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 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 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 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 service that monitors a network detects a plurality of anomalies in the network. The service uses data regarding the detected anomalies as input to one or more machine learning models. The service maps, using a conceptual space, outputs of the one or more machine learning models to symbols. The service applies a symbolic reasoning engine to the symbols, to rank the anomalies. The service sends an alert for a particular one of the detected anomalies to a user interface, based on its corresponding rank.

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 transmits data characterizing a holographic image to a physical environment. A visible rendering of the holographic image is created in the physical environment. The device uses a reasoning engine to make an inference regarding a state of the physical environment based on sensor data captured in the physical environment indicative of one or more real-time characteristics of the physical environment. In response to the inference, the device adjusts the holographic image. Then, the device transmits data characterizing the adjusted holographic image to the physical environment, and a visible rendering of the adjusted holographic image is created in the physical environment.

Abstract: In one embodiment, a video analysis service receives video data captured by one or more cameras at a particular location. The service applies a neural network-based model to portions of the video data, to identify objects within the video data. The service maps outputs of the neural network-based model to symbols using a conceptual space. The outputs of the model comprise the identified objects. The service applies a symbolic reasoning engine to the symbols, to generate an alert. The service sends the alert to a user interface in conjunction with the video 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 video analysis service receives video data captured by one or more cameras at a particular location. The service applies a neural network-based model to portions of the video data, to identify objects within the video data. The service maps outputs of the neural network-based model to symbols using a conceptual space. The outputs of the model comprise the identified objects. The service applies a symbolic reasoning engine to the symbols, to generate an alert. The service sends the alert to a user interface in conjunction with the video data.

Abstract: A method comprises collecting, by a computing device located at an edge of a network, data items corresponding to information transmitted by endpoints using the network, generating, by the computing device, a probabilistic hierarchy using the data items, generating, by the computing device using the probabilistic hierarchy and natural language data, a similarity metric, generating, by the computing device using the probabilistic hierarchy, the natural language data, and the similarity metric, an ontology, detecting, by the computing device using the ontology, an anomaly, and in response to detecting the anomaly, sending a notification.

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 deep fusion reasoning engine receives network telemetry data collected from a network. The deep fusion reasoning engine learns resource utilizations for different heuristic packages that can be used in the network to evaluate operation of the network. The deep fusion reasoning engine selects one of the heuristic packages based on the resource utilizations learned for the different heuristic packages. The selected heuristic package comprises a subservice and a set of rules to be evaluated. The deep fusion reasoning engine deploys the selected heuristic package for execution by a device in the network to evaluate operation of the network using the set of rules.

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.

Abstract: In one embodiment, a first deep fusion reasoning engine (DFRE) agent in a network receives first sensor data from a first set of one or more sensors in the network. The first DFRE agent translates the first sensor data into symbolic data. The first DFRE agent applies, using a symbolic knowledge base maintained by the first DFRE agent, symbolic reasoning to the symbolic data to make an inference regarding the first sensor data. The first DFRE agent updates, based on the inference regarding the first sensor data, the knowledge base. The first DFRE agent propagates the inference to one or more other DFRE agents in the network.

Abstract: In one embodiment, a controller in a network trains a deep reinforcement learning-based agent to predict traffic flows in the network. The controller determines one or more resource requirements for the predicted traffic flows. The controller assigns, using the deep reinforcement learning-based agent, paths in the network to the flows based on the determined one or more resource requirements, to avoid fragmentation of a flow during transmission of the flow through the network. The controller sends, to nodes in the network, assignment instructions that cause the flows to traverse the network via their assigned paths.

Abstract: In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.

Abstract: In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.