Abstract: The techniques disclosed herein enable systems to solve routing problems using machine learning augmented by optimization modules. To plot a route, a system receives a plurality of nodes from a problem space. The plurality of nodes is then analyzed by an optimization module and ranked based on various criteria such as distance from a reference node and deadline. Based on the ranking, the optimization module can select a smaller subset of nodes that is then processed by a machine learning model. The machine learning model can then select a node from the subset of nodes for addition to a route. This process can be repeated until a route is plotted for the full set of nodes within the problem space. In addition, the system can be configured to monitor current conditions of the problem space to modify the route in response to changes.

Abstract: Technologies for reinforcement learning-based optimization of manufacturing lines are disclosed. A reinforcement learning-based selector is trained utilizing reinforcement learning to select a reinforcement learning-based controller for controlling the operation of machines on a manufacturing line. The selection can be made based upon inputs from the machines on the manufacturing line indicating whether machines on the line are jammed, whether the manufacturing line is operating at a steady state, or other conditions. The selected reinforcement learning-based controller can generate outputs to adjust parameters of the machines on the manufacturing line, such as operating speed, in order to recover from the jamming of one or more machines, to transition to a steady state of operation, or to operate the manufacturing line in a steady state of operation. The selector and the reinforcement learning-based controllers can be trained using reinforcement learning on a simulation of the manufacturing line.

Abstract: A system for machine learning assisted speech scoring can include a neural network, a memory for storing executable software code, and a processor. The executable software code can include a software framework, a preprocessing submodule, a transcriber class, a confidence submodule, and an application programming interface. The processor can implement commands, including instantiating transcribers from the transcriber class, invoking the preprocessing submodule, and ensembling the transcribers. The preprocessing submodule can be configured to downsample a raw audio file into an audio file. Each node of the neural network can have one or more of the transcribers. The transcribers can be configured to create text from the audio file.