions executable by the processor to: determine that the first input has a linear relationship with a third input, wherein the third input is associated with a second decision making neuron; obtain a plurality of selected decisions and their resulting cycle outputs from the second decision making neuron; compare the obtained selected decisions and their resulting cycle outputs with a plurality of stored selected decisions and their resulting cycle outputs; and identify a best decision based on the comparison. 16. The apparatus of claim 14, wherein the instructions to determine the best decision based on the plurality of selected decisions and their resulting cycle outputs comprise instructions executable by the processor to: identify a critical data point based on the plurality of selected decisions and their resulting cycle outputs; and determine the best decision based on the identified critical data point. 17. The apparatus of claim 14, wherein the instructions to determine the best decision based on the plurality of selected decisions and their resulting cycle outputs comprise instructions executable by the processor to: obtain a plurality of global outputs, wherein a global output is based on at least one cycle output from at least one decision making neuron, and wherein the at least one cycle output includes the generated cycle output; identify a trend between the stored cycle outputs and the plurality of global outputs, wherein the trend relates stored cycle outputs with desirable global outputs; and determine the best decision based on the identified trend. 18. The apparatus of claim 17, wherein the instructions to determine the best decision based on the identified trend comprise instructions executable by the processor to: identify a stored decision where the resulting cycle output leads the trend, wherein the identified decision is the best decision. 19. The apparatus of claim 17, wherein the instructions are further executable by the processor to: associate each global output of the plurality of global outputs with its corresponding stored cycle output. 20. The apparatus of claim 12, wherein the random decision is based at least in part on the best decision. 21. The apparatus of claim 12, wherein the instructions are further executable by the processor to: identify a first data type for the first input; identify a second data type for the second input; and define a third data type for the cycle output based on the identified first data type and the identified second data type. 22. A method for machine learning, comprising: determining a first data type associated with a first input; determining a second data type associated with a second input; determining a first finite number of first parameters associated with the first data type; determining a second finite number of second parameters associated with the second data; and pairing each of the first parameters with each of the second parameters, wherein each pair of a first parameter and a second parameter is associated with a pair of inputs to a decision making neuron. 23. The method of claim 22, further comprising: obtaining a first value corresponding to a first parameter and a second value corresponding to a second parameter; and creating a decision making neuron for processing the first parameter and the second parameter, wherein the decision making neuron selects a decision to use for generating a cycle output based on a randomness factor. 24. The method of claim 22, further comprising: obtaining a result based at least on the selected decision; correlating the obtained results with metadata; and using the metadata to refine a solution. 25. A method for machine learning, comprising: obtaining a first input; obtaining a second input; identifying that the first input has a linear relationship with a third input, wherein the third input is processed by a second decision making neuron that is different from the first decision making neuron; obtaining a plurality of selected decisions and their resulting cycle outputs from the second decision making neuron; comparing the obtained selected decisions and their resulting cycle outputs with a plurality of stored selected decisions and their resulting cycle outputs; and determining a decision to use for generating a cycle output based on the comparison; generating a cycle output for the first and second inputs using the determined decision; and storing the selected decision and the resulting cycle output. 26. The method of claim 25, wherein determining the decision to use comprises: identifying a trend between the plurality of stored selected decisions and their resulting cycle outputs and a plurality of global outputs, wherein the trend relates cycle outputs with desirable global outputs; and identifying from the comparison a selected decision where the resulting cycle output leads the trend, wherein the identified decision comprises the decision.