Abstract:
In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.
Abstract:
Methods, apparatus, systems and articles of manufacture are disclosed that adjust autonomous vehicle driving software using machine programming. An example apparatus for adjusting autonomous driving software of a vehicle includes an input analyzer to determine a software adjustment based on an obtained driving input and a priority determiner to determine a priority level of the software adjustment. The apparatus further includes a program adjuster to, when the priority level is above a threshold, identify a parameter of the autonomous driving software of the vehicle associated with the software adjustment and adjust the parameter based on the software adjustment, the adjustment to the parameter to change driving characteristics of the vehicle.
Abstract:
Disclosed embodiments relate to an orchestrator and arbitrator in an Internet of Things (IoT) platform. In one example, a method of servicing a plurality of data flows of a plurality of wireless devices using a plurality of protocols includes: monitoring one or more interfaces that communicate using the plurality of protocols, activating a first interface upon detecting a demand to exchange data thereon, wherein a connectivity manager performs the monitoring, and activating, extracting, by a packet analyzer, packet metadata from one or more of the plurality of data flows, determining latency encountered and bandwidth utilized by the one or more data flows based on the packet metadata, applying, by an adaptive connectivity manager (ACM), a latency reduction strategy to attempt to comply with latency criteria, and applying, by a bandwidth utilization manager (BUM), a bandwidth reduction strategy to attempt to comply with bandwidth criteria.
Abstract:
In one embodiment, an apparatus comprises circuitry, wherein the circuitry is configured to: transmit, via a communications network, first context information of a first edge device to one or more second edge devices, wherein the first context information identifies an operating environment of the first edge device based on information from one or more sensors; receive, via the communications network, second context information of the one or more second edge devices, wherein the second context information identifies an operating environment of the one or more second edge devices based on information from one or more sensors; and perform a network management function based on the first context information and the second context information.
Abstract:
Methods, apparatus, systems and articles of manufacture are disclosed to add common sense to a human machine interface. Disclosed examples include a human machine interface system that having an actuator to cause artificial intelligence to execute in a virtual execution environment to generate a virtual response to a user input. The system also includes a virtual consequence evaluator to evaluate a virtual consequence that follows from the virtual response, the virtual consequence generated by executing a model of human interactions, and an output device controller to cause an output device to perform a non-virtual response to the user input when the virtual consequence evaluator evaluates the virtual consequence as positive.
Abstract:
Methods and apparatus to train interdependent autonomous machines are disclosed. An example method includes performing an action of a first sub-task of a collaborative task with a first collaborative robot in a robotic cell while a second collaborative robot operates in the robotic cell according to a first recorded action of the second collaborative robot, the first recorded action of the second collaborative robot recorded while a second robot controller associated with the second collaborative robot is trained to control the second collaborative robot to perform a second sub-task of the collaborative task, and training a first robot controller associated with the first collaborative robot based at least on a sensing of an interaction of the first collaborative robot with the second collaborative robot while the action of the first sub-task is performed by the first collaborative robot and the second collaborative robot operates according to the first recorded action.
Abstract:
Systems, methods, and computer-readable media are provided for managing mutual and transitive trust relationships between resources, such as Fog/Edge nodes, autonomous devices (e.g., IoT devices), and/or analog/biological resources to provide collaborative, trusted communication over a network for service delivery. Disclosed embodiments include a subject resource configured to assign an observed resource to a trust zone based on situational and contextual information. The situational information may indicate a vector of the observed resource with respect to the subject resource. The contextual information may be based in part on whether a relationship exists between the subject resource and the observed resource. The subject resource is configured to determine a trust level of the observed resource based on the determined trust zone. Other embodiments are disclosed and/or claimed.
Abstract:
Techniques are disclosed for streaming digital content from a server to a client device in a way that is tailored to the context in which the client device is used. The context in which a client device is used may refer to, for example, the operational characteristics of the device and/or the environmental conditions under which the device is used. A client device can be configured to collect contextual data characterizing its use context. The way in which streaming media is delivered to the client device can be adjusted based on such contextual data, and in particular, can be adjusted in a way that tailors the content delivery to the specific use context. This can improve user experience and conserve battery and network resources, for example, by avoiding the streaming of high definition content to a device that, due to its use context, is able to render standard definition content.
Abstract:
Technologies for performing contextually adaptive media streaming are described. In some embodiments, the technologies utilize contextual parameters leverage contextual information to alter the parameters of a content stream that is provided to a client device from a server. In some embodiments, the parameters of the content stream are altered by changing one or more input parameters (e.g., a report of network parameters) that is/are operated on by adaptive logic of a media player on the client device. Alternatively or additionally, in some embodiments the technologies leverage contextual information to alter the manner in which a client device processes content in a received content stream for consumption. Systems, devices, and methods employing the technologies are also described.
Abstract:
Apparatuses, storage media and methods associated with cognitive robot systems, such as ADAS for CAD vehicles, are disclosed herein. In some embodiments, an apparatus includes emotional circuitry to receive stimuli for a robot integrally having the robotic system, process the received stimuli to identify potential adversities, and output information describing the identified potential adversities; and thinking circuitry to receive the information describing the identified potential adversities, process the received information describing the identified potential adversities to determine respective fear levels for the identified potential adversities in view of a current context of the robot, and generate commands to the robot to respond to the identified potential adversities, based at least in part on the determined fear levels for the identified potential adversities. Other embodiments are also described and claimed.