Abstract: A system captures and utilizes expert knowledge in artificial intelligence. The system includes a knowledge capture module for extracting expert knowledge from subject matter experts in a conversational format and a knowledge management module for cataloging and summarizing the extracted knowledge. The system also includes a digital subject matter expert (dSME) module for ingesting the cataloged knowledge and using it to guide users in building AI models. A chatbot interacts with a user and selects the appropriate dSME module that is relevant to the user request. The system attempts to answer the user request based on the dSME module. If the dSME module lacks knowledge to solve the problem, the system uses a set of tools, for example, internet based search engine to solve the problem.

Abstract: An embodiment involves obtaining a tabular data set with columns that characterize items relating to behavior of components of a communication network; constructing a frequent-pattern tree, each node being associated with: (i) an item-name for representing an item, (ii) a count of transactions from a root node of the tree to the respective node, and (iii) node-links that refer to other nodes in the tree that represent items having the same item-name; traversing the tree to identify a set of nodes with counts greater than a predefined support threshold; generating, from the nodes, association-rules that are based on antecedent items associated with a target item; reducing the association-rules by (i) removing the association-rules in which the antecedent items thereof are a superset or subset of the antecedent items of a further association-rule, or (ii) combining the association-rules that have antecedent items that are at least partially disjoint and conditionally dependent.

Abstract: A system makes predictions using a machine learning model combined with a knowledge model. The system provides input data to a knowledge model and a machine learning based model. The machine learning based model is trained to make predictions based on input data. The system provides the outputs of the machine learning based model and the knowledge model to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system can be used for several applications. For example, the system may classify an input text based on a hierarchy of categories. The system may perform fault detection in time series data by identifying an anomaly data point and predicting whether the anomaly data point is a fault.

Abstract: A system makes predictions using a machine learning model combined with a knowledge model. The system provides input data to a knowledge model and a machine learning based model. The machine learning based model is trained to make predictions based on input data. The system provides the outputs of the machine learning based model and the knowledge model to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system can be used for several applications. For example, the system may classify an input text based on a hierarchy of categories. The system may perform fault detection in time series data by identifying an anomaly data point and predicting whether the anomaly data point is a fault.

Abstract: A system makes predictions using a machine learning model combined with a knowledge model. The system provides input data to a knowledge model and a machine learning based model. The machine learning based model is trained to make predictions based on input data. The system provides the outputs of the machine learning based model and the knowledge model to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system can be used for several applications. For example, the system may classify an input text based on a hierarchy of categories. The system may perform fault detection in time series data by identifying an anomaly data point and predicting whether the anomaly data point is a fault.

Abstract: An embodiment involves obtaining a tabular data set with columns that characterize items relating to behavior of components of a communication network; constructing a frequent-pattern tree, each node being associated with: (i) an item-name for representing an item, (ii) a count of transactions from a root node of the tree to the respective node, and (iii) node-links that refer to other nodes in the tree that represent items having the same item-name; traversing the tree to identify a set of nodes with counts greater than a predefined support threshold; generating, from the nodes, association-rules that are based on antecedent items associated with a target item; reducing the association-rules by (i) removing the association-rules in which the antecedent items thereof are a superset or subset of the antecedent items of a further association-rule, or (ii) combining the association-rules that have antecedent items that are at least partially disjoint and conditionally dependent.

Abstract: In general, in one aspect, a laptop computer includes two planes of orthogonal proximity sensors that are utilized to create a three dimensional user interface. The planes of proximity sensors are provided in the display and the keyboard. The intersections of the proximity sensors along three axis defines a plurality of coverage areas. An object (e.g., users finger, pointer) is determined to be within a coverage area if the corresponding intersecting proximity sensors indicate presence of the object. A user may identify an item on the display for selection by placing an object within an appropriate coverage area and select the item by moving the object toward the display. A user may identify an action to be taken on the display by moving an object through one or more of the coverage areas. A 3D image of an object may be generated based on the coverage areas indicating the object is located therein. The 3D image may utilized to authenticate a user.

Abstract: In general, in one aspect, a laptop computer includes two planes of orthogonal proximity sensors that are utilized to create a three dimensional user interface. The planes of proximity sensors are provided in the display and the, keyboard. The intersections of the proximity sensors along three axis defines a plurality of coverage areas. An object (e.g., users finger, pointer) is determined to be within a coverage area if the corresponding intersecting proximity sensors indicate presence of the object. A user may identify an item on the display for selection by placing an object within an appropriate coverage area and select the item by moving the object toward the display. A user may identify an action to be taken on the display by moving an object through one or more of the coverage areas. A 3D image of an object may be generated based on the coverage areas indicating the object is located therein. The 3D image may utilized to authenticate a user.

Abstract: An apparatus may include a processor and first logic operable on the processor to output a direct memory access (DMA) activity indicator to indicate a current state of activity of direct memory access data transfer operations. The apparatus may further include second logic operable on the processor to determine scheduled DMA activity to be performed; and third logic operable on the processor to output a pre-wake indicator to a controller before the scheduled DMA activity is to be performed, to satisfy both Quality of Service (QOS) and Power saving needs. Other embodiments are disclosed and claimed.