Abstract: Machine teaching in an embodiment can include receiving a user annotated concept for a given image in given context. The given image can be broken into parts and classified. Relationships can be determined associated with the parts. The created relationships can be stored along with the user annotated concept in a knowledge base. One or more similar images can be annotated using the parts and relationships. A second image associated the given context can be retrieved, decomposed into parts. The parts can be classified. Relationships can be determined associated with the second image's parts. Classifications and relationships associated with the second image's parts can be compared with classifications and relationships associated with the given image's parts. Based on comparing, the second image can be annotated with the user annotated concept for the given image.

Abstract: A method, computer program, and computer system are provided for selecting an artificial intelligence algorithm. Data corresponding to a user interaction with an artificial intelligence system is received. A need by the user for an explanation associated with the artificial intelligence system is identified based on the received data corresponding to the user interaction with the artificial intelligence system. An artificial intelligence algorithm is selected from among a plurality of artificial intelligence algorithms. The selected artificial intelligence algorithm includes an explainable artificial intelligence component. A model is created for the the selected artificial intelligence algorithm corresponding to the received data.

Abstract: A computer-implemented method, a computer system and a computer program product coordinates knowledge from visual elements in a collaboration session. The method includes obtaining collaboration session data. The collaboration session data includes a plurality of visual elements and a plurality of participant interactions. The method also includes identifying a subject and a link in the plurality of visual elements using a text recognition algorithm. In addition, the method includes applying a weight to the link based on the plurality of participant interactions and a distinct characteristic in the plurality of visual elements. Lastly, the method includes generating a knowledge graph. The nodes of the graph comprise the subjects and the edges of the graph comprise weighted links.

Abstract: A method and system of generating a documentation includes monitoring user actions regarding a model development, wherein the user actions are captured in a source code development environment. Semantic meaning is provided for each user action captured in the source code development environment. A degree of impact of each user action is determined in connection with the model. Actions having a degree of impact in the development of the model that is above a predetermined threshold are identified as impactful actions. An interactive knowledge graph is identified based on the user actions, semantic meaning of each action, and the determined degree of impact of each user action. The interactive knowledge graph is provided to be displayed in a navigable way.

Abstract: Machine teaching in an embodiment can include receiving a user annotated concept for a given image in given context. The given image can be broken into parts and classified. Relationships can be determined associated with the parts. The created relationships can be stored along with the user annotated concept in a knowledge base. One or more similar images can be annotated using the parts and relationships. A second image associated the given context can be retrieved, decomposed into parts. The parts can be classified. Relationships can be determined associated with the second image's parts. Classifications and relationships associated with the second image's parts can be compared with classifications and relationships associated with the given image's parts. Based on comparing, the second image can be annotated with the user annotated concept for the given image.

Abstract: Sensor data is detected from at least one sensor selected and installed for detecting operating conditions of at least one equipment. The sensor data includes an operating condition of at least one equipment. The sensor is selected dependent on indications of a user restriction and a predictive model. A sensor data signal is generated dependent on the sensor data. The sensor data signal is transmitted to a network device for collecting the sensor data and transforming the collected sensor data into a formatted transmission signal having a format for transmission over a network to a network server. The network server receives the formatted transmission signal for performing on-demand service of at least one of analysis, reporting and visualization dependent on the operating condition.

Abstract: Systems and methods are described for mapping input devices in real time. The method may include controlling a camera to capture a plurality of images, performing object recognition on the images to identify a plurality of objects, determining properties for each identified object that enable the corresponding object to provide spatial data and interaction data of a preferred input device, ranking the objects according to the determined properties, presenting the identified objects in an order corresponding to the rank that enables a user to select one of the presented objects, tracking the selected object and a human being, generating spatial data and interaction data from a result of the tracking, and providing the generated spatial data and interaction data to a computer to emulate the events of the preferred input device.

Abstract: Embodiments are directed to a computer-implemented method that includes receiving, at a triggering system, input data about an environment. Machine learning (ML) algorithms extract features from the input data and analyze relationships among and between the features to generate user-interaction-readiness model of person-A's readiness to participate in a human interaction with other persons in the environment. The ML algorithms to apply real-world data about person-A to the user-interaction-readiness model to generate a first classification output comprising data representing person-A's readiness to participate in a human interaction with other persons in the environment. Based at least in part on a determination that person-A and another person(s) in the environment are interaction candidates, the triggering system triggers the initiation of human interaction strategies configured to facilitate human interactions between person-A and the previously identified another person(s) in the environment.

Abstract: Embodiments are directed to a computer-implemented method that includes receiving, at a triggering system, input data about an environment. Machine learning (ML) algorithms extract features from the input data and analyze relationships among and between the features to generate user-interaction-readiness model of person-A's readiness to participate in a human interaction with other persons in the environment. The ML algorithms to apply real-world data about person-A to the user-interaction-readiness model to generate a first classification output comprising data representing person-A's readiness to participate in a human interaction with other persons in the environment. Based at least in part on a determination that person-A and another person(s) in the environment are interaction candidates, the triggering system triggers the initiation of human interaction strategies configured to facilitate human interactions between person-A and the previously identified another person(s) in the environment.

Abstract: Systems and methods are described for mapping input devices in real time. The method may include controlling a camera to capture a plurality of images, performing object recognition on the images to identify a plurality of objects, determining properties for each identified object that enable the corresponding object to provide spatial data and interaction data of a preferred input device, ranking the objects according to the determined properties, presenting the identified objects in an order corresponding to the rank that enables a user to select one of the presented objects, tracking the selected object and a human being, generating spatial data and interaction data from a result of the tracking, and providing the generated spatial data and interaction data to a computer to emulate the events of the preferred input device.

Abstract: One example of a computer-implemented method for adaptively placing weather sensors in response to dynamic local conditions includes obtaining a set of data indicating a dynamic local condition in a geographic location of interest and adaptively modifying a placement of a plurality of weather sensors in the geographic location of interest in response to the dynamic local condition.

Abstract: One example of a computer-implemented method for adaptively placing weather sensors in response to dynamic local conditions includes obtaining a set of data indicating a dynamic local condition in a geographic location of interest and adaptively modifying a placement of a plurality of weather sensors in the geographic location of interest in response to the dynamic local condition.

Abstract: One example of a computer-implemented method for adaptively placing weather sensors in response to dynamic local conditions includes obtaining a set of data indicating a dynamic local condition in a geographic location of interest and adaptively modifying a placement of a plurality of weather sensors in the geographic location of interest in response to the dynamic local condition.

Abstract: Sensor data is detected from at least one sensor selected and installed for detecting operating conditions of at least one equipment. The sensor data includes an operating condition of at least one equipment. The sensor is selected dependent on indications of a user restriction and a predictive model. A sensor data signal is generated dependent on the sensor data. The sensor data signal is transmitted to a network device for collecting the sensor data and transforming the collected sensor data into a formatted transmission signal having a format for transmission over a network to a network server. The network server receives the formatted transmission signal for performing on-demand service of at least one of analysis, reporting and visualization dependent on the operating condition.

Abstract: A system for managing items. The system includes a sensor for identifying a first physical object and a second physical object in a building. A computer interface in communication with the sensor is configured to receive a first identification of the first physical object and a second identification of the second physical object. A computer with at least one central processing unit is configured to automatically search for trend information about the first physical object, determine a first interaction between the first physical object and the second physical object based on the first identification of the first physical object and the second identification of the second physical object, and recommend disposition of the first physical object based on the first interaction between the first physical object and the second physical object, and the trend information about the first physical object.

Abstract: One example of a computer-implemented method for adaptively placing weather sensors in response to dynamic local conditions includes obtaining a set of data indicating a dynamic local condition in a geographic location of interest and adaptively modifying a placement of a plurality of weather sensors in the geographic location of interest in response to the dynamic local condition.