An inference engine is described for efficient machine learning. For example, an inference engine executes a plurality of ordered steps to carry out inference on the basis of observed data. For each step, a plurality of inputs to the step are received. A predictor predicts an output of the step and computes uncertainty of the prediction. Either the predicted output or a known output is selected on the basis of the uncertainty. If the known output is selected, the known output is computed, (for example, using a resource intensive, accurate process). The predictor is retrained using the known output and the plurality of inputs of the step as training data. For example, computing the prediction is fast and efficient as compared with computing the known output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An inference engine comprising: a computation logic arranged to carry out a plurality of ordered process steps; at least one uncertainty aware predictor having been trained to learn associations between inputs to one of the process steps and an output of the process step; the computation logic arranged to, receive a plurality of inputs to one of the process steps, and obtain a predicted output and uncertainty of the prediction using the predictor; the computation logic arranged to select either the predicted output or a known output on the basis of the uncertainty, and, if the known output is selected, compute the known output and use the known output and the plurality of inputs of the step to train the predictor using online training. 2. The inference engine of claim 1 wherein the inputs to the process step comprise image data from a frame of an image capture device. 3. The inference engine of claim 1 wherein the inputs to the process steps comprise parameters of one or more probability distributions representing a probabilistically defined variable. 4. The inference engine of claim 2 wherein the predicted output comprises a pose of an object depicted in the image data. 5. The inference engine of claim 3 wherein the predicted output comprises a factor to variable node message of a message passing algorithm. 6. The inference engine of claim 1 wherein computing the known output is computationally resource intensive as compared with obtaining the predicted output and the uncertainty of the prediction. 7. The inference engine of claim 1 wherein the uncertainty aware predictor comprises a first predictor and a second predictor, the first predictor having been trained to learn associations between inputs to one of the process steps and an output of the process step, and the second predictor having been trained to predict uncertainty associated with predictions of the first predictor. 8. A method at an inference engine comprising: executing a plurality of ordered steps to carry out inference on the basis of observed data; for each step, receiving a plurality of inputs to the step, and computing a predicted output and uncertainty of the prediction using a predictor; for each step, selecting either the predicted output or a known output on the basis of the uncertainty, and, if the known output is selected, computing the known output and retraining the predictor using the known output and the plurality of inputs of the step as training data. 9. A method as claimed in claim 8 wherein each of the steps comprises a message computation, each of the inputs being a message. 10. A method as claimed in claim 8 wherein each ordered step is a model fitting algorithm for tracking pose of an entity depicted in captured image data. 11. A method as claimed in claim 8 wherein the predictor is any of a random decision forest, a discriminative fern ensemble, a jungle of directed acyclic graphs, a support vector machine. 12. A method as claimed in claim 8 wherein the predictor is a random decision forest having been trained using an objective function which splits the training data at split nodes of the trees of the forest in a way that the relationship between inputs to the step and an output is well captured by a polynomial regression fitted to training examples reaching each child of a split node. 13. A method as claimed in claim 8 wherein computing the predicted output comprises inputting parameters of the inputs to the step to a random decision forest and aggregating results from a plurality of trees of the random decision forest using a moment average. 14. A method as claimed in claim 8 wherein retraining the predictor comprises pushing the training data through individual trees of a random decision forest and updating data stored at a leaf of each individual tree using the training data. 15. A method as claimed in claim 8 comprising selecting either the predicted output or the known output using a threshold uncertainty, the threshold uncertainty being pre-configured using training data. 16. A method as claimed in claim 15 comprising dynamically adjusting the threshold on the basis of frequency of selection of the known output. 17. One or more tangible device-readable media with device-executable instructions that, when executed by a computing system, direct the computing system to perform steps comprising: executing a plurality of ordered steps to carry out inference on the basis of observed data; for an individual step, receiving a plurality of inputs to the step, and computing a predicted output and uncertainty of the prediction using a predictor; for the individual step, selecting either the predicted output or a known output on the basis of the uncertainty, and, if the known output is selected, computing the known output and retraining the predictor using the known output and the plurality of inputs of the step as training data. 18. The one or more tangible device-readable media of claim 17 wherein computing the known output is computationally resource intensive as compared with obtaining the predicted output and the uncertainty of the prediction. 19. The one or more tangible device-readable media of claim 17 wherein each of the steps comprises a message computation, each of the inputs being a message. 20. The one or more tangible device-readable media of claim 17 wherein each ordered step is a model fitting algorithm for tracking pose of an entity depicted in captured image data.