Systems and methods are provided for managing organizational or corporate structures, including the employee roles or activities administered by human resources. A portion of the role datasets received within human resource records may be used to generate role tokens comprising unique datasets that have been truncated and deduped. Such tokens may be extracted based on assigned prioritization scores, and further assigned training labels representing categorical levels. Predictive labels may be assigned to a remaining portion of the extracted tokens via a logistic regression classifier, and a model organizational dataset may be generated based on the assigned training labels and the assigned predictive labels. The prediction certainty of the role tokens in the model organizational dataset may be used to map the identified role tokens to the roles represented in the human resource records.

Logical boundaries enclosing a physical area are defined. A segment of the logical boundaries is defined as a directional gate, wherein traversing the gate into the physical area is defined as an ingress and traversing the gate out of the physical area is defined as an egress. The directional gate is monitored, and ingresses and egresses are detected. An occupancy count of the physical area is maintained, based on monitoring the gate and detecting ingresses and egresses. One or more conditions are tracked in addition to the occupancy count. Artificial intelligence (AI) processing is applied to the maintained occupancy count and the additional tracked condition(s), in real-time as the monitoring, maintaining and tracking are occurring. One or more responsive actions are automatically taken as a result of applying the AI processing to the maintained occupancy count and the additional tracked condition(s).

A system and method is provided that tests and determines whether candidate artificial intelligence model contains a Trojan from when it was trained and using the outcome determination of the Trojan to determine whether the candidate artificial intelligence model should be deployed. The system utilizes a first artificial intelligence that operates as a data generator and a second artificial intelligence that operates as a discriminator to determine whether the candidate artificial intelligence contains a Trojan. The first artificial intelligence combines sets of data with random Trojan triggers and the second artificial intelligence discriminates output classifications from the candidate artificial intelligence model to determine whether the Trojan is present based on probability outputs.

Aspects of the present disclosure relate to machine learning techniques for identifying the incremental impact of different past events on the likelihood that a target outcome will occur. The technology can use a recurrent neural network to analyze two different representations of an event sequence—one in which some particular event occurs, and another in which that particular event does not occur. The incremental impact of that particular event can be determined based on the calculated difference between the probabilities of the target outcome occurring after these two sequences.

Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning function based on a combination of the first objective function and the second objective function; executing a tuning operation of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparameter-based points along a convex Pareto optimal curve.

Methods, systems, and devices for automated feature selection and model generation are described. A device (e.g., a server, user device, database, etc.) may perform model generation for an underlying dataset and a specified outcome variable. The device may determine relevance measurements (e.g., stump R-squared values) for a set of identified features of the dataset and can reduce the set of features based on these relevance measurements (e.g., according to a double-box procedure). Using this reduced set of features, the device may perform a least absolute shrinkage and selection operator (LASSO) regression procedure to sort the features. The device may then determine a set of nested linear models—where each successive model of the set includes an additional feature of the sorted features—and may select a “best” linear model for model generation based on this set of models and a model quality criterion (e.g., an Akaike information criterion (AIC)).

Methods, systems, and devices for automated feature selection and model generation are described. A device (e.g., a server, user device, database, etc.) may perform model generation for an underlying dataset and a specified outcome variable. The device may determine relevance measurements (e.g., stump R-squared values) for a set of identified features of the dataset and can reduce the set of features based on these relevance measurements (e.g., according to a double-box procedure). Using this reduced set of features, the device may perform a least absolute shrinkage and selection operator (LASSO) regression procedure to sort the features. The device may then determine a set of nested linear models—where each successive model of the set includes an additional feature of the sorted features—and may select a “best” linear model for model generation based on this set of models and a model quality criterion (e.g., an Akaike information criterion (AIC)).

One or more computing devices, systems, and/or methods are provided. In an example, a method comprises receiving, by a device, incoming motion data from a motion sensor, generating, by the device, an incoming embedding vector based on the incoming motion data, generating, by the device, a predicted embedding vector based on the incoming embedding vector, assigning, by the device, an activity classification based on the predicted embedding vector, and modifying an operating parameter of the device based on the activity classification.

A system performs continuous delivery of a data pipeline on a cloud platform. The system receives a specification of the data pipeline that is split into smaller specifications of data pipeline units. The system identifies a target cloud platform and generates a deployment package for each data pipeline unit for the target cloud platform. The system creates a connection with the target cloud platform and uses the connection to provision computing infrastructure on the target cloud platform for the data pipeline unit according to the system configuration of the data pipeline unit. The data pipeline may be implemented as a data mesh that is a directed acyclic graph of nodes, each node representing a data pipeline unit. Different portions of the data mesh may be modified independent of each other. Partial results stored in different portions of the data mesh may be recomputed starting from different points in time.

A method to prove the safety (e.g., value-alignment) and other properties of artificial intelligence systems possessing general and/or super-human intelligence (together, AGI). The method uses probabilistic proofs in Interactive proof systems (IPS), in which a Verifier queries a computationally more powerful Prover and reduces the probability of the Prover deceiving the Verifier to any specified low probability (e.g., 2.sup.−100) IPS-based procedures can be used to test AGI behavior control systems that incorporate hard-coded ethics or valuelearning methods. An embodiment of the method, mapping the axioms and transformation rules of a behavior control system to a finite set of prime numbers, makes it possible to validate safe behavior via IPS number-theoretic methods. Other IPS embodiments can prove an unlimited number of AGI properties. Multi-prover IPS, program-checking IPS, and probabilistically checkable proofs extend the power of the paradigm. The method applies to value-alignment between future AGI generations of disparate power.