Abstract: The disclosed embodiments include computer-implemented processes that generate adaptive textual explanations of output using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during a second temporal interval. Further, and based on an application of a trained explainability process to the input dataset, the apparatus may generate an element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, where the element of textual content is associated with a feature value of the input dataset. The apparatus may also transmit a portion of the output data and the element of textual content to a computing system.

Abstract: The disclosed embodiments include computer-implemented systems and processes that train adaptively and deployment of coupled machine-learning and explainability processes within distributed computing environments. By way of example, an apparatus may receive first interaction data associated with a first temporal interval from a computing system. Based on an application of a first and a second trained artificial-intelligence process to an input dataset that includes at least a subset of the first interaction data, the apparatus may generate output data indicative of a predicted likelihood of an occurrence of a target event during a second temporal interval, and may generate explainability data that characterizes the predicted likelihood. The apparatus may also transmit portions the output and explainability data to the computing system, and the computing system may modify an operation of an executed application program in accordance with at least one the output or explainability data.

Abstract: The disclosed embodiments include computer-implemented processes that generate adaptive textual explanations of output using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during a second temporal interval. Further, and based on an application of a trained explainability process to the input dataset, the apparatus may generate an element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, where the element of textual content is associated with a feature value of the input dataset. The apparatus may also transmit a portion of the output data and the element of textual content to a computing system.

Abstract: The disclosed embodiments include computer-implemented processes that flexibly and dynamically analyze a machine learning process, and that generate analytical output characterizing an operation of the machine learning process across multiple analytical periods. For example, an apparatus may receive an identifier of a dataset associated with the machine learning process and feature data that specifies an input feature of the machine learning process. The apparatus may access at least a portion of the dataset based on the received identifier, and obtain, from the accessed portion of the dataset, a feature vector associated with the machine learning process.

Abstract: The disclosed embodiments include computer-implemented processes that flexibly and dynamically analyze a machine learning process, and that generate analytical output characterizing an operation of the machine learning process across multiple analytical periods. For example, an apparatus may receive an identifier of a dataset associated with the machine learning process and feature data that specifies an input feature of the machine learning process. The apparatus may access at least a portion of the dataset based on the received identifier, and obtain, from the accessed portion of the dataset, a feature vector associated with the machine learning process.

Abstract: A modeling system trains a recurrent machine-learned model by determining a latent distribution and a prior distribution for a latent state. The parameters of the model are trained based on a divergence loss that penalizes significant deviations between the latent distribution the prior distribution. The latent distribution for a current observation is a distribution for the latent state given a value of the current observation and the latent state for the previous observation. The prior distribution for a current observation is a distribution for the latent state given the latent state for the previous observation independent of the value of the current observation, and represents a belief about the latent state before input evidence is taken into account.

Abstract: The disclosed embodiments include computer-implemented processes that flexibly and dynamically analyze a machine learning process, and that generate analytical output characterizing an operation of the machine learning process across multiple analytical periods. For example, an apparatus may receive an identifier of a dataset associated with the machine learning process and feature data that specifies an input feature of the machine learning process. The apparatus may access at least a portion of the dataset based on the received identifier, and obtain, from the accessed portion of the dataset, a feature vector associated with the machine learning process.

Abstract: A modeling system trains a recurrent machine-learned model by determining a latent distribution and a prior distribution for a latent state. The parameters of the model are trained based on a divergence loss that penalizes significant deviations between the latent distribution the prior distribution. The latent distribution for a current observation is a distribution for the latent state given a value of the current observation and the latent state for the previous observation. The prior distribution for a current observation is a distribution for the latent state given the latent state for the previous observation independent of the value of the current observation, and represents a belief about the latent state before input evidence is taken into account.

Abstract: To provide explanations for black box computer models, data samples are processed by the model to determine related feature attributions for each data sample, describing the extent to which feature values affect the model predictions for that data sample. A group of data samples is selected to be explained and the group is clustered into subgroups based on the feature attributions of the data samples. Because explanations related to feature attributions can be difficult to interpret or relate to input features, each of the subgroups is then described in the feature space, enabling ready interpretation of the groups at a semi-local level.

Abstract: A medical scope device such as an endoscope is produced using a cast aluminum process including a molten casting aluminum alloy including a maximum of 0.2-0.3% Si and at least 5% Zn. The process includes providing an investment casting mold, casting the aluminum alloy in the mold to create a component and removing the mold from the component, post-machining the component to meet a desired specification, and after post-machining the component, performing surface finishing, such as centrifugal barrel finishing (CBF) sufficient to remove impurities on casting surfaces by 2-3 mils, then coating the component with a micro-crystalline aluminum anodic coating of at least 0.5 mil thickness. A medical scope and product-by-process is also provided employing such techniques.

Abstract: A medical scope device such as an endoscope is produced using a cast aluminum process including a molten casting aluminum alloy including a maximum of 0.2-0.3% Si and at least 5% Zn. The process includes providing an investment casting mold, casting the aluminum alloy in the mold to create a component and removing the mold from the component, post-machining the component to meet a desired specification, and after post-machining the component, performing surface finishing, such as centrifugal barrel finishing (CBF) sufficient to remove impurities on casting surfaces by 2-3 mils, then coating the component with a micro-crystalline aluminum anodic coating of at least 0.5 mil thickness. A medical scope and product-by-process is also provided employing such techniques.

Abstract: A model evaluation system evaluates the effect of a feature value at a particular time in a time-series data record on predictions made by a time-series model. The time-series model may make predictions with black-box parameters that can impede explainability of the relationship between predictions for a data record and the values of the data record. To determine the relative importance of a feature occurring at a time and evaluated at an evaluation time, the model predictions are determined on the unmasked data record at the evaluation time and on the data record with feature values masked within a window between the time and the evaluation time, permitting comparison of the evaluation with the features and without the features. In addition, the contribution at the initial time in the window may be determined by comparing the score with another score determined by masking the values except for the initial time.

Abstract: The disclosed embodiments include computer-implemented processes that generate adaptive textual explanations of output using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during a second temporal interval. Further, and based on an application of a trained explainability process to the input dataset, the apparatus may generate an element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, where the element of textual content is associated with a feature value of the input dataset. The apparatus may also transmit a portion of the output data and the element of textual content to a computing system.

Abstract: A medical scope device such as an endoscope is produced using a cast aluminum process including a molten casting aluminum alloy including a maximum of 0.2 - 0.3% Si and at least 5% Zn. The process includes providing an investment casting mold, casting the aluminum alloy in the mold to create a component and removing the mold from the component, post-machining the component to meet a desired specification, and after post-machining the component, performing surface finishing, such as centrifugal barrel finishing (CBF) sufficient to remove impurities on casting surfaces by 2 - 3 mils, then coating the component with a micro-crystalline aluminum anodic coating of at least 0.5 mil thickness. A medical scope and product-by-process is also provided employing such techniques.

Abstract: A model visualization system analyzes model behavior to identify clusters of data instances with similar behavior. For a selected feature, data instances are modified to set the selected feature to different values evaluated by a model to determine corresponding model outputs. The feature values and outputs may be visualized in an instance-feature variation plot. The instance-feature variation plots for the different data instances may be clustered to identify latent differences in behavior of the model with respect to different data instances when varying the selected feature. The number of clusters for the clustering may be automatically determined, and the clusters may be further explored by identifying another feature which may explain the different behavior of the model for the clusters, or by identifying outlier data instances in the clusters.

Abstract: The disclosed embodiments include computer-implemented systems and processes that predict occurrences of targeted attrition events using trained artificial-intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a targeted participant during a first temporal interval. Based on an application of a trained artificial-intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an attrition event involving the targeted participant during a second temporal interval that is disposed subsequent to the first temporal interval, and that is separated from the first temporal interval by a buffer interval.

Abstract: The disclosed embodiments include computer-implemented processes that flexibly and dynamically analyze a machine learning process, and that generate analytical output characterizing an operation of the machine learning process across multiple analytical periods. For example, an apparatus may receive an identifier of a dataset associated with the machine learning process and feature data that specifies an input feature of the machine learning process. The apparatus may access at least a portion of the dataset based on the received identifier, and obtain, from the accessed portion of the dataset, a feature vector associated with the machine learning process.

Abstract: A modeling system trains a recurrent machine-learned model by determining a latent distribution and a prior distribution for a latent state. The parameters of the model are trained based on a divergence loss that penalizes significant deviations between the latent distribution the prior distribution. The latent distribution for a current observation is a distribution for the latent state given a value of the current observation and the latent state for the previous observation. The prior distribution for a current observation is a distribution for the latent state given the latent state for the previous observation independent of the value of the current observation, and represents a belief about the latent state before input evidence is taken into account.

Abstract: A method of packaging an ink tank for shipment, the method comprising: (a) sealing a first substratum to an ink tank to inhibit fluid communication between an interior of the ink tank and an external environment by way of an ink outlet port of the ink tank, the resultant seal between the first substratum and the ink tank includes at least one of an apex at least partially defined by two substantially linear segments being angled from one another between about 20 degrees to about 160 degrees and fractions separating portions of the substrate from one another; and (b) sealing a second substratum to the ink tank to inhibit fluid communication between the interior of the ink tank and the external environment by way of an ink vent of the ink tank, where the first substratum and the second substratum are removable from the ink tank.

Abstract: Printing systems such as those comprising a printing device operable for depositing one or more inks upon a substrate and a drying device, such as one operable for emitting radiation having a pre-selected electromagnetic wavelength, for the purpose of drying the one or more inks in a predetermined time period subsequent to the deposition of the one or more inks upon the substrate, wherein the printing device and the drying device are operated at about the same moving speed. Methods of printing, such as those comprising depositing one or more inks onto a substrate using a printing device and drying the one or more deposited inks using a drying device, such as one operable for emitting pre-selected wavelengths of energy that are focused onto the one or more deposited inks in a predetermined time period subsequent to ink deposition, wherein the depositing and the drying are synchronized.