Abstract: In one embodiment, a device tracks an object in video data captured by one or more cameras. The device represents spatial characteristics of the object over time as one or more timeseries. The device associates different portions of the one or more timeseries with behavioral regimes of the object. The device provides a portion of the video data for display as part of an alert that is based on a rate of change of transitions between the behavioral regimes of the object.

Abstract: In one embodiment, a device represents spatial characteristics over time of an object in video data as one or more timeseries. The device detects an event based on a rate of change of behavioral regimes associated with different portions of the one or more timeseries. The device selects contextual data for the event that comprises spatial timeseries information for different types of objects or different activities. The device provides an alert for the event to a user interface regarding the event that includes the contextual data.

Abstract: In one embodiment, a device obtains data models and workflow logic for a visual programming environment. The device constructs, based on the data models and workflow logic for the visual programming environment, a metamodel that comprises a knowledge graph. The device makes, using the metamodel, an evaluation of an interaction between a user and the visual programming environment. The device provides, based on the evaluation, visualization data to a user interface of the visual programming environment.

Abstract: In one embodiment, a device obtains data regarding generation of an artificial intelligence model. The device analyzes the data using a neuro-symbolic metamodel, to match the data to one or more concepts of a knowledge graph of the neuro-symbolic metamodel. The device makes one or more inferences about the artificial intelligence model, by applying a semantic reasoning engine to the one or more concepts of the knowledge graph. The device causes, based on the one or more inferences, generation of a replacement artificial intelligence model for the artificial intelligence model.

Abstract: In one embodiment, a device converts video data into a set of tracklets, each tracklet representing a different object depicted in the video data. The device identifies a particular object depicted in the video data as being an attractor or repulsor with respect to one or more other objects depicted in the video data, based on an analysis of their respective tracklets. The device makes, using a semantic reasoning engine, an inference about the video data, based in part on the particular object being identified as an attractor or repulsor. The device provides data based on the inference for display.

Abstract: In one embodiment, a device provides information for display regarding an artificial intelligence metamodel that includes at least a symbolic layer and a sub-symbolic layer. The device receives an indication of a target node in a network to which the artificial intelligence metamodel is to be deployed. The device generates a modified version of the artificial intelligence metamodel based on resources available at the target node. The device deploys the modified version of the artificial intelligence metamodel for execution by the target node.

Abstract: In one embodiment, a device identifies, using a semantic reasoning engine, activities in a location, based on sensor data obtained from a plurality of sensors deployed to the location. The device associates the activities with areas of the location in which they occurred. The device makes, using the semantic reasoning engine, an inference about a particular activity, based in part on where that activity occurred. The device raises, based on the inference, an alert regarding the particular activity.

Abstract: In one embodiment, a device detects a particular activity from sensor data generated by one or more sensors in a sensor network. The device identifies, using a semantic reasoning engine, relevant preceding activities to the particular activity that are relevant to the particular activity. The device makes, using the semantic reasoning engine, an inference about the relevant preceding activities and the particular activity. The device provides an activity timeline for display that indicates the particular activity, the relevant preceding activities, and the inference about the relevant preceding activities and the particular activity.

Abstract: In one embodiment, a device detects an object in an area based on sensor data generated by a plurality of sensors deployed to that area. The device uses a semantic reasoning engine to determine an appearance type of the object, based on the sensor data. The device uses the semantic reasoning engine to determine a behavioral type of the object, based on the sensor data. The device makes, using the semantic reasoning engine, a determination that the behavioral type of the object does not match the appearance type of the object. The device provides an indication of the determination for display.

Abstract: In one embodiment, a device extracts a voice command from audio data captured by a microphone. The device uses a semantic reasoning engine, to determine a goal of the voice command. The device determines that the goal of the voice command is consistent with prior voice commands issued to the device. The device raises an alert when the goal of the voice command is inconsistent with prior voice commands issued to the device.

Abstract: In one embodiment, a device receives sensor data from a plurality of sensors. The device detects, using a semantic reasoning engine, a disruption to a first shipment based on the sensor data. The device infers, using the semantic reasoning engine, that one or more other shipments are related to the first shipment. The device initiates a mitigation action for the disruption that is performed with respect to the one or more other shipments.

Abstract: In one embodiment, a device makes an inference about an event indicated by sensor data from a plurality of sources by applying a semantic reasoning engine to the sensor data. The device receives a selected semantic compression level from a user interface. The device selects a subset of the sensor data based on the inference and on the selected semantic compression level. The device exports the subset of the sensor data and the inference made by the semantic reasoning engine about the event.

Abstract: In one embodiment, a device obtains data models and workflow logic for a visual programming environment. The device constructs, based on the data models and workflow logic for the visual programming environment, a metamodel that comprises a knowledge graph. The device makes, using the metamodel, an evaluation of an interaction between a user and the visual programming environment. The device provides, based on the evaluation, visualization data to a user interface of the visual programming environment.

Abstract: In one embodiment, a reasoning engine executed by a device, identifies one or more structural breaks in a time series for a particular metric regarding a computer network. The reasoning engine associates the one or more structural breaks in the time series data with a network event. The reasoning engine determines, using symbolic reasoning, a root cause for the network event based on a symbolic knowledge base maintained by the reasoning engine. The reasoning engine provides an indication of the determined root cause for the network event to one or more devices.

Abstract: In one embodiment, a device maintains a metamodel that describes a monitored system. The metamodel comprises a plurality of layers ranging from a sub-symbolic space to a symbolic space. The device tracks updates to the metamodel over time. The device updates the metamodel based in part on sub-symbolic time series data generated by the monitored system. The device receives, from a learning agent, a request for the updates to a particular layer of the metamodel associated with a specified time period. The device provides, to the learning agent, data indicative of one or more updates to the particular layer of the metamodel associated with the specified time period.