Abstract: A system for detecting and responding to an anomaly in a chaotic environment, comprising one or more autonomous agent devices and a central server comprising a processor and non-transitory memory.

Abstract: A system for detecting a communications computer network node malfunction by analysis of network traffic output by the network node. Low latency packet capture nodes copy network traffic and transmit it to an analytics engine, which may use machine learning techniques, including long short-term memory (LSTM) neural networks, to determine a likelihood that the output of one data router in a network is suffering from a software malfunction, hardware malfunction, or network connectivity issue, and preserve overall data quality in the network by causing cessation of traffic by the malfunctioning node of the network.

Abstract: An artificial-intelligence system for manipulating resources to minimize exposure harm in a chaotic environment, comprising autonomous agent devices, remote electronic sensors, and a central server. The central server receives a first set of sensor readings from one or more remote electronic sensors, during a first time window, the sensor readings recording values of one or more variables in the chaotic environment; receives a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum permitted risk exposure for the time interval; determines a weighted total risk exposure during the critical time interval from the chaotic environment and the resources within the chaotic environment; determines that the weighted total risk exposure exceeds the maximum permitted risk exposure; and causes the autonomous agent devices to manipulate the one or more resources to decrease the weighted total risk exposure.

Abstract: A system for network message microtiming, comprising a central routing device, a first mesh network, and a second mesh network. The central routing device continuously receives utilization data from one or more devices of the first mesh network; continuously receives timing data from one of more devices of the second mesh network; builds and continuously updates a probabilistic future latency model based at least in part on the utilization data and the timing data; receives a message from a client computing device comprising data to be sent to at least one of a plurality of remote computing devices via the first mesh network; determines that a current expected latency according to the future latency model exceeds a predetermined threshold; and as a result delays transmission of a packet comprising the data to be sent to at the least one of the plurality of remote computing devices.

Abstract: A system for detecting and responding to an anomaly in a chaotic environment, comprising one or more autonomous agent devices and a central server comprising a processor and non-transitory memory.

Abstract: A system for detecting and responding to an anomaly in a chaotic environment, comprising one or more autonomous agent devices and a central server comprising a processor and non-transitory memory.

Abstract: A system for detecting a communications computer network node malfunction by analysis of network traffic output by the network node. Low latency packet capture nodes copy network traffic and transmit it to an analytics engine, which may use machine learning techniques, including long short-term memory (LSTM) neural networks, to determine a likelihood that the output of one data router in a network is suffering from a software malfunction, hardware malfunction, or network connectivity issue, and preserve overall data quality in the network by causing cessation of traffic by the malfunctioning node of the network.

Abstract: A system for network message microtiming, comprising a central routing device, a first mesh network, and a second mesh network. The central routing device continuously receives utilization data from one or more devices of the first mesh network; continuously receives timing data from one of more devices of the second mesh network; builds and continuously updates a probabilistic future latency model based at least in part on the utilization data and the timing data; receives a message from a client computing device comprising data to be sent to at least one of a plurality of remote computing devices via the first mesh network; determines that a current expected latency according to the future latency model exceeds a predetermined threshold; and as a result delays transmission of a packet comprising the data to be sent to at the least one of the plurality of remote computing devices.

Abstract: A system for detecting a communications computer network node malfunction by analysis of network traffic output by the network node. Low latency packet capture nodes copy network traffic and transmit it to an analytics engine, which may use machine learning techniques, including long short-term memory (LSTM) neural networks, to determine a likelihood that the output of one data router in a network is suffering from a software malfunction, hardware malfunction, or network connectivity issue, and preserve overall data quality in the network by causing cessation of traffic by the malfunctioning node of the network.

Abstract: An artificial-intelligence system for manipulating resources to minimize exposure harm in a chaotic environment, comprising autonomous agent devices, remote electronic sensors, and a central server. The central server receives a first set of sensor readings from one or more remote electronic sensors, during a first time window, the sensor readings recording values of one or more variables in the chaotic environment; receives a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum permitted risk exposure for the time interval; determines a weighted total risk exposure during the critical time interval from the chaotic environment and the resources within the chaotic environment; determines that the weighted total risk exposure exceeds the maximum permitted risk exposure; and causes the autonomous agent devices to manipulate the one or more resources to decrease the weighted total risk exposure.

Abstract: A system for detecting and responding to an anomaly in a chaotic environment, comprising one or more autonomous agent devices and a central server comprising a processor and non-transitory memory.

Abstract: A system for detecting a communications computer network node malfunction by analysis of network traffic output by the network node. Low latency packet capture nodes copy network traffic and transmit it to an analytics engine, which may use machine learning techniques, including long short-term memory (LSTM) neural networks, to determine a likelihood that the output of one data router in a network is suffering from a software malfunction, hardware malfunction, or network connectivity issue, and preserve overall data quality in the network by causing cessation of traffic by the malfunctioning node of the network.