Scalable-effort machine learning may automatically and dynamically adjust the amount of computational effort applied to input data based on the complexity of the input data. This is in contrast to fixed-effort machine learning, which uses a one-size-fits-all approach to applying a single classifier algorithm to both simple data and complex data. Scalable-effort machine learning involves, among other things, classifiers that may be arranged as a series of multiple classifier stages having increasing complexity (and accuracy). A first classifier stage may involve relatively simple machine learning models able to classify data that is relatively simple. Subsequent classifier stages have increasingly complex machine learning models and are able to classify more complex data. Scalable-effort machine learning includes algorithms that can differentiate among data based on complexity of the data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-20. (canceled) 21. A system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, configure the one or more processors to perform operations comprising: receiving an input value; iteratively performing the following steps until a predetermined threshold is exceed: applying a level of complexity of a machine learning model to the input value; determining whether a current level of complexity of the level of complexity is able to classify the input value; determining whether the predetermined threshold has been exceeded when the current level of complexity is not able to classify the input value; and increasing the level of complexity of the machine learning model when the current level of complexity is able to classify the input value. 22. The system of claim 21, wherein an amount of computing time used to classify the input value depends, at least in part, on a first level of complexity of the current level of complexity of the machine learning model. 23. The system of claim 21, the operations further comprising: if a first level of complexity of the level of complexity of the machine learning model is able to classify the input value, classifying the input value into one of two or more categories. 24. The system of claim 21, wherein applying the level of complexity of the machine learning model to the input value includes: applying at least one biased first level of complexity to the input value to generate at least one class label. 25. The system of claim 23, wherein applying at least one biased first level of complexity to the input value to generate at least one class label includes: applying a negatively biased first level of complexity to the input value to generate a first class label; and applying a positively biased first level of complexity to the input value to generate a second class label. 26. The system of claim 25, wherein determining whether the current level of complexity is able to classify the input value comprises: comparing the first class label to the second class label; and determining whether a consensus exists between the negatively biased first level of complexity and the positively biased first level of complexity based, at least in part, on the comparing. 27. The system of claim 25, the operations further comprising: adjusting one or both of i) the negatively biased first level of complexity and ii) the positively biased first level of complexity to modify a likelihood that the current level of complexity is able to classify the input value with a predetermined confidence level. 28. The system of claim 21, wherein the input value is based, at least in part, on collected information from one or more of the following: capturing an image, capturing an audio sample, or receiving a search query. 29. A computing device comprising: an input port to receive an input value having an initial level of complexity; a memory device storing a plurality of machine learning models, wherein abilities of the machine learning models to classify the input value are different from one another; and a processor to apply one or more of the plurality of the machine learning models based, at least in part, on the initial level of complexity of the input value, wherein the processor is configured to iteratively apply an increasing level of complexity of the machine learning models to the input value. 30. The computing device of claim 29, wherein the abilities of the machine learning models to classify the input value comprise: the abilities of the machine learning models to classify the input value into one of two or more categories. 31. The computing device of claim 29, wherein the configuration of the processor to iteratively apply the increasing level of complexity of the machine learning models to the input value includes the processor being configured to: iteratively perform the following steps until a predetermined threshold is exceed: apply a level of complexity of a machine learning model to the input value; determine whether a current level of complexity of the level of complexity is able to classify the input value; determine whether the predetermined threshold has been exceeded when the current level of complexity is not able to classify the input value; and increase the level of complexity of the machine learning model when the current level of complexity is able to classify the input value. 32. The computing device of claim 31, wherein the configuration of the processor to apply the level of complexity of the machine learning model to the input value includes the processor being configured to: apply at least one biased first level of complexity to the input value to generate at least one class label. 33. The computing device of claim 32, wherein the configuration of the processor to apply at least one biased first level of complexity to the input value to generate at least one class label includes the processor being configured to: apply a negatively biased level of complexity to the input value to generate a first class label; and apply a positively biased level of complexity to the input value to generate a second class label. 34. The computing device of claim 33, wherein the processor is configured to: compare the first class label to the second class label; and determine whether a consensus exists between the negatively biased level of complexity and the positively biased level of complexity based, at least in part, on the comparing. 35. The computing device of claim 33, wherein the processor is configured to: adjust one or both of i) the negatively biased level of complexity and ii) the positively biased level of complexity to modify a likelihood that the level of complexity is able to classify the input value. 36. The computing device of claim 29, wherein the processor is configured to apply the plurality of the machine learning models on the input value sequentially in order of increasing ability of the machine learning models to classify the input value. 37. The computing device of claim 29, wherein a computing cost of classifying the input value is proportional to the initial level of complexity of the input value. 38. One or more computer-readable storage media of a client device storing computer-executable instructions that, when executed by one or more processors of the client device, configure the one or more processors to perform operations comprising: receiving an input value; iteratively performing the following steps until a predetermined threshold is exceed: applying a level of complexity of a machine learning model to the input value; determining whether a current level of complexity of the level of complexity is able to classify the input value; determining whether the predetermined threshold has been exceeded when the current level of complexity is not able to classify the input value; and increasing the level of complexity of the machine learning model when the current level of complexity is able to classify the input value. 39. The one or more computer-readable storage media of claim 38, wherein applying the level of complexity of the machine learning model to the input value includes: applying at least one biased first level of complexity to the input value to generate at least one class label. 40. The one or more computer-readable storage media of claim 39, wherein applying at least one biased first level of complexity to the input value to generate at least one class label includes: applying a negatively biased first level of complexity to the input value to generate a first class label; and applying a positively biased first level of complexity to the input value to generate a second class label.